Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3- $\phi $ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3- $\phi $ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future.

Nowadays, one of the most serious ethical challenges in the financial arena is payment card skimming. Payment card fraud comes in many forms. It is a major and long-term menace to society, with a significant economic effect. With the advancement of contemporary technology, including worldwide interaction, fraud is on the rise. As a result, combating fraud has become a critical topic to investigate. There are a variety of machine learning methods that can assist us in classifying unusual or fraudulent transactions. The only requirements are historical data and an appropriate algorithm that can better match our data. The main agenda of the papers is to improve costs by reducing baseline with a new sample process, discuss the different choices of the optimization metric, and try to show the best performing models for fraud detection. For more details, this research study has used Baseline logistic regression, Gradient boosted model (GbM), Logistic regression, Logistic Regression Baseline Model (LgBm), Extreme Gradient (XGBoost) classifier with Synthetic Minority Oversampling Technique (SmOtE) and Adaptive Synthetic Sampling Method (AdAsYn), showed the accuracy. In the end, this research study reminds you that fraud, which represents 0.1731% of the studied dataset. The findings and conclusions are based on real-world transactional data given by a big European card processing business.

Glioblastoma is the most common adult brain tumor, significantly impacts disability and mortality. Early and accurate diagnosis of glioma subtypes is essential, but manual categorization is challenging due to their complexity, prompting the need for automated solutions. We developed an innovative mixed convolution‐transformer model to classify glioma subtypes, including astrocytoma, glioblastoma, oligodendroglioma, and normal brain tissue, using whole slide images. The novelty of this model lies in its remarkable efficiency and precise results. Multiple advanced and complex layers are incorporated during its development to enhance its performance, ensuring that it delivers fast and accurate classification results for glioma. This proposed model obtains an overall training accuracy of 97.41%, peaking at 98.12% for validation and 97.35% for testing. Next, our model architecture is independently evaluated by comparing its training performances on the CIFAR‐10 and CIFAR‐100 datasets with the vision transformer and compact convolutional transformer models. Results across various datasets demonstrate that the model consistently outperforms existing models. This performance underscores the effectiveness of our proposed approach in classifying glioma subtypes accurately and efficiently, highlighting its potential impact on healthcare and disability. This system enhances the classification of glioma subtypes and facilitates swift identification, ensuring appropriate and timely treatment.

In this work, a Twitter data-set was utilized to do sentiment analysis of people's thoughts on the corona-virus (COVID-19) period, which is a major concern throughout the world these days, impacting a number of nations. To better understand people's feelings about the epidemic, machine learning approaches (mla) and sentiment methodology such as Bert Model (BMO), Naive_Bayes_Bernoulli (nBB), Multi Nominal Naive_Bayes (mnNB), Support_ Vector_Machine (svM), Logistic_Regression (IR), Gradient_Boosting_ Classifier (gbR), Decision Tree Classifiers (dtC), K N eighbors(knN) and Random Forest Classifier (rfC) have been presented in this work. Also, we have classified that which Classifiers provides highest accuracy. Additionally, in this paper, we also analysis from the data set, the most that has been tweeted (hashtag), positive, negative as well as neutral with data visualization in the Covid-19 epidemic time.

Alzheimer's disease is a complex neurodegenerative disorder with profound implications for individuals and healthcare systems. Early and accurate diagnosis is critical for effective intervention and treatment. This study explores the potential of deep learning algorithms to predict Alzheimer's disease stages using MRI segmentation data. Four distinct algorithms, namely MobileNet, CNN, DenseNet, and Inception V3, were evaluated for their performance in classifying AD stages. MobileNet emerged as the top-performing algorithm, followed by CNN, DenseNet, and Inception V3. The study's findings highlight the promise of utilizing deep learning techniques for early Alzheimer's disease detection. However, the study's shortcomings, such as the quantity of the dataset and the assessment limits, are acknowledged. Despite these limitations, the findings are encouraging, and show the promise of using cutting-edge technologies to improve the early diagnosis and care of Alzheimer's More research is needed to have been validated to improve these systems for better patient care for better treatment of Alzheimer's disease.