Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there has been a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review of the relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified, and 77 met the final inclusion criteria. The key elements in the included papers are presented and conclusions are drawn from them. Results: The findings show that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for the choice of loss function in the deep learning models are presented. The limitations and lessons learnt are discussed, and some key recommendations are made. Conclusion: The discoveries and the conclusions of this work are organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

The resurgence of the coronavirus (COVID--19) disease is rapidly taking its toll on many nations across the globe. As the situation rapidly evolves, the entire world is faced with difficult and challenging moment and the need to be safe from the disease. Several international efforts are geared towards the production of effective vaccine for curing the disease. But, pending the availability of such vaccine, the need to flatten the curves is now the top priority of both governmental and non-governmental organisations in Nigeria. Estimation of the basic reproduction number of the disease is crucial to understanding its dynamics and making suitable preventive policies that will slow the spread and ultimately flatten the curves. In this work, we propose a compartmental--based model for analysing the dynamics of the pandemics' second wave in Nigeria taking into consideration, the current control measures. Using the Quasi--Newton algorithm, the model is fitted to the available data provided by Nigeria Centre for Disease Control (NCDC), World Health Organization (WHO) and accessible on the Wolfram Data Repository. The basic reproduction number of the second wave of the disease in Nigeria is estimated using the model. The model was also used to estimate the infection rate, average latent time, average recovery rate and average mortality rate of the disease. Efficiency of the current control measures is also measured. Forecast of the turning points and possible vanishing time of the virus in Nigeria are made. The infection rate, average latent time, average recovery rate and average mortality rate of the disease are estimated. Also, the basic reproduction number of the disease in Nigeria is estimated. Predictions on the turning points and possible vanishing time of the virus in Nigeria are made. Recommendations on how to manage the resurgence of the disease in Nigeria are also suggested.

Diabetes mellitus is a popular life-threatening disease and patients may gradually have started suffering from other diabetes-causing diseases such as heart attacks, stroke, hypertension, blurry vision, blindness, foot ulcer, amputation, kidney damage and other organ failures before diagnosis. Early detection can help reduce the fatality of this disease. Deep learning models have proven very useful in disease detection and computer-aided diagnosis. In this work, we proposed a deep unsupervised machine learning model for early detection of diabetes using voting ensemble feature selection and deep belief neural networks (DBN). Dataset was obtained from an online repository containing responses of prediagnosed patients to direct questionnaires administered in Sylhet Diabetes Hospital in Sylhet, Bangladesh. The dataset was preprocessed and preprocessed. Features were reduced using the ensemble feature selector. The DBN model was pretrained and tuned to obtain optimal performance. The model was also compared with other models with no multiple hidden layers. The DBN performed at its relative best with F1-measure, precision and recall of 1.00, 0.92 and 1.00 respectively. We conclude that DBN is a useful tool for an unsupervised early prediction of Type II diabetes mellitus.

This study delves into the intricate dynamics of sustainable fisheries through the lens of mathematical modeling, specifically employing the Lotka-Volterra equa-tions. The Lotka-Volterra equations, originally conceived to understand predator-prey interactions, offer a potent framework to model and predict fish population dynamics within aquatic ecosystems. The aim of this research is to demonstrate the applicability and efficacy of the Lotka-Volterra equations in understanding the delicate balance of fish populations and aiding sustainable fisheries man-agement. Through this mathematical approach, we seek to provide a structured methodology and present results that showcase the potential of this model in pre-dicting and managing fish populations. In our analysis, we adjust the parameters of the Lotka-Volterra equations to simulate various scenarios, enabling a compre-hensive understanding of sustainable fishing practices. Our findings underscore the significance of mathematical models, particularly the Lotka-Volterra equa-tions, in informing strategies to maintain fish populations in a sustainable and ecologically balanced manner, thereby ensuring the longevity of marine ecosys-tems and the fisheries that rely upon them.Keywords: Runge-Kutta, Two-step, Exponentially-fitted, Periodic; Oscillatory;

Twitter (X) has emerged as a vital platform for understanding public opinion and forecasting election outcomes through real-time sentiment analysis. This study systematically reviews machine learning (ML) and deep learning (DL) techniques applied to Twitter data for election prediction. Following PRISMA guidelines, 275 studies were screened, and 76 met the inclusion criteria. The review identifies dominant methods, performance trends, and research gaps in this field. Results show that pre-election data are most frequently analyzed, with the United States, India, and Indonesia leading the research landscape. Term Frequency–Inverse Document Frequency (TF–IDF), often paired with n-gram models, remains the most widely adopted feature extraction technique. Naïve Bayes and Support Vector Machines consistently deliver strong predictive performance, while newer DL models such as BERT and Bi-LSTM demonstrate improved contextual understanding. Approximately 78% of reviewed studies correctly predicted real election outcomes. This review consolidates methodological evidence, highlights emerging transformer-based innovations, and outlines directions for multilingual, cross-platform, and explainable AI approaches in electoral forecasting.