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Papers by Micheal Olusanya

Lecture notes in computer science, 2024

IEEE access, 2024

Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) often lacks explainability, h... more Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) often lacks explainability, hindering farmers from taking full advantage of their capabilities. This study tackles this gap by introducing a model that combines eXplainable Artificial Intelligence (XAI), with Predictive Maintenance (PdM). The model aims to provide both predictive insights and explanations across four key dimensions, namely data, model, outcome, and end-user. This approach marks a shift in agricultural AI, reshaping how these technologies are understood and applied. The model outperforms related studies, showing quantifiable improvements. Specifically, the Long-Short-Term Memory (LSTM) classifier shows a 5.81% rise in accuracy. The eXtreme Gradient Boosting (XGBoost) classifier exhibits a 7.09% higher F1 score, 10.66% increased accuracy, and a 4.29% increase in Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). These results could lead to more precise maintenance predictions in real-world settings. This study also provides insights into data purity, global and local explanations, and counterfactual scenarios for PdM in SAF. It advances AI by emphasising the importance of explainability beyond traditional accuracy metrics. The results confirm the superiority of the proposed model, marking a significant contribution to PdM in SAF. Moreover, this study promotes the understanding of AI in agriculture, emphasising explainability dimensions. Future research directions are advocated, including multi-modal data integration and implementing Human-in-the-Loop (HITL) systems aimed at improving the effectiveness of AI and addressing ethical concerns such as Fairness, Accountability, and Transparency (FAT) in agricultural AI applications.

International Journal of Environmental Research and Public Health

Soft-computing and statistical learning models have gained substantial momentum in predicting typ... more Soft-computing and statistical learning models have gained substantial momentum in predicting type 2 diabetes mellitus (T2DM) disease. This paper reviews recent soft-computing and statistical learning models in T2DM using a meta-analysis approach. We searched for papers using soft-computing and statistical learning models focused on T2DM published between 2010 and 2021 on three different search engines. Of 1215 studies identified, 34 with 136952 patients met our inclusion criteria. The pooled algorithm’s performance was able to predict T2DM with an overall accuracy of 0.86 (95% confidence interval [CI] of [0.82, 0.89]). The classification of diabetes prediction was significantly greater in models with a screening and diagnosis (pooled proportion [95% CI] = 0.91 [0.74, 0.97]) when compared to models with nephropathy (pooled proportion = 0.48 [0.76, 0.89] to 0.88 [0.83, 0.91]). For the prediction of T2DM, the decision trees (DT) models had a pooled accuracy of 0.88 [95% CI: 0.82, 0.92...

Applied sciences, May 25, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Copyright © 2015 Micheal O. Olusanya et al. This is an open access article distributed under the ... more Copyright © 2015 Micheal O. Olusanya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients ’ blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment...

Artificial Intelligence in Intelligent Systems, 2021

Intelligent Systems Applications in Software Engineering, 2019

Many engineering problems that occur in real-life are usually constrained by one or more factors ... more Many engineering problems that occur in real-life are usually constrained by one or more factors which constitute the basis for the complexity of obtaining optimal solutions. While some of these problems may be transformed to the unconstrained forms, there is a large pool of purely unconstrained optimization problems in engineering which have practical applications in the industry. One effective approach for solving this latter category of problems is the nonlinear conjugate gradient method (NCGM). Particularly, the NCGM uses an efficient recursive scheme to solve unconstrained optimization problems with very large dimensions. In this paper, a new hybrid NCGM is proposed based on the recent modifications of the Polak-Ribiere-Polyak (PRP) and Hestenes-Stiefel (HS) methods. Theoretical analyses and numerical computations using standard benchmark functions, as well as comparison with existing NCGM schemes show that the proposed PRP-HS type hybrid scheme is globally convergent and compu...

Applied Computational Intelligence and Mathematical Methods, 2017

The vehicle routing problem and its variants such as the multi-depot vehicle routing problem are ... more The vehicle routing problem and its variants such as the multi-depot vehicle routing problem are well-known NP-hard combinatorial optimization problems with wide engineering and theoretical background. In this paper a new hybrid technique based on intelligent water drop algorithm and simulated annealing is proposed to solve the multi-depot vehicle routing problem. The intelligent water drop algorithm is a stochastic population based metaheuristic optimization algorithm that uses a constructive approach to find optimal solutions of a given problem. Simulated annealing is a popular local search meta-heuristic approach with the key features of being able to provide a means to escape local optima by allowing hill-climbing moves with the hope of finding a global optimum. The performance of the hybrid algorithm is evaluated on a set of 23 benchmark instances and the results obtained compared with the best known solutions. The computational results show that the proposed method can produce good solutions, indicating that it is a good alternative algorithm for solving the multi-depot vehicle routing problem.

Computational Statistics and Mathematical Modeling Methods in Intelligent Systems, 2019

This paper presents a facility location model for improving the collection of solid waste materia... more This paper presents a facility location model for improving the collection of solid waste materials. The model is especially suitable for densely populated regions with several housing units as well as encourages initial sorting of wastes. Each individual house in the collection area is designated a customer, with randomly selected customers comprising the set of candidate hubs. The fundamental feature of the model is to group the customers into clusters by assigning each customer (house) to the nearest hub. Each cluster is then assigned to exactly one waste collection site drawn from the set of potential collection locations. The objective is to minimize the total number of activated waste collection sites such that all the customers' requests are satisfied without violating the capacity limit of each site. A simple Lagrangian relaxation heuristic is developed for the problem and solved with the CPLEX solver on the AMPL platform to find a feasible solution. Results from the numerical implementation of model show the model is efficient and competitive with existing solid waste collection facility location models.

2013 IEEE Global Humanitarian Technology Conference (GHTC), 2013

ABSTRACT

Master of Science in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, D... more Master of Science in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.

Expert Systems with Applications

PloS one, 2018

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the ch... more The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calc...

2012 IEEE Congress on Evolutionary Computation, 2012

Due to the critical blood shortages in South Africa and around the world, the assignment of blood... more Due to the critical blood shortages in South Africa and around the world, the assignment of blood can be considered an important real world optimization problem. This paper presents a mathematical model that facilitates good management and assignment of red blood cell units in order to minimize the quantity of imported units from outside the system. The model makes use of the Multiple Knapsack Algorithm, which is implemented using several optimization techniques, in order to determine the most optimal assignments. These include a Genetic Algorithm (GA), Adaptive Genetic Algorithm (AGA), Simulated Annealing Genetic Algorithm (SAGA), Adaptive Simulated Annealing Genetic Algorithm (ASAGA) and finally a Hill Climbing (HC) Algorithm. All techniques were capable of achieving the optimal fitnesses. The AGA, SAGA and ASAGA provide some desirable results over the standard GA, whilst the HC algorithm proves to demonstrate the best results overall.

2014 IEEE International Advance Computing Conference (IACC), 2014