Computational and Communication Science Engineering [ CoCSE]

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A Data-Driven Web-Based System for Regional Program Accreditation: The Case of Inter-University Council for East Africa (IUCEA)
(Springer Nature, 2026-04-01) Nkeshimana, Carmel; Ruhinda, Ben; Nyambo, Devotha; Kaijage, Shubi
In response to the evolving landscape of regional program accreditation, we undertook the development of a user-centric Regional Program Accreditation System (RPAS). This system was meticulously designed to cater to the diverse needs of accreditation bodies, educational institutions, and expert reviewers within the East African Region. Our journey commenced with an extensive requirement-gathering phase, engaging end-users to shape the system's design and functionality. The RPAS was built using cutting-edge technologies, including ReactJs, Django, API integration, Tailwind CSS, and MySQL, ensuring scalability, security, and flexibility. Agile methodologies, particularly Extreme Programming, were used, enabling expedited development and fostering close collaboration, ensuring adaptability to users’ evolving needs. The RPAS streamlines the accreditation process with a user-friendly interface and real-time collaboration features, thereby enhancing transparency and operational efficiency. Robust authentication mechanisms were implemented to ensure data security. Additional user-friendly features, such as night mode, QR code-based certificate verification, and automated notifications, were introduced for convenience. In the future, RPAS can be enhanced by implementing Machine Learning for Document Content Analysis, enhancing Reporting and Analytics Capabilities, and establishing Automated Reminders and Notifications. These enhancements will further empower RPAS, making it a valuable tool for streamlining accreditation processes and contributing to the elevation of program quality and educational excellence in the region.
A decentralized IoT and blockchain-based architecture for general-purpose vehicle speed data collection: a case study of Tanzania’s highways
(Sciendo, 2025-06-09) Njuu, Kevin; Dida, Mussa; Runyoro, Angela-Aida
This study addresses the limitations of Tanzania’s current vehiclespeed detection systems, which are mostly manual, costly, andvulnerable to corruption. Existing automated systems are limited inscope and depend on centralized architectures, making them prone to data manipulation. To overcome these issues, this study proposes a novel four-layer decentralized architecture that integrates Internet of things (IoT) and blockchain technologies, offering a secure, transparent, and automated solution tailored to the Tanzanian context. The system supports the collection of general-purpose speed data and utilizes hyperledger fabric (HLF) channels to ensure confidentiality and privacy. A proof-of-concept was tested in parallel mode, achieving a latency of 1 s, throughput of 1 record/s, and an error rate of 0.07% under varying record loads. When scaling the number of nodes (NN), it maintained 3 s latency, 0.4 records/s throughput, and 7.5% error rate, demonstrating a 50% latency reduction and over double the throughput compared with sequential and asynchronous modes. Future research should explore the integration of 5G for further improvements.
A Generative AI Method for Minority Class Handling in Anomaly Detection with Drift and Explainability Analysis
(Springer Nature, 2026-02-10) Mwiga, Kelvin; Dida, Mussa; Mohsin, Ahmad; Sarker, Iqbal
Artificial Intelligence, particularly machine learning (ML) algorithms, plays a crucial role in detecting cyberattacks, including anomalies and intrusions. However, machine learning models trained on imbalanced cybersecurity datasets often struggle to accurately detect minority data instances and potential threats, thereby weakening overall system security. Despite extensive research, a persistent challenge is the inadequate explanation for model predictions concerning minority data classes. This study aims to address these limitations by developing a generative AI-based approach to manage minority classes in anomaly detection, incorporating concept drift handling and explainability analysis. We introduce an over-sampling technique, CGGReaT, designed to enhance the presence of minority classes in the anomaly detection domain. Leveraging Large Language Models (LLMs) as a hybrid approach, we use pre-trained transformer-based LLM DistilGPT-2 for generating synthetic tabular data. Extensive experiments on two publicly available benchmark datasets, UNSW NB15 and CIC-IDS2017, underscore the efficacy of our proposed approach. We employed concept drift detection and adaptation techniques to maintain reliable and sustainable ML performance. To enhance interpretability, eXplainable Artificial Intelligence (XAI) methods, including SHAP and LIME, are employed to quantify feature contributions to model outputs. Extensive experiments reveal that testing ML algorithms on datasets balanced with synthetic samples generated by cGGReaT boosts the prediction accuracy on the UNSW NB15 and CIC-IDS2017 datasets, compared to classifiers tested on imbalanced datasets.
A Hybrid Deep Learning Model for SQL Injection Attack Detection
(IEEE Access, 2026-01-12) Casmiry, Emanuel; Sinde, Ramadhani; Mduma, Neema
An increasing number of web application services raises significant security concerns. Online access to these applications exposes them to multiple cyberattacks. The Open Web Application Security Project has consistently reported SQL injection attacks among the top 10 cyber threats for over a decade, highlighting the need for more effective detection and prevention strategies. Traditional detection methods, primarily signature-based, offer basic protection but struggle to identify new signatures embedded within web requests. An alternative involves Machine Learning (ML) and Deep Learning predictive analytics, which provide effective solutions for analyzing large datasets to detect and prevent SQL Injection Attacks (SQLIA). However, these models often face challenges such as high false alarm rates and low accuracy. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model for SQL injection detection. Five architectures were compared: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid CNN-LSTM, and hybrid CNN-GRU. Among them, CNN-LSTM achieved the best results, with 99.85% accuracy, 99.86% precision, 99.85% recall, and a 99.85% F1-score, while also recording the lowest false positive rate (0.06%) and misclassification rate (0.15%). CNN-GRU, CNN, and GRU followed closely, though GRU reported the highest false positive rate (0.16%). The standalone LSTM model performed slightly weaker, with 99.80% accuracy, 99.80% precision, 99.79% recall, and 99.80% F1-score, along with false positive and misclassification rates of 0.06% and 0.20%, respectively. Overall, the findings show that all models achieved near-perfect performance, with hybrid architectures outperforming single-architecture models.
A Hybrid Machine Learning and Signature-Based Approach for Detecting Network Pivoting in BYOD Environments
(Asosiasi Doktor Sistem Informasi Indonesia, 2026-02-25) Amour, Nassor; Leo, Judith; Dida, Mussa
This study addresses the challenge of detecting network pivoting, a lateral movement technique that is difficult to identify in insider and BYOD environments because malicious transitions can resemble normal internal activity. The objective was to improve detection of both known and unknown pivoting behaviours while supporting practical triage in resource-constrained institutions. A hybrid detection framework was developed that fuses Snort signature alerts with machine learning classification and unsupervised anomaly detection using behavioural features derived from BYOD-like network traffic. The approach was evaluated in a controlled testbed and supported by organisational survey findings on awareness and monitoring practice. Results show the hybrid system achieved 96.2% classification accuracy with a 4.5% false positive rate when distinguishing normal traffic, suspicious activity, and pivoting attacks. Compared with signature-only and machine-learning-only baselines, the hybrid design detected simulated pivoting attempts earlier and more consistently. User acceptance testing also reported strong satisfaction with the integrated dashboard for monitoring, filtering, and reporting. The key contribution is a unified, dashboard-oriented fusion of signature and behavioural evidence that strengthens early lateral movement detection and reduces manual correlation effort.
A labelled dataset of healthy and diseased common bean (Phaseolus vulgaris) from Tanzania
(Elsevier Inc., 2026-05-31) Mduma, Neema; Laizer, Hudson
Common bean (Phaseolus vulgaris) is an important food and cash crop in Tanzania, where it contributes to household nutrition and income among smallholder farmers. Its production is, however, constrained by diseases such as bean rust and bean anthracnose, which can cause substantial yield losses and reduce crop quality. This article presents a labelled image dataset of common bean leaves collected under field conditions in Northern Tanzania to support research and application development in computer vision, machine learning and digital crop health. The dataset comprises 155,842 labelled multi-season images belonging to three classes: healthy leaves, bean rust and bean anthracnose. Images were acquired from farms in Arusha, Kilimanjaro and Manyara regions using smartphone cameras and were subsequently reviewed and validated with support from agricultural extension officers and plant pathologists to improve annotation reliability. The dataset is organized into class-specific compressed files and is publicly available through the Zenodo repository. By providing a large field-based image resource captured under variable real-world conditions, this dataset can support the development, training and evaluation of image-based models for common bean disease identification.
A Loan Application Management System for Efficient Loan Processing: A Case of Muhimbili SACCOS LTD
(Springer Nature, 2024-06-30) Murimi, Luciana; Siebert, Marius; Salira, Godwin; Mkoba, Elizabeth; Ally, Mussa
Major parts of the population in emerging markets are still unbanked. The 2021 Global Findex Database shows that only 52% of Tanzanian adults own a formal financial account. Unbanked individuals cannot access capital to grow their businesses. In Tanzania, Savings and Credit Cooperative Organization Societies (SACCOS) have traditionally provided services and products such as loans and savings tailored to fit the needs of the financially excluded. By doing so, tremendous success has been achieved in attaining financial inclusion. However, inefficient manual business processes still pose a great challenge, hindering SACCOS performance and sustainability. Whereas digital solutions such as web and mobile applications have been widely adopted to improve business processes in various sectors, this adoption has been quite slow in the SACCOS sector. Lack of affordable entry-level solutions has resulted in most SACCOS relying on manual paper-based processes. There is therefore need, for the design and implementation of affordable, entry-level digital solutions. This study presents the implementation of a loan application management system: a case of Muhimbili SACCOS LTD. Qualitative methods of data collection were used in identifying system requirements. An Android tablet-based loan application management system was implemented, allowing loan officers to capture rich information required to determine members’ loan eligibility. Through the application, loan officers can retrieve stored loan applications and generate the required templates needed for further processing. For integration with the Core Banking System (CBS), a schema is generated that can be uploaded to the CBS for further loan processing. Thus, achieving an efficient loan application process.
A Mathematical and Optimal Control Model for Rabies Transmission Dynamics Among Humans and Dogs with Environmental Effects.
(WILEY Online Library, 2025-09-02) Charles,Mfano.; Mfinanga,Sayoki.; G,Lyakurwa.; Torres,Delfim.; Masanja,Verdiana.
This study presents a deterministic model to investigate rabies transmission dynamics, incorporating envi- ronmental effects and control strategies using optimal control theory. Qualitative and quantitative analyses reveal that the disease-free equilibrium is stable when the effective reproduction number Re < 1, and un- stable when Re > 1. Mesh and contour plots illustrate an inverse relationship between Re and control strategies, including dog vaccination, health promotion, and post-exposure treatment. Increased interven- tion reduces transmission, while higher contact rates among dogs raise Re. Numerical simulations with optimal control confirm the effectiveness of integrated strategies. Vaccination and treatment are identified as key interventions for achieving rabies elimination within five years.
A Mathematical Model for Smooth Muscle Cell Phenotype Switching In Atherosclerotic Plaque
(Springer Nature, 2026) Ndenda, Joseph; Watson, Michael; Misra, Ashish; Myerscough, Mary
Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. They ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch to a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMCs, MDMs and smooth muscle cell-derived macrophages (SDMs) and the internalised lipid load in each population. We use this model to explore the effect on plaque fate of SMC phenotype switching. We find that when SMCs switch to a macrophage-like phenotype, there is an increase in the lipid quantity in the model plaque that is internalised inside cells. Additionally, removal of SMCs from the model plaque via phenotype switching reduces the number of SMCs in the fibrous cap, increases the lipid in the necrotic core, and increases plaque inflammation. These features are hallmarks of vulnerable plaques, whose rupture can cause heart attacks or strokes. When SDMs are highly proliferative or resistant to cell death, the model plaque becomes increasingly pathological. The model suggests that the switch of SMCs to a macrophage-like phenotype may drive the development of unstable and pathological plaques.
A mathematical model to explore the role of tolerance in marriage stabilityand divorce dynamics
(Elservier Inc., 2026-01-12) Buxay, Lydia; Chuma, Furaha; Mayengo, Maranya
The purpose of this study is to develop a mathematical model to analyze the impact of tolerance on marriage stability and divorce dynamics. A deterministic mathematical model is formulated to describe marriage and divorce dynamics, incorporating tolerance as a modifying parameter. The standard incidence function is employed to represent the force of marital disunity. The local and global stability of the divorce-free equilibrium are examined using the Routh–Hurwitz criterion and the Metzler matrix approach, respectively, confirming its stability in both cases. Additionally, the global stability of the divorce endemic equilibrium is analyzed using a Lyapunov function, and the results indicate that it is globally stable. The divorce reproduction number is derived using the Next-Generation Matrix approach. To ensure model accuracy, model fitting is performed using empirical data by employing the Least squares method. The most influential parameters are identified using the normalized forward sensitivity index method. Both analytical and numerical results indicate that an increase in the tolerance factor is associated with a corresponding decrease in the divorce rate. The findings suggest that fostering tolerance in marriages could serve as a practical intervention to enhance marriage stability. Additionally, reducing contact between divorced individuals and married couples may help mitigate the spread of marital disunity. These insights provide valuable guidance for policymakers, marriage counselors, and social scientists in designing programs that promote enduring marital relationships.
A Mobile-Based Machine-Learning Model for Predicting Maintenance of Dental Machines for Health Facilities: A Case of Ministry of Health in Uganda
(Springer Nature, 2026-04-01) Ayebare, Famina; Kaijage, Shubi; Mduma, Neema; Kiwoli, Liston; Kaluuma, Hillary; Byarugaba, Lambert; Ssanyu, Lydia
The health sector remains underfunded, falling short of the 15% budget allocation recommended by the Abuja Declaration of which Uganda is a member. Between 2010 and 2016, the budget of the health sector was an average of 7.8% of the national budget. In 2020/2021, it accounted for 5.1% of the national budget, from the previous 7.9% in the previous financial year. Major issues facing the health system include grossly underpaid health practitioners, scarcity of health workers, and necessary equipment in government facilities. The concept of machine learning is ubiquitous, and different algorithms have been used to train and build models to come up with different solution in the health sector. The objective of this project was to come up with a mobile-based machine-learning model for predicting the maintenance of dental machines based on the failure type to determine whether the machine is in good condition to work on patients efficiently or needs repair and maintenance. The project used both quantitative and qualitative methods of data collection. Random Forest and XG boost classifiers were used to train and test the model using structured data. The dataset contained 8 features, which were the product ID, type of machine, UDI (unique identifier), air temperature, process temperature, rotational speed, torque, and tool wear. Heat dissipation, power failure, random failure, overstain, and no failure were labels used to predict the type of failure that could occur, XG boost classifier emerged as the best with accuracy score of 97.3% in comparison with Random Forest after the accuracy score and confusion matrix of the two algorithms.
A Mobile-Based Machine-Learning Model for Predicting Maintenance of Dental Machines for Health Facilities: A Case of Ministry of Health in Uganda
(Springer Nature, 2026-04-01) Ayebare, Famina; Kaijage, Shubi; Mduma, Neema; Kiwoli, Liston; Kaluuma, Hillary; Byarugaba, Lambert; Ssanyu, Lydia
The health sector remains underfunded, falling short of the 15% budget allocation recommended by the Abuja Declaration of which Uganda is a member. Between 2010 and 2016, the budget of the health sector was an average of 7.8% of the national budget. In 2020/2021, it accounted for 5.1% of the national budget, from the previous 7.9% in the previous financial year. Major issues facing the health system include grossly underpaid health practitioners, scarcity of health workers, and necessary equipment in government facilities. The concept of machine learning is ubiquitous, and different algorithms have been used to train and build models to come up with different solution in the health sector. The objective of this project was to come up with a mobile-based machine-learning model for predicting the maintenance of dental machines based on the failure type to determine whether the machine is in good condition to work on patients efficiently or needs repair and maintenance. The project used both quantitative and qualitative methods of data collection. Random Forest and XG boost classifiers were used to train and test the model using structured data. The dataset contained 8 features, which were the product ID, type of machine, UDI (unique identifier), air temperature, process temperature, rotational speed, torque, and tool wear. Heat dissipation, power failure, random failure, overstain, and no failure were labels used to predict the type of failure that could occur, XG boost classifier emerged as the best with accuracy score of 97.3% in comparison with Random Forest after the accuracy score and confusion matrix of the two algorithms.
A Response-by-Retrieval Chatbot for Enhancing Horticulture Extension Services in Tanzania
(ETASR, 2025-10) Lubawa, Amos; Nyambo, Devotha; Mduma, Neema; Sinde, Ramadhani
Horticulture, which encompasses the cultivation of flowers, fruits, herbs, and vegetables, is a key contributor to Tanzania’s export revenue generation. Smallholder farmers are the primary producers of these crops, and they rely heavily on extension services for critical information that shapes both their economic success and long-term sustainability. However, the delivery of such services from the government and other stakeholders faces challenges, including constraints in human capital, geographic barriers, misaligned information needs, as well as issues with the timeliness of information dissemination. To address these challenges, this study developed a Swahili-language chatbot designed to provide timely, context-specific information tailored to the needs of farmers. To ensure credibility and relevance, key private and public stakeholders were consulted, and comprehensive farming guides were collected to build a custom dataset. This dataset consisted of 307 passages and 2,231 question-answer pairs. Four multilingual models, Multilingual Bidirectional Encoder Representations from Transformers (mBERT), Cross-lingual Language Model Pretraining RoBERTa (XLM-R), Multilingual Decoding-Enhanced BERT with Disentangled Attention (mDeBERTa), and Afro Cross-lingual Language Model Pretraining RoBERTa (AfroXLMR), were finetuned on this dataset for a question-answering task. Among them, the mDeBERTa model achieved the strongest performance, with an Exact Match (EM) score of 62.69% and an F1 score of 75.35%. These results demonstrate the potential of adapting advanced language models for specialized, low-resource language tasks in agriculture. The deployment of mDeBERTa in a prototype chatbot highlights a promising pathway to bridge information gaps and enhance the accessibility of extension services for Tanzania’s smallholder farmers.
A Secured Energy Saving With Federated Assisted Modified Actor-Critic Framework for 6G Networks
(IEEE, 2025-06) Abubakar, Attai; Mollel, Michael; Ozturk, Metin; Ramza, Naeem
Ultra-dense base station deployments must be operated in a way that allows the network's energy consumption to be adapted to the spatio-temporal traffic dynamics, thereby minimizing overall energy consumption. To achieve this goal, we leverage two artificial intelligence algorithms—federated learning and actor-critic—to develop a proactive and intelligent cell switching framework. This framework can learn the operating policy of small base stations in an ultra-dense heterogeneous network, resulting in maximum energy savings while respecting quality of service (QoS) constraints. Additionally, the use of federated learning enhances the security of the system as only the model parameters are shared among the entities, rather than the raw and potentially sensitive data. In other words, in the event of a data breach or eavesdropping, only the parameters of the developed artificial intelligence model would be compromised. This approach ensures that the network remains secure against attacks targeting confidential and sensitive data. The performance evaluation reveals that the proposed framework can achieve an energy saving that is about 77% more than that of the state-of-the-art solutions while respecting the QoS constraints of the network.
A stacking model integrating GARCH and LSTM with feature interactions for time series volatility prediction
(Elsevier Inc., 2026-02-04) Peter, Michael; Mirau, Silas; Sinkwembe, Emmanuel; Kasumo, Christian; Guambe, Calisto
Volatility forecasting remains a cornerstone of quantitative finance, underpinning risk management, portfolio optimization, and regulatory oversight. This study introduces a novel stacking model that integrates the generalized autoregressive conditional heteroskedasticity (GARCH) framework with long short-term memory (LSTM) networks to capture both econometric structure and nonlinear temporal dependencies in finan cial time series. Unlike conventional hybrid approaches that sequentially cascade outputs, the proposed framework employs GARCH and LSTM as parallel base learners, with their predictions intelligently fused through a meta-learner that exploits feature interactions and cross-model synergies. The empirical evaluation benchmarks the stacking ensemble against state-of-the-art alternatives, including DLINEAR, CKAN, N-BEATS, and individual GARCH and LSTM specifications across multiple performance metrics. Results demonstrate consistent superiority across RMSE, MAE, accuracy, RAMP, geometric mean, Hausdorff distance, and AUC metrics, validating the synergistic benefits of integrating econometric and machine learning paradigms within a theoretically grounded architecture. The model’s superior performance stems from leveraging GARCH’s parametric efficiency in modeling volatility clustering while harnessing LSTM’s capacity to capture complex nonlinear temporal patterns. Beyond methodological contributions, the framework offers practical value for enhancing systemic risk monitoring, improving stress testing frameworks, and optimizing investment strategies across diverse market conditions. The demonstrated robustness across different market regimes underscores its potential for adoption in both routine operations and crisis contexts. This research establishes stacking-based ensemble modeling as a powerful paradigm for advancing volatility prediction and provides a foundation for next-generation financial forecasting systems.
A systematic review on computer vision-based methods for cervical cancer detection
(Elsevier, 2026) Mbelwa, Hope; Leo, Judith; Kahesa, Crispin; Mkoba, Elizabeth
Cervical cancer is a leading cause of mortality among women globally, especially in regions where access to timely screening remains a challenge. With such concerns, accurate detection of cervical lesions is essential for effective diagnosis and treatment. This review aimed to explore the application of computer vision-based methods for detecting cervical cancer, identifying their potential, setbacks and areas for future development. A comprehensive literature search across Scopus, IEEE Xplore, PubMed, and Google Scholar identified 96 relevant studies published between 2014 and August 2025. These studies applied computer vision methods including CNNs, Vision Transformers, and multimodal models to cervical cancer detection using Pap smear, colposcopy, and histopathology images. They were analyzed based on the techniques employed, datasets used, evaluation metrics adopted, and reported results. This review highlights significant advancements in the field, particularly in lesion classification, precise segmentation of affected regions, and accurate detection of cancerous regions. However, some challenges were identified, including limited image datasets with insufficiently distributed normal and abnormal cases, aggravated by privacy issues and accurate labeling of medical images, which is critical and rigorous, often leading to annotation inconsistencies. Lastly, this study revealed that integrating Natural Language Processing and Computer Vision can enhance cervical cancer diagnosis through multi-modal models that combine both clinical text and imaging data. Additionally, this study proposes the use of techniques like annotation-efficient learning to manage limited labeled datasets using methods such as semi-supervised and transfer learning as well as the use of federated learning to ensure privacy in computer-aided diagnostic systems.
A Web-based Data Visualization Tool Regarding School Dropouts and User Asssesment
(Engineering, Technology and Applied Science Research, 2020-08) Kayanda, Angelika; Machuve, Dina
Data visualization is important for understanding the enormous amount of data generated daily. The education domain generates and owns huge amounts of data. Presentation of these data in a way that gives users quick and meaningful insights is very important. One of the biggest challenges in education is school dropouts, which is observed from basic education levels to colleges and universities. This paper presents a web-based data visualization tool for school dropouts in Tanzania targeting primary and secondary schools, together with the users’ feedback regarding the developed tool. We collected data from the United Republic of Tanzania Government Open Data Portal and the President’s Office - Regional Administration and Local Government (PO-RALG). Python was then used to preprocess the data, and finally, with JavaScript, a web-based tool was developed for data visualization. User acceptance testing was conducted and the majority agreed that data visualization is very helpful for quickly understanding data, reporting, and decision making. It was also noted that the developed tool could be useful not only in the education domain but it could also be adopted by other departments and organizations of the government.
Access and use of agricultural market information by smallholder farmers: Measuring informational capabilities
(WILEY, 2020-04-03) Magesa, Mawazo; Michael, Kisangiri; Ko, Jesuk
While farmers sell their crops, middlemen provide a linkage between them, markets and buyers. Middlemen have good knowledge of working conditions of markets and have access to agricultural market information. Due to poor access to markets and agricultural market information by smallholders, there is a feeling that middlemen benefit more while farmers sell their crops. Good access to markets and market information may help farmers bypass middlemen while selling crops and thus benefit more. Thus, it is best to improve the informational capabilities (ICs) of farmers in agricultural marketing. Thus, this research measured ICs of farmers accessing market information, through a program NINAYO, while selling their crops. The research utilized the informational, psychological, social, and economic dimensions of the empowerment framework in identifying capability indicators to formulate survey questions. Data were collected from smallholders in six regions in Tanzania. The analysis utilized measures of life satisfaction and results showed that about half of the variation in the dependent variable, satisfaction with capabilities, was explained by the model. Backward elimination analysis confirmed that life satisfaction is multidimensional. Robustness test confirmed a positive relationship between satisfaction and capabilities. Overall, results confirmed ICs are multidimensions, their improvement empowers farmers in agricultural marketing.
Access to Agricultural Market Information by Rural Farmers in Tanzania
(International Journal of Information and Communication Technology Research, 2014-07) Magesa, Mawazo M.; Michael, Kisangiri; Ko, Jesuk
Access to agricultural markets and agricultural market information is essential for participating in agricultural markets. A skilled and well-equipped participant benefits more in the agricultural marketing chain. Due to poor access to agricultural markets, rural farmers have for so long depended on subsistence farming living other participants (traders, consumers, intermediaries) benefiting more. Poor access to markets by these rural farmers is attributed by poor road infrastructure, lack of transporting means, and broadly by lack of agricultural market information. Due to lack of market information such as price of produce at the markets, quality and quantity of produces required at the markets, rural farmers negotiate on prices of their produce based on the information provided by traders. These factors significantly reduce the bargaining power of rural farmers and thus promote development of uncompetitive markets. It is envisaged that if rural farmers get a fair share of their produce, they can shift from subsistence farming and consider agriculture as their main economic activity. Dependence on radio programs and mobile phone calls to get agricultural market information has not well benefited remote rural farmers. This study proposes to establish a platform of framework where agricultural market participants can share market information. With this platform, real-time market information can be made available to market participants. The overall goal is to ensure farmers are assisted to get a fair share of their produce.
Accessibility Features for Augmented Reality Indoor Navigation Systems
(Springer Link, 2024-06-29) Samson, Frank; Dida, Mussa; Leo, Judith; Shidende, Deogratias; Naman, Godfrey
Accessibility plays a pivotal role in developing technological tools that strive to promote inclusivity for users of all abilities. Regrettably, many technological advancements have traditionally disregarded accessibility, assuming homogeneous user abilities or treating it as an afterthought. This paper endeavors to provide a review of accessibility considerations on augmented reality indoor navigation systems, to enhance navigational experiences in indoor learning environments. To achieve this, interviews were conducted with visually impaired individuals, to investigate their existing methods of navigation, identify challenges they face, and uncover potential accessibility features that could enhance indoor navigation systems. Additionally, a literature review was undertaken to explore various accessibility features in the context of indoor navigation systems, including localization technologies and pathfinding algorithms employed in indoor navigation applications. Finally, the paper concludes by offering insights into accessibility features specifically tailored for individuals with visual impairments, to facilitate efficient indoor navigation.

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