Journal of ICT Systems

Assessment of Vulnerabilities in Student Records Web-Based Systems for Public and Private Higher Learning Institutions in Tanzania

2024-06-22T22:16:01+00:00

In spite that HLIs in Tanzania use web-based systems for managing, storing and processing of HLIs information and data such as website contents, academic results and financial records. The HLIs web-based system have been compromised by attackers due to presence of vulnerabilities. The main objective of this study is to assess the vulnerabilities of Students Records Web-based Systems (SRWBS) for private and public Higher Learning Institutions (HLIs) in Tanzania using black-box testing methodology by employing two automatic vulnerability scanners namely OWASP ZAP (Open Webs Application Security Project Zed Attack Proxy; open-source tool) and Acunetix (proprietary tool). This study assesses the vulnerability of SRWBS for 3 private HLIs and 5 public HLIs in Tanzania. The results reveal the total of 29 vulnerabilities which include but are not limited to Broken Authentication and Session Management, Broken Access Control, Security Misconfiguration, Sensitive Data Exposure, Vulnerable JS (Java Script) Libraries, CSRF (Cros Site Request Forgery), Using Components with Known Vulnerabilities, XSS (Cross Site Script), DOM (Document Object Model) based XSS and Reflected XSS. SRWBS of public HLIs were found more vulnerable by average 44.2% than the SRWBS of private HLIs which were vulnerable by average of 37%. Based on these results, this study provides some recommendations for mitigating vulnerabilities and improving the security of SRWBS for private and public HLIs in Tanzania.

Advancing Face Recognition Technologies: The Role of Decision Trees in Classifying Complex Image Pairs

2024-08-22T16:33:56+00:00

The advancement of face recognition technologies has been pivotal in various applications, from security systems to personalized user experiences. There are significant efforts already devoted to solving challenges of multimodality and pose variation in face recognition. Some studies focus on multimodality but pose-invariant, and other studies focus on pose variation but single modality. Despite significant progress, various face recognition algorithms do not consider both multimodality and pose variation constraints in their proposed methods. Recognizing face images presented both in a different modality and in a different pose presents serious challenges to current algorithms. This paper proposes an algorithm that combines the strengths of deep learning with decision trees to improve face recognition performance across modalities and poses in constrained and unconstrained environments. This hybrid approach leverages the representational power of deep learning and the interpretability and simplicity of decision trees. The findings indicate significant improvements over existing methodologies, particularly in challenging conditions like when multimodality and pose variation constraints are compounded together in the input face images in both constrained and unconstrained environments. The proposed algorithm not only addresses the limitations of current face recognition systems but also offers scalable, efficient solutions suitable for real-world applications.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

2024-05-10T05:56:29+00:00

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Forecasting Pediatric Medical Expenses using Machine Learning: A Case Study of the Toto Afya Card in Tanzania

2024-12-07T19:30:17+00:00

In Tanzania, the discontinuation of the "Toto Afya" insurance package in March 2023 highlighted the challenges of managing escalating medical costs and ensuring the sustainability of child health insurance schemes. This research aimed to predict children's medical expenses using machine learning (ML) algorithms to aid the National Health Insurance Fund (NHIF) in effective cost management. Data from 1,212 registered children, comprising 7 variables, was used to train and test four ML models: linear regression, random forest, XGBoost, and CatBoost. CatBoost emerged as the best-performing model with an accuracy score of 82.1%, followed by XGBoost (79.2%), Random Forest (77.3%), and Linear Regression (76.4%). Additionally, CatBoost and XGBoost achieved the highest F1 scores at 81%. These results underscore CatBoost's effectiveness in accurately predicting children's medical expenses, which is crucial for NHIF's cost management and sustainability of insurance schemes. By leveraging ML algorithms like CatBoost, NHIF can improve evidence-based decision-making for policymakers and healthcare providers, ultimately enhancing access to affordable healthcare for children. This approach supports NHIF's goals and contributes to better healthcare outcomes and sustainability.

Revenue loss reduction in the electrical distribution networks using distributed generators: A case of Tanzania electrical distribution network

2024-12-20T04:30:56+00:00

Most power losses in power systems occur within distribution networks, resulting in poor service quality, higher electricity costs for consumers, and revenue loss for utility companies. This study suggests strategies for minimizing these revenue losses by integrating distributed generators (DGs) into the distribution networks. The process involves identifying optimal locations and determining the best power output for each DG based on fluctuating power system variables, making it a complex NP-hard optimization problem. Therefore, this study proposes the use of metaheuristic algorithms to solve the DG power dispatch problem and reduce revenue losses in the electrical distribution networks. The proposed technique has been tested in the Tanzanian electrical distribution network using an hourly load profile and tariff D1, T1 and T2 users. Four metaheuristic algorithms were used, namely Golden jackal optimization (GJO), Grey wolf optimizer (GWO), Walrus optimizer (WO) and Marine Predators Algorithm (MPA). The algorithms were evaluated based on convergence profiles and computational times, with GWO performing the best. The study analyzed the impact of the number of DGs on revenue loss reduction, finding that the revenue losses decrease with the increase in the number of DGs. The analysis of the impact of the tariffs category on revenue loss reduction shows that integrating DGs reduced revenue loss by over 56% in all cases. These results demonstrate that DG integration can effectively decrease revenue losses in distribution networks.

Hybrid Communication Architecture Based on Hierarchical Computing for Low Voltage Power Network Automation in Developing Countries

2024-12-09T06:26:58+00:00

Utility companies in developing countries, such as Tanzania, have made significant advancements in automating the generation, transmission, and primary distribution segments of their electrical grids. However, automation in the Low Voltage Power Network (LVPN) remains minimal and largely unmonitored, leading to manual fault management that results in losses and inconveniences for both utilities and customers. Effective Smart Grid (SG) infrastructure relies on seamless communication between end-devices and controlling systems to ensure timely execution of critical applications. Key SG applications for LVPN automation include Advanced Metering Infrastructure (AMI), Distributed Energy Resource (DER) coordination, and Distribution Automation (DA), each with specific latency requirements that must be met. Current architectures outlined in the literature present challenges for utility companies attempting to implement these applications while satisfying latency criteria for automation. This paper introduces a hybrid communication architecture based on a three-level hierarchical computing model specifically designed for LVPN automation. Laboratory results indicate that this architecture achieves an average delay of 7.059ms, significantly below the maximum allowable latency of 20ms for AMI, DER, and DA which demonstrates the architecture's capability for effective LVPN automation.

Fog-Based Computing Architecture for Enhancing Secondary Distribution Grid Services Management

2024-12-21T14:27:38+00:00

In monitoring the health of secondary distribution networks (SDNs), power utility providers have faced an increasing need to deploy intelligent solutions with affordable sensing and data-driven technologies. Existing manual-based approaches are not capable of collecting large volumes of real-time operational data to achieve significant monitoring of the SDN network for reliable power distribution. Effective monitoring would require real-time sensing, scalable high-performance computing, and appropriate grid-based applications designed for efficient data processing. This paper presents a computing architecture for grid services monitoring to enhance real-time fault management in SDN. The architecture leverages wireless sensor networks, a hybrid cloud-fog computing architecture, and a heuristic-based application coordination mechanism to efficiently manage grid applications. Experimental results indicate that coordination mechanism improved workload distribution by up to 70% in fog nodes and to 40% in the cloud. A fog-based architecture provided low latency improvements of 70% compared with that of cloud-only architectures. This signifies that most of the data processing was pushed to the local fog nodes, which is crucial for distributed fault management applications.

The A Systematic Review of Big Data Techniques, Opportunities, and Challenges for Developing Countries: The Case of Social Media Networks Mining and Analytics

2024-12-27T04:45:42+00:00

Social media networks (SMNs) serve as global communication platforms where users can share content, images, and videos as well as post comments, follow friends, and share their thoughts. However, developing countries are lagging behind in understanding the techniques, challenges, and opportunities associated with mining and analytics of SMNs Big Data (BD). The study's objective was to review relevant literature to establish awareness and understanding in developing countries about these techniques, opportunities, and challenges associated with mining and analytics of SMNs BD. A systematic literature review analysis was used to address the study objective. The SMNs BD mining and analytics techniques resulting from the review include, but are not limited to, data mining, value chain technique, infosphere big insights, and SMNs BD sentiment analysis. Three categories of challenges discovered on the subject under investigation are process challenges, data challenges, management challenges, and infrastructure challenges. Opportunities discovered during the review include, but are not limited to, business improvements and adjustments, constructing intelligent networks, and customer engagement boosts, among others. Based on the review results, the study proposed SMNs BD management, mining, and analytics steps to guide developing countries in any endeavors aiming at utilizing SMNs BD mining and analytics initiatives.