Conference Proceedings

Permanent URI for this collection

https://dir.muni.ac.ug/handle/20.500.12260/18

Browse

Now showing 1 - 13 of 13

A framework for e-Health information management in Ugandan hospitals: a case of Kampala and Arua.
(IEEE, 2021-10-26) Ajidiru, Hope Sally; Nkamwesiga, Lawrence; Nakakawa, Agnes
The study aims at developing a framework for guiding the establishment of an e-health information management solution in a developing country including Uganda focusing on: the challenges faced and the requirements to address them; designing a framework and evaluating it. The study used Qualitative and Quantitative research designs. Data was collected from 6 health facilities in Arua and 10 from Kampala using 48 respondents for qualitative inquiry and 16 for expert review. Interviews were used for qualitative and questionnaires for quantitative data. Purposive sampling technique was used for qualitative while simple random sampling for quantitative. Thematic analysis was used to analyse qualitative data while quantitative data was analysed using SPSS version 23. Findings reveal that the design decisions are likely to fulfil their purposes. The study can be used by e-health software developers, government to formulate eHealth policies, and researchers on eHealth information management.
Relationship between semantic layer, technical infrastructure with eHealth interoperability in Ugandan public hospitals.
(IEEE., 2023-11) Nkamwesiga, Lawrence; Kituyi, Geoffrey Mayoka
Although eHealth technology has been adopted in developed countries for some years, it seems to be in its early stages in developing countries like Uganda. In this study, the connection between Technical Infrastructure, Semantic layer and eHealth interoperability in Ugandan Public Hospitals was investigated. Quantitative research methods to collect and analyze data were employed in this study. Stratified sampling was used to select three public general hospitals of Kitagata, Nebbi, and Naguru while simple random sampling technique was used to collect data from a total of 14 administrative staff, 44 medical workers and 32 patients from each of these hospitals at a response rate of 89.3 percent. Data was collected using a self-administered questionnaire. Descriptive statistics were used to derive background information of the respondents while regression analysis techniques were used to analyze variable relationships and also to test the predicting power of the independent variables on the dependent variable. The final model was confirmed using structural equation modelling analysis tools. The key findings indicated a significant positive relation between Technical Infrastructure and eHealth Interoperability (Beta = 0.475, p<0.001), Semantic Layer and eHealth Interoperability (Beta=0.595, p<0.001). The proposed model showed a significant relation between Technical Infrastructure with eHealth Interoperability, Semantic Layer with eHealth Interoperability in Ugandan Public Hospitals. It is recommended that stakeholders implementing eHealth in Ugandan Public Hospitals to consider Technical Infrastructure, Semantic Layer, and eHealth Interoperability relationships for effective healthcare systems leading to quality eHealth care.
Machine learning for medical image feature extraction
(IEEE, 2025-05-09) Rathore, Saurabh Pratap Singh; Ali, Guma; Chamoli, Sakshi; Lotus, Rayappan; Kumar, Yogendra; Sikarwar, Shailendra Singh
Feature extraction from medical images is crucial for harnessing the vast information they contain, aiding in diagnosis, treatment planning, and disease monitoring. Traditional feature extraction methods often struggle to capture the complex patterns and subtle variations in medical images. Recently, machine learning techniques have become powerful tools for automatically extracting discriminative features, enabling more accurate and efficient analysis. This paper provides a comprehensive review of advanced machine learning approaches for medical image feature extraction. It covers various methods, including deep learning architectures, convolutional neural networks (CNNs), and feature learning techniques, highlighting their applications across different medical imaging modalities such as MRI, CT, and X-ray. Our CNN model achieves an average classification accuracy of approximately 94%, outperforming the pneumonia detection accuracy of KNN (91%) and SVM (92%).
AI Driven railway crack detection system using Convolutional Neural Network and IoT
(IEEE, 2025-06-02) Sudha, M.; Saranya, R.; Ali, Guma; Ganesh, C; Umamaheswari, S.
The Railway Track fractures identification Using AI with IoT project aims to increase railway safety by automating the identification of fractures in railway tracks using artificial intelligence (AI) and Internet of Things (IoT) technology. Even minor cracks in railway tracks, which are crucial components of the transportation system, can cause major accidents if they not immediately identified also repaired. Conventional manual assessing technique are expensive, time-consuming, and error-prone. Convolutional Neural Networks, a kind deep learning model, will used in this study to automatically identify cracks in photos of railroad tracks. The model trained on a dataset of track photographs in order to discern parts are defective (cracked) and non-defective (intact). Once trained, The CNN model is employed to analyze images captured by cameras mounted on trains or inspection vehicles as part of a real-time monitoring system driven by the Internet of Things. Every time a fracture is discovered, the system sends the information to the Blynk IoT platform, notifying and alerting maintenance personnel. Additionally, an LCD display and a buzzer alarm are activated in the field to alert technicians to the detected defect. By combining AI and IoT, the initiative aims to reduce overall maintenance costs, improve safety through early fault detection, and speed up the track inspection process. By providing a more automated, accurate, and efficient method of tracking the present state of railroad lines, the system ultimately increases the harmlessness and reliability of railway transit.
Next-gen non-invasive glucose monitoring using microwave sensors and ai-based thumb positioning analysis
(IEEE, 2025-06-25) Uma, S.; Soundharya, P.; Charisma, S.; Jackson, Beulah; Ganapathi, G.; Ali, Guma
Because of discomfort and accuracy problems, the existing non-invasive glucose monitoring devices include optical sensors and finger-prick tests have long been under doubt for their viability. Variations in skin type and environmental interference might make these techniques unreliable. To get past these constraints, the study presents a fresh approach combining artificial intelligence (AI)-based thumb positioning analysis with microwave sensors. By spotting changes in the dielectric characteristics of the skin brought on by glucose, the microwave sensors offer exact, invasive, real-time readings. Through best sensor alignment, AI removes thumb position errors. Performance assessment indicates substantially better outcomes than the existing systems with an R2 of 0.98, RMSE of 7.1 mg/dL, and MAE of 5.2 mg/dL. The great flexibility of the proposed system to a wide spectrum of demographics and excellent user compliance (95%) underlines its possibility for efficient and comfortable diabetes control. The technique represents a substantial development in non-invasive glucose monitoring.
IoT-enabled smart farming: Harnessing LoRa technology for sustainable agriculture
(IEEE, 2025-07-15) Kalaivanan, T.; Sethuraman, Priya; Abirami, B.; Rajkumar, L.; Sathish, A.; Ali, Guma
The present-day speed of Internet of Things (IoT) technology development delivers real-time monitoring and decision-making features to precision agriculture. This research seeks to establish an IoT-smart farming system built around LoRa (Long Range) technology to create more efficient resource management while maintaining sustainable farming routines. The LoRaWAN-based sensor network distributed across different topographical areas measured environmental parameters such as soil moisture and temperature, humidity. The obtained sensor data was sent through LoRa gateways to establish real-time processing at a cloud server. The radio signals maintained peak power levels in open agricultural fields yet they faced minor reductions in receiving strength in dense farmland regions. Experimental data revealed that LoRa established reliable communication which delivered -70 dBm average RSSI in open fields coupled with -85 dBm RSSI in dense crop areas as well as achievement of over 90% data transmission success across a 5 km distance using minimal power. Results from experiments proved that LoRa technology establishes dependable long-distance transmission while using minimal energy.
Scalable multi-connectivity approaches for AR/VR traffic management in 5G networks
(IEEE, 2025-07-15) Dhamini, V.; Subathra, Y.; Aruna, V.; Jothi, J Salomi Backia; Sathish, A.; Ali, Guma
The expanding use of AR/VR applications in 5G networks needs an efficient traffic management system for reaching the network requirements concerning latency and bandwidth usage. The current network architectures show limitations during changes in traffic flow patterns which results in performance deterioration. A Scalable Multi-Connectivity Approach based on Software-Defined Networking (SDN) with Artificial Intelligence (AI) traffic balancing and Multi-Access Edge Computing (MEC) serves to improve 5G network management of AR/VR traffic. The system implementation includes the combination of AI-based load balancing and QoS-aware network slicing and SDN-based adaptive routing for distributing traffic optimally across 5G, LTE, and Wi-Fi networks. Real-time immersive experiences improve significantly through simulation results which show a reduction in AR/VR jitter by 28 % together with an average latency of 15.3 ms. The study shows that artificial intelligence-based multi-connectivity methods create successful traffic management for AR/VR applications through optimized resource utilization in networks.
FPGA-based High-precision MCU testing using pattern latching and DDLL
(IEEE, 2025-07-25) Arunkumar, K; Vani, R; Ali, Guma
Performance testing of microcontrollers (MCUs) is very crucial especially when it comes to safety critical systems such as those in automotive electronics field that will require high accuracy and reliability. One of the essential criteria for performance testing are results checking for getting the maximum possible clock frequency of the MCU possible to influence the automobile units’ efficiency. The traditional method of testing using the ring oscillator approach might not be sufficient in giving MCU performance. To this, the system under consideration includes a pattern latching algorithm with an included configurable ring oscillator. The utilization of a clocked latch with a synchronous gate is used to synchronize ring oscillator circuits so as to do precise pattern analysis. The system makes use of a digital delay locked loop (DDLL) to provide high accuracy when measuring performance by dynamically compensating for delays. Such a strategy increases the validity of performance screening, with improved estimation of the MCU’s operating abilities in adverse conditions.
Next-Gen smart homes: AI-enhanced Li-Fi for superior efficiency and protection
(IEEE, 2025-09-04) Rajasubha, J.; Sathish, A.; Jackson, Beulah; Hemavathi, U.; Ajay, A. P.; Ali, Guma
The system proposed optimizes dynamically the Li-Fi transmission parameters using a Deep Reinforcement Learning (DRL) adaptive modulation algorithm, resulting in a bit error rate of 0.00034 and a throughput of 986.45 Mbps. An intrusion detection system can enhance cybersecurity by utilizing Convolutional Neural Networks, which yield a detection rate of 99.81%. The future paradigm is more secure, reliable, and programmable than conventional Wi-Fi and fixed-modulation Li-Fi systems. The Li-Fi architecture with AI support possesses a unique edge in intelligent home applications because of its scalability and reliability compared to previous, less dependable wireless networks. This work promises a safe and effective form of data transmission by creating a platform for upcoming advancements in optical wireless communication based on AI. The Li-Fi system proposed that is supported by AI works far better on smart home wireless networking problems of low speeds, insufficient customization, and security. The optimized transmission parameters were used in real-time by the system through a DRL adaptive modulation approach, and this resulted in reducing the bit error rate to 0.00034 and boosting the throughput to 986.45 Mbps.
AI-driven pathogen detection systems for rapid and accurate diagnosis
(IEEE, 2025-09-24) Deepika, M.; Murugan, Sundara Bala; Subasini, V.; Rufus, N. Herald Anantha; Atkinswestley, A.; Ali, Guma
Pathogen detection at speed along with precision serves as a basis for disease diagnosis and overall control effectiveness. Several current detection methods based on culture-based techniques and PCR face drawbacks of extended processing times, high expenses, and the need for expert operators. It adopts deep learning Convolutional Neural Networks with an attention mechanism in a system designed to overcome existing limitations of pathogen detection. The model was trained using a multi-modal dataset that combined spectral and biological signals through feature extraction optimization and privacy-protected training methods based on federated learning. The proposed system reaches a 96.8% detection accuracy together with 15% reduced false alarm rates and 35% faster operation speeds than conventional models. Experimental data measurements showed that this system achieved 92.3% F1-score together with 97.1% AUC for real-time identification of pathogens. AI has proven its effectiveness through these findings in changing the way pathogen diagnostics operate. Further work will concentrate on extending datasets while creating real-time deployment protocols for clinical use.
Creating electric vehicle battery management with IoT: Using intelligent algorithms to enhance safety, efficiency, and charging time
(IEEE, 2025-10-28) Balapriya, S.; Pandi, V. Samuthira; Sabitha, M.; Veena, K.; Balasubramaniyan, R.; Ali, Guma
The expanding electric vehicles (EV) market has raised the demand for better-optimized intelligent battery management systems (BMS) that can improve safety, performance, and charging time. Older BMS representative solutions employ earlier monitoring and control techniques, often without real-time adaptability and predictive capabilities. This paper investigated Internet of Things (IoT)-specialized and smart algorithms-integrated EV battery management to increase efficiency, safety, and a superior charging process. The proposed architecture uses machine learning model, data analytics, and IoT-enabled sensors to enable real-time monitoring of critical battery parameters such as state of charge (SoC), state of health (SoH), temperature, and voltage variations. Predictive analytics enable the early detection of potential battery degradation, minimize thermal runaway, and enhance energy distribution among individual battery cells. Furthermore, advanced charging algorithms optimize charging rates in response to instantaneous battery states and grid demand, maximizing charging speed while avoiding overcharging and wearout. Cloud-hosted IoT platforms enable remote monitoring and data-based decision-making, improving user experience and prolonging battery life. We develop a prototype implementation to showcase the effectiveness of the system, which results in efficient energy management, early faults detection, and reduced charging cycles. Application of the proposed system was compared against the state-of-the-art BMS and used as a reference standard and the comparison results revealed excellent performance in terms of safety, compactness and flexibility of the system with the existing BMS. The study utilizing IoT as well as artificial intelligence helps to further emerge smart electric vehicle technology that leads human being for sustainable and rich electric mobility solution.
Multi-modal biometric authentication integrating gait and face recognition for mobile security
(IEEE, 2025-10-28) Nivedita, V.; Joseph, Juno Ann; Varghese, Divya Thankom; Nithyanandh, S; Sundaram, Jayavelu; Ali, Guma
In the surroundings of mobile security systems, traditional biometric methods such as fingerprint and facial recognition can be readily disputed and misled. These systems change user appearance by failing in different illumination conditions or spoofing attacks with false fingerprints or images. Overcoming these constraints, the proposed multi-modal biometric identification system integrates gait with facial recognition. Combining facial data with gait traits observed by accelerometers and gyroscopes helps to increase security and reduce false positives and negatives. Results show the system achieves 97.8% accuracy, significantly above the existing systems using a low false acceptance rate (significantly) of 1.5% and a false rejection rate (FRR) of 0.7%. It has a superior resilience to spoofing techniques, including stride imitation and image/video spoofing; it also shines in tough conditions, including poor lighting and appearance alterations. Despite a slight increase in processing time, the method offers a more robust and reliable solution for mobile authentication.
Augmented heritage: AR and QR code integration for interactive cultural storytelling in the UAE
(IEEE, 2025-12-05) Lal, Mily; Dash, Soumyakant; Keerthika, J; Kumar, Bura Vijay; Ali, Guma; Shukla, Vinod Kumar
This study has outlined a mobile-augmented reality (AR) storytelling framework utilizing Quick Response (QR) code technology to provide cultural engagement at heritage sites in the United Arab Emirates. The project set out to merge traditional heritage with augmented immersive technology and focused on a four-phase methodology: (1) Content curation and narrative design through historians and cultural experts; (2) Planning QR codes in spaces cooperatively designed through spatial design and semiotics of culture; (3) Content development of AR experiences in Unity and Vuforia using finite state machine for stability and usability; and (4) User testing in three major heritage locations. The quantitative and qualitative evaluations measured user engagement, narrative retention, usability of the system, and cultural sensemaking. Relative to baseline digital experiences, the AR-QR platform saw significant gains: 25-30% improvement in engagement; 20-25% improvement in narrative retention; and over 15% improvement in usability and cultural relevance. The study evidence demonstrates the promise of AR-QR storytelling and approach to deliver contextually rich and interactive heritage experiences. Although there are limitations in device affordances/conceivability and longer-term evaluations, this study suggests---immersive storytelling can make heritage more engaging, accessible, and meaningful to a diverse population.

Browse

Browsing Conference Proceedings by Issue Date

Results Per Page

Sort Options