Computational and Communication Science Engineering [ CoCSE]

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Browsing Computational and Communication Science Engineering [ CoCSE] by Author "Alfred, Reuben"

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    Design of an Integrated Android Mobile Application and Web-Based System (IAMAWBS) as a Solution to Concerns of Passengers Using Bus Rapid Transit System for Public Transportation in Dar Es Salaam
    (Modern Education and Computer Science Press, 2019-02-08) Alfred, Reuben; Kaijage, Shubi
    The rapid population growth in Dar Es Salaam has prompted the demand of effective transport system in the city. This tremendous rise of population led to serious road traffic congestions, which brings a number of challenges into the city and other growing urban areas. City authorities attempted various solutions to control the traffic congestions such as construction of new roads, expansion of existing roads, installation of traffic lights and other transportation infrastructures such as reestablishment of commuter train to operate within the city but they couldn’t effectively relieve the problem. Eventually, the Government of Tanzania (GoT) supported the city’s effort by establishing the organ called Dar Es Salaam Rapid Transit (DART) to supervise the implementation and operation of Bus Rapid Transit (BRT) system. The BRT system provided direct benefits to passengers such as minimal travel time, improved reliability as compared to other public transport commonly known as daladala, and reduced accident as BRT buses travel in their dedicated lanes. Despite these benefits there still persist transportation challenges with the BRT, where passengers still suffer from waiting on very long queue during ticket booking, shortage of smart cards, they are unable to check balance direct from their mobile phones, as well as they fail to top-up onto their card’s balance using their smart phones. This paper presents a software technology approach that would help passengers to check balance, send request specifying station to board a bus and check the bus arrival time at any station.
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    Detectron2-enhanced mask R-CNN for precise instance segmentation of rice blast disease in Tanzania: Supporting timely intervention and data-driven severity assessment
    (Elservier, 2025-08-09) Alfred, Reuben; Leo, Judith; Kaijage, Shubi
    Rice blast, caused by Magnaporthe oryzae, poses a significant threat to rice production in Tanzania and across Africa, affecting food security and farmers’ livelihoods. Traditional inspection methods are slow and often overlook early symptoms, leading to delayed responses. Although progress has been made with deep learning diagnostics, many approaches still depend on whole-image classification or broad bounding boxes, lacking them pixel-level detail needed to assess infection severity. This study introduces a Mask R-CNN instance segmentation model developed within the Detectron2 framework to accurately detect and segment blast lesions (BL), blast- infected leaves (BIL), and healthy leaves (HL) at the pixel level. In addition to detection, the model quantifies the lesion severity by computing the proportion of infected leaf area, supporting informed evaluation and improved disease management decisions. Built on a ResNet-50 backbone with a Feature Pyramid Network (FPN), it achieved a mean average precision (mAP) of 89.4 %, with an AP50 of 94.6 % and an AP75 of 90.5 %. The model exhibited consistent performance across object scales, achieving an AP of 81.31 % for small objects and 86.06 % for large objects. Furthermore, testing on unseen images (images not used in the training process) demonstrated strong generalization, with detection confidence above 99 % and accurate masks that provide reliable severity scores. By enabling pixel-level severity assessment without expensive sensors or UAVs, this study offers a practical and affordable solution for disease monitoring in resource constrained farming communities. It equips Tanzanian smallholder farmers with timely, accessible tools for effective blast detection and data-driven decision making.
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    An integrated mobile and web-based system for bus rapid transit (brt) in Dar es Salaam
    (NM-AIST, 2020-02) Alfred, Reuben
    The rapid population growth in Dar Es Salaam has prompted the demand of effective transport system in the city. This tremendous rise of population leads to a serious road traffic congestion, which brings a number of challenges into the city and other growing urban areas. The city’s local governments attempted various solutions to control the traffic congestions including building of other roads, expansion and extension of present roads, installation of traffic lights and other transportation infrastructures though they have not relieved the problem. The Government of Tanzania (GoT) supported the city’s efforts by establishing the administrative organ called Dar Es Salaam Rapid Transit (DART) to administer the implementation of Bus Rapid Transit (BRT) system. The government through DART has improved the public transport in the city. The BRT system provided direct benefits to passengers like minimal travelling time, improved reliability as compared to other public transport commonly known as daladala and accident reduction because BRT buses use their exclusive lanes. Besides its advantages, the newly adopted transport system has brought some challenges to its customers. Among others are; a queue during ticket booking, shortage of smart cards, and inability to check and top-up balance using passengers' mobile phones. Therefore, the study presents a software-based solution that will help passengers to check balance, send request by specifying station to board a bus and check or predict bus arrival time at any station.
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    Optimizing dataset diversity for a robust deep-learning model in rice blast disease identification to enhance crop health assessment across diverse conditions
    (Elsevier, 2025-03) Alfred, Reuben; Leo, Judith; Kaijage, Shubi
    Magnaporthe oryzae, the pathogen that causes rice blast disease, poses a significant global threat to rice pro duction. This disease may lead to yield losses exceeding 30 % in susceptible rice varieties. There is an urgent need for more effective detection solutions, as traditional methods—primarily based on visual inspection—are time- consuming and prone to errors. Deep-learning models presented effective solutions for disease identification due to their ability to analyze large datasets. However, the diversity of the training dataset is significant for optimal performance and generalizability of the model. This study evaluated the impact of dataset diversity on model performance and generalizability by developing two models, referred to in this study as the High-Diverse Model and the Low-Diverse Model. The High-Diverse Model was trained on a diverse dataset comprising images from different geographical regions, rice species, environmental conditions, plant growth stages, and disease severity levels. In contrast, the Low-Diverse Model was trained on a less diverse dataset with significantly limited variability. The results showed that the High-Diverse Model significantly outperformed the Low-Diverse Model, achieving a training accuracy of 95.26 % and a validation accuracy of 94.43 %, indicating effective general ization. The Low-Diverse Model achieved an accuracy of 98.37 % on the training data but only 35.38 % on the validation data, indicating a severe overfitting issue associated with limited dataset diversity. This highlights the importance of dataset diversity in developing effective and scalable deep-learning models for crop health assessment.