Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.

Detecting cardiovascular irregularities in a timely manner is crucial for preventing any fatal risks. This research aims to devise an efficient forecasting algorithm for the timely prognosis of Coronary Heart Disease (CHD). The study includes a diverse sample of individuals from Framingham, Massachusetts, with varying demographic, clinical, and co-morbidity parameters. We aim to achieve this with a two-step ensemble Machine Learning model. Firstly, feature importance is integrated with conventional classifiers to build Feature Weighted Meta-Models with a Forward feature selection algorithm. Subsequently, the top-performing Meta-Models are combined to design the Hybrid Voting Models to predict the risk of CHD in a ten-year timeframe by minimizing the misclassification rate. The proposed models undergo vetting using multiple metrics, including F1 score, Matthew’s Correlation Coefficient (MCC), Misclassification Ratio (MCR), and Accuracy. Given the high cost associated with misclassification in the healthcare domain, these metrics are carefully considered. The resulting model demonstrated strong predictive capability for CHD risk, achieving an overall accuracy rate of 95.87%. The F1 score is calculated to be 0.91, the MCC is 0.83, and the MCR is 0.041. Notably, the model achieved these impressive results using only seven features, reducing the time complexity of the prediction. In comparison to conventional classifiers, our model achieved a 23.94% improvement in accuracy, and a 17.23% improvement over average Meta-models accuracy, highlighting its effectiveness in predicting CHD risk.

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease.

Palm-leaf manuscripts are historical records written mainly in many local languages in many South Asian countries. These texts have suffered damage over many years and centuries. Many organizations have scanned and digitally imaged palm-leaf manuscripts to preserve these potentially priceless records. Machine readable text conversion should be done on these images for the easy retrieval and processing. A comparative study of deep learning models for recognizing palm-leaf Malayalam characters (PLMC) was conducted in this work. Characters were segmented directly from palm leaves using the contour method. Various CNN architectures were employed to recognize these segmented characters, namely, VGG16, LeNet, MobileNet, Resnet152v2, VGG19, and DenseNet121. Additionally, a custom CNN architecture was developed and experimented with. It was observed that the custom CNN architecture provides the best recognition accuracy at 96%.