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In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.

The diversity of enterprise domains is one of the significant factors that add to the challenges confronting organizations when managing compliance, and it, therefore, contributes to the cost of compliance. In particular, there is an indication that the inadequacy of a general approach to analyzing compliance documents impedes adequate compliance management practice. Taking an algorithmic-centric approach to tackling this challenge, this paper proposed algorithms and techniques intended to aid useful extraction, classification and analysis of dataflow, control flow, and time constraints in compliance management domain for different stakeholders. The algorithms and techniques were implemented and validated through two case studies and afterward evaluated for its adoption in practice.

Use case has embedded potentials that enable the diversity in its adoption to addressing other issues in software engineering. This objective of this paper is to gathered and classified use case re...

Security risk management is a knowledge-intensive procedure that requires monitoring and capturing relevant information that can assist in making the right decision by managers. In this paper, a semantically enhanced model for security management during the information system lifetime is proposed. The model supports the continuous collection of identified threat behaviours from the intrusion detection system, filtering and analysis of the threats within a time snapshot and re-appraiser of IS security countermeasures which involves the security administrator (S-Admin), managers, IS and security management system as stakeholders. The probe agent categorizes the security threats identified by the IDS using the developed ontology-driven knowledge base, while the likelihood of threats occurring in real time was obtained using long-term frequency probability. The case-based reasoning paradigm is employed for the security solution reasoning of identified threat risk. The suggested security solutions are based on CASE base built on existing threat ontology. The re-appraiser is based on the success likelihood of potential ongoing threats. The system facilitates management decision with regard to security control selection so that they can have a maximum Return on Security Investment. The proposed Collect–Probe–Analyse–Reason–Reappraise model is illustrated using an e-banking system.

One crucial aspect that had cost business organizations so much is management of compliance requirements from various regulatory sources. In a bid to avoid being penalized, some organizations have adopted various techniques to accomplish this task. However, literature revealed that few thorough reviews have been centered on this subject in a systematic way. This implies that a review that systematically captured the entire crucial elements such as implementation environment, constraints types addressed, main contributions and strengths of the existing techniques is missing. This has led to the lack of sufficiently good context of operation. A systematic review on existing literatures is presented in this paper, which focuses on the management of business process compliance requirements in order to present summarized evidences and provide a lead-up for appropriately positioning new research activities. The guideline for conducting systematic literature review in software engineering by Kitchenham was employed in carrying out the systematic review as well as a review planning template to execute the review. Results showed that control flow and data flow requirements have been addressed most in recent time. The temporal and resource allocation requirements have been under researched. The approaches that have been employed in business process compliance requirements management are model checking, patterns, semantic, formal, ontology, goal-based requirements analysis and network analysis. The traditional business environment has been put into consideration more than the cloud environment. The summary of research contributions revealed that the approaches have been more of formal techniques compared to model checking and semantics. This shows that there is a need for more research on business process compliance that will be centered on the cloud environment. Researchers will be able to suggest the technique to be adopted based on the combined importance of each criterion that was defined in this work.

Part 3: eBusiness; International audience; Sustainable food security (Sfs) desires not only that people always have passage to ample and nutritious food, but also that this food be formed with minimum environmental impact. This research is aimed at developing a mobile application for marketing organic farm products with the functionality of automated geo-location services, preferred goods delivery services, easy access to different organic farm produce. The mobile user platform consists of the Presentation, Business, Data and Data storage which is a slight variation of the Model-View-Controller architecture. It also takes into deep awareness the configuration, security and communications aspect as these are the underlying factors for the robustness of mobile application. The Presentation Tier consists of the user interface and the logic used to navigate around the user interface meaningfully. It modifies and shows information into a human distinct pattern. The Business Logic tier is essentially efficient for information transfer between the user interface and the database of the project. The last layer of the three-tiered architecture is the Data Access tier, which is made up of the Database servers. The application was implemented in Java, SQLite (local) and PHP for the backend server and Amazon Web Services for cloud infrastructure. Farmers were engaged in cursory testing. Users find the application concept very interesting.

Employee performance appraisal is often considered a core management task in most standard organizations because it could potentially inspire an employee towards the pursuit of organizational goals and objectives. Meanwhile, issues such as delay in appraisal processes, inevitable human error, and emotional status of the appraiser, are common with the traditional appraisal methods. Hence, this research proposes a fuzzy decision-support system based on Henri Fayol's 14 principles of management for the appraisal of employees' performance. A questionnaire consisting of 44 structured questions was designed based on these principles and administered to the employees of Babcock University, Ilisan-Remo, Ogun State, Nigeria. Fuzzy inference system that incorporates Mamdani computational technique was built based on the feedback extracted from the questionnaires, and used for appraising the employee performance. Experimental results show that the proposed system could predict the appraisal status of an employee with 75% accuracy in comparison to the conventional appraisal method.

Despite the attractive benefits of cloud-based business processes, security issues, cloud attacks, and privacy are some of the challenges that prevent many organizations from using this technology. This review seeks to know the level of integration of security risk management process at each phase of the Business Process Life Cycle (BPLC) for securing cloud-based business processes; usage of an existing risk analysis technique as the basis of risk assessment model, usage of security risk standard, and the classification of cloud security risks in a cloud-based business process. In light of these objectives, this study presented an exhaustive review of the current state-of-the-art methodology for managing cloud-based business process security risk. Eleven electronic databases (ACM, IEEE, Science Direct, Google Scholar, Springer, Wiley, Taylor and Francis, IEEE cloud computing Conference, ICSE conference, COMPSAC conference, ICCSA conference, Computer Standards and Interfaces Journal) were used for the selected publications. A total of 1243 articles were found. After using the selection criteria, 93 articles were selected, while 17 articles were found eligible for in-depth evaluation. For the results of the business process lifecycle evaluation, 17% of the approaches integrated security risk management into one of the phases of the business process, while others did not. For the influence of the results of the domain assessment of risk management, three key indicators (domain applicability, use of existing risk management techniques, and integration of risk standards) were used to substantiate our findings. The evaluation result of domain applicability showed that 53% of the approaches had been testing run in real-time, thereby making these works reusable. The result of the usage of existing risk analysis showed that 52.9% of the authors implemented their work using existing risk analysis techniques while 29.4% of the authors partially integrated security risk standards into their work. Based on these findings and results, security risk management, the usage of existing security risk management techniques, and security risk standards should be integrated with business process phases to protect against security issues in cloud services.

Electronic examination systems are becoming increasingly complex and intensive to develop with the introduction of virtual invigilator in proctored examinations. In order to address this complexity issue, there is evident need to have a global model that is extensible. Modeling software systems enables developers to better understand the system they are building and offers opportunities for simplification and reuse. This article presents an extension to UMLsec, by introducing three (3) new stereotypes which were added to the UMLsec Profile for a Proctored e-Exam model. The model was validated and converted to a platform specific model using the Java stereotype available on Papyrus. This enabled the model generate Java classes which can be used for the implementation of a secure proctored e-exam system. The model allows developers with little or no knowledge in security to use the model to build proctored e-exam systems and to incorporate all known security requirements. The model can also be extended to accommodate new security solutions for e-exam systems as they are discovered.

Employee performance appraisal is often considered a core management task in most standard organizations because it could potentially inspire an employee towards the pursuit of organizational goals and objectives. Meanwhile, issues such as delay in appraisal processes, inevitable human error, and emotional status of the appraiser, are common with the traditional appraisal methods. Hence, this research proposes a fuzzy decision-support system based on Henri Fayol's 14 principles of management for the appraisal of employees' performance. A questionnaire consisting of 44 structured questions was designed based on these principles and administered to the employees of Babcock University, Ilisan-Remo, Ogun State, Nigeria. Fuzzy inference system that incorporates Mamdani computational technique was built based on the feedback extracted from the questionnaires, and used for appraising the employee performance. Experimental results show that the proposed system could predict the appraisal status of an employee with 75% accuracy in comparison to the conventional appraisal method.

Decision making in agriculture affects both crops in all their phases (planting, management, costs, etc.) as well as the distribution and organization of crop production systems. Generating rules and connecting the rules into the fuzzy inference system for decision making is a very delicate task when associated with soil fertility test. Some of the constraints with previous study is the problem of uncertainty and imprecise complexity of experimenting and interpreting DSS system in real-life. The application of fuzzy-logic provides an effective decision-making tool for dealing the aforementioned challenges. This paper proposed a Mandani-type fuzzy inference System for testing soil fertility and increasing overall crop yield. This study was conducted in FUNAAB farm and seven inputs (nitrogen, phosphorous, potassium, organic matter, boron, zinc, and PH of water) with their nutrients percentage were fed into a fuzzy inference system. Three major inputs were varied based on their major role in soil constituents (nitrogen, phosphorous and potassium (NPK)). Our experimental result showed that irrespective of the level of the three major inputs (NPK) at every given time, the values of the output did not significantly change, it remained at 0.5 and it represents an average output on the crops considered for this purpose of this work. This study concludes that the application of fuzzy logic rule (mandani –type FIS model) in soil fertility test for cultivating four crops (Yam, Cassava, Maize and Cocoyam) have proven to be effective measure and thereby ensuring effective decision making.

Core and integral to the fourth industrial revolution, knowledge economy, and beyond is information and communication technology (ICT); more so, during and post the novel coronavirus pandemic. Yet, there exists a skills gap in ICT networking and networks engineering. Not only do students perceive ICT networking to be difficult to comprehend, lecturers and institutions grapple with the adequacy of ICT networking equipment. Real-life simulators, like the Cisco Packet Tracer, hold the promise of alternate teaching opportunities and evidenced-based environments for (higher-order) assessment. Research in the last decade on ontology for assessments have focused on taxonomy and multiple-choice questions and auto-generation and marking of assessments. This chapter extends the body of knowledge through its ontology-based model for enabling and auto-assessing performance-based and/or pseudo-psychomotor assessment. The auto-grading online submission system assists with authenticity and enables authentic and/or sustainable assessments.

Effective market information systems help to reduce information asymmetries, increase competitiveness, and improve efficiency in the marketing network. Thus, lack of dissemination of market information and the bargaining capability of the traders across the agricultural supply chain is a major concern among small holder farmers in Nigeria. The advent of mobile phones serves as a great tool for awareness and information dissemination to people. A Mobile Based Market Information System is presented in this study. It serves as a means of enhancing farmers marketing strategy by providing market visibility for transacting business both within and outside the region. The proposed system employs Google map API which shows the location of the registered markets. The system was implemented using JavaScript, PHP/MySQL and Phonegap/Cordova. XAMMP database server was used for data management. The application was tested and validated by 50 respondents, the results returned high acceptance rate, high perception on usage and ease of use.

Fake COVID-19 tweets appear as legitimate and appealing to unsuspecting internet users because of lack of prior knowledge of the novel pandemic. Such news could be misleading, counterproductive, unethical, unprofessional, and sometimes, constitute a log in the wheel of global efforts toward flattening the virus spread curve. Therefore, aside the COVID-19 pandemic, dealing with fake news and myths about the virus constitute an infodemic issue which must be tackled to ensure that only valid information is consumed by the public. Following the research approach, this chapter aims at a predictive analytics of COVID-19 infodemic tweets that generates a classification rule and validates genuine information from verified accredited health institutions/sources. On deployment of classifier Vote ensembles formed by base classifiers SMO, Voted Perceptron, Liblinear, Reptree, and Decision Stump on dataset of tokenized 81,456 Bag of Words which encapsulate 2964 COVID-19 tweet instances and 3169 extracted numeric vector attributes, experimental result shows a novel 99.93% prediction accuracy on 10-fold cross validation while the information gain of each 3169 extracted attributes is ranked to ascertain the most significant COVID-19 tweet-words for the detection system. Other performance metrics including ROC area and Relief-F validates the reliability of the model and returns SMO as the most efficient base classifier. The thrust of the model centered more on the trustworthiness of COVID-19 tweet source than the truthfulness of the tweet which underscores the prominence of verified health institutions as well as it contributes to discourse on inhibition and impact of fake news especially on societal pandemics. The COVID-19 infodemic detection algorithm provides insight into new spin on fake news in the age of social media and era of pandemics.

Agriculture as the oldest profession dates back to the Stone Age. Advances in technologies like diesel engine tractors and other tools with hydrostatic capability and control brought about agricultural mechanization which increased food productivity and industrialization. The aim of this research work is to design a mobile application for distributing or leasing agricultural machineries to farmers using locations-based services. The design also took into consideration the configuration of the various topologies and other factors that could enhance the flexibility of a mobile application of this nature. The user platform is categorized into three sections: the presentation layer, the business layer, and the data layer. The presentation layer is focused on the design logic and the navigational tools used in locating the right hiring type. It is also responsible for choosing the right data format using data validation technique to protect the app from invalid data entry. The business layer is responsible for logging in, authentication, exception handling, and security matters. While the data layer is focused on facilitating secure data transactions, the beauty of it is that it can be rescaled over time to meet the challenges of the time. The application was developed using JavaScript and MySQL with Phonegap/Cordova, XAMMP, and PHP for the backend. It was validated using formative evaluation which was conducted using interviews and open-ended questionnaires. The results of usability test obtained are promising.

Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.

Cloud computing has permeated and penetrated every aspect of our lives both personal and professional. This technology has gained a phenomenal acceptance as it provides opportunities for organizations, by offering large collections of easily accessible data. This has transformed the way businesses are done. The ever-changing demands of users, fierce competitive activities, and rapidly evolving technology pose a great challenge to software vendors to be innovative and constantly seek to be on top of their game. Cloud computing allows business to scale efficiently as they constantly adjust their operations to the new realities that help them to optimize cost, quality, and time. The main objective of this project is to ascertain if cloud computing offered capability is a threat to software vendors, and the extent of the threat, thus proposing solutions. This study used multi-stage research technique and the research instrument being questionnaire. The completed questionnaire forms were collated, coded, and analyzed using both descriptive and parametric statistics. Quantitative data was coded and entered into statistical packages for social scientists and analyzed using descriptive statistics for expressing the demographic characteristics of the respondents. Qualitative data was analyzed based on the content of the responses. Responses with common themes or patterns were grouped together into coherent categories. Chi-square analysis and logistic analysis were used to show dependencies and relationships between the variables and also to determine the probability that software vendors are aware of cloud computing threats and the severity of those threats. Quantitative data was rendered in tables and explanation was presented in logical prose.

The optimizations approaches of ant colony optimization (ACO) and particle swarm optimization (PSO) were targeted at improving the outcomes of artificial neural networks for finding best solution in the space. Both ACO and PSO are derived from the artificial intelligence concept that imitate the natural behaviors of animals in finding best path to foodstuff location relative and back their nest. The artificial neural networks (ANNs) are reliant on estimated research scheme in which models are generated for unspecified function in order find suitable interrelationships in input and output datasets. These are not without challenges including large time of computation, expansive hidden layer size, and poor accuracy. This paper examines the effects of pretraining dataset with ACO and PSO prior training process of ANN in order to overcome the aforementioned problems of speed and accuracy through optimization of the local and global minima. The outcomes of the study revealed that the ACO outperformed PSO in conjunction with ANN in terms of RAE, MSE, RMSE, and MAPE utilized. The error rates of ANN pretrained with ACO and PSO distinctively are 62 and 73% accordingly. Benchmarking the results against the solution optimization studies, ACO and PSO algorithms are most preferred in finding the best solution or nearest-optimal in search spaces.

The need to improve on the existing performance metrics of plant biometric researches in literature is more than addendum but a critical juncture towards the path of enhancing classification accuracies hence the purpose of this work aimed at establishing yardsticks for improved image processing phase precursor to machine learning. In this study, we applied seven dissimilar filtering iterations on three distinct plant image datasets and appraise their influence on plant biometrics to evaluate the implications of five image filtering parameters. The supervised machine learning classification was conducted on Sequential Minimal Optimization algorithm with 10-fold cross validation after oversampling of minority classes was conducted to reduce level of classification biasedness. Classification enhanced with 1.2739% improvement through inter-filter mixture over highest single filter result while insignificant poorer result was achieved with intra-filter mixture returning 0.637% decline when compared with inter-filter. Larger attribute-extraction filters returned a 4.1402% classification improvement over filters with lower attributes extraction and a 1.3169% improvement was recorded by colour-attribute-based filter over texture-based on a coloured image dataset. Image pixel representations and dataset sizes are other parameters that influence the accuracy level of image predictive analytics. The experimental results establishes the advantage of intra-filter combination over inter option and the prominence of the size of extracted image attributes over obtaining good results. Nature of image dataset in terms of colour or greyscale likewise must inform the choice of filter to be deployed according to experimental results obtained from this work.

Facial recognition is a type of biometric that deals with the facial region of a human image. The low recognition accuracy of existing Facial Recognition Systems (FRS) is due to the low quality of captured images. The assessment of image quality, therefore, becomes a requirement to be taken before passing such an image through the FRS. This study presents a proposed Facial Image Quality Assessment (FIQA) model using an ensemble of pre-trained deep learning models (EFQnet). The system known as EFQnet is an ensemble of ResNet-50, DenseNet, and Inception-Net CNN pre-trained models. It utilizes a performance-based ground truth that forecasts a quality score for the input image between 0 to 1. The three models are ensemble using full fully connected Feedforward Neural Network and AMSGrad stochastic gradient Descent algorithm. When trained and fully implemented EFQnet will be evaluated on standard IQA databases and finally deployed in a Personal Identity Verification (PIV) scenario.

presented by regulatory bodies or standards which must be complied with and followed maximally. Failure or in instances slight deviations from these requirements had cost a number of firms and affected their reputations. These could be traced to inefficient techniques employed in analyzing these requirements. This paper presents an efficient pre-analysis and analysis procedure that employs both goal based engineering and cerno-framework approach in acquiring, extracting and classifying requirements as well as presenting algorithms that will diligently take care of previous analysis lapses. The work was applied to the health domain utilizing the Health Insurance Protection and Accountability Act (HIPAA). This was used placing in mind that such industry is fast adopting cloud services with the consideration of the security constraints that are essential in such domain. This approach will aid a full compliance consideration in the management of business or software processes aiding analysts in preventing incomplete adherence to regulatory guidelines or policies and enhancing correctness.

Many information assets have been exposed to threats as a result of business owners not having access to an efficient model for classifying their information assets into appropriate security risk levels, required level of protection and priority. Manual and machine learning among several approaches have been employed in classifying information; these were not effective in considering users’ preferences and are also not based on security standard. This study proposed a Fuzzy-based Information Asset Classification and Labeling framework based on ISO/IEC 27001 security standard. The framework comprises of six major phases: expert based information asset security assessment and classification (IAC), fuzzy based information assets security assessment and classification (FIACL), information asset labeling, information security risk assessment, and information asset handling and information asset storage. The implementation of the model will be done by developing an automated system with an enhanced classification using fuzzy logic and Delphi method for the security risk assessment. The comparative analysis revealed that FIACL was more efficient and accurate in classifying information assets for proper security risks assessment and control. This model will foster effective classification of information assets in any organization.

The goal of organic agriculture is to show the interrelationship between farm biota, its production, and the overall environment. In cooperation with various Organic Farming unions, a system called the organic farming mobile agricultural extension services (OrgFarMob) was developed to provide farmers with instant information to problems with organic crop cultivation. Hence, this project provides organic farmers with crop lifecycle support system, leading to effective management and sustainable economic development in organic agriculture. The system powered by Azure cloud platform, was developed using Model-View-Architecture and programmed with AngularJS and Ionic. The design followed the computer research framework with incorporate users’ requirement elucidation and validation. The system was designed and evaluated using data obtained through the collection of materials from professionals in the field of organic agriculture. The results showed that the system can be used in real-time and foster organic agriculture cultivation.

Computation is concerned with the validation of algorithm, estimation of complexity and optimization. This requires large dataset analysis for the purpose of finding the unknown optimal solution. Aside the intricacies in completing tasks, this process is expensive and time inefficient; attaining solutions with conventional mathematical approaches are unrealizable. Search algorithms were advanced to improve solution approaches for optimization problems by finding the possible sets of solution to a particular problem as contained in search space. However, metaheuristic algorithms suggest three solutions to optimization problems on the basis of the application areas in real-life situations including: near optima, the optimal or the best solution. This paper analyses the decision-making processes of two nature-inspired search algorithms namely: Backpropagation search algorithm and ant colony optimization (ACO). The results revealed that, backpropagation search algorithm without ACO training trailed those trained with ACO for MSE, RMSE, RAE and MAPE. Again, forecasts errors estimated in the neural network set-up were smaller due to directional search mechanism of the ACO as against the approach provided in neuro-fuzzy rules set tuning by Rajab and Sharma (Soft Comput 23:921–936, 2017) [1]. There is need to consider metaheuristic algorithms approaches to obtain better solutions or nearest optimal values to the optimization problems in neural networks.

Securing information system (IS) has become a critical concern within many sectors of business organisations with significant resources being devoted to the control of security threats. Recently, it has been discovered that incorporating security at the time of development is the best option for having a robust system. This study explores factors that motivate IS owner’s willingness to pay extra cost for a secure software development and validates the relationships among the various variables. Enhanced Technology Acceptance Model (TAM) was used to investigate the factors that influences IS owner’s willingness to pay extra cost for secured software development. Out of all the constructs considered, Self-Efficacy (SE) is found to be significant (β= 0.617, P

Genetic Algorithm has been successfully applied for solving systems of Linear Equations; however the effects of varying the various Genetic Algorithms parameters on the GA systems of Linear Equations solver have not been investigated. Varying the GA parameters produces new and exciting information on the behaviour of the GA Linear Equation solver. In this paper, a general introduction on the Genetic Algorithm, its application on finding solutions to the Systems of Linear equation as well as the effects of varying the Population size and Number of Generation is presented. The genetic algorithm simultaneous linear equation solver program was run several times using different sets of simultaneous linear equation while varying the population sizes as well as the number of generations in order to observe their effects on the solution generation. It was observed that small population size does not produce perfect solutions as fast as when large population size is used and small or large number of generations did not really have much impact on the attainment of perfect solution as much as population size.

Today’s business entities face an ever-growing number of laws and regulations due to recent high profile business scandals and failures. Small and medium scale enterprises (SMSE) in developing countries do not have an efficient compliance checking mechanism to make their business processes compliant with these regulatory standards. This checking mechanism is needed to give the enterprises full assurance of complete adherence to regulatory standards, bodies, or Service Level Agreements. Therefore, a structured and efficient compliance management model is needed to aid SMSE in launching their businesses safely and to ensure business processes fit into the classical regulatory standards. This paper presents a business rules compliance checking model and architecture for SMSEs in developing countries to verify and monitor their business process models at design time and at run time. It involves a systematic compliance requirements classification and analysis that employs a goal based requirement engineering approach prior to design time verification. It also introduces the idea and demonstration of network analysis for runtime business processes monitoring. The business process model will be verified at design time using a Simple PROMELA Interpreter model checker through Linear Temporal Logic rules. The approaches were tested on a financial institution in Nigeria, a developing nation in Africa at the time of this research. In order to ensure that the choice of the requirements analysis approach was efficient, a number of standard metrics for evaluating requirements engineering techniques were used and promising results were obtained. We also carried out a comparative analysis of the proposed approach in this paper with the approaches of previous research papers. This approach proved to be effective in terms of clarity, simplicity, flexibility and expressiveness while reducing incomplete adherence of business processes and enhancing the correctness of the business process.

Weight equalisation (WE) is the technique of grouping experimental subjects with the aim of reducing the variability in the starting weight among experiment treatment groups. Existing methods are cumbersome, especially when dealing with large number of animals. This work developed a supporting tool for WE called WETool. It was implemented as an Android mobile application adopting quick sorting technique, Z score outlier detection with sinusoidal arrangement as the method for partitioning and sorting the experimental subjects into groups. Experimental data of 24 subjects were sorted using random sorting and WETool into six groups of four experimental subjects per group. Assigning the subjects resulted in group minimum weight that ranged from 13.75-10.125 and 12.5-12; with a difference of 3.625 and 0.5, per group; variance of 1.915 and 0.046 for random sorting and WETool respectively. The results of the acceptability evaluation of WETool were also very promising.

Resource allocation decisions can be enhanced by performing risk assessment during the early development phase. In order to improve and maintain the security of the Information System (IS, hereafter), there is need to build risk analysis model that can dynamically analyze threat data collected during the operational lifetime of the IS. In this paper the authors propose an ontological approach to accomplishing this goal. They present analyzer model and architecture, an agent-based risk analysis system (ARAS) which gathers identified threats events, probe them and correlates those using ontologies. It explores both quantitative and qualitative risk analysis techniques using real events data for probability predictions of threats based on an existing designed security ontology. To validate the feasibility of the approach a case study on e-banking system has been conducted. Simulated IDS output serves as input into the risk analysis system. The authors used JADE to implement the agents, protégé OWL to create the ontology and ORACLE 11g SQL developer for the database. Optimistic results were obtained.

This study presents an agent based approach to resolve issues related to the collection and classification of software application anomalies and misuses with the aim of facilitating the reappraisal of security controls of information system (IS). The proposed system is assumed to be integrated with the existing IS in order to enhance information system security maintenance by continuously collecting identified threat behaviour from the application intrusion detection system (IDS). The system comprises of several functional agents like the input collector agent, classifier agent, and tracking agent. The collector agent collects the identified threats by the IDS, the categoriser agent categorises according to STRIDE model using pattern matching algorithm on the content of security knowledge base. The security knowledge repository is developed based on existing security ontology. The classifier classifies based on the threats IP address while the tracking agent collates all the threats profile. The collect-categorise-classify-track (C3T) model is hereby presented. The potential usability of this work is demonstrated by a case study and its useful integration with further studies is also discussed.

Despite the great benefits of cloud computing on businesses, some organizations have had to withdraw from its service due to incomplete compliance to conditions from standards, bodies, or Service Level Agreements which have led to their being penalized. A structured and efficient model is helpful to aid small and medium scale enterprises (SMSEs) utilize cloud services safely and ascertain their fitting into necessary requirements before adoption. In this paper, a compliance management architecture is proposed to efficiently aid SMSE efficiently validate and examine at the time of design and running their business process. The verification of the model will be done using SPIN model checker based on linear temporal logic rules. The network analysis approach is proposed for monitoring the models at run time. The presented approach will allow for effective coordination of SMSE business adherence to any domain regulatory requirements. It will also allow for update with new requirements.

Misuse cases are currently used to identify safety and security threats and subsequently capture safety and security requirements. There is limited consensus to the precise meaning of the basic terminology used for use/misuse case concepts. This paper delves into the use of ontology for the formal representation of the use-misuse case domain knowledge for eliciting safety and security requirements. We classify misuse cases into different category to reflect different type of misusers. This will allow participants during the requirement engineering stage to have a common understanding of the problem domain. We enhanced the misuse case domain to include abusive misuse case and vulnerable use case in order to boost the elicitation of safety requirements. The proposed ontological approach will allow developer to share and reuse the knowledge represented in the ontology thereby avoiding ambiguity and inconsistency in capturing safety and security requirements. OWL protege 3.3.1 editor was used for the ontology coding. An illustration of the use of the ontology is given with examples from the health care information system.

This paper proposes an enhanced use-misuse case model that allows both safety and security requirements to be captured during requirements elicitation. The proposed model extends the concept of misuse case by incorporating vulnerable use case and abuse case notations and relations that allows understanding and modeling different attackers and abusers behaviors during early stage of system development life cycle and finishes with a practical consistent combined model for engineering safety and security requirements.The model was successfully applied using health care information system gathered through the university of Kansas HISPC project. The authors were able to capture both security and safety requirements necessary for effective functioning of the system. In order to enhance the integration of the proposed model into risk analysis, the authors give both textual and detailed description of the model. The authors compare the proposed approach with other existing methods that identify and analyze safety and security requirements and discovered that it captures more security and safety threats.

This paper presents an application of Prim's Algorithm to local access network in rural areas. The minimum spanning tree problem has important applications in network design which has been extensively studied in literature. The minimum spanning tree problem on a graph with edge costs and vertex profits asks for a sub tree maximizing the difference between the total cost of all edges in the sub tree and the total profits of all vertices contained in the sub tree. Minimum spanning tree problem appears in the design of utility networks (e.g. bus services, electrifications) where villages and the network connecting them have to be chosen in the most profitable way. The application of Prim's algorithms is demonstrated to the design of local access networks in a particular local government area of about 88 villages. The case study is analyzed and interesting results are obtained. The results obtained justify the need to apply this kind of algorithm for efficiency and profitability

One-tenth of the world’s ever-growing internet population shops online, representing a staggering 627 million people. As more organization adopts e-payment methods, changes in the market-place for business-to-business (B2B) payments increasingly demand executives’ attention. Businesses are beginning to realize they must add new payment options to a process still dominated by paper checks, wire transfers, and automated clearinghouse (ACH) transactions. Extreme programming and other emerging agile methods represent an important new direction in software engineering and development. This paper explores the integration of both usage-centred engineering and extreme programming for epayment model using open network system. This paper describes the development of a B2B E-payment system using Use-Case modelling techniques as well as, ESignatures and XML. We propose an open network system which can adapt to users changing needs as well as allowing effective and secured transaction via any customers’ bank account.

the introduction of modularity, increasing student numbers and the continued expansion of university departments, space in Nigerian Universities is becoming an increasingly precious commodity. To address this, some institutions have tried to ensure efficient space utilization by employing different proposed solutions to space allocation problems especially during examination period. A number of approaches have been explored in the casting of examination timetables for academic institutions. The approach to be discussed here applies genetic algorithm using hierarchy of constraints. This hierarchy can incorporate individual requests or organizational requirements by weighing them according to some criteria. In this paper, we present a new real-world examination timetabling dataset at the University of Agriculture, Abeokuta Nigeria that will hopefully be used as a future benchmark problem. In addition, a new objective function that attempts to spread exams throughout the examination period is also introduced. This objective function that taking into account both timeslots and days assigned to each exam, is different from the often used objective function from the literature that only considers timeslot adjacency. Also room capacity for each room is included in the examination datasets specification. This approach has been tested with real data from the university and numerical results is presented and discussed. General Terms: Scheduling, Algorithms, NP hard