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All Journal African Journal of Sciences and Traditional Medicine African Journal of Medicine, Surgery and Public Health Research
Abel Onolunosen Abhadionmhen
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Chemistry Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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Enhancing Water Health Monitoring with ML Techniques for Detection of Coliform Bacteria: A Review Abel Onolunosen Abhadionmhen; Stanley Ebhohimhen Abhadiomhen
African Journal of Sciences and Traditional Medicine Vol 1 No 1 (2024): African Journal of Sciences and Traditional Medicine
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstm.v1i1.3690

Abstract

Water health monitoring is critical for ensuring safe drinking water and preventing waterborne diseases. Traditional methods for detecting coliform bacteria, including culture-based techniques and biochemical tests, are well-established but face limitations such as time consumption, high costs, and labor intensity, particularly in resource-limited settings like Nigeria. Recent cholera outbreaks in Nigeria have underscored the urgent need for more effective and timely water quality monitoring solutions. This review explores the application of machine learning (ML) techniques in enhancing the detection of coliform bacteria, offering a promising alternative to traditional methods. ML approaches, including Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Ensemble Methods, are evaluated for their potential to provide faster, more accurate, and scalable detection of coliform contamination. The review highlights key challenges, such as data quality, computational demands, and infrastructure limitations, and discusses real-world case studies demonstrating the practical applications and limitations of ML techniques. The integration of ML models into water monitoring systems shows considerable promise but requires addressing critical issues related to data quality and model feasibility in low-resource settings. Future research directions include exploring hybrid systems that combine ML with traditional methods, leveraging emerging technologies like edge computing, and enhancing model robustness through innovative data strategies. By advancing the application of ML in water health monitoring, it is possible to improve public health outcomes and effectively manage waterborne diseases.
Genomic Insights into Antimicrobial Resistance in Salmonella typhi: A Bioinformatics-Based Surveillance Model from Public Datasets with Implications for Resource-Limited Settings Ehizokhale Jude Usiabulu; Abel Onolunosen Abhadionmhen; Husseni Iduku
African Journal of Sciences and Traditional Medicine Vol 2 No 2 (2025): African Journal of Sciences and Traditional Medicine
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstm.v2i2.6485

Abstract

Antimicrobial resistance (AMR) in Salmonella typhi represents an escalating global health challenge, particularly in regions with limited capacity for surveillance and treatment. This study investigates the genetic diversity and AMR mechanisms of S. typhi strains using publicly available genomic data. Twenty genomes were retrieved from GenBank and analyzed to identify resistance genes and genetic variations. The analysis focused on key AMR determinants, including blaTEM (beta-lactam resistance), qnrS (quinolone resistance), and aac(3)-I (aminoglycoside resistance), assessing their distribution across isolates. Phylogenetic analysis revealed substantial genetic diversity and indicated clonal dissemination of strains with similar resistance profiles. Mutation screening of gyrA and parC genes associated with fluoroquinolone resistance identified recurrent mutations, underscoring their role in resistance development. Bioinformatics tools such as BLAST+, Prokka, ResFinder, and iTOL were employed for sequence alignment, gene annotation, AMR gene detection, and phylogenetic reconstruction. The findings demonstrate the effectiveness of bioinformatics approaches in AMR surveillance, especially in resource-constrained settings where direct sample collection is often impractical. This study highlights the pervasive presence of AMR genes in S. typhi and reinforces the value of genomic surveillance in tracking resistance trends and informing targeted public health interventions. The research offers a novel and efficient model for AMR monitoring and provides foundational insights into resistance mechanisms in S. typhi, with implications for regions affected by AMR, such as Northeast Nigeria.
ML-Powered Privacy Preservation in Biomedical Data Sharing Ehizokhale Jude Usiabulu; Abel Onolunosen Abhadionmhen; Husseni Iduku
African Journal of Medicine, Surgery and Public Health Research Vol 2 No 3 (2025): African Journal of Medicine, Surgery and Public Health Research
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajmsphr.v2i3.6143

Abstract

The sharing of biomedical data is essential for accelerating healthcare research, fostering medical innovation, and improving patient outcomes. Such data encompasses a wide range of sensitive information, including electronic health records, genomic sequences, and clinical trial results. Despite its value, biomedical data sharing poses significant privacy risks, such as patient re-identification, unauthorized access, and regulatory non-compliance. These concerns necessitate advanced techniques that balance the need for data utility with stringent privacy protection. Machine learning (ML) has emerged as a powerful tool to facilitate privacy-preserving biomedical data sharing. This manuscript presents a comprehensive review of state-of-the-art ML-based privacy preservation methods, including differential privacy, federated learning, homomorphic encryption, secure multi-party computation, and synthetic data generation through generative models. Each technique offers unique mechanisms to protect sensitive information while enabling collaborative analysis and predictive modeling. These methods have been applied practically across various biomedical domains, including collaborative disease risk prediction and genomic research, clinical trial data analysis, remote patient monitoring, and public health surveillance. Additionally, we evaluate relevant privacy and utility metrics that assess the effectiveness of privacy guarantees and the impact on model performance. The review further examines limitations and challenges—including computational overhead, data heterogeneity, privacy-utility trade-offs, and ethical considerations—that must be addressed to ensure robust and scalable solutions. Looking forward, the manuscript highlights promising future directions, such as hybrid privacy frameworks, enhanced synthetic data generation, real-time privacy-preserving analytics, standardization of evaluation protocols, and interdisciplinary policy development. By integrating these advancements, biomedical research can achieve safer and more effective data sharing, ultimately fostering innovation while respecting patient confidentiality and trust.
Co-Authors Ehizokhale Jude Usiabulu Husseni Iduku Stanley Ebhohimhen Abhadiomhen