Diabetes is a major health problem that affects a large number of people worldwide. We proposed an effective and reliable method to diagnose diabetic readmission in this research based on machine learning models. The model was developed on tree-based ensemble classification algorithms, such as Decision Tree, Extreme Gradient Boost, Ada Boost, and CatBoost. In addition, to increase accuracy, we stacked the models with Stack Classifier using Catboost classifier as the final estimator. We carried out the experiments on the diabetic readmission dataset obtained from the VCI machine learning repository. We used the Grid Search technique to learn the best practices for model evaluation and hyperparameter tuning. The performances of all six algorithms are measured using different metrics such as AUC, Accuracy, and Recall. AUC measures a classifier's ability to distinguish between classes. CatBoost Classifier outperforms other algorithms in terms of AUC, Recall, and Accuracy, with 68 percent, 58.7 percent, and 63.2 percent, respectively. These findings are validated using ROC (Receiver Operating Characteristic) curves correctly and systematically.

Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.

There has been significant growth in big data technology in healthcare in recent years. However, the potential of big data analytics is affected by various ethical and security concerns, which have hampered the application of big data analytics in healthcare. Recently, numerous studies have been conducted on the emerging big data ethical issues in healthcare. While most of the journal reflects on privacy and security questions, it did not examine; objectively the possible discriminatory impact of big data analytics has no. This mixed-method project aims to highlight various ethical problems in big data analytics while also providing an in-depth insight into the biased results derivable from big data analytics and the effects of such outcomes.

Background And Objectives: Actualizing constant supply of electricity is the desire of every nation as it facilitates economic development and national growth, some nations had gone the way of privatisation The privatisation of the power sector had been the strategy embarked upon across the globe aiming at finding probable last solution to the menace of incessant power failure most especially in the developing nations which is detrimental to their economic development. The consideration for adequate functioning of electrical infrastructure facilities before the full privatisation of the power sector as fundamental to effective electricity service delivery is the main focus of this study as it applies to Nigeria. The paper stresses that the poor functional condition of these electrical facilities was one of the causes of the poor performance of electricity service delivery after privatisation. Hence, the paper opined that the functional condition of these electrical infrastructure facilities ought to have been put into consideration before embarking on the privatisation of electricity service delivery. Methods: The study draws its information from the extant publications on infrastructure facilities that are relevant to the case study. The study adopted on the spot observation and assessment of facilities in the study area. The electrical facilities considered in this study were the distribution transformer, electric poles, and electric wire and cables. Tertiary data were also made use. Finding: The study based on the findings from the literature recommended that the policymakers, decision-makers, relevant government officials, and all concerned stakeholders should go back to the roundtable and hold an in-depth, thoughtful deliberation that will factor in the consideration for the indispensability of electrical infrastructure facilities to successful privatisation of power sector. Conclusion: Conclusively, figure 4,5,6,7, and 8 gave the visual condition of the electrical facilities state. Hence, prioritising these facilities before privatisation is imperative.