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Purpose: The purpose of this study is to propose an advanced methodology for automated diagnosis and classification of heart conditions using electrocardiography (ECG) in order to address the rising death rate from cardiovascular disease (CVD). Methods: Buffered ECG pulses from the MIT-BIH Arrhythmia dataset are integrated using a multi-modal fusion framework, refined using Gradient Descent optimization, and classified using the K-Means technique based on pulse magnitudes. Convolutional Neural Networks (CNNs) are used to detect anomalies. Results: The study achieves an average accuracy of 98%, outperforming current state-of-the-art methods. Sensitivity, specificity, and other metrics show significant improvements. The results also show the type of Cardiovascular disease detected using Confusion matrix plots. Conclusion: The proposed methodology demonstrates the utility of advanced machine learning, particularly deep learning, in the assessment of cardiovascular health. Based on the MIT-BIH Arrhythmia dataset, this study contributes to the development of accurate and efficient diagnostic tools for addressing urgent cardiac health challenges.

Appraisal systems hold critical importance in organisational human resource management. The way HR departments have developed over the period to the recent trends of AI-based human resource management systems and practices reflect on the emerging importance of effective HRM. In this present work, one of the key functionalities of the HRM process, the Appraisal system, is focused upon. This work presents a comprehensive model of appraisal system that relies on the machine learning solution for predicting evaluating the appraisal score. The developed model is trained with SVM classifier and is tested with 600+ records for evaluation. The precision and recall values indicated by the test results reflect that the model is potential and if more effectively pursued in terms of training and incorporating more in-depth analysis, the model can be a sustainable solution for human resource appraisal system.

Purpose: For radiologists, identifying and assessing thelung nodules of cancerous form from CT scans is a difficult and laborious task. As a result, early lung growing prediction is required for the investigation technique, and hence it increases the chances of a successful treatment. To ease this problem, computer-aided diagnostic (CAD) solutions have been deployed. The main purpose of the work is to detect the nodules are malignant or not and to provide the results with better accuracy. Methods: A neural network model that incorporates a feedback loop is the recurrent neural network. Evolutionary algorithms such as the Grey Wolf Optimization Algorithm and Recurrent Neural Network (RNN) Techniques are investigated utilising the Matlab Tool in this work. Statistical attributes are also produced and compared with other RNN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)combinations for study. Results: The proposed method produced very high accuracy, sensitivity, specificity, and precision and compared with other state of art methods. Because of its simplicity and possible global search capabilities, evolutionary algorithms have shown tremendous promise in the area of feature selection in the latest years. Conclusion: The proposed techniques have demonstrated outstanding outcomes in various disciplines, outperforming classical methods. Early detection of lung nodules will aid in determining whether the nodules will become malignant or not.