All Outputs (7)

A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges (2024)
Journal Article
Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024). A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges. Informatics in Medicine Unlocked, 51, Article 101587. https://doi.org/10.1016/j.imu.2024.101587

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balan... Read More about A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges.

Enhancing Energy Access in Africa through Artificial Intelligence: Opportunities and Challenges (2024)
Presentation / Conference Contribution

Africa faces significant challenges in achieving uni- versal energy access, a fundamental prerequisite for sustainable development. This paper explores the potential of artificial intelli- gence (AI) in bridging this gap by enhancing grid management,... Read More about Enhancing Energy Access in Africa through Artificial Intelligence: Opportunities and Challenges.

Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects (2024)
Journal Article

Drug discovery and development is a time-consuming process that involves identifying, designing, and testing new drugs to address critical medical needs. In recent years, machine learning (ML) has played a vital role in technological advancements and... Read More about Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects.

A Survey of Bias and Fairness in Healthcare AI (2024)
Presentation / Conference Contribution

The integration of artificial intelligence (AI) tech-nologies in healthcare has shown great promise in improving patient outcomes. However, there have been concerns about bias and ethical implications. This review explores the key issues of bias, fai... Read More about A Survey of Bias and Fairness in Healthcare AI.

Proposed New SD-WAN Architecture to Facilitate Dynamic Load Balancing (2024)
Thesis

SD-WAN (Software Defined Wide Area Networking) is widely regarded as the future of networking as it integrates programmable features into its architecture. However, there is currently no all-encompassing solution for traffic monitoring policies in th... Read More about Proposed New SD-WAN Architecture to Facilitate Dynamic Load Balancing.

Comparative study of EIGRP and OSPF protocols based on network convergence (2020)
Journal Article
Okonkwo, I., & Emmanuel, I. Comparative study of EIGRP and OSPF protocols based on network convergence. International Journal of Advanced Computer Science and Applications, 11(6), 39-45. https://doi.org/10.14569/IJACSA.2020.0110605

Dynamic routing protocols are one of the fastest growing routing protocols in networking technologies because of their characteristics such as high throughput, flexibility, low overhead, scalability, easy configuration, bandwidth, and CPU utilization... Read More about Comparative study of EIGRP and OSPF protocols based on network convergence.

Sparse noise minimization in image classification using Genetic Algorithm and DenseNet
Presentation / Conference
Mienye, I., Kenneth Ainah, P., Emmanuel, I., & Esenogho, E. Sparse noise minimization in image classification using Genetic Algorithm and DenseNet. Presented at 2021 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa

Noise handling is a critical aspect of image processing, which can significantly affect the accuracy of classification and recognition algorithms. In this paper, we propose a technique for improved noise handling in sparse input feature maps where th... Read More about Sparse noise minimization in image classification using Genetic Algorithm and DenseNet.