Absalom El-Shamir Ezugwu

North-West University | NWU · Unit for Data Science and Computing

A Discrete Symbiotic Organisms Search (DSOS) algorithm for finding a near optimal solution for the Travelling Salesman Problem (TSP) is proposed. The SOS is a metaheuristic search optimization algorithm, inspired by the symbiotic interaction strategies often adopted by organisms in the ecosystem for survival and propagation. This new optimization a...

The unrelated parallel machine scheduling problem with sequence-dependent setup times is addressed in this paper with the objective of minimizing the elapsed time between the start and finish of a sequence of operations in a set of unrelated machines. The machines are considered unrelated because the processing speed is dependent on the job being e...

Symbiotic Organisms Search (SOS) algorithm is an effective new metaheuristic search algorithm, which has recently recorded wider application in solving complex optimization problems. SOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival. This paper, presents a study on the application of SOS with Simula...

The Cluster Validity Index is an integral part of clustering algorithms. It evaluates inter-cluster separation and intra-cluster cohesion of candidate clusters to determine the quality of potential solutions. Several cluster validity indices have been suggested for both classical clustering algorithms and automatic metaheuristic-based clustering al...

In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain g...

In the current landscape, there is a rapid increase in the creation of new algorithms designed for specialized problem scenarios. The performance of these algorithms in unfamiliar or practical settings often remains untested. This paper presents a new development, the multi-objective Runge–Kutta optimizer (MORKO), which is built upon the principles...

The advent of the Internet of Things (IoT) has transformed the concept of smart home automation, thereby allowing users to remotely interact with their houses and control home appliances for resource efficiency. This technological development has significantly improved convenience, safety, and overall lifestyles for homeowners. The impact of smart...

Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces a novel hybrid optimization algorithm, the Hybrid Crayfish Optimization Algorithm with Differential Evolution (HCOADE), which addresses the limitations of premature convergence and...

Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertens...

South Africa faces a critical shortage of blood donors, leading to substantial deficits in the national blood supply. Blood donations are vital for the treatment of life-threatening conditions, making it crucial to develop efficient models for the management of blood stocks. This paper presents a mathematical model to optimize blood donation and en...

Crop diseases pose a significant threat to global food security, with both economic and environmental consequences. Early and accurate detection is essential for timely intervention and sustainable farming. This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector...

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professio...

The Gazelle Optimization Algorithm (GOA) is an innovative metaheuristic inspired by the survival tactics of gazelles in predator-rich environments. While GOA demonstrates notable advantages in solving unimodal, multimodal, and engineering optimization problems, it struggles with local optima and slow convergence in high-dimensional and non-convex s...

In this research, enhanced versions of the Artificial Hummingbird Algorithm are used to accurately identify unknown parameters in Proton Exchange Membrane Fuel Cell (PEMFC) models. In particular, we propose a multi strategy variant, the Lévy Chaotic Artificial Hummingbird Algorithm (LCAHA), which combines sinusoidal chaotic mapping, Lévy flights an...

Proton Exchange Membrane Fuel Cell (PEMFC) models require parameter tuning for their design and performance improvement. In this study, Depth Information-Based Differential Evolution (Di-DE) algorithm, a novel and efficient metaheuristic approach, is applied to the complex, nonlinear optimization problem of PEMFC parameter estimation. The Di-DE alg...

The widespread utilization of direct current (DC) motors in real-life engineering applications has led to the need for precise speed control, making controllers a crucial aspect of DC motor systems. Proportional-integral-derivative (PID) controllers have been widely adopted due to their simplicity and effectiveness. However, recent advancements hav...

For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a mathematical model which uses empirical or semi-empirical equations to estimate unknown model parameters through optimization techniques. This thesis calculates, analyzes and di...

Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, and machine learning, excel in extracting insights from large datasets and improving predictive models to boost their utility and accuracy. Though research in this a...

Nature-inspired methods are finding more and more practical applications. At present, one can observe a strong development of these techniques associated with the design of new algorithms or new modifications of currently known algorithms. Among the whole family of nature-based optimization algorithms, swarm intelligence algorithms take a special p...

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate pre-dictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State...

Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the significant classical ML algorithms and examines the state-of-the-art publications spanning twelve decades through an extensive bibliometric analysis study. We analyzed a da...

This paper introduces the SIR Optimizer (SIRO), a novel next-generation learned metaheuristic algorithm inspired by biological systems and deep learning techniques. The optimizer uses the susceptible-infected-removed (SIR) epidemiological model to predict the population’s susceptibility, active infections, and recoveries. To enhance the search proc...

Wheat (Triticum aestivum) yield predictions can be improved by using multispectral remote sensing to identify different genotypes and crop growth stages. We propose an innovative machine learning technique aimed at classifying diverse wheat crop genotypes and providing accurate estimations of plant age. Multispectral reflectance data was obtained f...

This paper introduces a novel metaheuristic technique, the Greater Cane Rat Algorithm (GCRA), for solving optimization problems. GCRA's optimization process is inspired by the intelligent foraging behaviours of greater cane rats during and outside the mating season. These nocturnal animals leave trails as they forage through reeds and grass, which...

Even though data wrangling (DW) accounts for more than half of the machine learning (ML) process, there is a dearth of research on data wrangling and dataset preparation. Consequently, we present a valuable state-of-the-art dataset prepared based on medical and pharmaceutical claims, as well as patient-level data collected from the transactional pr...

Deep learning stands at the forefront of contemporary machine learning techniques and is well-known for its outstanding predictive accuracy, adaptability to data variability, and remarkable ability to generalize across diverse domains. These attributes have spurred rapid progress and the emergence of novel iterations within the discipline. Yet, thi...

Lung adenocarcinoma (LUAD), a prevalent histological type of lung cancer and a subtype of non-small cell lung cancer (NSCLC), accounts for 45–55% of all lung cancer cases. Various factors, including environmental influences and genetics, have been identified as contributors to the initiation and progression of LUAD. Recent large-scale analyses have...

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to...

The objective function used in global optimization issues as often as possible features a big computing complexity, conditionality, and a nonclear scene. Such jobs are immensely useful, and a variety of methodologies have been proposed as a foundation for solving them. In this study, we will discuss the krill herd (KH), an ecologically inspired app...

This chapter provides an introduction to the crow search algorithm (CSA) as well as a discussion to keep scholars engaged in swarm intelligence techniques and optimization problem-solving. CSA is a newly created swarm intelligence program that mimics crow behavior in the storage and retrieval of surplus food. There is a solution that can be found b...

To solve new real-world problems, many metaheuristic optimization methods have been invented. One of these methods is called Henry gas solubility optimization (HGSO); it is a physics-based algorithm which simulates the manners managed by Henry’s law to resolve contesting optimization issues. This survey shows the procedure of Henry’s law and the re...

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted p...

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 i...

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of...

Lung cancer, a life-threatening disease primarily affecting lung tissue, remains a significant contributor to mortality in both developed and developing nations. Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search...

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of...

In the context of wireless sensor networks (WSNs), the utilization of artificial intelligence (AI)-based solutions and systems is on the ascent. These technologies offer significant potential for optimizing services in today's interconnected world. AI and nature-inspired algorithms have emerged as promising approaches to tackle various challenges i...

The Dwarf Mongoose Optimization Algorithm (DMOA) is an innovative nature-inspired optimization technique that draws inspiration from the cooperative and adaptive behaviours observed in dwarf mongooses in their natural habitat. This research paper offers a comprehensive exploration of DMOA, including its theoretical underpinnings, recent advancement...

Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-l...

Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm...

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment o...

Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using me...

Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contribution...

Generative adversarial networks (GAN) represent two deep learning (DL) models positioned in an adversarial manner to generate and evaluate images. This area of research promises to address several issues associated with medical image analysis using deep learning architectures and has been applied to medical image synthesis. The histopathology image...

This paper focuses on addressing the urgent need for efficient and accurate automated screening tools for COVID-19 detection. Inspired by existing research efforts, we propose two framework models to tackle this challenge. The first model combines a conventional CNN architecture as a feature extractor with XGBoost as the classifier. The second mode...

Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learni...

Breast cancer is a prevalent form of cancer among women, with over 1.5 million women being diagnosed each year. Unfortunately, the survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low, with only 40% of diagnosed patients surviving beyond five years. The inadequate availability of resource...

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation, and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many t...

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many te...

There is a surge in the application of population-based metaheuristic algorithms to find the optimal feature subset from high dimensional datasets. Many of these approaches cannot properly scale especially as they are expected to maintain two opposing goals: maximizing the accuracy of classification while at the same time minimizing the number of f...

Medication nonadherence is a significant public health concern that leads to ineffective treatment, which in turn engenders complications such as increased morbidity risks, unnecessary hospitalisations, and premature mortality. Technologies of the Fourth Industrial Revolution, such as machine learning, provide breakthroughs in identifying the most...

Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. The evaluation and suitability of these selected features are often analyzed using classifiers. These features are locked with data increasingly being generated from different sources such as socia...