The shift from centralized to distributed generation and the need to address energy shortage and achieve the sustainability goals are among the important factors that drive increasing interests of governments, planners, and other relevant stakeholders in microgrid systems. Apart from the distributed renewable energy resources, fuel cells (FCs) are a clean, pollution-free, highly efficient, flexible, and promising energy resource for microgrid applications that need more attention in research and development terms. Furthermore, they can offer continuous operation and do not require recharging. This paper examines the exciting potential of FCs and their utilization in microgrid systems. It presents a comprehensive review of FCs, with emphasis on the developmental status of the different technologies, comparison of operational characteristics, and the prevailing techno-economic barriers to their progress and the future outlook. Furthermore, particular attention is paid to the applications of the FC technologies in microgrid systems such as grid-integrated, grid-parallel, stand-alone, backup or emergency power, and direct current systems, including the FC control mechanisms and hybrid designs, and the technical challenges faced when employing FCs in microgrids based on recent developments. Microgrids can help to strengthen the existing power grid and are also suitable for mitigating the problem of energy poverty in remote locations. The paper is expected to provide useful insights into advancing research and developments in clean energy generation through microgrid systems based on FCs.

Early detection and treatment of cervical cancer is crucial to patients' recovery with a reported success rate of nearly 100%. Presently, Pap smear test which is a visual inspection of cells collected from the ectocervix is the screening tool mainly used in cancer prevention programs. The Pap smear is relatively easy to handle however, it is time-consuming and requires wet fixation of the cytological material. Thus, there is great demand for an automated-screening system that exhibits high sensitivity, high specificity and highthroughput. Hence, a textural based cervical cancer classification system has been developed in this research work. The wavelet transform was used to denoise 120 Pap smear images to enhance its visual quality while the images were segmented using the morphological operations. Eight textural features of GLCM that serve as inputs into the k-NN and SVM classifiers were extracted from each of the images and the performance was evaluated using accuracy, sensitivity and specificity. The result of the developed system shows that clustering shade SVM classifier out-performs entropy k-NN classifier in terms of classification accuracy of 90.0% and 88.3% respectively and vice visa in terms of sensitivity and specificity.

Energy systems modelling and design are a critical aspect of planning and development among researchers, electricity planners, infrastructure developers, utilities, decision-makers, and other relevant stakeholders. However, to achieve a sustainable energy supply, the energy planning approach needs to integrate some key dimensions. Importantly, these dimensions are necessary to guide the simulation and evaluation. It is against this backdrop that this paper focuses on the simulation and analysis approaches for sustainable planning, design, and development of microgrids based on clean energy resources. The paper first provides a comprehensive review of the existing simulation tools and approaches used for designing energy generation technologies. It then discusses and compares the traditional strategies and the emerging trends in energy systems simulation based on the software employed, the type of problem to be solved, input parameters provided, and the expected output. The paper introduces a practical simulation framework for sustainable energy planning, which is based on the social-technical-economic-environmental-policy (STEEP) model. The STEEP represents a holistic sustainability model that considers the key energy systems planning dimensions compared to the traditional techno-economic model used in several existing simulation tools and analyses. The paper provides insights into data-driven analysis and energy modelling software development applications.

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Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed.