Bachelor of Science in Computer Science
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Item A hybrid machine learning network intrusion detection system for Small and Medium Enterprises (SMEs)(Uganda Christian University, 2026-05-25) Absolom Orianga; Mark Travis Lufene; Allan Kagimu SsebattaCybersecurity remains a critical challenge for Small and Medium Enterprises (SMEs) in Uganda, where cybercrime increased by approximately 93.5% in 2024, causing losses of approximately UGX 72 billion. Approximately 40% of Ugandan SMEs have experienced cyberattacks, yet most continue to rely on basic signature-based tools that are incapable of detecting sophisticated or zero-day attacks. This project presents Vanguard-NIDS, a hybrid machine learning-based Network Intrusion Detection System designed for real-time intrusion monitoring in SME environments. The system captures live network packets using Scapy, extracts flow-level and statistical traffic features, and analyses them through a three-pronged detection pipeline comprising signature-based pattern matching, supervised machine learning via an ensemble of Random Forest, SVM, XGBoost, and LightGBM classifiers, and unsupervised anomaly detection via Isolation Forest, One-Class SVM, and Autoencoder models. A result fusion engine combines these predictions into a unified threat score. Models were trained and validated on the CICIDS2017, NSL-KDD, and UNSW-NB15 benchmark datasets. The Random Forest classifier achieved approximately 95% detection accuracy with a false positive rate of approximately 3%. A real-time React-based dashboard delivers live traffic monitoring, alert management, and system metrics via WebSocket communication, with an average end-to-end detection latency of under 25 milliseconds.Item Digital performance tracking for amateur football(Uganda Christian University, 2026-06-04) Gareth Neville Kisuze; Andrew Ogwang; Derrick Elvan KatendeGrassroots football draws millions of participants worldwide, yet most players still record performance through scattered channels such as paper notes, group chats, and one o! social posts. Records are hard to compare, easy to lose, and rarely support any real analysis. Tools built for elite clubs depend on GPS feeds, video pipelines, and specialist sta! that community leagues simply do not have. This report presents FootyStats, a mobile platform that lets amateur players and tournament organisers log match events during play, store them in a shared database, and view leaderboards, player profiles, and simple performance trends. The work combines user centred design with a lightweight client server setup and automated aggregation so that useful summaries can be drawn from ordinary event data rather than costly tracking hardware. Prototype testing showed that structured, low friction capture cuts down the fragmentation seen with informal methods, and that even basic match records can support meaningful summaries for grassroots users. The study offers both a practical path toward deployable amateur sports informatics and evidence that analytics can be delivered under the resource limits typical of community football.Item A Yolo-based robotic system for waste detection and Collection(Uganda Christian University, 2026-06-02) Isaac Nabasa; Apophia Atwijukire; Leticia LackiaWaste management remains a critical challenge in Kampala where approximately 63% of daily generated waste goes uncollected, leading to environmental pollution, blocked drainage systems, flooding and public health risks. Traditional manual collection methods are inefficient, labor intensive and inadequate to handle the growing volume of lightweight litter such as paper,npolythene bags, and plastic bottles, especially in busy public spaces. This project developed ECO-BOT, a low-cost autonomous robotic system that integrates computer vision and robotics to address these challenges. Following the engineering design method ology, the robot was built using the Hiwonder mobile platform, Raspberry Pi 5, a lightweight YOLOv8n object detection model, and a 5-DOF robotic arm. The system was designed to detect, navigate towards, pick up and store lightweight waste items. Individual components were developed and tested separately before full system integration. The complete prototype was evaluated in outdoor environments on surfaces including tarmac, short grass and light gravel and also inside Uganda Christian University Dining hall which has cemented ground and some tables. Results demonstrated promising performance with 87% detection accuracy, 78% collection success rate, and 84% navigation success across tests, achieving an average collection time of 13.5 seconds per object. While limitations such as occasional processing delays and challenges with certain waste types were observed, ECOBOT successfully shows that affordable YOLO-based robotics can provide a practical, scalable solution for improving waste collection in resource limited urban settings like Kampala. This work contributes to smarter, technology driven waste management practices in Uganda.Item Harmony Guardian: A wearable multi-sensor assistive system for stress and behavioural monitoring in individuals with neurodevelopmental disorders(Uganda Christian University, 2026-06-06) Wangobi K Nicholas; Mawejje J Paul; Asingura K PhilipNeurodevelopmental disorders (NDDs), such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), often impair an individual’s ability to regulate emotions, process sensory stimuli, and communicate distress effectively. These challenges can result in behavioural escalations, including meltdowns and shutdowns, which are commonly preceded by measurable physiological changes. In low-resource settings such as Uganda, caregivers largely depend on subjective observation due to the lack of affordable, real-time monitoring systems, leading to delayed interventions and reduced quality of care. This project addresses this problem through the development of Harmony Guardian, a personalised, lightweight, and sensory-friendly wearable monitoring system designed for individuals with neurodevelopmental disorders. The system integrates multiple physiological sensors, including heart rate variability (HRV), galvanic skin response (GSR), and motion sensors, to continuously monitor stress-related indicators and behavioural patterns. Embedded processing and real-time data analysis techniques are used to identify early signs of stress and behavioural dysregulation, after which alerts are transmitted to caregivers for timely intervention. The proposed solution was designed with emphasis on affordability, usability, portability, and suitability for under-resourced environments. System evaluation demonstrated that the wearable device was capable of reliably capturing physiological changes associated with stress responses and providing timely notifications to caregivers. The results indicate that the system can improve early intervention, enhance caregiving efficiency, and contribute to better behavioural and health outcomes for individuals with neurodevelopmental disorders. Overall, the project demonstrates the potential of accessible wearable technologies in supporting inclusive and proactive healthcare for people with special needs.Item AquaGuardian: An IoT-driven real time water quality monitoring and machine learning prediction for aquaculture systems in Uganda(Uganda Christian University, 2026-06-08) Calvin Tendo; Ronald Austine Ezamamti; Emmanuel KisaUganda’s aquaculture sector faces a critical productivity crisis, with national fish production declining by 27.8% and export revenue falling 21.9% between 2023 and 2024. This decline is largely attributed to inadequate real-time water quality monitoring among smallholder farmers, who continue to rely on manual testing methods that fail to detect sudden changes in critical parameters. This project presents AquaGuardian, an IoT-based smart fish pond monitoring and management system designed specifically for the Ugandan context. The system integrates a Raspberry Pi 4B controller with an integrated RS485 NPK sensor probe, a digital turbidity sensor and a GSM-based SMS alert module to enable continuous, real-time environmental surveillance. The architecture comprises five functional layers: Sensing, Processing, Actuation, Communication and Cloud Logging. A Random Forest machine learning model, trained on 3,887 samples from the Aquaculture Water Quality Dataset (AWD), classifies pond water quality into three categories: Excellent, Good and Poor. By utilizing biologically-informed feature engineering derived from NPK, pH and temperature data, the model achieved an 85.5% theoretical accuracy and 100% precision on the critical ”Poor” water quality class. Field testing validated the system’s ability to deliver real-time sensor visualization via a cloudhosted Flask dashboard deployed on Render and Neon.tech. The system successfully activated automated aeration and circulation pumps within five seconds of detecting a critical threshold and dispatched SMS alerts to farmers within thirty seconds. However, the evaluation also highlighted the limitations of using Nitrogen-based surrogate substitution for Nitrite inputs, a necessary compromise due to local hardware market constraints. This project demonstrates how the integration of IoT, machine learning and cloud technologies can bridge the monitoring gap in smallholder aquaculture,while providing a transparent assessment of the hardware-software alignment challenges encountered in resource-constrained environments.Item Tomato Doctor: An AI Advisory System for tomato leaf disease detection and retrieval-augmented agronomic guidance for smallholder tomato production(Uganda Christian University, 2026-06-11) Newton Anguyi; Timothy OpifudriraSmallholder tomato farmers require timely, practical, and localized guidance for tomato crop management decisions, yet many lack fast access to expert support when Tomato Early Blight (Alternaria solani) or Tomato Late Blight (Phytophthora infestans) symptoms first appear on Solanum lycopersicum. In this project, we built a full-stack Smart AI Advisory System for Smallholder Farmers, delivered to end users as the Tomato Doctor mobile application. The prototype integrates three tightly-coupled capabilities into a single workflow: (1) AI-based tomato leaf disease diagnosis from images, (2) a contextual advisory chatbot that turns a tomato diagnosis into actionable management guidance, and (3) optional field-feedback capture to support future dataset improvement and retraining. The system is intentionally narrowed to three classes only: Tomato Early Blight, Tomato Late Blight, and Healthy (Tomato Leaf). The disease diagnosis pipeline is implemented with transfer learning using MobileNetV2 [1], trained on a 3-class subset of PlantVillage [2] and then fine-tuned using field images to improve real-world robustness. The primary deployed model is the field fine-tuned checkpoint mobilenetv2_field_3class_fieldda ta.pth. Inference includes uncertainty gating to handle images that are not tomato leaves or are unclear; instead of forcing a wrong class, the API returns an “unsupported/unclear” response. Robustness is further improved by lightweight test-time augmentation (original + horizontal flip) and careful image preprocessing that preserves aspect ratio and corrects EXIF orientation. Explainability (Grad-CAM) [3] is generated for Early Blight and Late Blight predictions only, and severity estimation (lesion coverage %, lesion count, and severity stage) is applied to Early Blight detections to support urgency-aware recommendations. The advisory feature is implemented as an authenticated Django REST API that combines a retrieval-augmented generation (RAG) pipeline (sentence-transformers [4] with ChromaDB [5]), disease-context prompting using optional fields disease_name and detection_context, and a rule-based fallback for greetings and very short messages. Field evaluation on 89 held-out real photos shows the impact of fine-tuning: the base model achieved approximately 28% accuracy on field images, while the fine-tuned 3-class model achieved 85.4% accuracy with balanced precision/recall. The final outcome is a v1.0 proof-of-concept that integrates tomato foliar disease detection (Early Blight, Late Blight, Healthy), explainability, and grounded advisory guidance in a single mobile-first workflow, with clear limitations for real-world use.Item A hybrid intent-driven and retrieval-augmented conversational framework for reliable University information access(Uganda Christian University, 2026) Nicole Mary Johnson; Humphery Mubiru; Ethan ArikoStudents at Uganda Christian University frequently struggle to access timely and accurate institutional information because relevant details are scattered across notice boards, social media groups, and outdated web pages. This report presents the design, implementation, and evaluation of a hybrid multi-channel chatbot that merges intent-based routing, keyword retrieval, vector-based semantic search, and large-language-model generation to deliver context-grounded answers drawn from seventeen verified university documents. The system was deployed as both a web application and a WhatsApp integration, offering multilingual support in seven languages through the Sunbird translation service. Functional testing across representative query categories showed that the hybrid retrieval strategy returned relevant context for the majority of test queries, while a confidence-aware fallback mechanism directed users to appropriate university offices when the knowledge base could not provide a reliable answer. The findings indicate that combining complementary retrieval methods with grounded generation is a viable approach for building dependable information assistants in resource-constrained university settings.Item AI Powered Computer Vision-Based Framework For Real-Time Road User Classification And Intelligent Traffic Signal Control(Uganda Christian University, 2026-06-04) Rachel Mbeiza Isooba; Promise Pierre Mokili; Joshua Owor GenoTraffic congestion is a leading cause of economic and productivity loss in Uganda. The Greater Kampala Metropolitan Area carries roughly half of the country’s registered vehicles, and peak-hour speeds on central corridors drop to as low as 11 km/h. At the same time, more than 70% of road traffic fatalities in the country fall on vulnerable road users, that is pedestrians, cyclists and boda-boda motorcyclists. Most of Kampala’s signalised junctions still operate on fixed-time plans that cannot respond to real demand, and none of them offers cyclists a dedicated signal phase. This report describes RoadWise, a group final-year project that builds a low-cost, AI-powered traffic management framework for classifying road users in real time and controlling intersection signals dynamically. The system is organised in four layers: a sensing layer made of USB cameras and IoT sensors, an intelligence layer that runs a YOLOv8 detector and a Firebase cloud backend, an action layer that drives smart traffic lights, and an interface layer made of a traffic officer dashboard and a road user mobile web app. A custom scale model traffic setup was built, covering both three-way and four-way junction layouts, so that the full detection to actuation loop could be tested under controlled conditions.The work was grounded in a PRISMA compliant systematic literature review that screened 4,419 records and retained 12 primary studies. The review confirmed that, although YOLO based vision and adaptive signal control are individually mature, no reviewed system offers a dedicated cyclist priority phase in mixed traffic. RoadWise closes that gap through what we call a blue light phase, which is activated whenever cyclist presence at a junction passes a configurable threshold. On the prototype, the detector reached 100% vehicle detection and 85% cyclist detection accuracy under varied lighting, with a best validation mAP@0.5 of 0.977 on the miniature model dataset. The adaptive controller reduced junction waiting times by up to 40% compared with a fixed time baseline, and the web to hardware synchronisation protocol achieved 100% state consistency over serial acknowledgement feedback. The same controller was shown to run simultaneously across three-way and four-way configurations, which supports scalability toward real junctions. The report also discusses the limits of the work, in particular the scale model-to-real domain gap, the narrow class schema, and the remaining steps needed before a pilot deployment on a real KCCA junction can be attempted.Item AI-Powered Robotics for Post-Disaster Survivor Detection and Rescue Path Mapping(Uganda Christian University, 2025-05-06) Diana NansubugaNatural and man-made disasters often highlight the weaknesses in traditional emergency response efforts, especially in countries with limited resources like Uganda. Challenges such as slow response times, difficulty reaching affected areas, and a lack of real-time information frequently result in lost lives and misused resources. This study introduces a robotic dog system enhanced by artificial intelligence, designed to improve the effectiveness of search and rescue missions following disasters. The system incorporates real-time human detection using AI, SLAM for creating environmental maps, and the A* algorithm to plan efficient rescue routes. The robot is powered by a Raspberry Pi and managed through a Flask-based web platform. It includes multiple sensors, such as GPS, ultrasonic detectors, a night vision camera, and directional microphones, allowing it to navigate independently, avoid obstacles, and communicate wirelessly with remote rescue teams. Testing the system in a controlled, disaster-like setting confirmed its ability to locate victims, generate accurate maps, and determine safe paths with high reliability. The results suggest that. This low-cost, locally adaptable solution could play a vital role in speeding up rescue efforts and minimising risks to human responders. By addressing key limitations in current practices, the project adds valuable insights to the field of disaster robotics, particularly In settings where advanced tools are not readily available.Item Gandabert: Transfer Learning With Mbert for Luganda News Classification(Uganda Christian University, 2025-05) Seth MbashaLuganda, spoken by over 21 million Ugandans, is significantly under‐resourced in Natural Language Processing (NLP), lacking effective tools like news classifiers. This gap hinders digital information access and contributes to the digital language divide. This research project addressed this challenge by developing GandaBERT, a model for Luganda news classification. The methodology involved fine‐tuning the multilingual BERT (mBERT) model on a novel multi‐source dataset comprising 2,609 native, translated, and synthetic Luganda news articles across five categories (Politics, Business, Sports, Health, Religion). Evaluation on a held‐out test set showed GandaBERT achieved an overall accuracy of 85.7%. While demonstrating strong performance in certain categories like Politics, challenges and variations across topics were observed, partly linked to overfitting during training. This study confirms the viability of applying transfer learning with mBERT for practical Luganda NLP tasks, provides a valuable classification tool, and contributes towards enhancing digital resources for this low‐resource language.Item An Embedded and Machine Learning Based Early Flood Monitoring and Warning System, the Case of River Manafwa(Uganda Christian University, 2025-05-06) Daniel Lukyamuzi WavamunnoFlooding remains a serious threat in many parts of Uganda, especially in regions with limited access to early warning systems. This project introduces a practical solution that combines embedded hardware and machine learning to monitor and predict flood events in real time. Using a flow sensor and an ultrasonic sensor connected to an ESP32 device, the system captures data on water movement and levels. These readings are automatically logged to Google Sheets, allowing for easy data management and access. A backend built with FastAPI processes this information, using a trained Random Forest algorithm to forecast potential flood risks. The results, along with past records, are displayed on an interactive dashboard developed in React. By merging simple electronics with predictive analytics, the system provides an affordable and adaptable tool to support timely flood response efforts in vulnerable areas.Item AI Image-based System for Lumpy Skin Disease Detection in Cattle(Uganda Christian University, 2025) Amos MugabiLumpy Skin Disease (LSD) remains a significant threat to cattle health across Uganda, with conventional disease detection methods being slow, centralized, and reliant on clinical expertise that is often unavailable in field settings. This project proposes an innovative solution through an AI-powered, image-based detection system capable of identifying LSD from cattle images. The system employs a two-stage deep learning architecture: a YOLOv8 object detection model locates individual cattle within images, followed by a convolutional neural network (CNN) that classifies each animal as either healthy or infected based on visible skin lesions. Trained on a diverse dataset of annotated cattle images, the integrated model achieved a high detection precision and classification accuracy, demonstrating strong reliability in recognizing signs of LSD. Furthermore, the system offers real-time feedback via an interactive web interface, enabling farmers and veterinary personnel to quickly assess cattle health with images. This approach not only enhances detection and control measures but also sets the stage for broader adoption of AI in livestock health management within low-resource environments. The system’s design aligns with global goals of smart agriculture, offering a scalable tool that supports both food security and disease resilience.Item SMART-AG : A Precision Agriculture AI-Powered Edge Computing System(Uganda Christian University, 2025-05-06) Totit Kabuya BushenyulaFood insecurity is a critical issue in Africa, with over 296 million people affected by hunger. Despite efforts to increase food production, food loss remains a major contributor to this crisis, particularly in low-income countries where inefficiencies in farming and post-harvest handling are common. While 42% of Africa's workforce is employed in agriculture, there is a low adoption of modern agricultural technologies, primarily due to lack of internet access, technical skills, and high costs. This results in many farmers continuing to rely on traditional methods, which limits their productivity and exacerbates food insecurity. Existing solutions to improve farming practices are often too complex or require constant internet access, leaving many farmers unable to benefit from them. Therefore, a practical, affordable, and internet-independent solution is needed to help farmers increase yields and reduce food losses. This project proposes SMART-AG, an AI-powered edge computing system that provides actionable farming advice via SMS without requiring internet access. SMART-AG aims to empower farmers with insights on soil health, crop selection, and nutrient management, improving productivity and contributing to food security in Africa.Item Potato Disease Diagnosis Using YOLOv5 and Web-Based Deployment(Uganda Christian University, 2025-05-06) James Alala MunjwokAgriculture plays a central role in Uganda’s economy, and potatoes are among the most important staple crops. However, potato yields are significantly threatened by diseases such as early blight and late blight. Timely detection of these diseases is critical to reduce losses, minimize pesticide misuse, and enhance food security. This project presents a web-based potato leaf disease diagnosis system using YOLOv5, a state-of-the-art deep learning object detection model. The system classifies potato leaves as healthy, early blight, or late blight. The backend is implemented using FastAPI and deployed to Render, while the frontend is built in React and hosted on Vercel, ensuring accessibility via modern web browsers. The model was trained on the PlantVillage dataset. Evaluation results show that the system achieved high accuracy and fast inference times, making it suitable for use by farmers and agricultural officers. This report details the system design, methodology, model training, deployment, and perfor- mance evaluation. Limitations such as environmental noise, internet dependency, and limited disease coverage are acknowledged, and future work includes expanding the disease scope, offline deployment, and integrating treatment recommendations. This work contributes to the growing field of AI-powered agriculture in Uganda and aligns with the Sustainable Development Goals for food security and smart farming.Item A Digital Predictive Healthcare Management System for Sickle Cell Disease Using Machine Learning and Data Visualization Techniques: A Case Study of Uganda(Uganda Christian University, 2025-05-06) Tirza AtwiineThis project presents the design and development of a Digital Predictive Healthcare Management System for Sickle Cell Disease (SCD) using Machine Learning and Data Visualization techniques, with a focus on Uganda. Sickle Cell Disease remains a significant public health challenge in Uganda, with limited access to timely diagnosis, treatment monitoring, and personalized care. The proposed system leverages machine learning algorithms; Random Forest Classifier and LSTM(Long-Short-Term-Memory), to predict potential health risks, analyse and predict future patient data, and support early interventions. Interactive dashboards and visual tools created using React provide healthcare professionals and patients with actionable insights for better disease management. This project aims to enhance decision-making, improve patient outcomes, and support national efforts in digital health transformation, particularly in under-resourced settings.Item A Machine Learning Based Web Application for Pre-Eclampsia Risk Prediction, Awareness and Management(Uganda Christian University, 2025-05-06) Angela Nina Twine MukiizaPre-eclampsia is a critical condition affecting pregnant women that is characterized by high blood pressure and potential damage to vital organs. This research focuses on developing a machine learning-based web application designed to predict the risk of pre-eclampsia, enhance awareness and provide management strategies. Utilizing patient data, the application aims to offer accurate predictions and a recommendation. The project involves data collection, model training and application deployment emphasizing the integration of user- friendly interfaces and real-time data processing. The research underscores the importance of early detection and intervention potentially reducing the adverse outcomes associated with pre-eclampsia. By leveraging machine learning algorithms and web technologies, this application aspires to empower healthcare providers and expectant mothers with actionable insights fostering better health outcomes and informed decision-making. This work represents a significant stride towards improving maternal health care through innovative technological solutions.Item For the Sake of Food: A Comprehensive Nutrition Platform for Promoting Healthier Lifestyles in Uganda(Uganda Christian University, 2024-05-10) Aguma Destiny Kampumure; Tracy Majorie Najjoba; Rochelle Katukunda; Moses AyebareThis document provides a comprehensive overview of the development process behind the "For the Sake of Food" nutrition recipe web application. For the Sake of Food’s primary aim was to empower individuals to make healthier dietary choices by offering convenient access to nutritious recipes tailored to their preferences. For the Sake of Food embarked on a methodical journey, employing a variety of research techniques to gain insights into user needs and behaviors. Through interviews and surveys, For the Sake of Food delved into the diverse dietary habits and technological proficiency levels of our target audience, ensuring the app's design catered to a broad spectrum of users. User testing played a pivotal role in refining the application's usability and functionality, with feedback from real users guiding iterative improvements. The team’s findings illuminated key user demographics, dietary preferences, and technological inclinations, informing the development of features such as personalized recipe recommendations and intuitive interfaces. Ethical considerations remained paramount throughout the project, with stringent measures in place to safeguard user privacy and ensure data security. “For The Sake of Food” also acknowledged inherent limitations, such as sample representativeness and resource constraints, which shaped the scope and depth of our research efforts. Ultimately, the systematic approach culminated in the creation of an application poised to positively impact users' dietary habits and overall well-being. By harnessing technology to promote healthier lifestyles, we endeavor to contribute to a healthier society.Item Project MindPeace Report(Uganda Christian University, 2024-04-26) Isaiah Mukisa ; Desire Namanya; Soul Solomon Sekamatte ; Peter Paul Jamugisa ; Conrad William Mabira1 in 5 people experience mental health challenges, the prevalence of mental health issues globally remains a pressing concern, with a significant proportion of affected individuals not receiving the necessary treatment due to barriers such as stigma, cost, and a shortage of skilled professionals. This report presents the MindPeace project, an online application designed to address these challenges by providing a user-friendly platform that connects working-class individuals aged 20 to 65 with skilled mental health practitioners. MindPeace aims to bridge the gap in access to mental health care, focusing on specific demographics and offering an alternative to informal support and self-medication. The project encompasses a comprehensive suite of features, including booking of sessions, an emergency helpline for crisis support, and a compatibility quiz for personalized counseling experiences. By leveraging technologies such as Cal.com integration for efficient appointment management, secure video chat for confidential sessions, and a robust emergency response system, MindPeace seeks to reduce the reliance on self-medication and improve mental health outcomes for its users. This report outlines the project's objectives, scope, and the functionalities it offers, highlighting its potential to significantly impact the accessibility and quality of mental health care for underserved populations.Item Pillpop Final Year Group Project Report(Uganda Christian University, 2024-05-10) Sarah Nsereko ; Daphine Kamusiime; Nkata Joshua Luyombya; Mukisa Hassan Bahati; Kasagga Gordon Kimera; Allan Smith NiringiyeA web application aimed at reminding users to take their medications which is a major problem in Uganda also called medication non-adherence called Pillpop was developed to solve this problem with additional features to further solve this problem, The application was developed due to a recent study on the health sector, which displayed that medication non-adherence is one of the problems troubling the health sector. Moreover, the problem was reflected in my lifestyle when it came to taking medications. The development of the application was done based on using email as a format of reminding users and all the other features within rotated based on the user’s data. Using different technologies e.g Nextjs and Django to develop the application, which relies on the data storage or backend a lot, and displays through the frontend hence the need for an interface that is easy to navigate for the users. During the application development, a lot of hostility was found especially when it came to collecting data and the effectiveness of reminding a user which is hard to achieve 100% because it all draws back to user integrity and I found that it not possible to solve all the problems within the one application but instead one problem at a time is better. The significance of the findings is that investment in technology can help or improve the efficiency of health since the digital generation/age is now the common or the norm meaning with the help of technology, the health of the users can improve, and it also a call to find ways of utilizing the available technology to improve one’s life quality .Item Help Annonymous Project Report(Uganda Christian University, 2024-04-26) Joshua Jasper Ashaba ; Nahum Okello ; Nabil Sengooba ; Gensi Collin Ikiriza; Karongo Keron Kansiime ; Elizabeth AmandaNamaleThe "Help Anonymous" project, developed by a team from Uganda Christian University, represents a transformative approach to mental health support. Our application creates a safe space for anonymous dialogue, offering peer support, professional advice, and educational resources, thereby making mental health care accessible to a broader audience. This community-driven platform has shown significant user engagement and an increase in mental health awareness, demonstrating the effectiveness of our strategies. As a collective, we are proud to contribute to the United Nations' Sustainable Development Goals, specifically SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities), by providing a platform that fosters open conversations on mental health and supports inclusivity. Our project underscores the importance of community and technology in breaking down barriers to mental health care and destigmatizing the pursuit of help. Moving forward, we are committed to enhancing our platform's reach and impact, driving positive change in the realm of mental health.