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- Pan-African Journal for Health and Environmental Science (AJHES) The Pan-African Journal of Education and Social Sciences (PAJES) Pan African Journal of Theology (PAJOT)
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Recent Submissions
Charitable giving
(Steward Ministries, 2022-06) Prof. Sampson M. Nwaomah
Charitable giving may be defined as the willingness and decision to give money and other resources directly or doing so through organizations for worthy causes. Charitable giving is the kind act of making donations for the benefit of the underprivileged or for other worthy causes. Generally, charitable giving be could be secular or religious on nature. Secular charitable giving may include giving for the benefit of persons or causes without religious motivations. In some parts of the world, there are registered foundations/organizations which either give or receive money and other resources for this kind of purposes Religious charitable giving may be considered as the commitment to cheerfully give money or other resources beyond the regular tithes and offerings for a religious or other worthy cause(s). This kind of giving could be considered as part of a Christian’s response of gratitude to the unsurpassed sacrifice of God to save humanity by supporting worthy causes through his/her resources.
An Evaluation of Machine Learning Techniques for Crop Detection from Garden Images
(IEEE Xplore, 2023) Samson O. Ooko; Grace Kwagalakwe; Lossan Bonde
Agriculture is a major driver for different economies across the globe. With the continued advancement in technology, there is a move towards precision agriculture. A major area of research interest is the detection of crops by use of computer vision. Such can help in controlling pests and diseases and thus increase yields. However, given the wide range of techniques applied, there is a need to evaluate them to recommend the most suitable for the detection of different crops. This study was thus aimed at evaluating both traditional Machine Learning (ML) and Deep Learning (DL) techniques for the detection of banana and cassava crops from a set of garden images. First, a crop classification model was built using the traditional machine learning model with feature
extraction algorithms being applied before training using K-Nearest Neighbor (KNN) and Naïve Bayes. Another crop classification model was also built using deep learning (CNN) on an annotated dataset. The performance of both models was evaluated with deep learning giving the best result. Deep learning is thus recommended as the best model for crop detection.
Tiny Machine Learning (TinyML) Based Self Diagnostic Kit for Respiratory Diseases
(IEEE Xplore, 2023) Samson O. Ooko; Jimmy Nsenga
The World Health Organization (WHO) 2022 statistics show that over 3 million deaths globally result from chronic respiratory diseases yearly. Interestingly 90% of these deaths are reported from middle- and low-income countries. This may be attributed to the poor health care systems with few medical experts to enable early diagnosis and treatment of the diseases. There is therefore a need for cheap and accessible solutions to help solve the problems. The use of the latest technologies of Machine Learning (ML) and Internet of Things (IoT) provide capabilities that can enable cheap and convenient detection and monitoring of respiratory diseases. However, existing solutions are cloud-based and thus depend on the availability of internet connectivity to function. This poses privacy, security, and even connectivity challenges, especially in Africa. The concept of using an emerging ML technique for inferencing on resource-constrained devices known as Tiny ML was used as a solution enabling the development of a self diagnostic kit. The system captures breath Volatile Organic Compounds (VOC) the collected data will be processed on the device and a Tiny ML algorithm used to detect if the sample is infected or not. The designed prototype was used to collect from healthy and unhealthy volunteers. The collected data was then used to train the prediction model. The ML model predicts respiratory diseases with an accuracy of 95.4% using less than 20% of the device resources. The proposed solution will reduce the dependency on medical experts and healthcare facilities and enable early detection of respiratory diseases.
Use of Machine Learning for Realtime Water Quality Prediction
(IEEE Xplore, 2023) Samson Otieno Ooko; Elaine Kansiime Pamela; Grace Kwagalakwe
Water is an essential resource that is a foundation
not only for people’s lives but also for economic development.
According to statistics from the World Health Organization (WHO) in 2021, over 2 billion people across the globe use water from contaminated sources leading to life-threatening diseases with over 485,000 deaths as a result of waterborne diseases being
reported annually. There is therefore a need for tools and techniques that can be used to ensure water quality. Traditional water quality index calculations consume time and are often prone to errors. In the recent past use of Machine Learning (ML) in predicting water quality has generated interest among researchers as a real-time solution. From the reviewed literature, existing models only consider a few chemical properties of water while ignoring others. In addition, there is a need to compare the performance of different models. This study thus presents a supervised learning model for predicting water quality. Our hypotheses were that (i) most of such parameters are all important and none can be ignored and (ii) with supervised learning one can predict water quality based on its physio-chemical properties. Open data sets are explored as inputs with different models being evaluated. The results show that the Random Forest algorithm gives the best results with a 79 percent accuracy. The use of this model will go a long way towards ensuring water quality and thus reducing related diseases and fatalities.
The Significance of Τοῦτο in Act 2:16 to the Eschatology of Acts
(Pan-African Journal of Theology, 2024-07-12) Odhiambo David Odhiambo
The demonstrative pronoun “τοῦτό” in Acts 2:16 carries pragmatic weight, suggesting markedness and emphasizing the eschaton (v. 17). Despite scholarly oversight, verse 16’s syntactical placement within the main clause underscores its significance. This article seeks to rectify this neglect by examining Acts 2:16’s theological and syntactical importance, particularly the usage of “Τοῦτο” and its implications for understanding Acts’ eschatological framework. Through linguistic analysis, theological exploration, and intertextual connections, this study aims to reveal the profound implications of Acts 2:16 for the eschatological discourse in Luke’s narrative.