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Every year, the Joint Admission Board (JAB) is tasked to determine those students who are expected to join various Kenyan public universities under the government sponsorship scheme. This exercise is usually extensive because of the large number of qualified students compared to the very limited number of slots at various institutions and the shortage of funding from the government. Further, this is made complex by the fact that the selections are done against a predefined cluster subjects vis a vis the student's preferred and applied for academic courses. Minimum requirements exist for each course and only students having the prescribed grades in specific subjects are eligible to join that course. Due to this, students are often admitted to courses they consider irrelevant to their career prospects and not their preferred choices. This process is tiresome, costly, and prone to bias, errors, or favour, leading to disadvantaging innocent students. This paper examines the potential use of artificial neural networks at the JAB for the process of selecting students for university courses. Based on the fact that Artificial Neural Networks (ANNs) have been tested and used in classification, the paper explains how a trained neural network can be used to perform the students' placement effectively and efficiently. JAB will be able, therefore, to undertake the students' placement thoroughly and be able to accomplish it with minimal wastage of time and resources respectively without having to utilise unnecessary effort. The paper outlines how the various metrics can be coded and used as input to the ANNs. Ultimately, the paper underscores the various merits that would accompany the adoption of this technique. By making use of neural networks in the university career choices, student placement at JAB will enhance the chances of students being placed into courses they prefer as part of their career choice. This is likely to motivate the students, making them work harder and leading to improved performance and improved completion rate. The ANN application may also reduce the cost spend on the application processing and the time the applicants have to wait for the outcome. The ANN application could further increase the chances of high quality applicants getting admission to career courses for which they qualify. (Contains 5 figures and 3 tables.)

Objective: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya., Approach: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya., Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches., Conclusion: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance., (© 2023. BioMed Central Ltd., part of Springer Nature.)

This paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses. However, the two traditional measurement methods, mid‐upper arm circumference (MUAC) and weight‐for‐height (WFH), are impractical in conflict and catastrophic disaster situations. They require well‐trained enumerators, cumbersome equipment, and close supervision. The Method for Extremely Rapid Observation of Nutritional Status (MERON) addresses the problem, using simple facial photographs. Facial features are extracted to predict Body Mass Index (BMI) in adults and Weight for Height Z Score (WFHZ) in children under five. MERON correctly predicts adult BMI classification with 78% accuracy. A variant of the model, trained on a sample of 3167 children in Kenya, successfully classified 60% of cases. On most measures, MERON was easier and more culturally acceptable to use than the traditional measurement methods. If MERON were to be trained and validated on a larger sample, with more extreme cases, it would provide a practical solution to a recurrent humanitarian problem. [ABSTRACT FROM AUTHOR]

This study investigates the application of Artificial Intelligence (AI) in monitoring and managing marine ecosystems to address the impacts of climate change on Kenya's Blue Economy. It aims to assess the threats posed by climate change to these ecosystems and explore the potential of AI solutions to enhance adaptation and resilience. The research employs a comprehensive review of secondary data sources, including academic publications, reports from reputable institutions, and other relevant materials. The study analyzes existing literature on AI applications in marine ecosystem management and climate change mitigation, focusing on the specific context of Kenya's Blue Economy. The study reveals that climate change poses significant threats to Kenya's marine ecosystems, including coral bleaching, ocean acidification, sea-level rise, and disruptions to ocean currents. AI technologies offer promising solutions for monitoring and managing these impacts, with applications in predictive modeling, resource optimization, and decision support. The research highlights the need for further exploration into specific AI applications tailored to Kenya's unique coastal challenges and the importance of incorporating diverse stakeholder perspectives. Additionally, it emphasizes the necessity for longterm impact assessments of AI technologies in the context of climate change mitigation. This study contributes to the growing body of knowledge on AI applications in environmental management, particularly within the context of Kenya's Blue Economy. By identifying the potential of AI to enhance resilience and sustainability in marine ecosystems, the research offers valuable insights for policymakers, researchers, and practitioners involved in climate change mitigation and adaptation efforts. [ABSTRACT FROM AUTHOR]

Broken water pumps continue to impede efforts to deliver clean and economically-viable water to the global poor. The literature has demonstrated that customers’ health benefits and willingness to pay for clean water are best realized when clean water infrastructure performs extremely well (>99% uptime). In this paper, we used sensor data from 42 Afridev-brand handpumps observed for 14 months in western Kenya to demonstrate how sensors and supervised ensemble machine learning could be used to increase total fleet uptime from a best-practices baseline of about 70% to >99%. We accomplish this increase in uptime by forecasting pump failures and identifying existing failures very quickly. Comparing the costs of operating the pump per functional year over a lifetime of 10 years, we estimate that implementing this algorithm would save 7% on the levelized cost of water relative to a sensor-less scheduled maintenance program. Combined with a rigorous system for dispatching maintenance personnel, implementing this algorithm in a real-world program could significantly improve health outcomes and customers’ willingness to pay for water services. [ABSTRACT FROM AUTHOR]

Background: Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries. Methodology/Principal findings: Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples. Conclusions/Significance: Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs. Author summary: In this school survey conducted within a primary healthcare setting in rural Kenya, a deep-learning system (DLS) was developed for detection of eggs secreted in human stool from intestinal worms, so called soil-transmitted helminths (STHs). Infections caused by STHs are the most common neglected tropical diseases and a major cause of health impairment in low- and middle-income countries. Novel diagnostic methods are needed to improve disease control. In the current study, stool samples were collected from children attending local primary schools and digitized using a portable slide scanner and uploaded via mobile networks to a cloud repository. Using the digital samples, a DLS was trained and tested for the detection of eggs of the most common STHs (Ascaris lumbricoides, Tricuris trichiura and hookworms) and compared to conventional manual microscopy analysis of the samples. The results showed that the DLS was able to accurately detect STHs in the digital samples. Although the DLS detected some false positive parasites, it was able to detect a significant number of parasites that had been missed with conventional microscopy analysis. The study concludes that digital microscopy, supported by image-based artificial intelligence, can be implemented in a primary health care setting and may provide a future tool for improved STH detection and, thereby, better monitoring and evaluation of control programs. The digital method is especially promising in light intensity infections, with only a few parasite eggs per sample. [ABSTRACT FROM AUTHOR]

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Artificial intelligence technology (AI) is an array of computer technologies that provide machines with human-like abilities in perception, action, and cognition. It can mimic human intellect in a broad variety of situations thanks to its advanced processing of information and mega-thinking procedures and powers. AI's power to mimic the intellect of humans means it will disrupt or significantly affect practically every major industry, including but not limited to manufacturing, healthcare, agriculture, and logistics. AI helps several industries move forward. Developed Western nations and leading digital titans like Google and Facebook have invested significantly in AI, utilizing its human skills in various vital societal sectors. Regrettably, most poor countries, particularly those in the sub-Saharan African region, do not have the necessary administration, learning, data reserves, and legislation to support the adoption and implementation of AI. Nevertheless, certain countries in sub-Saharan Africa, such as Kenya, have incorporated and used AI technologies in several key areas for the benefit of their inhabitants. This article explores how AI has affected Kenya and demonstrates how other subSaharan African countries might use AI-related technology to their full potential. [ABSTRACT FROM AUTHOR]

This study addresses the underlying challenges of computer vision adoption in the Kenyan agricultural sector and how to solve these hurdles to commercialize this technology. Technological advancements have revolutionized the agriculture sector, where artificial intelligence enhances yields, mitigates losses, and manages natural resources, leading to increased productivity. Kenya is still lagging in the commercialization of computer vision to improve its agricultural sector, which is the largest source of GDP. Kenya has remarkable skills and expertise in artificial intelligence that can support artificial intelligence implementation; the government policies, data availability, and high cost incurred in starting a computer vision company are problematic. Through better government policies on subsidies and data, research and development investments, and AI forums, Kenya will solve the challenges of adopting computer vision. While computer vision has the potential to revolutionize the agricultural industry by improving crop yield, detecting diseases, and increasing efficiency, there are several barriers to its adoption, including inadequate infrastructure, lack of technical expertise, and limited funding. This study aims to identify the challenges hindering the implementation of computer vision technology in the Kenyan agricultural sector and propose potential solutions to address these challenges. [ABSTRACT FROM AUTHOR]

Background: Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes. We document the development and validation of the web app and report its predictive accuracy. Methods: CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen's kappa coefficients. Results: Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was 'substantial' to 'almost perfect', as suggested by observed percentage agreements of 88–95% and Cohen's kappa values of 0.7–0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was 'moderate', suggested by observed percentage agreements of 73–78% and Cohen's kappa values of 0.51–0.56. Conclusions: Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. Trial registration: This was not a clinical trial and was therefore not registered as such. [ABSTRACT FROM AUTHOR]

Modelling of emerging vector borne diseases serves as an important complement to clinical studies of modern zoonoses. This article presents an archaeo‐historic epidemiological modelling study of Rift Valley fever (RVF), using data‐driven neural network technology. RVF affects both human and animal populations, can rapidly decimate herds causing catastrophic economic hardship, and is identified as a Category A biodefense pathogen by the US Center for Disease Control. Despite recent origins circa the early 1900s, little is known about the circumstances of its inception nor the relationships between factors that affect transmission. This evidence could be vital as the disease continues to expand from its epicentre in Kenya to other parts of Africa and the Arabian Peninsula. RVF is a relevant case for archaeological/palaeopathological investigations of disease as it intersects between numerous human, animal, spatial, temporal, and sociopolitical dimensions. By integrating landscape archaeology, historical evidence, and climatic data, with evidence of human behaviour gathered through ethnoarchaeological study, this article presents an applied framework for human–animal palaeopathology. This framework aligns with the One Health approach that observes disease to be intrinsically tied to ecological and societal factors. We provide a useable alternative way of thinking about disease modelling in the present and the past, ultimately seeking to support efforts to accurately predict future impacts. Tapping into longitudinal evidence from the last 50–300 years offers a powerful way to respond to the threat zoonoses will pose to human populations around the world as the climate warms. [ABSTRACT FROM AUTHOR]