Browsing by Author "Ally, Mussa"

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Blockchain Technology in Sub-Saharan Africa: Where does it fit in Healthcare Systems: A case of Tanzania
(Journal of Health Informatics in Developing Countries, 2019-09) Kombe, Cleverence; Sam, Anael E.; Ally, Mussa; Finne, Auvo
Background: Blockchain technology is a distributed electronic ledger containing digital records, transactions or events that are protected with advanced encryptions, extremely hard to tamper, and updateable through a consensus algorithm agreeable to all connected network nodes. In Sub-Sahara Africa, the technology has started to be adopted in real estate, supply chain, agriculture, and financial sector. Unfortunately, there is a lack of effort in introducing this technology in the healthcare sector. Therefore, this study aims to explore the issues facing electronic healthcare systems in Sub-Sahara Africa taking Tanzania as a case study and introduce blockchain-based solutions for the discovered issues. Methods: The study used qualitative methods for data collection and analysis. Data were collected through interviews, observation and documentary analysis. Interviews were done with the sample size of 50 participants who were selected from groups of healthcare facility leaders, ICT experts, government representatives, doctors, nurses, laboratory technicians, pharmacists, accountants, and receptionists. Direct observation and participatory observation were used to assess different electronic healthcare records systems’ functions. Moreover, researchers used document analysis to collect data from public records (like policy manuals), personal documents (like incident reports), and physical evidence (like training materials and handbooks). NVivo 11 software was applied in managing and organizing data analysis. Results: Out of 710 healthcare facilities involved in this study, 34.5% fully implemented electronic healthcare records systems and 78% installed Mfumo wa Taarifa za Uendeshaji Huduma za Afya (MTUHA) also known as (District Health Information Software (DHIS) II). The findings showed that the issues facing electronic healthcare records are; difficulties in taking care of the patients’ private information, problems in safely sharing medical information between healthcare facilities, bandwidth issues, and improper handling of data integrity.
A Deep Learning Model for Classifying Black Sigatoka Disease in Banana Leaves Based on Infection Stages
(IJST, 2024-10-04) Nyambo, Devotha; Kambo, Edwin; Leo, Judith; Ally, Mussa
Objective: This research study aims to develop an efficient deep-learning model to detect and classify stages of Black Sigatoka disease in banana plants. Methods: In this study, deep learning techniques, specifically the basic Convolutional Neural Network (CNN) and VGG16 models, were used to address the challenge of identifying Black Sigatoka disease in banana leaves early on. The tests were conducted on a dataset containing labelled images of banana leaves, assessing their effectiveness based on criteria such as accuracy, precision, recall, and F1-score after adjusting hyperparameters for optimal outcomes. Findings: The results of the trials revealed that the basic CNN model attained a training accuracy of 96% and a validation accuracy of 89%, surpassing the performance of the VGG16 model. The VGG16 model, on the other hand, had a training accuracy of 92% and a validation accuracy of 89%. Across precision, recall, and F1 score measurements, the basic CNN model consistently outperformed the VGG16 model, with scores averaging 0.90 for all three metrics compared to VGG16’s precision of 0.80, recall of 0.75, and F1 score of 0.75. The CNN model demonstrated its efficiency by stopping training at 26 epochs, whereas VGG16 completed training in 21 epochs. This demonstrates its effectiveness in detecting Black Sigatoka while utilising minimal resources. Novelty: A significant component of this study is its emphasis on identifying the stages of Black Sigatoka disease, which is commonly overlooked in research. By studying disease progression, this study provides insights for early intervention and disease management, aiding efforts to lessen the impact of Black Sigatoka on banana farming.
IoT-Based Intelligent Charging System for Kayoola EVs Buses at Kiira Motors Corporation in Uganda
(Research Square, 2024-02-16) Ntwali, Benjamin; Sinde, Ramadhan; Ally, Mussa; Naman, Godfrey; Ntambala, Boniface
The increasing popularity of Electric Vehicles (EVs) has led to a surge in the need for charging stations in Kayoola EV buses. Most of existing EV charging stations have outdated features which is challenging to be remotely controlled. However, current EV charging systems experience with no remote battery operational charging status, unsafety control if charging station is faulted, and insecure charging RFID card payment. This paper, an IoT-based system was aimed to manage and monitor these EV charging stations. The battery management system (BMS) sends charging voltage and current information to charger via Controller Area Network (CAN) bus. Then, the Raspberry Pi4 receives and decode CAN charging data to be processed, analyzed and transmits to the cloud server. Each charger is equipped with sensors monitoring parameters like charging status, energy consumption, voltage, current, and time. The user can access that decoded charging information via android mobile application and desk remote management system. Additionally, the system server calculates the battery charging levels and commend RFID card transaction payment. The results show that developed IoT-based intelligent charging system provides and outperforms minimum and maximum cell voltages of 2.82V and 4.1V, min. and max. cell temperatures of 37℃ and 40℃ respectively. The charged energy of 10kWh, used energy of 0kWh, charging state indication, low-cell voltage as error state indication, charging price rate of 500Ugx/kWh, and full-latch of 0, pack voltage of 483.9V, pack current of 100.1A, battery health of 97%, battery state of charge (SoC) of 100% and remaining charging time of 38 mins were also detected and remotely monitored. In conclusion, the developed system proves 100% of real-time and remote data access and accuracy, efficiency, security, accessibility, sustainability, safety charging payment, and remote battery status monitoring system of EV charging infrastructure compared to the current charging where it offers only 58.75% of charging rate.