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UTILIZATION OF BLOCKCHAIN TECHNOLOGY BY UPDF FOR SUPPLY CHAIN TRANSPARENCY AND TRACEABILITY
(Journal of Applied Sciences, Information and Computing (JASIC), 2023-11) Sulle Tabu Ale & V. S. Manjula
To sustain ethical sourcing practices, prevent the sale of counterfeit goods, and increase consumer confidence, organizations must provide transparency and traceability in their supply chains. This case study focuses on the Uganda People's Defense Forces (UPDF) and examines the creation and implementation of a blockchain-based application for improving supply chain transparency and traceability. The main goal is to look into how a blockchain application may enhance supply chain management, decrease corruption, and increase accountability in the UPDF's logistics and procurement procedures. In order to provide transparency, immutability, and decentralized control, the app will use blockchain technology to record and verify each stage of the supply chain, from purchase through delivery. The investigation will take into account whether the UPDF has access to the infrastructure and training required to use blockchain technology. A mixed-methods approach will be used to combine quantitative data from transaction logs and supply chain records with qualitative data from stakeholder interviews and observations. The study will evaluate the app's effects on supply chain efficiency, transparency, and traceability as well as any risks and rewards of integrating blockchain technology into the UPDF's supply chain ecosystem. Finally, this technique seeks to advance knowledge of how blockchain technology might enhance supply chain management in governmental and military contexts by examining the adoption of a blockchain-based app within the UPDF's supply chain. The results will advise policymakers, procurement officers, and R researchers on the applicability of block chain based apps for boosting supply chain transparency. They will offer insights into the viability, effectiveness, and potential barriers of using blockchain solutions
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ICT-ENABLED SOLUTIONS TO FARMERS INCREASE CROP YIELDS WHILE REDUCINGTHEIRUSE OFENERGY
(2023-07) Dr. V. S. Manjula , Fatou Marega , Robert Ssali Balagadde
This book chapter explores the role of Information and Communication Technology (ICT)-enabled solutions in agriculture to increase crop yields while simultaneously reducing energy consumption. It discusses various ICT tools and technologies, including precision agriculture, data analytics, remote sensing, and smart irrigation systems, and their applications in optimizing resource allocation,improving decision-making processes, and promoting sustainable practices. The chapter highlights the benefits, challenges, and future prospects of implementing these ICT-enabled solutions in agricultural systems.
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IMPACT OF DIGITIZATION OF SUSTAINABLE AGRICULTURE IN UGANDA: A CASE STUDY
(Journal of Applied Sciences, Information and Computing (JASIC), 2023-07) Nabulongo Ali, V. S. Manjula and Fatou Marega
Uganda has made outstanding progress in agricultural output, which is crucial for preserving the safety of the country's food supply. The role of technology and digitization in promoting sustainable agriculture practices in Uganda. The case study will focus on various innovative initiatives and solutions that have been implemented in the country to enhance agricultural productivity, reduce environmental impact, and improve livelihoods. By examining the current state of technology adoption in Ugandan agriculture, analyzing successful case studies, and identifying challenges and opportunities, this research will provide valuable insights into the potential of technology and digitization to drive sustainable agricultural development in Uganda."
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IDENTIFICATION AND MITIGATION OF THE VULNERABILITY OF WEB APPLICATIONS IN INSTITUTIONS OF HIGHER EDUCATION
(Journal of Applied Sciences, Information and Computing, 2023-07) Sabo Muhammed, Muwanga Zake, V.S. Manjula and Auwal Saleh
The security of information technology, specifically web applications, has become an area of concern today. Computer cybercrime is now a significant problem that affects more than just businesses and organizations. Higher education institutions also began to experience computer threats that revealed their information assets. Universities, polytechnics, colleges of education, research centers, and other postsecondary institutions are probably the most vulnerable because they house sensitive data on their faculty, staff, and students, as well as academic records of scientific and technological advancements and research. The first step in an information system security strategy is risk analysis management It helps in assessing the risk of information assets to know their security level or status, and assist in define a security control measures and implementation of technical plan to avoid threats that exploit some vulnerability that could cause severe damage to an asset or infrastructure of institutions higher education (IHEs). This article presents some recommendations to perform a risk analysis management in IHEs to accessed threats and vulnerability that helps to lower the risk of their information assets. This article presents existing educational threat and vulnerability on their web applications. Ensuring security is a goal of every organization regardless of its size or purpose and also proposed a risk management model. With the information technology, an organization may be considered secure when it ensures the confidentiality, integrity, and availability of information and IT assets. Confidentiality may be broken due to theft of sensitive information such as trade secrets, clients’ personal information.
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A REVIEW OF CLUSTER UNDER-SAMPLING IN UNBALANCED DATASET AS A METHODS FOR IMPROVING SOFTWARE DEFECT PREDICTION
(Journal of Applied Sciences, Information and Computing (JASIC), 2023) Abdulhamid Sani, V. S. Manjula and Musa Ahmed Zayyad
In many real-world machine learning applications, including software defect prediction, detecting fraud, detection of network intrusion and penetration, managing risk, and medical dataset, class imbalance is an inherent issue. It happens when there aren't many instances of a certain class mostly the class the procedure is meant to identify because the occurrence the class reflects is rare. The considerable priority placed on correctly classifying the relatively minority instances—which incur a higher cost if incorrectly categorized than the majority instances—is a major driving force for class imbalance learning. Supervised models are often designed to maximize the overall classification accuracy; however, because minority examples are rare in the training data, they typically misclassify minority instances. Training a model is facilitated by balancing the dataset since it keeps the model from becoming biased in favor of one class. Put another way, just because the model has more data, it won't automatically favor the majority class. One method of reducing the issue of class imbalance before training classification models is data sampling; however, the majority of the methods now in use introduce additional issues during the sampling process and frequently overlook other concerns related to the quality of the data. Therefore, the goal of this work is to create an effective sampling algorithm that, by employing a straightforward logical framework, enhances the performance of classification algorithms. By providing a thorough literature on class imbalance while developing and putting into practice a novel Cluster Under Sampling Technique (CUST), this research advances both academia and industry. It has been demonstrated that CUST greatly enhances the performance of popular classification techniques like C 4.5 decision tree and One Rule when learning from imbalance datasets.