338 10 3494 https://omeka.ibu.edu.ba/files/original/173680acbb933aed28bb44102ca00405.pdf 0d0fc48c2c249e7e9811fba7c9bc6847 PDF Text Text Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 Understanding Forms and Models of Cloud Computing Technologies Adopted in the Selected Institutions in Southwestern Nigeria Gbonjubola Oluwafunmilayo BINUYO1 1- African Institute for Science Policy and Innovation, Obafemi Awolowo University, Nigeria gobinuyo@gmail.com Abstract - The study examined the forms and models of cloud computing technology adopted in the selected institutions from four states in Southwestern Nigeria. The three purposively selected institutions were Federal, State and Private owned making twelve institutions. However, the administered questionnaire was filled in by the ten (10) IT personnel, ten (10) lecturers and five (5) students from each of the selected institutions making 300 respondents. The questionnaire elicited information on the forms and models of cloud computing technology adopted and the extent of use of the adopted cloud computing technologies in the selected institutions. Secondary data were obtained from relevant literature. Data collected were analysed with descriptive and inferential statistics. The study concludes that the forms of cloud computing technology adopted by the selected institutions in Southwestern Nigeria are infrastructure-as-a-service (IaaS), software-as-a-service (SaaS) and platform-as-a-service (PaaS) while software-as-a-service (SaaS) is often used by the institutions. Also, the models of adopted cloud computing technology are private, public, hybrid and community cloud computing by the selected institutions in Southwestern Nigeria. The adopted forms and models of cloud computing technology are used for different business functions such as payroll, procurement, human resources, accounting and finance, CRM, application development, and project management. Keywords-Cloud computing, Institutions and Nigeria 1. Introduction The aim of this study is to explicate the forms and model of cloud computing technology adopted in the selected institutions and determine the extent of use of forms of cloud computing technology and the business function deployed on cloud computing technology adopted by the selected institutions in Southwestern Nigeria. Scholars have defined cloud computing from their perspectives. Cloud computing depends on subscription service to accessing networked storage space and computer resources [1]. By implication, it is a paid service(s) to securing online information and communications technologies’ services. As cited in [1] that not all establishment are leapfrogging to adopting cloud computing technologies especially established institutions in developing countries like Nigeria [2]. Globally, higher institutions are encountering with the challenges of needed level of information and communications technology (ICT) required to enhancing good quality education and R&D activities especially in developing countries [3]. Giving yearly educational report of Republic of Yemen, it indicates that the educational sectors are challenged with hindrances to carrying out required quality education to the populace in the country. Among the hindrances to delivering good quality education at Republic of Yemen are due to inadequate needed infrastructure resources, under budget allocation to ICT, absence of ICT technical and teaching personnel [4]. At present, majority of activities are been conducted online. Among the activities are online document editing and writing, email checking, online interaction, collaboration, among others. Therefore, it is imperative globally for educational system to meet up with the advancement in ICT technology for rendering quality education [3]. Also, given the high cost attached to providing and maintaining the needed hardware and software, it is highly needed for educational system to adopt low cost advanced technology such as cloud computing. This cloud �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 computing addresses the challenge of high cost attached to both computer software and hardware needed to rendering quality education to the populace by providing ICT resources on a pay per use basis [3]. There have been diverse empirical studies on cloud computing technologies adopted in institutions [5-11]. Although, there are some theoretical review studies on the same phenomenon [4, 12-15] . However, scholars have noted that there is dearth of empirical studies on cloud computing technology in institutions especially Nigerian institutions [13,15,16]. Also, there is dearth of information on the forms and model of computing technology adopted in Universities in Nigeria, this is because cloud computing research is nascent in Nigeria [16], hence the need for this study. The remaining part of this paper is ordered as follows such as review of related literatures, method of research deployed, the study results and discussion, conclusion and recommendations. 2. Literature Review There is an increasing empirical research interest in cloud computing from both developing and developed economies. This cloud computing research interest have engineered vast intellectual and financial investment in cloud R&D [16]. Given that, it is highly imperative to know that cloud computing can be inform of service model and deployment model [16-18]. (a) cloud computing as a service model: It is service model when it entails Software, Platform and Infrastructure [17]. The discussion of cloud computing as a service is stated below: (i) Software as service (SaaS) was defined as distribution model that allows users to access applications run on their servers over the Internet and charged customers per usage [18]. In other words, it is a remote online application accessed by users/customers via the network using a simple web navigator [18]. In general, SaaS refers to any online services (cloud services) that users can access remotely or subscribed to and pay per usage basis. These types of cloud services entail accounting, invoicing, performance monitoring, communications, tracking sales and planning among others. Furthermore, using SaaS is like renting rather than purchasing it [18]. Unlike mainstream traditional software with limited license and the number of devices that can use it. SaaS offers the users the opportunity of subscribing to the software instead of purchasing it. (ii) Platform as a service (PaaS) allows for clients or customers to hire software, hardware, repository and network capacity through Internet. PaaS is of great interest to application developers because it provides for easy changes and upgrades to the features of the operating system in use and also allows for an application to be developed by developers distributed over different geographical locations across international boundaries. Costs can be reduced by the use of infrastructure services from a single cloud computing service provider rather than have and maintain several hardware facilities that often do identical functions. Examples of PaaS include Salesforce, IBM Bluemix, Cloudbees and Microsoft Azure among others. (iii) Infrastructure-as-a-Service (IaaS): This service delivery model enables clients to rent the equipment used in service operations and control the deployed applications and operating systems among others. Given that, �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 however, updating and patching of operating system at IaaS level are the responsibility of the users within the contractual period [19]. (b) Cloud computing as deployment model entails public, private, community and hybrid cloud [17, 20]. These models are discussed below: (i) Public Cloud: The most common type of cloud computing services skewed towards the public cloud deployment model because as the name implies, are publicly and openly available. Even though they can exist in private clouds, SaaS provisions like cloud storage, online office applications and IaaS and PaaS contributions like cloud-based web application development environments and hosting is in related to public cloud model. Public clouds are also deployed when organisations or individuals do not require the level of infrastructure and security present in private cloud model [21]. Intuitively, large organisations or enterprises may still deploy public clouds in situations where privacy is not required, such as online document collaboration, webmail or storage of non-sensitive documents. (ii) Private Cloud: It does not allow cloud resources to be shared with unknown third parties. It is otherwise known as internal cloud that is strictly for internal use of an establishment [22]. Private cloud loud resources perhaps located either onsite or offsite premises of the organization, hence, this model does not come with the benefit of reduced investment or expenditure in IT infrastructure or equipment. (iii) Community cloud: This type of model is solely for a group or collection of users within an organisation having a shared or common goal [23]. Here, IT resources are provided as a service to group of users in order to enable an elastic collaborative use of computing resource. It is often limited to selected or limited set of employees within an organisation such as security department, head of departments, a team or sub-unit in an organisation. (iv) Hybrid cloud: This model integrates two different deployment models such as public, private and community models. Organisations often combine two differing models to form a hybrid cloud in a bid to maximise efficiencies. In hybrid cloud, the combined clouds retain their identities but are bound together by standardized or proprietary technology [24]. Given cloud computing as service and deployment models, however, measuring the contribution of Nigerian scholars to the number and impact of cloud computing study was needed [16]. Content analysis and bibliometric was deployed in papers extracted from Scopus database within the specified time and country (2016 and Nigeria). The analysis of the extracted papers shows that majority of cloud computing study in Nigeria tend towards Education and Saas model of cloud computing [16]. In support of that assertion, [11] studied the effect and challenges of adopting cloud computing technology in government owned universities in the Southwestern Nigeria. In the study, one hundred (100) IT (information technology) personnel, fifty (50) para-IT personnel and fifty (50) students making two hundred (200) respondents in total were selected in each of the selected ten (10) universities using stratified sampling techniques with the aid of questionnaire. Out of the two thousand (2,000) questionnaire administered, one thousand, seven hundred and forty-two (1742) were retrieved which represents a respondent rate of 87.1%. Microsoft excel was used to analyse the data descriptively. The outcome of the study implies that the adoption of cloud computing has an important effect on enhanced availability, cost effectiveness, low environmental impact, reduced and reduced investment in physical asset among others. �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 Hence, the main issues challenging the use of cloud were data insecurity, regulatory compliance concerns, lock-in and privacy concerns. Cloud computing is an avenue to experience efficient and optimize IT (information and technology) services at least cost which is induced by pay as you use (PAYU) to cloud service providers [3]. There are other benefits attached to the use of cloud computing, among the benefits is high return on investment [25]. Given the benefits attached to the adoption and use of cloud computing, however, many sectors especially the higher education are skeptical in adopting cloud computing technology [3, 25]. On a contrary, cloud computing technology is highly being adopted by higher institutions mainly because of financial reasons [4]. Thinking beyond financial reason for adopting cloud computing, among the technical reasons for adopting cloud computing by IT manager or decision maker can be attributed to organizational, environment, technological and individual factors [4]. Cloud computing is a feasible in meeting the technological needs of an ogranisation efficiently, effectively and at reduced investment on physical asset with least environmental impact and IT complexity [1, 11]. [1] examined the behavioural intent to adopting cloud computing technology in large and small organization using an Enhanced Technology Acceptance Model (ETAM). [1] concluded that attitude and adopters’ ability to use cloud computing (self-efficacy) were better predictor of intention to adopt cloud computing technology. Perceived usefulness and perceived ease of use of cloud computing were better predictor of attitude to adopt cloud computing technology and perceived ease of use and the relevant of cloud computing to adopters’ work (job relevance) were the predictor of perceived usefulness. Recently, [15] systematically reviewed empirical studies on cloud computing technologies. The study showed from the reviewed studies that empirical studies on cloud computing technology are dearth of cloud computing usage/utilization. The study also identified challenges and benefits attributed to cloud computing adoption. The study empirically showed that universities in the selected area are willing to adopting cloud computing technologies. Meanwhile, [14] had earlier concluded from the reviewed literature on cloud computing technology adoption in organisations that the factors that determines the adoption of cloud computing technologies varies. [14] further noted that most of the reviewed studies operationalised the intention to adopt cloud computing in a binary form rather than the actual use of the technology. Meanwhile,[13] showed from the systematic literature review on empirical studies carried out on cloud computing technology adoption in universities that several universities have utilized different types of cloud computing service models. [25] examined the perception of IT and non-IT personnel on factors associated to the poor adoption of cloud computing technologies in African enterprises with Nigeria as a case study. The study concluded that the fear of unknown such as job loss, cyber threat, privacy issue and data theft were the hindrances to the adoption of cloud computing technology. In addition to that, [26] showed that top management support, competitive pressure, and compatibility are the factors attributed to cloud computing technologies. Based on the aforementioned studies, this paper adopts theory of Technology Acceptance Model (TAM) as a focusing device for the analysis of this study. Technology Acceptance Model explains the perceive usefulness of technology, perceive ease of use of technology and attitude toward using technology [27]. The three constructs are key determinants of technology adoption model. First, perceived usefulness (PU) explains thus that people �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 tend to use or not use a technology based on the usefulness perception of the technology. Second, perceived ease of use (PEOU) explains that potential users of technology are of the opinion that a given technology is useful and requires less effort to use it. Third, attitude of a user toward a technology was a major determinant of whether the user will actually use or reject the innovation [27]. Based on that, the applicable research method is adopted for this study. 3. Research Method This study deployed multi-stage sampling technique in data collection. Four states were randomly selected from six in Southwestern Nigeria. Three institutions otherwise called universities were purposively selected from each of the selected states. The justification for the purposive selection is to comprise one federal, one state and one private owned university from each of the selected four states making twelve universities in total. Furthermore, questionnaire was administered and filled in by the personnel in the purposive selected institutions: ten (10) IT personnel, ten (10) lecturers and five (5) students were considered from each of the selected institutions making three hundred (300) respondents. The yardstick for selecting the institutions is based on those institutions that are using cloud computing technologies while the purposive selection of the respondents in the institutions were based on referrer of expertise personnel on the subject matter. The questionnaire elicited information on the forms and models of cloud computing technology adopted. The respondents were asked to tick the forms and models of cloud computing adopted in their institutions. The forms of cloud computing adopted for this study include Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) while the models of cloud computing include private, public, hybrid and community cloud computing. Furthermore, respondents were to rank in five scales (5) the extent of use of the adopted cloud computing technologies in the selected institutions such as: no use (A), little use (B), moderate use (C), highly use (D) and lastly, often use (E); where Alphabet A is the lowest and Alphabet E is the highest. The respondents were further asked to indicate appropriately (multiple response is allowed) the type of cloud computing technologies deployed in the institutions such as Gmail-Based Institution Email Service, Dropbox, Docusign, Skydrive, Netsuite, Cisco-WebEx, Amazon Elastic or Web Services, Learning Management Systems (LMS), Microsoft Azure Cloud, Integrated Development Environments (IDEs), Cloud based APIs, and Cloud based .NET Platforms. In addition to that, the respondents were asked to rank the extent of use of the adopted cloud computing technologies for business function in five scales such as not applicable (A), little use (B), moderate use (C), highly use (D) and often use (E) where Alphabet A is the lowest and Alphabet E is the highest. The variables for business functions include payroll, application development, project management, accounting and financing, CRM/sales management, procurements, human resources and messaging and collaboration. Data collected were analysed with descriptive statistics such as frequencies and crosstabulation. 4. Results and Discussion The Table 1 in this study explains the three intuitions selected for this study such as Federal owned institutions, State owned institutions and Private owned institutions. Not only that, the table further shows the number of questionnaires administered to the selected institutions and the number of questionnaire retrieved. �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 The table shows that out of three hundred (300) questionnaires administered, 56.3% (169) were retrieved and used for the analysis of this study. Meanwhile, from the perspective of [16] majority of cloud computing study in Nigeria tend towards Education and SaaS model of cloud computing, hence, this further contributes to those studies. Categories of the institutions Questionnaire Administered Questionnaire Retrieved Frequency Percentage Frequency Percentage Federal owned institution 100 33.3 57 19 State owned institution 100 33.3 63 21 Private owned institution 100 33.3 49 16.3 Total 300 100 169 56.3 Table 1 Number of Institutions Selected Table 2 explains the forms and models of cloud computing technology adopted in the selected institutions. The table shows that majority (78.3%) of the institutions adopts software-as-a-service, while 65.1% and 54.3% of the institutions also adopts platform-as-a-service and infrastructure-as-a-service respectively. The adoption of forms of cloud computing corroborates the reports of previous scholars on the forms of cloud computing technology adopted in institutions [17] [28] [29] and [30]. Hence, the adoption of these technologies will reduce the cost of operations of the selected institutions from keeping hardware, storage facilities, maintenance cost among others. Concerning models of cloud computing technology adopted by the selected institutions in the study area. Table 2 further shows that the selected institutions adopts private cloud computing (53.5%), public cloud computing (54.3%), hybrid cloud computing (51.9%) and community cloud computing (51.2%). This is line with posits of previous scholars on the models of cloud computing technologies adopted by institutions [20-23, 31]. In addition to that, this study corroborated [13] that several universities have utilized different types of cloud computing service models. By implication, universities in the study area adopted different forms and models of cloud computing based on their discretion, cost reduction, needful, necessity, and industrial revolution, technology push and demand among others. In support of the adopted theory for this study, the selected universities inductively adopted cloud computing technology based on perceive usefulness, perceive ease of use and attitude of user toward a technology as indicated as element of technology acceptance model by [27]. Table 2: Forms and Models of Cloud Computing Technology Adopted Characteristics Frequency Percent (%) Software-as-a-Service (SaaS) 101 78.3 Platform-as-a-Service (PaaS) 84 65.1 Forms of Cloud Computing �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 Infrastructure-as-a-Service (IaaS) 70 54.3 Private Cloud 69 53.5 Public Cloud 70 54.3 Hybrid Cloud 67 51.9 Community Cloud 66 51.2 Models of Cloud Computing *Multiple response is applicable Table 3 explains the level of institutional use of the forms of cloud computing technology adopted by the selected institutions. Table 3 shows that majority (38.8%) the selected institutions that adopted infrastructure-as-a-service moderately use the technology follow by 24.8% of the institutions that highly use the infrastructure-as-a-service. Concerning the use of software-as-a-service by the selected institutions, Table 3 further shows that majority (34.9% and 32.6%) of the selected institutions moderately and highly use software-as-a-service respectively. Concerning the use of platform-as-a-service by the selected institutions, Table 3 shows that majority (26.4% and 41.1%) of the selected institutions little use and moderately use platform-as-a-service respectively. By implication, Table 3 shows that software-as-a-service (SaaS) is mostly used by the selected institutions in Southwestern Nigeria. This might be as a result of idiosyncratic of SaaS that connotes any cloud services that users can access remotely or subscribed to and pay per usage basis [18]. Among the SaaS cloud services that can be subscribed to or use remotely are accounting, invoicing, performance monitoring, communications, tracking sales and planning [18]. In addition to that, this study corroborates [16] that, majority of cloud computing study in Nigeria tend towards Saas model of cloud computing. Table 3: Level of Institutional Use of Cloud Computing Technology Characteristics Level of cloud computing usage (%) Forms of cloud computing A B C D E IaaS 14 7 38.8 24.8 0.8 SaaS 1.6 14 34.9 32.6 3.9 PaaS 10.9 26.4 41.1 3.9 1.6 *Multiple response is applicable Key: A = No use; B = Little use; C = Moderate use; D = Highly use; E = Often use Table 4 shows the cloud computing technology adopted by the selected institutions in the study area. The table shows that most of the cloud computing technologies adopted in the selected institutions are cloud based APIs �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 (55.8%), cloud based.NET Platforms (51.9%), Cisco-WebEx (48.8%), integrated development environment (IDEs) (43.4%), Amazon Elastic or Web Services (31.8%). More also, other cloud computing technologies adopted by the institutions includes Gmail-Based Institution Email Service (26.4%), Microsoft Azure Cloud (18.6%), Learning Management Systems (LMS) (16.3%), Skydrive (12.4%), Netsuite (8.5%), Dropbox (7.8%), and Docusign (0.8%). This shows that the selected institutions exhibited some level of cloud computing technologies. Perhaps, the necessity to adopt low cost advanced technology such as cloud computing warrant the selected institutions to adopting the cloud technologies. Meanwhile, [3] had postulated earlier that cloud computing technologies addresses the challenge of high cost attached to both computer software and hardware needed to rendering quality education to the populace by providing ICT resources on a pay per use basis. By implication, the selected institutions adopted cloud computing technologies so as to providing high quality that is affordable, accessible at least cost for the stakeholders in the institutions. Table 4: Cloud Computing Technology Adopted by the Selected Institutions Characteristics Frequency Percent (N=111) Gmail-Based Institution Email Service 34 26.4 Dropbox 10 7.8 Docusign 1 0.8 Skydrive 16 12.4 Netsuite 11 8.5 Cisco-WebEx 63 48.8 Amazon Elastic or Web Services 41 31.8 Learning Management Systems (LMS) 21 16.3 Microsoft Azure Cloud 24 18.6 Integrated Development Environments (IDEs) 56 43.4 Cloud based APIs 72 55.8 Cloud based .NET Platforms 67 51.9 *Multiple response is applicable The Table 5 in this study shows the extent of cloud computing technology in business function in the selected institutions in the study area. The selected institutions highly use (30.2%) and often use cloud computing technology in their payroll function. In addition to that, the table shows that the selected institutions highly (34.1%) and often use (25.6%) cloud computing technology in their application development function. Furthermore, Table 5 shows that the selected institutions moderately use (25.6%) and highly use (22.5%) cloud computing technology in their project management functions. The table shows that the selected institutions �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 moderately use (33.3%) cloud computing technology in their accounting and financing functions. Also, the institutions little use (27.9%) and moderately use (31.8%) cloud computing technology in their CRM/sales management function. This table shows that the selected institutions moderately use (39.5%) cloud computing technology in their procurements function. In addition, the selected institutions moderately use (37.2%) cloud computing technology in their human resources. Lastly, the selected institutions little use (34.9%) and moderately use (32.6%) cloud computing technology in managing and collaboration function. By implication, the payroll functions of the selected institutions have been digitised and can be done anywhere in the world (telecommuting). Not only that, the selected institutions have deployed cloud computing technologies in their project management, accounting and financing, CRM/sales management, procurements, human resources, managing and collaboration functions. Table 5: Extent of Use of Cloud Computing Technology in Business Function Characteristics Extent of use of cloud computing technology Business Function A B C D E Payroll 17.8 9.3 18.6 30.2 11.6 Application Development 10.1 7 8.5 34.1 25.6 Project Management 16.3 15.5 25.6 22.5 3.9 Accounting and Financing 17.1 24 33.3 7 0.8 CRM/Sales Management 21.7 27.9 31.8 3.1 - Procurements 22.5 21.7 39.5 2.3 - Human Resources 20.2 23.3 37.2 3.9 1.6 Messaging and Collaboration 11.6 34.9 32.6 7 3.1 *Multiple response is applicable Key:A = Not applicable; B = Little use; C = Moderate use; D = Highly use; E = Often use 5. Conclusion The study concludes that the forms of cloud computing technology adopted by the selected institutions in Southwestern Nigeria are infrastructure-as-a-service (IaaS), software-as-a-service (SaaS) and platform-as-a-service (PaaS) while software-as-a-service (SaaS) is often used by the institutions. Also, the models of adopted cloud computing technology are private, public, hybrid and community cloud computing by the selected institutions in Southwestern Nigeria. The adopted forms and models of cloud computing technology are used for different business functions such as payroll, procurement, human resources, accounting and finance, CRM, application development, and project management. �Journal of Natural Sciences and Engineering, Vol. 2, No.2 (2020) DOI number: 10.14706/JONSAE2021311 6. Limitations and future work This study is limited to universities in Southwestern Nigeria, further studies perhaps consider the whole universities in Nigeria. 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Arinze, (2010), "Factors that Determine the Adoption of Cloud Computing: A Global Perspective", International Journal of Enterprise Information Systems, IJEIS, 6(4): 55-68. [30] R. Miller, (2011), “Understanding the Different Levels http://www.businessservicemanagementhub.com/2011/03/16/understanding-the- of Cloud Computing", different-levels of- Cloud-computing/ accessed 7th October, 2020. [31] F. Shimba, "Cloud Computing: Strategies for Cloud Computing Adoption". Masters Dissertation at the school of computing Dublin. Dublin Institute of Technology, 2010. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Understanding Forms and Models of Cloud Computing Technologies Adopted in the Selected Institutions in Southwestern Nigeria Author Author Gbonjubola Oluwafunmilayo Binuyo Abstract A summary of the resource. The study examined the forms and models of cloud computing technology adopted in the selected institutions from four states in Southwestern Nigeria. The three purposively selected institutions were Federal, State and Private owned making twelve institutions. However, the administered questionnaire was filled in by the ten (10) IT personnel, ten (10) lecturers and five (5) students from each of the selected institutions making 300 respondents. The questionnaire elicited information on the forms and models of cloud computing technology adopted and the extent of use of the adopted cloud computing technologies in the selected institutions. Secondary data were obtained from relevant literature. Data collected were analysed with descriptive and inferential statistics. The study concludes that the forms of cloud computing technology adopted by the selected institutions in Southwestern Nigeria are infrastructure-as-a-service (IaaS), software-as-a-service (SaaS) and platform-as-a-service (PaaS) while software-as-a-service (SaaS) is often used by the institutions. Also, the models of adopted cloud computing technology are private, public, hybrid and community cloud computing by the selected institutions in Southwestern Nigeria. The adopted forms and models of cloud computing technology are used for different business functions such as payroll, procurement, human resources, accounting and finance, CRM, application development, and project management. Keywords Keywords. Cloud computing, Institutions and Nigeria Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021318 https://omeka.ibu.edu.ba/files/original/d8bd5c4881ddc5399123b176dd9fbcd2.pdf 1d4855c1060aa995fd1a0d8cbff1e775 PDF Text Text Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 Feedback System Using Sentiment Analysis Abdulrahman Almonajed 1, Dino Kečo1, 1 International Burch University, Sarajevo, Bosnia and Herzegovina abdulrahman.almonajed@stu.ibu.edu.ba dino.keco@ibu.edu.ba Abstract – Today, when looking at the quality of an online item, the feedback itself plays a very important role. Based on the feedback we can decide whether the desired item is good or not, get a picture of the seller and so on. Many companies that have online shops display the most positive feedback while hiding bad ones or display only a few of them. In this research, we will help people by automating the process of deciding whether a feedback is positive or negative, which will give them time for other jobs and save money for hiring people who will work on the feedback. Since feedback on online articles is very important today, the process of determining positive and negative feedback should be made as quick and easy as possible. In this research, we will show a very simple and fast way to classify feedback as positive or negative, which means that the main question of this research is how to facilitate and speed up the process of determining the polarity of the feedback. We will use NLP using Python’s library called TextBlob. The used algorithm is called Naïve Bayes, it gave the accuracy of around 80%. Keywords - feedback, online article, sentiment analysis 1. Introduction These days, the number of online stores is growing very fast [1]. We can see that today we can buy whatever we want online. Also, through online shopping we can save a lot of money by being able to find things much cheaper than they are in local stores. By shopping through online shops, we can "escape" arrogant sellers, as well as annoying sellers who follow us during the shopping and "force" us to buy their products. Also, we can save a lot of time by avoiding traffic jams, waiting in line at the store, saving money by not paying for parking, saving our fuel, etc. We can even buy things we don’t have in our city or country. For leading companies such as Amazon, Alibaba, eBay, and so on, feedback from every user is very important. They receive thousands of feedback a day, which is very difficult to read and analyze, which is why they need to automate the process. Understanding and analyzing the feedback can improve the user experience, improve the products, and so on, but can also help the online shop owners to know which seller is not doing their job properly, whether it is cheating, etc. Also, there are online applications where we can book an apartment, rent a car, etc., such as on our BTT (Balkan Tourist Travel) application. This kind of web application is now well known in our region, so we decided to create one to facilitate the tourism process in Bosnia and Herzegovina. The application is intended for tourists who visit our country in large numbers. BTT application will make it easier for them to book everything they need during their stay in our country with a few clicks. The main goal of the application is to avoid numerous calls and �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 misunderstandings between our people and tourists. On the BTT we value feedback, so users can leave feedback on everything that they have used on our application. By doing so, we give our customers the opportunity to express their opinions, which will help us to achieve the best possible service. In this research, we will use the BTT application to apply and test our classification method. For the best process of development, we will be using only one part of the BTT web application. We will perform all the tests and modifications to achieve the best possible results. And if the results are satisfying we will include all the other parts of the application. Customers' opinion is not only important to large companies it is also important to small companies that are just getting started [2]. Therefore, determining whether the opinion is positive or negative must be automated as soon as possible and in the best possible way. This research will solve this problem and determine whether customers’ opinion is positive or negative in a very quick and easy way. The biggest problem this research solves is the hard work of reading the opinions, which can be praise or criticism, of users and determining whether it is positive or negative or spending the extra money to hire people to do that. Later, it will help identify whether the comment is spam or not, which can reduce time determining feedback's polarity, determine the language of the comment, and so on. 2. Literature Review Sentimental analysis, which will be used in this research, has been studied in detail for the last few years. There are a lot of research papers regarding sentimental analysis, but we will present only the ones that are useful for our research. In the paper [3], authors Akanksha Sharma and Dr. Ashim performed a Comparative Study of Different Approaches Used For Sentiment Analysis from customer reviews, where they stated that this process helps the owners of the online shop to make the right decision regarding their items. In their research, they have divided the feedback into three categories: positive, negative, and neutral. Where we can notice that in our research the classification of feedback is similar, from -1 to 1. 1 represents positive, 0 represents neutral and -1 represents negative. Their research is very similar to ours. They gathered feedback from e-shops, analyzed the feedback, and finally classified them. The authors mentioned Support Vector Machine (SVM), Naive Bayes, Lexicon Method, etc. At the end of their research, SVM was the best compared to other methods. Research paper [4], also performed a sentiment analysis on user feedback from online shops. Michael Gamon, the author of this research, uses over 40.000 feedbacks that he collected from two different sources, Global Support Services, and Knowledge Base Surveys. The author divided the feedback on a scale between 1 and 4, where 1 represented dissatisfied and 4 for very satisfied. In his research, he used a linear Support Vector Machine (SVM) for feedback classification with 10-fold cross-validation. As a result of his research, Michael created two clear classifications (classes). The first class determines �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 whether the feedback falls under 1 or 4 on the scale, the second class determines whether the feedback falls under 1 or 2 and 3 or 4. He used 10-fold cross-validation on both classifications with the first 2000 feedbacks in his dataset. The first class (whether the feedback falls under 1 or 4 on the scale) proved to be more accurate. The precision was 85.47 for the first class, while the second class was 69.23. Prashali et al. [5], the authors of the research, collected their research data for the classification from Kaggle website. The data was in excel format, containing 186 feedback. The goal of their research was to see how to improve the teaching and learning program. Their dataset was composed of students’ feedback on the teaching program. The result of their research was divided, as in our research, between -1 and 1. As we mentioned before 1 represents positive, 0 neutral, and -1 negative. We have to mention that in their research, they used polarity from sentiment analysis to determine whether the feedback is classified as positive, negative, or neutral. In the paper [6], the authors wrote about how owners of online stores should analyze every feedback they get in the shortest time possible. Since this affects their further business and cooperation with the seller on their online shop. Robots can cause fraud to star ratings on items on online shops, for that reason feedback on online shops must be analyzed using natural language processing (NLP). In this way, we can delete false feedback and quickly analyze feedback received. Swati N. Manke and Nitin Shivale classify their results in two categories, positive and negative. Author Peter D. Turney in his research paper [7], applied semantic orientation for determining whether the feedback is positive or negative. For his research, Peter used 410 samples of feedback, which he acquired from 4 different domains (banks, automobile, movie, and travel destination). He used an unsupervised learning algorithm to classify feedback as positive or negative. The precision of his algorithm was averaging 74%, the highest precision was on automobile 84%, while the lowest one was on movie 66%. The reason for the difference between the precision of automobiles and movies, which was a pretty huge one was because of some words depending on the context. In the domain of automobiles, some adjectives may have a negative meaning whereas in the movie sphere it can be the exact opposite meaning. For example, the adjective “unpredictable” would have a negative meaning in an automobile but in the movie a positive one. For assessing feedback to be positive or negative, the author Peter followed 3 steps: ● Draw out sentences which contain adjectives and adverbs, ● Predict semantic orientation of each extracted sentence, ● Categorize feedback as positive or negative according to the semantic orientation of the sentence. In [8], the authors used a model to analyze text from feedback written by the users in their research. Also, the number of stars of the star rating given by the user was taken for determining the results. Joachim Büschken and Greg M. Allenby tested their model on a hotel and restaurant dataset, which contained the feedback and the star rating. Their model was built based on Latent Dirichlet Allocation (LDA). In the �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 restaurant dataset, there are 696 samples (feedback and star rating) from different Italian restaurants. While in the hotel dataset, feedback and star ratings were collected from two hotels, one in New York and the second near the JFK airport. The number of samples collected from the hotel in New York is 3.212, while the second hotel is 1.255, which sums up to 4.467 feedback from the hotels. At the end of their research, the authors believe that bag-of-sentence is better than bag-of-words for user speech analysis. Saleem Abuleil and Khalid Alsamara in their research paper [9], wrote about analyzing user feedback using Natural Language Processing (NLP). The authors presented feedback in two formats, rating (structured data) and textual (unstructured data). Their research was applied on feedback that has been written in the Arabic language. In the Arabic language, adjectives take the form of describing another person or thing in a sentence. In their research, the authors convert unstructured data (text) into structured data (numerical). They categorized their results into two classes, positive and negative feedback. In the research paper [10], authors write about measuring customers’ satisfaction using sentiment analysis. For the classification method, they used sentiment classifier support vector machine (SVM). The main reason for that was that SVM gave the best results on the basis of the research paper [11]. The data set was collected from Twitter API. It contained the following: ● Likes (lists of users that liked specific tweet) ● Followers (lists of users that follow specific tweet) ● Mentions (lists of users that was mentioned on a specific tweet) ● Replies (lists of replies on a specific tweet), and ● Re-tweet (lists of users that share specific tweet) In this research, they used the database MySQL Database Management System. The authors classified their results in two classes, positive and negative. At the end of their research, their algorithm gave a precision of around 87%. 3. Methods and Materials The data that will be used in this research will be taken from the BTT web application. The number of feedback samples is more than 1000. The application consists of multiple feedback sites, but this research will be based on feedback from the rent-a-car section/site. The number of data we will test in this research will depend on the number of feedbacks on the BTT web application. Right now, there are more than 1000 feedback for the rent-a-car section, if new feedback is added, the system will cover them automatically once it runs. We only used cars’ feedback from the BTT web application. We take data in HTML format where we have only feedback, without other attributes from the table that are related to feedback for business logic. The attributes that we will not use are ID, user, and car_ID since it means nothing to us in determining whether the feedback is positive or negative. This means that only one column is left since the table contains 4 columns (ID, name, carID, and feedback), which we can see in the figure below. �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 Figure 1. Feedback in MySQL As we mentioned before, we will only use one column for the table, which is the feedback column. Figure 2 shows only feedback from the table in the HTML web page, from where we will take the feedback. Figure 2. Feedback on HTML page A. Data preprocessing Since this research is based on working with text, in the process of determining whether the given text is positive or negative, that text must be analyzed and processed. The system will be based solely on working with English text. We will implement natural language processing (NLP) in the process of further analyzing and processing the feedback. For the whole process, we will use the python programming language with its library TextBlob. The library TextBlob will be used to determine if the given feedback is positive or negative. TextBlob is a python library used for basic text tasks, such as sentiment analysis, translation, language determination, and so on. All of these tasks can be classified under NLP tasks. TextBlob allows us to view objects as a regular string in the python for processing the desired task [12]. The processes and analyzes done in this research are removing HTML tags, removing non-letters, removing whitespaces and empty elements, lowercase, tokenization, spell checking and correct misspelled words, and etc. To reduce the number of words of the feedback and make the classification as accurate as possible, usually removing stopwords is used [13]. When we check the list of nltk’s stopwords, we can see that it’s not a good idea to always remove stopwords from the dataset. For example, the stopword “not” it can change the meaning of the sentence at all. Since, the sentence “This car is not good” after removing stopwords will be “car good”. We can see that the original sentence is �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 negative, while the sentence after removing stopwords is positive. Of course, it's not always the case that removing stopwords will change the meaning of the sentence. Because of that, before removing stopwords it is good to know the sentences inside the dataset. The figure below shows the example of how removing stopwords can change the meaning of the sentence. Figure 3. Example of removing stopwords In Figure 4. we prove that removing stopwords sometimes can cause an issue. We can see that first sentence as polarity result -35, which means it's negative, while after removing stopwords from the sentence, the meaning is changes and polarity result became 70, which means the sentence is positive. Figure 4. Polarity result before and after removing stopwords 4. Results After processing the above analyzes and processes on each feedback we took from the BTT web application, we will begin the process of determining whether it is positive or negative. Here we come to sentiment analysis, which will be used from the mentioned python library. From TextBlob's sentiment analysis, we will use the polarity part which will give us a result between -1 and 1. Where -1 indicates very negative results, in our case very bad feedback, and 1 is positive. In Figure 5, we show the implementation of textblob's sentiment polarity and the polarity result or score. Figure 5. Implementation and result of TextBlob's sentiment property �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 From the figure above, we can see that the result is not so readable, where we can only check for the polarity result but we don't know for which feedback is that result. So we combined or merged the polarity score and feedback, to make the result more readable and understandable. The figure below, shows the way we combined the feedback and polarity score, and how the result became more readable and understandable from before. Figure 6. Feedbacks' polarity result The table below shows the total accuracy of our algorithm. Table 1. Result 5. Algorithm Approximate result Naïve Bayes ~ 80% Discussion Considering the research papers related to our research, which are already mentioned in the Section 2, we have notice that it is much faster and easier to determine if the feedback is positive or negative using the Python’s library TextBlob. As we mentioned before, it is not always good idea to remove stopwords from the text, as it can change the meaning of the sentences. In some researches, Naïve Bayes algorithm didn’t give the best result. There may be more causes such as, huge dataset with unnecessary sample or information, stopwords are removed, preprocessing is not done properly, and so on. In the table below, we showed the algorithms and results of several previous researches. �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 Table 2. Conspectus of previous works Author(s) Algorithm Result Joachim Büschken and Greg M. LDA (Latent Dirichlet Allocation ) 60-70% Allenby Michael Gamon SVM (Support Vector Machine) – 85.47% for the first class, while two classes the second class was 69.23% Al-Otaibi Shaha, Alnassar Allulo, SVM (Support Vector Machine) Alshahrani Asma, Al-Mubarak Amany, Albugami Sara, Almutiri Nada, Albugami Aisha Peter D. Turney 6. Around 87% PMI-IR (Pointwise Mutual Around 74% Information Information Retrieval) Conclusion To conclude the results, the feedback has been divided into two groups, positive and negative. Feedback, like in every web site helps the users that are first time on the online shop to determine which product is of good quality. In this research we proved that removing stopwrods in not always a good idea, because it can change the meaning of the sentence. Also the research will make it easier for the online shop owners to determine which feedback is positive and which is negative. In this way the owner will be able to recognize the quality sellers in a very easy and simple way. In the near future we are planning to improve this research by adding 'minus'. The minus will be added to sellers for every bad/negative feedback on his items. In that way we will be able to isolate bad sellers with bad items. If the seller receives a certain number of minuses he will be warned. If the sellers item gets a certain amount of minuses it will be automatically deleted. Also a method for recognising whether a feedback is spam or not will be implemented. This process will be initiated before the sentimental analysis. Since we want to perform the sentimental analysis only on „real“ feedback. This will speed up the process because we will not analyse large numbers of spam feedback. Also methods for translating foregin feedback to english language will be added. This research will be open-source so that every company or person will be able to use it, of course they will need to own a shop which receives feedback. �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 10.14706/JONSAE2019114 REFERENCES [1] S. CK i G. Edwin, “Online Shopping - An Overview,” June 2014. [Na mreži]. Available: https://www.researchgate.net/publication/264556861_Online_Shopping_-_An_Overview. [2] A. Fundin i B. Bergman, “Exploring the customer feedback process,” June 2003. [Na mreži]. Available: https://www.researchgate.net/publication/240260148_Exploring_the_customer_feedback_process. [3] A. Sharma i A. Dr. Saha, “A comparative Study of different Approaches Used for Sentiment Analysis From Customer Reviews,” 14 Dec 2018. [Na mreži]. Available: https://poseidon01.ssrn.com/delivery.php?ID=5751021240710700920281070710870230680370490 040060050301200900180960751141181190270711060980510290180320020670021111090060881 06122026094048065075111125088015087089126069002034074006017116005086091025113001 0930131. [4] M. Gamon, “Sentiment classification on customer feedback: Noisy data, large feature vectors, and the role of linguistic analysis,” January 2004. [Na mreži]. Available: https://www.researchgate.net/publication/215470705_Sentiment_classification_on_customer_feedb ack_data_Noisy_data_large_feature_vectors_and_the_role_of_linguistic_analysis . [5] S. S. Prashali , R. K. Asmita , S. P. Rutuja i U. W. Yamini , “Sentiment Analysis of Feddback Data,” March 2019. [Na mreži]. Available: https://www.ijtsrd.com/papers/ijtsrd23090.pdf. [6] N. M. Swati i Nitin Shivale, “A Review onL Opinion Mining and Sentiment Analysis based on Natural Language Processing,” International Journal of Coumputer Applications, pp. 29-32, 2015. [7] D. T. Peter, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” July 2002. [Na mreži]. Available: https://www.aclweb.org/anthology/P02-1053.pdf. [8] J. Büschken i G. M. Allenby, “Sentence-Based Text Analysis for Customer Reviews,” 2016. [Na mreži]. Available: https://www.ku.de/fileadmin/160102/WiSe2015_2016/mksc.2016.0993-ePDF3.pdf. [9] S. Abuleil i K. Alsamara, “Using NLP Approach for Analyzing Customer Reviews,” 2018. [Na mreži]. Available: https://www.slideshare.net/cscpconf/using-nlp-approach-for-analyzing-customer-reviews-86265367. [10] S. Al-Otaibi, A. Alnassar, A. Alshahrani, A. Al-Mubarak, S. Albugami , N. Almutiri i A. Albugami, “Customer Satisfaction Measurement using Sentiment Analysis,” International Journal of Advanced Computer Science and Application, pp. 106-117, 2018. [11] J. Brynielsson, F. Johansson, C. Jonsson i A. Westling, “Emotion classification of social media posts for estimating people's reactions to communicated alert messages during crises,” 2014. [Na mreži]. Available: https://docplayer.net/11592731-Emotion-classification-of-social-media-posts-for-estimating-peoples-reactions-to-communicated-alert-messages-during-crises.html. [12] S. Loria, “textblob Documentation,” 26 April 2020. [Na mreži]. Available: https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf. [13] S. Bird, E. Klein i E. Loper, Natural Language Processing with Python, O'REILLY, 2009. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Feedback System Using Sentiment Analysis Author Author Abdulrahman Almonajed Dino Kečo Abstract A summary of the resource. Today, when looking at the quality of an online item, the feedback itself plays a very important role. Based on the feedback we can decide whether the desired item is good or not, get a picture of the seller and so on. Many companies that have online shops display the most positive feedback while hiding bad ones or display only a few of them. In this research, we will help people by automating the process of deciding whether a feedback is positive or negative, which will give them time for other jobs and save money for hiring people who will work on the feedback. Since feedback on online articles is very important today, the process of determining positive and negative feedback should be made as quick and easy as possible. In this research, we will show a very simple and fast way to classify feedback as positive or negative, which means that the main question of this research is how to facilitate and speed up the process of determining the polarity of the feedback. We will use NLP using Python’s library called TextBlob. The used algorithm is called Naïve Bayes, it gave the accuracy of around 80%. Keywords Keywords. feedback, online article, sentiment analysis Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021319 https://omeka.ibu.edu.ba/files/original/41cddaabb1e237ad0c086dbca13071d0.pdf 369ba7ca22ee871fc9b74adde3cf1d69 PDF Text Text Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Using Exploratory Data Analysis and Big Data Analytics for Detecting Anomalies in Cloud Computing Ibrahim Muzaferija1, Zerina Mašetić1 1 International Burch University, Sarajevo, Bosnia and Herzegovina ibrahim.muzaferija@stu.ibu.edu.ba zerina.masetic@ibu.edu.ba Abstract – While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection, which is a necessary part of the management of modern applications. As the record of user behavior, weblogs surely become the research item related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where anomalous behavior needs to be detected in understanding phases prior to modeling a system for such use. Big Data Analytics often ignores anomalous point due to high volume of data. To address this problem, we propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis, which assists in gaining insight into data relationships without the classical hypothesis modeling. In that way, we can gain better understanding of the patterns and spot anomalies. Results show that Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business Understanding phase, making it one of the key steps in the Data Understanding phase. Keywords - Cloud Computing, Big Data, Data Mining, Anomaly Detection 1. Introduction With constant growth and advancements of the Internet, there are more systems connected to other connected systems, constantly generating and exchanging data. That data is referred to as Big Data and is constantly targeted by cyber-attacks as it contains sensitive and valuable information. The term “big data” refers to data that is so large, complex, or rapid that it’s not possible to process using traditional computing and data management tools. Big Data provides opportunities to improve research, operational efficiency, and decision-support applications with increased value for digital applications [1]. At the same time, Big Data represents the challenges to store, transport, process, mine, and serve the data. Data that is high in volume, velocity, variety, and veracity must be processed with advanced analytical tools and algorithms to reveal meaningful information and provide value. Cloud computing represents the use of distributed and shared resources such as computing, storage, networking, and analytical software, and provides fundamental support to address the challenges of Big 1 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Data. Cloud computing serves both as a technological enabler and producer of big data [1]. Anomalies represent unusual or behaviors that deviate from the normal. In efforts to increase cloud computing reliability, anomaly detection poses a frequent problem in threat detection and identification, as reported by Cloud Security Alliance (CSA) [2] which represents the world’s leading organization dedicated to securing cloud computing environments, conducts annual research with an aim to raise awareness of threats, risks, and vulnerabilities in the cloud environment. In their latest (2019) report [3], CSA re-examined the risks with cloud security and took a new approach, examining the problems in configuration and authentication, rather than the traditional focus on vulnerabilities and malware, highlighting the following threats: 1. Data Breaches 2. Misconfiguration and inadequate change control 3. Lack of cloud security architecture and strategy 4. Insufficient identity, credential, access, and key management 5. Account hijacking 6. Insider threat 7. Insecure interfaces and APIs 8. Weak control plane 9. Metastructure and applistructure failures 10. Limited cloud usage visibility 11. Abuse and nefarious use of cloud services In this research, we aim to address the threats which can be traced in user logs (numbered 1, 4, 5, 6, 8, 9 and 11) by utilizing Big Data Analytics and Exploratory Data Analysis in order to discover anomalies and contribute to increase of security in Cloud Computing applications. 2. Literature Review Anomaly detection in the cloud infrastructure and big data environment has been the topic of many research studies in the literature. Since the first introduction of cloud infrastructure in 2006 [4], cloud computing has greatly impacted the industries. The rapid development of Internet and Big Data technologies has resulted in increased service development on cloud computing, such as online banking services, electronic news services, government information systems, mobile services, etc. These systems handle sensitive and confidential data, making the anomaly detection mechanisms one of its core security requirements. In the review paper by Arif Sari [4], [5], different techniques and mechanisms used in the detection of anomalous activities within the cloud environment are described: threshold detection, statistical analysis, rule-based measures, data mining, and machine learning. We aim to apply statistical techniques and EDA 2 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 (Exploratory Data Analysis) in order to discover anomalies. In the “Big Data processing for Anomaly Detection” survey [6], Ariyaluran et al. present the details of the comparative analysis and the relationship of three different domains, which are anomaly detection, machine-learning algorithms, and real-time big data processing. This paper aims to contribute to complemented techniques for anomaly detection. Once anomalies are detected, we can utilize Machine Learning and real-time anomaly detection for future improvements. In their research, Dalal and Rele [6], [7] emphasize the steps in creating effective and reliable mechanisms for threat detection. They highlight the importance of the first CRISP-DM (Cross Industry Standardized Process for Data Mining) phase named “Develop Business Understanding”, where reasons for defects and answers for maintenance are taken into consideration. They discuss the phase “Analyze Data and Data Dependencies” where the aim is to analyze, combine, and compare the data with the present situation, without proposing EDA as a baseline for data understanding. Our work aims to employ EDA in order to complement the methodology. Also, they highlight the step named “Engage with Subject Matter Experts (SME’s)” for better dataset examination and analysis of the anomaly situation, along with a grouping of the threat factors. By employing these methods, we aim to set transparent expectations and bring out clarity to our results. In further research, we work closely with application development technical lead which serves as SME, and facilitates in clarification of log data, as well as threats, anomalies and our results 3. Methodology The research is implemented using a portion of the CRISP-DM (Cross Industry Standardized Process for Data Mining) methodology [8], which represents the common standards used by data scientists and data mining experts in order to build analytical and machine learning models. Prior to analytical and machine learning model creation, we need to construct a clean dataset of user behavior with anomalies labeled for future modeling. To do so, in this research we focus on the first three phases: Business Understanding, Data Understanding, and Data Preparation, as highlighted with red color in the figure below. Modeling and subsequent phases are researched in our extended study of anomaly detection in cloud computing. 3 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Figure 1. CRISP-DM workflow In the Business Understanding phase, the goal is to determine business objectives, assess the situation from a business perspective, discuss with subject matter experts, determine data mining goals, and produce a project plan. In the Data Understanding, we collect and select raw data, describe and explore the data, consult with subject matter experts, and verify data quality. In the Data Preparation phase, which is often the most time-consuming phase, we select and clean the data, format data, and construct a clean dataset. We approach the mentioned phases using Big Data Analytics and Exploratory Data Analysis (EDA). Big Data Analytics examines large amounts of data in a non-traditional manner, that is using distributed and shared resources to support the data quantity and complexity [8], [9]. Exploratory Data Analysis [10] is an approach to analyzing data in order to summarize their main characteristics and uncover the underlying structure using statistical and visual methods. 3.1. Data Collection and Selection Cloud-based enterprise web application logs are produced by multiple servers and services, which are streamed to Elasticsearch [11] service, an open-source search, and analytics engine for all types of data. Elasticsearch is distributed, fast, and scalable, which makes it an ideal environment for big data ingestion, enrichment, storage, analysis, and visualization. 4 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Figure 2. Raw data access from Kibana Raw data is accessed by locally restoring the Elasticsearch cluster snapshot taken for a period of three months. The cluster contains around 20 GB of semi-structured data collected from different application services and levels, indexed by a timestamp. Application logs are mapped to 175 attributes and accessed using Kibana [12], the Elastic Stack service for data analysis and visualization. Attribute selection is a part of the “Business understanding” and “Data understanding” phase, implemented together in consultations with application development technical lead, i.e., subject matter expert (which we’ll refer to as SME). The attributes describing the user’s application usage that were the most relevant for anomaly detection are selected for further analysis. The following table displays statistical information for selected attributes. 5 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Table 1. Selected data statistical information Attribute name Description Data type Range Missing timestamp Timestamp Date Time [2020-01-05 21:17, 0.0 % 2020-03-26 21:06] account_id Account ID, Nominal unique company f6afd09c-****-****-****- 8.87 % c30a935ccc37, ... account identifier client_country User country Nominal BA, US, ... 9.53 % company_name Company Name Nominal Company A, Company B, 10.17 % ... platform Application Nominal platform BrowserMNC, 0.0 % BackendMNC, ... principal_id User email Nominal developer@**.com, ... 9.64 % remote_address User IP address Nominal [ 0.0.0.0. - 255.255.255.255 9.12 % ] user_agent User-agent Nominal Mozilla/5.0 ( Windows NT 0.0 % 10.0; Win64; x64) … , ... error_message Error message Nominal validation error, auth error, 99.96 % ... message Log message Nominal Profiling, FrontTimings, ... 0.18 % level Log level Nominal Info, error 0.0 % path Parameterized Nominal PUT 99.78 % resource request /customer/***/ticket/***, ... resource Request Nominal (GET) /invoices, ... 0.0 % status_code Response code Nominal 200, 404, ... 10.17 % Once the relevant data is selected, we utilize Elastic Stack service named Logstash [13] for collecting the data, that is, obtaining the initial dataset in CSV format for further work. 6 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 3.2. Data Cleansing and Engineering In order to get an insight into data quality, graphical and statistical methods were used to detect anomalies, faults, outliers, missing values, etc. Moreover, we engineer new attributes in order to increase the interpretability or decrease data complexity. Exploratory Data Analysis assists understanding of relations between attributes and allows us to spot tendencies, as well as to identify the necessary cleaning steps we have to take. First, we apply filters to remove log data from automated services, such as health-checks and other application services that don't reflect the user’s interactions. Next, we remove attributes that contain a high fraction of missing values because the informational significance of attributes is inconsiderable. Values of “status_code” attribute are mapped to the corresponding descriptions for better interpretability. We engineer new attributes: “resource_method”, “resource_base” and “user_os”. The “resource_method” and “resource_base” attributes are created from the values of the “resource” attribute by using regular expressions to extract the relevant information. The “user_os” attribute is created in a similar manner, extracting the relevant information using regular expressions from the “user agent” attribute. Creation of these attributes allows us to focus on the most relevant information and decrease the cardinality of original attributes. 3.3. Dataset Creation The clean dataset contains 16 attributes describing the application usage, and 522,763 rows with a timestamp attribute range from 6th January to 26th March (81 days). Data is imported to RapidMiner [14], a data science software platform that provides an integrated environment for data preparation, visualization, machine learning, text mining, and predictive analytics. It is open source and used for commercial applications, as well as for research, education, training, rapid prototyping. In this phase, we continue with Exploratory Data Analysis in order to discover patterns beyond formal modeling or hypothesis testing tasks. Our aim is to utilize the business understanding to increase the understanding of data and relationships between attributes in order to spot anomalous trends. As the application is B2B based, we analyze the company data first: company account histogram, statistics and distribution. Next, we analyze the behaviors of users in company and general context. By analyzing the “user” and “user domain” attribute, we spot trends in company context usage and behavior. Analysis of application resource requests allows us to understand the usage in general context. 7 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Figure 3. Counts of application resource requests From the figure above, we can spot trends and further analyze the resource usage. The resource request represents a user action, thus are highly valuable for the context of anomaly detection. Moreover, granular analysis facilitates the business understanding as we gain deeper insight into user generated data. Next, we analyze the application errors which are often one of the most informative attributes for the anomaly detection. Anomalies and cyber-attacks are often causing application errors, allowing us to quickly analyze error data and make distinctions between application anomalies, user anomalies and possible threats. Figure 4. Application error logs histogram 8 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Figure 5. Application logs status codes histogram Application status codes are highly correlated with application resource usage. By analyzing status codes, we gain insight into applications performance and usage trends. Anomalies are most visible when analyzing the status codes. Dataset creation is concluded with the creation of an “anomaly” attribute, which represents whether a specific application log instance is anomalous. The criteria for creation of such attribute are drawn from the discoveries of EDA and confirmed through the consultations with SME. By addressing the CRISP-DM phases for Business Understanding, Data Understanding, and Data Preparation with the application of Exploratory Data Analysis, we are able to discover anomalies in application usage and user behavior. 4. Results and Discussion As web application has busines-to-busines context, we approach the analysis of log data from a company perspective. We find that companies using the application can have their application usage segmented into three categories: heavy, medium, and light users, as shown below in the Figure 6. Heavy users are the companies responsible for application development and support. Medium users reflect the companies with frequent application usage, while light users represent the companies that are onboarding to application or in initial phases of application usage. Distinction of company users per their level of usage helps us create a better business understanding. Because of unbalanced level of application usage per 9 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 company, we can expect an increased number of anomalies for heavy users, while companies with medium and light usage may have decreased the number of anomalies. Regarding the percentage of anomalies, it varies between companies with no specific pattern. Figure 6. Application usage per company When analyzing the histogram of application resource methods through the “resource_method” attribute, we find an anomalous request pattern, as shown below in the Figure 7. Consultations with SME yielded that resource request method anomaly corresponds to the service whose use has ceased, and the service behavior can be identified as anomaly. 10 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Figure 7. Application resource methods histogram anomaly When analyzing individual users, we perform segmentation per company using the domain name in user email address. The histogram of user domains contributes to business understanding as we can spot user trends per each company. In the figure below, we present the user domain histogram focused on anomalous application usage of unknown domains. We discover that usage from unknown domains tends to be increased in the monthly peaks of application usage. Figure 8. User domain histogram focused on unknown domains Consultations with SME clarified that unknown domains such as “gmail.com”, “hotmail.com”, and 11 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 “outlook.com” are used by quality assurance developers and were marked as such. This has further decreased the number of visits from unknown domains. Moreover, consultations showed that users from unknown domains are companies in the trial phase, that is application demonstration phase, and are still eligible for anomaly detection. Application usage from other user domains is distributed as expected: two development companies take up the most traffic while others are medium and light users. Figure 9. Log message histogram anomalies In the figure above, we present an analysis result of log message histogram with revealed anomalies. We find that anomalies are caused by application development or, more specifically, integration attempts with other companies using the application. In the figure below, we present results from correlation analysis of the dataset. The correlation matrix shows increased correlation between attributes such as “platform” and “message”. These results help us to identify and discard highly correlated attributes and decrease the dataset complexity. 12 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Figure 10. Correlation matrix Correlation matrix also shows that attributes “status code” and “level” have a level of correlation. This indicates that application errors can be sourced from application status codes. In the figure below, status code histogram focused on error status code is depicted. We can spot the error trends together with identification of error sources. Figure 11. Status code histogram focused on error status codes With application of EDA, the resulting anomalies are used in the creation of labeled dataset for anomaly detection purposes. The dataset can serve as a baseline for creating various analytical and machine learning anomaly detection models such as frequency threshold detection, supervised anomaly prediction, unsupervised anomaly detection, etc. In the Table 2, we present the final dataset statistical information. 13 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 Table 2. Dataset statistical information Attribute name Type Missing Least / Min Most / Max Range timestamp Date and 0 Jan 6, 2020 Mar 26, 2020 9:06 80d 14h 48min 6:18 AM PM 58710 (3) 12345 (131,132) time account_id Nominal 3 12345, c84c286[...]ffea5, [52 more] company_name Nominal 3 Company XYZ Company A Company A, (3) (131,132) Company B, [52 more] country Nominal 3 XX (29) US (399,465) US, BA, IN, [12 more] platform Nominal 0 Backend (45%) Browser (55%) Browser, Backend user Nominal 6 fk***@*.com fs***@*.com fs***@*.com, (4) (48,738) de***@*.com, [209 more] remote_address Nominal 3 184.*.*.22 (3) 77.*.*.171 (41,561) 77.*.*.171, 144.*.*.229, [302 more] user_agent Nominal 0 Mozilla/[...]4.1 Mozilla/[...]ri/537.3 Mozilla/[...]36, (3) 6 (77,449) Mozilla/[...].0, [114 more] 14 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 error_msg Nominal 467,22 Getaddr[...].co ESOCKET[...]UT ESOCKET[...]UT, 5 m (1) (89) 502, [3 more] level Nominal 0 error (159) info (467,225) Info, Error message Nominal 0 Integ[...]led Profiling (264,851) Profiling, (159) frontTimigs, [1 more] status_code Nominal 93 405 Method 200 OK (453,461) [...]ed (1) resource_method Nominal 0 PUT (97) 200 OK, 204 No Content, [8 more] GET (373,123) GET, POST, [3 more] resource_base Nominal 0 produ[...]ile (8) endpoints (98,191) endpoints, customers, [17 more] user_domain Nominal 6 C*** (272) A*** (351,885) A***, M***, [9 more] user_agent_os Nominal 0 Unknown (3) Windows (411,762) Windows, OS X, [2 more] anomaly Binomina 0 True (882) False (466,502) False, True l 5. Conclusion This study has shown that the use of Exploratory Data Analysis contributes to and complements the implementation of CRISP-DM methodology phases: business understanding, data understanding, and data preparation. Moreover, we demonstrate that Exploratory Data Analysis is efficient method for detecting anomalies in big data. Summarizing data characteristics and discovering underlying patterns for data and its distribution brings value for both data understanding and data preparation phase. We confirm the benefits of proven method from previous studies: consultations with SME play a crucial role in the business understanding phase and give a valuable contribution in data understanding phase Next, consultations in the data understanding and data preparation phase facilitates the workflow and can help us increase the data value. Future efforts can be placed in implementation of subsequent CRISP-DM phases, that is, modeling, 15 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 evaluation and deployment. Modeling data using Machine Learning techniques enables complex pattern discovery, as suitable for big data datasets, and further improves anomaly detection as underlying mathematical relationships can be leveraged. While this has been proven in majority of studies conducted in the field of anomaly detection and supervised machine learning, we propose a use of unsupervised machine learning for finding new anomalies that will enable a creation of extended labeled dataset which can then be used for creation of supervised machine learning model for anomaly detection and prediction. 6. [1] References “Big Data and cloud computing: innovation opportunities and challenges” [Online]. Available: https://www.tandfonline.com/doi/full/10.1080/17538947.2016.1239771. [Accessed: 04-Sep-2020] [2] “Cloud Security Alliance (CSA)” [Online]. Available: https://cloudsecurityalliance.org/. [Accessed: 04-Sep-2020] [3] “Top Threats to Cloud Computing: Egregious.” [Online]. Available: https://cloudsecurityalliance.org/artifacts/top-threats-to-cloud-computing-egregious-eleven/. [Accessed: 04-Sep-2020] [4] “About AWS.” [Online]. Available: https://aws.amazon.com/about-aws/. [Accessed: 04-Sep-2020] [5] A. Sari, “A Review of Anomaly Detection Systems in Cloud Networks and Survey of Cloud Security Measures in Cloud Storage Applications,” Journal of Information Security, vol. 6, no. 2, pp. 142–154, Mar. 2015. [6] “Real-time big data processing for anomaly detection: A Survey,” Int. J. Inf. Manage., vol. 45, pp. 289–307, Apr. 2019. [7] “Cyber Security: Threat Detection Model based on Machine learning Algorithm - IEEE Conference Publication.” [Online]. Available: https://ieeexplore.ieee.org/document/8724096. [Accessed: 04-Sep-2020] [8] “DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM model,” Procedia CIRP, vol. 79, pp. 403–408, Jan. 2019. [9] “A Reference Model for Big Data Analytics” [Online]. Available: https://www.researchgate.net/publication/327728739_A_Reference_Model_for_Big_Data_Analytic s. [Accessed: 04-Sep-2020] [10] “Exploratory data analysis” [Online]. Available: https://psycnet.apa.org/record/2011-23865-003. [Accessed: 04-Sep-2020] [11] “Open Source Search: The Creators of Elasticsearch, ELK Stack & Kibana.” [Online]. Available: https://www.elastic.co/. [Accessed: 04-Sep-2020] [12] “Kibana.” [Online]. Available: https://www.elastic.co/kibana. [Accessed: 04-Sep-2020] 16 �Journal of Natural Sciences and Engineering, Vol. 3, (2020) DOI number: 12.34567/JONSAE2020123 [13] “Logstash.” [Online]. Available: https://www.elastic.co/logstash. [Accessed: 04-Sep-2020] [14] “RapidMiner.” [Online]. Available: https://rapidminer.com/. [Accessed: 04-Sep-2020] 17 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Exploratory Data Analysis and Big Data Analytics for Detecting Anomalies in Cloud Computing Author Author Ibrahim Muzaferija, Zerina Mašetić Abstract A summary of the resource. – While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection, which is a necessary part of the management of modern applications. As the record of user behavior, weblogs surely become the research item related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where anomalous behavior needs to be detected in understanding phases prior to modeling a system for such use. Big Data Analytics often ignores anomalous point due to high volume of data. To address this problem, we propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis, which assists in gaining insight into data relationships without the classical hypothesis modeling. In that way, we can gain better understanding of the patterns and spot anomalies. Results show that Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business Understanding phase, making it one of the key steps in the Data Understanding phase. Keywords Keywords. Cloud Computing, Big Data, Data Mining, Anomaly Detection Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021320 https://omeka.ibu.edu.ba/files/original/0bed6a65d0b57cf646e0783e059b7814.pdf af8c307a067e56185e57c3d303df8cfc PDF Text Text Effect of Vaccinium vitis-idaea tea and Arctostaphylos uva-ursi tea on growth of causative agents of urinary tract infections Lamija Hafizović1, Selma Karup1, Almin Hadžialić 1 1 International Burch University, Sarajevo, Bosnia and Herzegovina lamija.hafizovic@stu.ibu.edu.ba selma.karup@stu.ibu.edu.ba almin.hadzialic@stu.ibu.edu.ba Abstract - Urinary tract infections pose a serious problem to people, both in the hospital environment and outside world. They are characterized by high mortality and ability to cause health problems in areas of the human body other than the urinary tract. It has been long clinical practice to treat these infections with antibiotics, a tactic made very ineffective with the advent of antibiotic-resistant microbial strains. The research has turned to alternative modes of treatment, such as use of herbal remedies to combat urinary tract infections. Effect of two types of herbal teas was observed through use of broth microdilution assay, to test varying concentrations of teas on the growth of selected microorganisms. Results were verified by assessment of colony growth on Mueller Hinton Agar plates. Tested microorganisms exhibited very dense colony growth. Similarity of conditions between urinary retention and conditions under which microorganisms were cultured in 96-well plates possible reason for density of growth. Methods with higher degree of confidence in treatment of urinary tract infections could likely be the combination of antibiotics with herbal teas. Keywords: antibiotic resistance, Arctostaphylos uva-ursi, broth microdilution assay, urinary tract infections, Vaccinium vitis-idaea 1. Introduction Urinary tract infections (UTIs) are, by definition, categorized as diseases according to clinical symptoms, laboratory indicators and microbiological findings, and are most often caused by various bacterial species. They are designated as cystitis or as infections affecting lower urinary tract, or as prostatitis. Based on clinical factors, they are classified in different groups: acute uncomplicated cystitis, urinary tract infections caused by indwelling catheters, recurrent cystitis in young women, urinary tract infections in men, complicated urinary tract infections and asymptomatic bacteriuria [1,2]. In the cases of often repeated and inadequately treated urinary tract infections, these infections may become permanent or chronic diseases [3], which can lead to development of other types of diseases, and further increase the already high mortality rate exhibited by urinary tract infections [4]. Antibiotics are the most common method of treatment for urinary tract infections. Some of the antibiotics have been highly effective in treatment of UTIs, while others had little to no effect. However, the misuse of antibiotics has led to development of bacterial resistance, where increasing numbers of antibiotics have no effect in treatment of urinary tract infections [5,6]. This has led to discovery of alternative methods of treatment for UTIs, such as use of herbal products to combat these infections. Certain herbs have �exhibited ability to prevent bacterial invasion into the urinary tract (Agropyron repens), to impede adhesion of bacteria to bladder walls (Urtica spp., Betula spp.), and to inhibit formation of bacterial colonies [7,8]. Bioactive compounds of Vaccinium vitis-idaea (lingonberry, mountain cranberry) have demonstrated antimicrobial, anti-inflammatory and antioxidative activity [9,10], while Arctostaphylos uva-ursi (bearberry) was also noted to have anti-inflammatory effect in lower urinary tract [11]. The objective of this experiment was to determine the effect of Vaccinium vitis-idaea tea and Artctostaphylos uva-ursi tea on growth of primarily bacteria which are responsible for the development of various urinary tract infections. 2. Materials and methods Microorganisms used in this experiment were Escherichia coli ATCC 14169, Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, Staphylococcus aureus ATCC 6538, Staphylococcus aureus ATCC 12493, Enterococcus faecalis ATCC 29212, Candida albicans ATCC 10231 and Pseudomonas aeruginosa ATCC 27853. They were cultured and kept in Tryptic Soy Broth (TSB) containing 50% glycerol, since it was necessary to store them at -80˚C. The herbal teas chosen for this experiment were the Vaccinium vitis-idaea tea and Arctostaphylos uva-ursi tea, both of which were acquired from herbal pharmacy ″Faveda″ in Sarajevo. Teas were made and tested in two different concentrations: in concentration recommended on tea packaging and in concentration two times stronger than the recommended one. Since the objective of this experiment was to study the effect of teas on bacterial growth, it was necessary to detect minimal inhibitory concentration (MIC) for both teas. Broth microdilution assay was used for detection of minimal inhibitory concentrations for each of the tested microorganisms. Tested microorganisms were first grown in the medium Tryptic Soy Broth until desired growth phase. In each well of microtiter plate a 100μl of medium was pipetted. Tea, also made in TSB medium, was added in column 3 (100μl) and mixed using micropipette to suck the liquid up and down a few times. Then, a 100μl of dilution was transferred from column 3 to column 4 and mixed using micropipette. The procedure was repeated until the last column, so the concentration of tea was lessened by half in each subsequent well. Following that, a 10 μl of microorganism was added (each tested microorganism to an individual plate) in columns 2 to 12. Column 1 did not contain microbes, as it represented negative control. Plates were incubated overnight at 37˚C. After incubation, microbes from wells containing different concentrations were inoculated onto Mueller Hinton Agar plates and incubated overnight at 37˚C. Bacterial growth was observed from the plates and it was visually assessed and numerically classified by the density of formed colonies, as shown in Table 1. 3. Results Bacterial growth was assessed from Mueller Hinton Agar plates. The objective was to find minimal inhibitory concentration, so microorganisms were inoculated only from certain wells of 96-well plates (where the probability of finding minimal inhibitory concentrations seemed the highest). Starting concentrations of tea were the recommended concentration (RC) on the tea packaging (Table 2 and 3), �adjusted for the volume of the well, and the second concentration that was twice as strong as the recommended one (Tables 4 and 5). Using broth microdilution assay as described in the previous section, the concentration of tea was lowered by half in each subsequent well. Recommended concentration on the tea packaging was one coffee spoon (10 g) per 200 ml of liquid (TSB, in this case). Table 1. Evaluation of microbial growth Evaluation Area of plate covered by microbial growth 10^7 100% 10^6 85%-90% 10^5 70%-75% 10^4 50% 10^3 35% 10^2 15% 10^1 <15% Sterile 0% Table 2. Effect of recommended concentration of Vaccinium vitis-idaea tea on bacterial growth 0,5*RC 0,25*RC 0,125*RC 0,0625*RC E. faecalis 29212 10ˆ7 10ˆ7 / / C. albicans 10231 10ˆ7 10ˆ7 / / P. aeruginosa 27853 10ˆ7 10ˆ7 / / E. coli 25922 10ˆ7 10ˆ7 / / E. coli 14169 10ˆ7 10ˆ7 10ˆ7 10ˆ7 S. aureus 25923 10ˆ7 10ˆ7 / / S. aureus 12693 10ˆ7 10ˆ7 / / S. aureus 6538 10ˆ7 10ˆ7 / / Table 3. Effect of recommended concentration of Arctostaphylos uva-ursi tea on bacterial growth 0,5*RC 0,25*RC 0,125*RC 0,0625*RC E. faecalis 29212 10ˆ7 10ˆ6 / / C. albicans 10231 10ˆ6 10ˆ7 10ˆ7 10ˆ7 �P. aeruginosa 27853 10ˆ6 10ˆ7 10ˆ7 / E. coli 25922 10ˆ7 10ˆ7 / / E. coli 14169 10ˆ7 10ˆ7 / / S. aureus 25923 10ˆ7 10ˆ7 / / S. aureus 12693 10ˆ6 10ˆ6 10ˆ6 10ˆ6 S. aureus 6538 / 10ˆ7 10ˆ7 10ˆ7 Table 4. Effect of second concentration of Vaccinium vitis-idaea tea on bacterial growth RC 0,5*RC 0,25*RC E. faecalis 29212 10ˆ7 10ˆ7 10ˆ7 C. albicans 10231 10ˆ7 / / P. aeruginosa 27853 10ˆ7 10ˆ7 / E. coli 25922 10ˆ7 / / E. coli 14169 10ˆ7 10ˆ7 / S. aureus 25923 10ˆ7 10ˆ7 / S. aureus 12693 10ˆ7 10ˆ7 / S. aureus 6538 10ˆ7 10ˆ7 / Table 5. Effect of second concentration of Arctostaphylos uva-ursi tea on bacterial growth RC 0,5*RC 0,25*RC 0,125*RC E. faecalis 29212 / 10ˆ7 10ˆ7 10ˆ7 C. albicans 10231 10ˆ7 10ˆ7 / / P. aeruginosa 27853 10ˆ7 10ˆ7 / / E. coli 25922 10ˆ7 10ˆ7 / / E. coli 14169 10ˆ6 10ˆ7 10ˆ7 / S. aureus 25923 10ˆ7 10ˆ7 / / �S. aureus 12693 10ˆ7 10ˆ7 10ˆ7 / S. aureus 6538 10ˆ7 10ˆ7 / / As observed from results presented in tables above, microorganisms have demonstrated formation of highly dense bacterial colonies, covering the entirety of inoculated surface of agar plates, both in case of recommended tea concentrations and the second tested concentration. 4. Discussion Urinary tract infections present a great issue, since they are capable of causing a multitude of other health complications, as well as possessing a high mortality rate, which is 3% in women and 1% in men [4]. A fact which further exacerbates this problem is the ability of microorganisms to form structures such as biofilm and to produce antibiotic-negating enzymes, resulting in bacteria being capable of withstanding and surviving antibiotic activity, which is a characteristic termed antibiotic resistance. Microbial strains resistant to a variety of different antibiotics, designated as multidrug-resistant strains, have also emerged [6]. It is likewise highly concerning that the discovery of new types of antibiotics capable of successfully combating UTIs has substantially decreased, which has led researchers to turn their investigations to alternative methods of treatment for UTIs. One of these alternative methods is the use of herbal products, since some of their compounds are capable of mitigating or eliminating the symptoms of UTIs. In this experiment, we observed the growth of microorganisms under influence of herbal tea. One of the objectives was to determine whether tea alone has the ability to impede the growth of bacteria. Broth microdilution assay was used to test the effect of different concentrations of tea on microbial growth, because it provides quantitative data and it is possible to use this method in any laboratory. Inoculation onto Mueller Hinton Agar plates was used to verify the results. After overnight incubation at 37˚C, it was observed that tested microorganisms exhibited highly dense growth, under both normal and doubled concentrations. Conclusion is that this might have happened because the conditions in 96-well plates, during the performance of broth microdilution assay, were similar to those occurring in urinary retention. Urinary retention is an inability to completely remove urine from the bladder, which can lead to kidney and bladder damage, and development of urinary tract infections. Bacteria within urine, which are normally mostly harmless since they are removed with urine in healthy people, in case of urinary retention are able to accumulate and cause the development of various urinary tract infections [12]. Increased liquid intake was reported to have beneficial effects for the patients affected by UTIs, since it causes dilution of metabolic waste products which serve as nutrients for microorganisms [13]. In addition, diuresis that results from increased liquid intake yields the benefit of so-called mechanical ″flushing″, meaning that bacteria are physically removed from the urinary tract along with urine, denying them the time necessary to accumulate and cause further issues. As noted in introduction, some herbs possess compounds which exhibit the ability to prevent bacterial adhesion or inhibit bacterial growth [7,8]. ″Flushing″ the urinary tract with herbal tea used for alleviating the symptoms of UTIs can possibly exhibit higher effectiveness in treatment of these infections. �Herbal teas used in this experiment consisted of dried leaves (in case of A. uva-ursi) and dried fruits or baccae (in case of V. vitis-idaea). This might have contributed to their low effect on microbial growth. Extracts of fresh leaves and fruits could be more effective in treatment of urinary tract infections. Despite not being able to inhibit the growth of microbes on their own, both V. vitis-idaea and A. uva-ursi have properties which make them useful in treatment of UTIs, such as the anti-inflammatory activity and antioxidative activity, as well as other properties. Their use in treatment of UTIs is a subject of many studies [9-11,14-17]. Antibiotic resistance of microorganisms responsible for development of UTIs extends to some of the most commonly used antibiotics in treatment of UTIs. However, there are several antibiotics which still demonstrate significant effect in inhibiting the growth of UTI causative agents. There is a high possibility that the combined effect of antibiotics and herbal teas could result in greater effectiveness of treatment of urinary tract infections. Further research into combined use of antibiotics and herbal remedies should be done, with aim of reduction of unnecessary antibiotic use, which leads to the development of antibiotic resistance. 5. References [1] Grabe, M., Bjerklund-Johansen, T. E., Botto, H., Çek, M., Naber, K. G., Pickard, R. S., ... and Wullt, B. (2013). Guidelines on urological infections. European Association of Urology guidelines. [2] Stamm, W. E., and Hooton, T. M. (1993). Management of urinary tract infections in adults. New England journal of medicine, 329(18), 1328-1334. [3] Finer, G., and Landau, D. (2004). Pathogenesis of urinary tract infections with normal female anatomy. The Lancet infectious diseases, 4(10), 631-635. [4] Daswani, P. G. (2019). Non-antibiotic potential of medicinal plants to combat urinary tract infections. CURRENT SCIENCE, 117(9), 1459. [5] Sabir, N., Ikram, A., Zaman, G., Satti, L., Gardezi, A., Ahmed, A., and Ahmed, P. (2017). Bacterial biofilm-based catheter-associated urinary tract infections: Causative pathogens and antibiotic resistance. American Journal Of Infection Control, 45(10), 1101-1105. [6] Gopichand, P., Agarwal, G., Natarajan, M., Mandal, J., Deepanjali, S., Parameswaran, S., and Dorairajan, L. N. (2019). In vitro effect of fosfomycin on multi-drug resistant gram-negative bacteria causing urinary tract infections. Infection and drug resistance, 12, 2005. [7] Rafsanjany, N., Lechtenberg, M., Petereit, F., and Hensel, A. (2013). Antiadhesion as a functional concept for protection against uropathogenic Escherichia coli: In vitro studies with traditionally used plants with antiadhesive activity against uropathognic Escherichia coli. Journal Of Ethnopharmacology, 145(2), 591-597. [8] Beydokthi, S., Sendker, J., Brandt, S., and Hensel, A. (2017). Traditionally used medicinal plants against uncomplicated urinary tract infections: Hexadecyl coumaric acid ester from the rhizomes of Agropyron repens (L.) P. Beauv. with antiadhesive activity against uropathogenic E. coli. Fitoterapia, 117, 22-27. [9] Wang, S. Y., Feng, R., Bowman, L., Penhallegon, R., Ding, M., and Lu, Y. (2005). Antioxidant activity in lingonberries (Vaccinium vitis-idaea L.) and its inhibitory effect on activator �protein-1, nuclear factor-κB, and mitogen-activated protein kinases activation. Journal of agricultural and food chemistry, 53(8), 3156-3166. [10] Puišo, J., Jonkuvienė, D., Mačionienė, I., Šalomskienė, J., Jasutienė, I., and Kondrotas, R. (2014). Biosynthesis of silver nanoparticles using lingonberry and cranberry juices and their antimicrobial activity. Colloids and Surfaces B: Biointerfaces, 121, 214-221. [11] Schink, A., Neumann, J., Leifke, A. L., Ziegler, K., Fröhlich-Nowoisky, J., Cremer, C., ... and Lucas, K. (2018). Screening of herbal extracts for TLR2-and TLR4-dependent anti-inflammatory effects. PloS one, 13(10), e0203907. [12] Barrisford GW and Steele GS. Acute urinary retention. Post T, ed. UpToDate. Waltham, MA: UpToDate; 2018. [13] Tian, Y., Cai, X., Wazir, R., Wang, K., and Li, H. (2016). Water consumption and urinary tract infections: an in vitro study. International Urology and Nephrology, 48(6), 949-954. [14] Moore, M., Trill, J., Simpson, C., Webley, F., Radford, M., Stanton, L., ... & Griffiths, G. (2019). Uva-ursi extract and ibuprofen as alternative treatments for uncomplicated urinary tract infection in women (ATAFUTI): a factorial randomized trial. Clinical Microbiology and Infection, 25(8), 973-980. [15] Afshar, K., Fleischmann, N., Schmiemann, G., Bleidorn, J., Hummers-Pradier, E., Friede, T., ... & Gágyor, I. (2018). Reducing antibiotic use for uncomplicated urinary tract infection in general practice by treatment with uva-ursi (REGATTA)–a double-blind, randomized, controlled comparative effectiveness trial. BMC complementary and alternative medicine, 18(1), 1-8. [16] Ek, S., Kartimo, H., Mattila, S., & Tolonen, A. (2006). Characterization of phenolic compounds from lingonberry (Vaccinium vitis-idaea). Journal of agricultural and food chemistry, 54(26), 9834-9842. [17] Antolak, H., Czyzowska, A., Sakač, M., Mišan, A., Đuragić, O., and Kregiel, D. (2017). Phenolic compounds contained in little-known wild fruits as antiadhesive agents against the beverage-spoiling bacteria Asaia spp. Molecules, 22(8), 1256-1274. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Effect of Vaccinium vitis-idaea tea and Arctostaphylos uva-ursi tea on growth of causative agents of urinary tract infections Author Author Lamija Hafizović, Selma Karup, Almin Hadžialić Abstract A summary of the resource. Urinary tract infections pose a serious problem to people, both in the hospital environment and outside world. They are characterized by high mortality and ability to cause health problems in areas of the human body other than the urinary tract. It has been long clinical practice to treat these infections with antibiotics, a tactic made very ineffective with the advent of antibiotic-resistant microbial strains. The research has turned to alternative modes of treatment, such as use of herbal remedies to combat urinary tract infections. Effect of two types of herbal teas was observed through use of broth microdilution assay, to test varying concentrations of teas on the growth of selected microorganisms. Results were verified by assessment of colony growth on Mueller Hinton Agar plates. Tested microorganisms exhibited very dense colony growth. Similarity of conditions between urinary retention and conditions under which microorganisms were cultured in 96-well plates possible reason for density of growth. Methods with higher degree of confidence in treatment of urinary tract infections could likely be the combination of antibiotics with herbal teas. Keywords Keywords. antibiotic resistance, Arctostaphylos uva-ursi, broth microdilution assay, urinary tract infections, Vaccinium vitis-idaea Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021321 https://omeka.ibu.edu.ba/files/original/837ee7ba8524552cc47c30a81ccd37ff.pdf fd1278d02233437664771934e94dee9c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource INFLUENCE OF THE DISTRIBUTED GENERATION ON THE POWER QUALITY IN DISTRIBUTION NETWORK Author Author Alma Halilović, Lejla Mujanović, Jasna Hivziefendić Abstract A summary of the resource. The aim of this paper is to present and discuss the influence of distributed generation on power quality. Nowadays, interest in power quality has increased since it has become a very important issue in power system delivery. One of the major problems of ensuring a certain level of power quality are harmonics. The aim of this project is to investigate an impact of photovoltaic (PV) on harmonic voltage distortion (HD) in real MV distribution network. Different scenarios will be implemented where solar power plant is going to be modelled with high variability of load and generation to see their effects on the systems power quality (PQ). Those scenarios are when PV is disconnected from the grid and PVs are connected with 2 different powers. Results presented below showed that PV improves power quality of the system, because their inverters are source of harmonics and they increase HD. However, that impact is not very significant and harmonic limits are not violated. A load flow analysis is done for the model of test system 110/35/10kV in which a distributed generator is added, that is on-grid or off-grid. The network modelling and simulation is done in DIgSILENT PowerFactory software. Keywords Keywords. DIgSILENT, power system harmonics, PVs, power quality, harmonic distortion. Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021322 https://omeka.ibu.edu.ba/files/original/75d3d0af9ea8e10805ad5c3282bba357.pdf 7331ebb69ef6ccfd5bd7b7b6a4f1b3ca PDF Text Text Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 Effect of metals on antibiotic sensitivity, growth, and biofilm-forming capacity of B. subtilis subsp. spizizenii Selma Cifric1 1 International Burch University, Sarajevo, Bosnia and Herzegovina selma.cifric@stu.ibu.edu.ba Abstract – B. subtilis is normally considered a soil organism, it can be also found in the animal and human gastrointestinal tract. Bacillus subtilis subsp. spizizenii is a type of Bacillus subtilis complex. It shares up to 99% of homology with B. subtilis CU1, which can be represented as a probiotic strain. Metal compounds found in soil or used in agriculture can easily enter the food chain and end up in our gut. Gram-positive bacteria (e.g. Bacillus spp.) have good adsorptive capacity for metals due to high peptidoglycan and teichoic acid content in cell walls. There is some evidence that certain metals inside the intestine play an important role in influencing growth and functionality of specific probiotic strains. Some of them have inhibitory, while others have an activating effect on bacteria. This study revealed that metal compounds increased antibiotic susceptibility of B. subtilis subsp. spizizenii. Higher concentrations of metal solutions inhibited growth of tested bacteria. Culture did not show affinity to form biofilms before or after addition of metal solutions. Keywords – antibiotic susceptibility, biofilms, MIC, metals. 1. Introduction Various bacteria reside in the gut or arrive there by food consumption. A microbiome is the overall collection of the genetic material of all microorganisms that live on or inside our body or collection of the genetic material of microorganisms in a particular environment (e.g., in your gut). Bacteria within our gut have an important role in digesting food, modulating the immune system, providing protection against harmful microbes, and more. Multiple factors including genotype, antibiotics, mode of delivery, dietary habits, lifestyle, social interactions and environmental factors shape the gut microbiota to make everyone’s microbiome unique [1, 2, 3]. Metal compounds can cause alterations in the composition of the gut microbiota. Usually, decrease in richness as well as the diversity of gut microbiota, is observed after exposure to metals [4, 5]. Gram-positive bacteria (e.g. Bacillus spp.) have good adsorptive capacity for metals due to high peptidoglycan and teichoic acid content in cell walls, in contrast to Gram-negative bacteria [6]. The phylum Firmicutes found in colon is mostly composed of gram-positive species, such as Clostridium and Bacillus. There is some evidence that certain metals inside the intestine play an important role in influencing growth and functionality of specific probiotic strains. Some of them have inhibitory, while others have an activating effect on bacteria. It has been concluded that many effects of metals are strain-specific [7]. �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 Bacillus subtilis is a gram positive and catalase positive rods. It is spore-forming bacteria. Although normally considered a soil organism, it is also found in the animal and human gastrointestinal tract [8]. Bacillus subtilis subsp. spizizenii is a type of Bacillus subtilis complex. It shares up to 99% of homology with Bacillus subtilis CU1, which can be represented as a probiotic strain that can have specific outcomes on the immune system of the elderly [9, 10]. Probiotics are commensal bacteria in the gut that have a health beneficial effect on the host organism. However, there are still a few unresolved questions regarding the safety of certain Bacillus strains, which is the main reason for their still limited application as probiotics [11, 12]. Biofilms are communities of bacteria joined together by a sticky extracellular matrix. This extracellular matrix is also responsible for adherent biofilms to various surfaces. Probiotic bacteria in the gut also use biofilm attachment to bind to the mucosa layer of the intestine. Biofilm attachment improves their survival rate. Specifically, biofilms provide protection against antibiotics and enzymes [13, 14, 15]. Antibiotics are antimicrobial agents active against bacteria. Their mode of action can be bactericidal or bacteriostatic. Application of antibiotics influences intestinal microbiota. It affects growth, diversity and antibiotic resistance of bacteria. Since Bacillus subtilis are partially considered as probiotic bacteria, normally found in the human gastrointestinal tract, this study will show their antibiotic susceptibility in the presence of metal compounds that can end up in our gut via food intake [16, 17]. In this paper, the effect of metal compounds on biofilm forming capacity, bacterial growth, and changes in antibiotic sensitivity is examined. It is assumed that metal compounds would increase antibiotic sensitivity and suppress growth. 2. Methods 1. Cultivation of B. subtilis subsp. spizizenii strain Bacillus subtilis subsp. spizizenii (ATCC 6633) was cultivated on solid and liquid media (trypticase soy broth (TSB) broth, TSB agar). After overnight incubation at 37 C, the turbidity of bacterial density is adjusted to 0.5 McFarland standard, as such was used for further tests. 2. Determination of antibiotic susceptibility before the addition of metal supplements Bacteria is previously cultivated on TSB agar. Susceptibility to fifteen types of antibiotics will be performed using the standard Kirby-Bauer disk diffusion method [18]. Antibiotics (Liofilchem) are listed in Table 1 below. 3. Microbroth dilution method Microbroth dilution method will be used to determine the minimal dose of metal supplement necessary to inhibit the growth of bacteria (minimum inhibitory concentration - MIC). It is accomplished through the standardized broth microdilution assay procedure [19, 20]. 96-well microtiter plates were used. The metal salts were aseptically diluted in TSB broth in the following w/V solutions: 1%, 0.5%, 0.25%, 0.12%, 0.06%, 0.03%, 0.015%, 0.007%, 0.003%, 0.0018%, 0.0009%. The 96-well plate contained 100 ul of different concentrations of metal solutions (CuSO4, ZnSO4 x 7H2O, Fe(NO3)3, and Mg), 100 ul TSB broth, and 20 ul of B. subtilis subsp. spizizenii (0.5 McFarland standard). This test was done in triplets. The �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 purpose was to determine the exact concentration of each metal that inhibits bacterial growth. After overnight incubation at 37 C visible growth of bacteria is recorded and MICs have been determined. 4. Determination of biofilm forming capacity This test determines how different concentrations of CuSO4 - copper (II) sulfate pentahydrate (Sigma-Aldrich), ZnSO4 x 7H2O - zinc sulfate heptahydrate (Sigma-Aldrich), Fe(NO3)3 - iron (III) nitrate (Fisher Scientific), and magnesium complex (Twinlab - dietary supplement from local pharmacy) will facilitate the biofilm formation. This test will be performed using TCP method. The 96-well plate contained different concentrations of metals, TSB medium, and 20 ul of B. subtilis subsp. spizizenii (0.5 McFarland standard). The inoculated plate should be covered with a lid and incubated for 24 h at 37 C. After incubation the content of the plates is discarded and washed. Crystal violet assay is used as a method of indirect biofilm quantification. Each microtiter-plate well is stained with 120 ul of 0.1% crystal violet and set aside for 10 minutes. Microliter-plate is decanted again and washed with distilled water. The test is done in triplets [21, 22]. 5. Determination of antibiotic susceptibility after addition of metals Susceptibility to fifteen types of antibiotics (Table 1) after addition of metal solutions will be performed using the Kirby-Bauer disk diffusion method [18]. Table 1. List of fifteen antibiotic discs used for antibiotic susceptibility testing. Name of antibiotic Micrograms Abbreviation Cefoxitin 30 FOX30 Gentamicin 10 CN10 Oxacillin 1 OX1 Amoxicillin 10 AML10 Ceftazidime + clavulanic acid 40 CAL40 Ciprofloxacin 5 CIP5 Streptomycin 10 S10 Vancomycin 30 VA30 Erythromycin 15 E15 Ceftazidime 10 CAZ10 Amoxicillin-clavulanic acid 30 AUG30 Azithromycin 15 AZM15 Kanamycin 30 K30 Tetracycline 30 TE30 Ampicillin 2 AMP2 �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 3. Results After testing the effect of metal compounds on growth, antibiotic susceptibility, and biofilm forming capacity, the following results were obtained. Table 2 shows results obtained after performing antibiotic susceptibility test for B. subtilis subsp. spizizenii. It compares diameters of the inhibition zone (in millimeters), before and after addition of four different metal compounds. Table 2. Antibiotic susceptibility test for B. subtilis subsp. spizizenii. Diameter of the zone of inhibition is in millimeters. (* - partially bactericidal) B. spizizenii B. spizizenii + Mg B. spizizenii + ZnSO4 x 7H2O B. spizizenii + Fe(NO3)3 B. spizizenii + CuSO4 FOX30 25 28 27 28 24 CN10 20 22 21 20 21 OX1 15 18 18 17 15 AML1 0 9 12* 15* 14* 11 CAL40 0 0 0 0 0 CIP5 29 32 32 32 35 S10 19 20 18 19 19 VA30 18 21 20 20 20 E15 21 25 28 24 25 CAZ10 0 0 6 8 0 AGU30 21 23 25 24 22 AZM1 5 20 22 21 23 22 K30 22 24 24 23 24 TE30 26 31 29 30 27 AMP2 0 0 0 0 0 Since diameters of inhibition zones for fifteen antibiotics were measured manually, Figure 1 visualizes sizes of diameters and possible manual errors during the measurement process. �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 Figure 1. Antibiotic susceptibility to fifteen antibiotics measured by zone of inhibition (in millimeters). Results of microbroth dilution tests are presented in Table 3 and Figure 2. Table 3 shows how different concentrations of metal (w/V) solutions affect growth of B. subtilis subsp. spizizenii, while minimum inhibitory concentrations of metals are summarized in Figure 2. Table 3. Growth of B. subtilis subsp. spizizenii under different concentrations of metal solutions w/V solution 1% 0.5% 0.25% 0.12% 0.06% 0.03% 0.015% 0.007% 0.003% 0.0018% 0.0009% Mg Fe(NO3)3 CuSO4 ZnSO4 x 7H20 No growth No growth No growth No growth No growth No growth No growth No growth No growth Growth No growth No growth No growth Growth No growth No growth Growth Growth No growth No growth Growth Growth Growth No growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 Figure 2. Minimum inhibitory concentration (MIC) of metal compound for growth of B. subtilis subsp. spizizenii B. subtilis subsp. spizizenii did not show affinity to form biofilms before (visible to the naked eye) or after addition of metal solutions at any concentration (w/V). The limitation of this study might be that the optical density of each microplate was not measured using ELISA reader. 4. Discussion In order to test the antibiotic sensitivity and growth, for this particular experiment, different metals had been taken to test this effect. In this particular experiment, one of the metals that had been used was zinc sulfate, a specific solid that can have a colorless crystalline structure. In a historical approach, it is known that zinc could be found in soil where different plants are harvested, but in different areas there is something known as solid deficiency, where plants cannot develop properly and grow because of the lack of zinc. And in order for this to be corrected, people have experimented and found out that in order to correct this deficiency, zinc sulfate can be added to the soil in order to have the proper growth of different crops. Because these metals are used in order to grow crops, this may have a different effect when the crops are consumed as a food source. [23, 24, 25]. Copper (II) sulfate pentahydrate is most commonly described as an inorganic compound that could be found in copper in the form of salt. It is highly soluble in water. This type of salt has a usage as an additive in order to recover pentose sugars from the fronts of palm oils. It had been used as well to prove specific antimicrobial properties when working with specific types of bacteria, but most importantly here with Bacillus subtilis [26, 27]. Copper (II) sulfate is used as fungicide in agriculture, as an additive for fertilizers and food [28]. �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 Iron (III) nitrate, or in other words ferric nitrate, is a type of metal that can be used in many fields. This type of compound can be used to treat different sludges and wastewaters, it can be used to remove nitrogen from different plants and it can also be used in analytical chemistry [29, 30]. All three of metals aforementioned, zinc sulfate, copper (II) sulfate pentahydrate, and iron (III) nitrate, can be found in soil or are used in agriculture. In that way they can get into the food chain and enter the human gut. One of the most abundant minerals that are important for different metabolic processes in the human body is magnesium. It can be found in over 300 enzymes as a cofactor and it regulates different biochemical reactions that are processed in the human body. Usually, magnesium is provided as a type of dietary supplement, people consume it in order for their body to function properly, and different amounts of these minerals are given to people based on various factors [31]. For example, magnesium citrate helps with constipation, it acts as laxative, while magnesium aspartate is important for digestion of macronutrients [32]. B. subtilis subsp. spizizenii showed visible growth at 0.06% (w/V) magnesium solution (Table 3). Dietary supplement was used as a source of magnesium. No significant changes were recorded in antibiotic susceptibility tests in presence of Mg, except with amoxicillin. Addition of Mg solution slightly changed property of B. subtilis subsp. spizizenii. According to obtained results amoxicillin was partially bactericidal (a few colonies appeared within the inhibition zone) for tested bateria, in the presence of magnesium. Susceptibility to fifteen types of antibiotics (Table 1), before and after addition of metal solutions, will be performed using Kirby-Bauer disk diffusion method. This test showed that B. subtilis subsp. spizizenii is completely resistant to ampicillin (AMP2), as well as to ceftazidime+clavulanic acid (CAL40). Antibiotic sensitivity of B. subtilis subsp. spizizenii did not significantly change for the following antibiotics: gentamicin (CN10), streptomicin (S10), vancomycin (VA30), azithromycin (AZM15), kanamycin (K30), cefoxitin (FOX30), oxacillin (OX1), ciprofloxacin (CIP5). Change in diameter was less or equal to 3 mm. Note that diameters were measured manually, and manual errors (gross errors) should be taken into account. Difference in diameter of zone inhibition of erythromycin (E15) with addition of zinc sulfate heptahydrate and without metal solution is 7 mm. There was an increase in diameter size of the inhibition zone for tetracycline (TE30) and amoxicillin (AML10) in presence of magnesium, zinc sulfate heptahydrate, and iron (III) nitrate solutions, compared to diameters of inhibition zones before addition of metal compounds. Besides that, a few colonies of bacteria were observed within amoxicillin zones of inhibition. Amoxicillin was partially bactericidal for B. subtilis subsp. spizizenii, in presence of magnesium, zinc sulfate heptahydrate, and iron (III) nitrate solutions. �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 B. subtilis subsp. spizizenii without presence of metal solutions was resistant to ceftazidime (CAZ10). With addition of zinc sulfate heptahydrate, and iron (III) nitrate solutions, zones of inhibition were 6 and 8, respectively. Since B. subtilis subsp. spizizenii shares the biochemical similarities with Bacillus subtilis subsp. subtilis results for these two strains can be compared. There is up to 58 to 68% is the DNA relatedness between these two bacteria [10, 33, 34]. According to Silman et al. vancomycin showed great bactericidal effect for B. subtilis in general [35]. Our data shows that zones of inhibition obtained by vancomycin (VA30) are ~20 mm, while the largest zones of inhibition were recorded in presence of ciprofloxacin (CIP5) ranging from 32-35 mm in diameter (Figure 1). Sim et al. obtained similar results about CIP5 and TE30, where zones of inhibition were 32 and 31, respectively [36]. Bacterial growth was registered for all four metal compounds at different concentrations (Table 3). No bacterial growth was registered for Mg at the concentrations 0.1%, 0.5%, 0.25%, 0.12%, while bacterial growth occured at all other tested w/v solutions (Table 3). B. subtilis spizizenii growth occurred at all other w/v solutions of iron (III) nitrate except at the concentrations 0.1% and 0.5%. Growth of bacteria in the presence of copper (II) sulfate w/v solution occurred at concentrations 0.03-0.0009%. The lowest growth rate was observed in the presence of zinc sulfate heptahydrate solution, bacterial growth occurred only on concentrations 0.015-0.0009% (Table 3). The lowest concentration of chemical (drug, antimicrobial) that inhibits visible growth of microorganism (in this case bacteria) in overnight culture is known as minimum inhibitory concentration (MIC) [37]. After overnight incubation at 37 C MICs were recorded (Figure 2). Obtained MICs of metal solutions that inhibit growth of B. subtilis subsp. spizizenii are: magnesium 0.12%, iron (III) nitrate 0.50%, 0.06% copper (II) sulfate, 0.03% zinc sulphate heptahydrate. Considering that, growth of tested bacteria is slightly inhibited by iron (III) nitrate solution (bacteria is growing in presence of metal solution whose concentration is <0.50%), while it is tolerating much lower concentrations of zinc sulfate heptahydrate solution (<0.03%). For this experiment laboratory strain of B. subtilis subsp. spizizenii was used. This strain did not form biofilms at all. According to other studies, during domestication of laboratory strains of B. subtilis accumulation of mutation can occur which can lead to their inability to form well-structured biofilms. Compared to the laboratory strains, undomesticated strains of B. subtilis usually form rich and strong biofilms [38, 39]. 5. Conclusion B. subtilis complex is normally found in soil, however it is also found in the human gut as harmless bacteria. Further research is needed for its wider application on the probiotic market due to safety concerns. Metal traces can be found in soil, wastewaters, products used in agriculture, fungicides, etc. as �Journal of Natural Sciences and Engineering, Vol. 1, (2019) DOI number: 10.14706/JONSAE2019114 such they can easily enter our food chain and end up in the human gut. This study investigated how specific metal compounds influence growth, antibiotic susceptibility, and biofilm forming capacity of B. subtilis subsp. spizizenii. Based on the results that have been retrieved, we can conclude that higher concentrations of metal solutions inhibited growth of tested bacteria, while it showed good tolerance to majority of lower concentrations of metals. Generally, culture showed increased sensitivity against antibiotics after addition of metal solutions. B. subtilis subsp spizizenii used in this experiment was laboratory strain and was not able to form biofilms. No influence of metals was recorded there. 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Determination of minimum inhibitory concentrations. Journal of antimicrobial Chemotherapy, 48(suppl_1), 5-16. [38] Bate, A. R., Bonneau, R., & Eichenberger, P. (2016). Bacillus subtilis systems biology: applications of‐omics techniques to the study of endospore formation. The Bacterial Spore: from Molecules to Systems, 129-144. [39] McLoon, A. L., Guttenplan, S. B., Kearns, D. B., Kolter, R., & Losick, R. (2011). Tracing the domestication of a biofilm-forming bacterium. Journal of bacteriology, 193(8), 2027-2034. � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Effect of metals on antibiotic sensitivity, growth, and biofilm-forming capacity of B. subtilis subsp. spizizenii Author Author Selma Cifric Abstract A summary of the resource. B. subtilis is normally considered a soil organism, it can be also found in the animal and human gastrointestinal tract. Bacillus subtilis subsp. spizizenii is a type of Bacillus subtilis complex. It shares up to 99% of homology with B. subtilis CU1, which can be represented as a probiotic strain. Metal compounds found in soil or used in agriculture can easily enter the food chain and end up in our gut. Gram-positive bacteria (e.g. Bacillus spp.) have good adsorptive capacity for metals due to high peptidoglycan and teichoic acid content in cell walls. There is some evidence that certain metals inside the intestine play an important role in influencing growth and functionality of specific probiotic strains. Some of them have inhibitory, while others have an activating effect on bacteria. This study revealed that metal compounds increased antibiotic susceptibility of B. subtilis subsp. spizizenii. Higher concentrations of metal solutions inhibited growth of tested bacteria. Culture did not show affinity to form biofilms before or after addition of metal solutions Keywords Keywords. antibiotic susceptibility, biofilms, MIC, metals Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021323 https://omeka.ibu.edu.ba/files/original/d576b84246d647befe028e10e6cd3d5f.pdf 1cf9dbb6a9979d6c63430c29348c1585 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Potential of Biomaterial-Based Solutions in Cancer Research and Treatment Author Author Hannah Boone Abstract A summary of the resource. Cancer is a very troubling disease due to its unique morphological characteristics, capacity for drug resistance, and immunosuppressive abilities. Traditional methods used both for research of cancer and its subsequent treatment have fallen short of being able to accurately understand and ultimately defeat cancer within the body. Biomaterials present a unique solution to many problems associated with cancer. The use of biomaterials in cancer cell modeling has promoted a better understanding of tumor microenvironments. Biomaterials can also serve as drug and adjuvant carriers that are more likely to reach their target cancer cells. Many biomaterials also have standalone antitumor properties, and can also help in modulating the immune response, triggering various immune cells to attack cancerous cells. Naturally derived biomaterials include polysaccharides, lipids, polypeptides, vitamin E derivatives, and even plant extracts like curcumin. Biomaterial-based cancer treatments tend to have a longerlasting and more dependable effect inside the body and can come in many different forms, from polymeric scaffolds to injectable nanoparticles. Keywords Keywords. Adjuvant therapy, biomaterials, cancer treatment, carrier, immunotherapy. Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021321 Publisher An entity responsible for making the resource available International Burch University Language A language of the resource English Type The nature or genre of the resource Literature review https://omeka.ibu.edu.ba/files/original/ade2f568548c50da88671751de1d9811.pdf 6570cefe0cdbb13adcd981ced48fd2a6 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of High School Graduate Data Using Database Analytics Tools Author Author Ezana Ćeman, Ajdin Salihović, Samed Jukić Abstract A summary of the resource. It can be confidently stated that access to education is one of the most prized possessions available to us today. Although there are underlying factors such as the discrepancies in the education being provided worldwide, it is imperative that data scientists and all those interested take advantage of the data publicly available to draw necessary insights into how to better the education sector in our respective countries. The purpose of this research is to showcase various analytical insights into the 2020 New York State (NYS) high school graduation rate data using various advanced database systems techniques, specifically using SQL. With these analyses, further studies and conclusions can be drawn for local governments to implement into their plans to increase the quality of the schooling system, to aim for equality for all without regard to cultural and ethnic background, and to find discrepancies within the current system.</div> <quillbot-extension-portal></quillbot-extension-portal> Keywords Keywords. Database, data analysis, graduate, high school, New York State, SQL Identifier An unambiguous reference to the resource within a given context ISSN 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021322 Publisher An entity responsible for making the resource available International Burch University Language A language of the resource English language Type The nature or genre of the resource Original research https://omeka.ibu.edu.ba/files/original/47803e1a263bd6b4f783f59726b172fb.pdf 8395c7aba58aaf5fd5f5cbca54796c01 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Frequency Locked Loop in Estimating Active, Reactive and Apparent Powers Author Author Meris Lihić, Slobodan Lubura Abstract A summary of the resource. In this paper, a new power calculation method has been presented. This method is based on a second-order generalized integrator frequency locked loop (SOGIFLL) and has enhanced features over classical methods for power calculation widely used in industry. The FLLs have a wide variety of applications such as power converters, grid synchronization, sensorless flux estimation, and control of motor drives. The nature of the FLL allows for it to be a potentially perfect calculation method for power calculation. The obtained results showcase the effectiveness of the proposed power calculation method. Keywords Keywords. Power measurement, addition theorems for trigonometry, DC component elimination, gain normalization, grid frequency synchronization, MAF filters, MATLAB/Simulink. Identifier An unambiguous reference to the resource within a given context ISSN 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021323 Publisher An entity responsible for making the resource available International Burch University Language A language of the resource English language Type The nature or genre of the resource Original research https://omeka.ibu.edu.ba/files/original/9b9af352702f480808cfc7cf8737cb81.pdf baee14a6a0aa63930e7a0c90e038676d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Journal of Natural Sciences and Engineering Identifier An unambiguous reference to the resource within a given context 2637-2835 DOI Digital object identifier 10.14706 Publisher An entity responsible for making the resource available International Burch University Description An account of the resource Journal of Natural Sciences and Engineering (JONSAE) is a peer-reviewed, biannually published international journal focusing on empirical and theoretical research in all branches of Engineering and Natural Sciences. It is published on the behalf of Faculty of Engineering and Natural Sciences of International Burch University and aims to provide the best content regarding by publishing original research papers, review articles, special issues, feature articles, and book reviews. All manuscript submissions are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous referees. All peer review is double-blind and submission is online. The journal welcomes theoretical, applied, interdisciplinary and methodological work, with preference on empirical research, critical approach and problem-solving methods in manuscripts. Language A language of the resource English Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of Solved Homicides Using Classification Method Author Author Lamija Zukić, Samed Jukić Abstract A summary of the resource. Homicide rates are still high in the world and they are the worst crime in human existence. Despite all the technological advances and usage of information by various agencies, the number of homicides is not decreasing. Homicide prediction in certain countries should notably be the number one priority, which can help the government to easily identify the kind of profile they are looking for, or even help them prevent those cases. This paper compares different Machine Learning Techniques classifications of homicide prediction. Random Forest (RF), Random Tree, J48, Naive Bayes and k-Nearest-Neighbor (KNN) were tested to determine which method provides the best results in homicide prediction classification. The results of sample accuracy for all algorithms were around 99%, which clearly shows that all algorithms give great results. However, J48 is the best technique applied on the dataset, as it classified all instances correctly. Keywords Keywords. Classification, data analysis, homicide, machine learning, prediction. Identifier An unambiguous reference to the resource within a given context ISSN 2637-2835 DOI Digital object identifier 10.14706/JONSAE2021324 Publisher An entity responsible for making the resource available International Burch University Language A language of the resource English language Type The nature or genre of the resource Original research