The strategic deployment of EV (EV) charging stations is crucial for promoting sustainable transportation and facilitating the widespread adoption of EVs. However, the lack of readily accessible charging station continues to be a significant barrier to the mainstream adoption of EVs. This study presents a comprehensive approach to optimizing the location of charging stations using a custom K-means clustering algorithm. The algorithm incorporates various factors including charging demand, energy consumption, population density, and existing stations, to identify optimal locations for the charging stations. The proposed methodology aims to minimize the distance between charging stations and areas with high EV demand while considering energy efficiency and avoiding redundancy near low-weighted point locations. The algorithm iteratively assigns data points to clusters and updates the centroids, converging to an optimal solution that balances coverage, accessibility, and sustainability. Folium map in Python and Python code was used for case study analysis. The innovation of the finding shows the effectiveness of the custom K-means clustering algorithm in optimizing charging station placement, by introducing a penalty term to avoid repositioning charging stations close to a low-weighted (charging demand) point charging station, also ensuring convenient access for EV owners, and promoting efficient energy utilization. The study emphasizes on strategic planning and data-driven approaches in developing a comprehensive and sustainable EV charging station network to address the challenge of limited charging station and support the growth of electric mobility.

With the increased accessibility of global trade information, transaction fraud has become a major worry in global banking and commerce security. The incidence and magnitude of transaction fraud are increasing daily, resulting in significant financial losses for both customers and financial professionals. With improvements in data mining and machine learning in computer science, the capacity to detect transaction fraud is becoming increasingly attainable. The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud. The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023, comprising around 550,000 records. The study uses this dataset to assess the performance of well-established machine learning models, measuring their accuracy, recall, and F1 score. In addition, the study includes a confusion matrix for all models to aid in evaluation and training time duration. Machin learning models, including Logistic regression, random forest, extra trees, and LGBM, achieve high accuracy and precision in the credit card fraud detection dataset, with a reported accuracy, recall, and F1 score of 1.00 for both classes.

The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, including Logistic Regression, Random Forest, Decision Tree, Extra Trees classifier, Support Vector Machine (SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), GaussianNB, and Multilayer Perceptron (MLP). The machine learning models are applied to a concrete dataset acquired from Kaggle. The models underwent training using a dataset that was partitioned into an 80:20 ratio. Machine learning model evaluation involves the utilization of performance measurements such as Accuracy, Precision, Recall, and ROC curves. An exhaustive evaluation is carried out to gauge the efficacy of the models.

Accurate forecasting of energy demand for electric vehicles (EVs) is crucial for maintaining the stability and reliability of power systems. By predicting demand over various periods, charging station owners can ensure a continuous energy supply. Medium-term and long-term demand predictions, which extend from a few weeks to several days, help analyze charging demand across different periods based on historical trends. This study proposes a Transformer model that utilizes an LSTM-based encoder-decoder for forecasting the demand at electric vehicle charging stations (EVCS). The proposed model is compared with traditional deep learning-based LSTM and Transformer models. The research employs open datasets from ACN, including charging data from Caltech and JPL. Both datasets are used to train and test the models. Predictions are made for 30 days, 120 days, and 240 days ahead, with results compared to actual demand. Performance is evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE). Compared to baseline models, the proposed LSTM-Transformer model for Caltech data shows a significant improvement at the 30-day horizon, lowering MAE by up to 17.27% and MSE by 19.79%. The accuracy improvement are minor but consistent in longer horizons (120 and 240 days), with an MAE and MSE improvement of up to 5.71% and 4.85%, respectively. The LSTM-Transformer model also shows better accuracy across all horizons for JPL data by reducing MAE and MSE by up to 24.91% and 23.17% at 30 days, 5.00% and 5.17% at 120 days, and 3.90% and 4.86% at 240 days. The results indicate that the Hybrid Transformer model outperforms the baseline models for both datasets in medium-term and long-term predictions. The 30, 120, and 240-day predictions demonstrate lower error rates with the proposed model when utilizing Caltech and JPL charging data for these time frames.