Globalfoodsecurity, precise market pricing, and efficient quality con trol are all hampered by the subjective, labor-intensive, and error-prone nature of traditional manual classification techniques for rice, a staple commodity. Much research into machine vision and deep learning technologies has been prompted by the growing need for automated, non-destructive, and effective methods for rice va riety identification. Despite their notable achievements in this field, Convolutional Neural Networks (CNNs) frequently struggle to capture long-range relationships and achieve optimal generalization across a variety of visually similar and distinct rice types, which is a constant problem. Using the sophisticated capabilities of Vision Transformer (ViT) models, this research suggests a novel method for au tomated rice type detection. In comparison to conventional CNN architectures, ViTs are highly respected for their capacity to manage global dependencies and continuously produce competitive, and frequently better, performance in challeng ing image classification tasks. The suggested ViT-based approach is intended to get over the inherent difficulties of differentiating minute details, such as specific morphological, morphological, and color traits, among different species of rice. The model is set up for effective feature extraction and reliable pattern learning straight from image data by using its potent self-attention mechanism, negating the need for extensive pre-processing for raw images. The goal of this research is to create a reliable and extremely accurate classification system for a variety of rice types, taking inspiration from prior works that show high classification accuracies, such as RiceSeedNet, which achieved 97% for 13 rice seed variants and 99% for 8 rice grain varieties. The successful implementation of this Vision Transformer model is anticipated to significantly enhance precision agriculture by providing a more reliable, consistent, and scalable solution for the identification of rice seeds and grains, thereby supporting farmers and the broader agricultural industry in ensuring product quality and contributing to global food security.

The use of credit cards and online banking is expanding exponentially. As more individuals use debit cards, credit cards, and internet banking, the likelihood of falling victim to various forms of fraud also rises. Due to ignorance, credit card company customers have provided their card details, personal information, and one-time password to an unknown bogus caller on several occasions in the recent past. One of the biggest issues facing financial institutions and customers alike is credit card theft. It may result in large financial losses and harm to the financial institution's image. While there are several ways to identify credit card fraud, one of the best is machine learning.By using past data, machine learning algorithms may be trained to recognize trends and abnormalities that point to fraud. The state-of-the-art in machine learning for credit card fraud detection is reviewed in this study. Numerous research on the same subject have been conducted in the past with a variety of well-known machine-learning techniques. However, with a grasp of the notion of hybrid machine learning, we will now examine how well hybrid machine learning performs on the same problem statement.

The rapid advancement of deep learning technologies has markedly influenced numerous sectors, particularly agriculture. This survey paper presents an exhaustive review of contemporary trends, challenges, and future perspectives in the application of deep learning to agricultural tasks. By meticulously analyzing 95 research papers published in 2020, this review categorizes studies based on application areas, deep learning methodologies, data sources, targeted crops, and utilized frameworks. The findings highlight the predominance of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with Keras and TensorFlow emerging as the most frequently employed frameworks. Primary data sources include camera and satellite imagery. Key applications explored encompass plant disease detection, weather forecasting, crop yield prediction, and plant classification. Additionally, the paper underscores performance metrics and model accuracies, with disease detection models frequently surpassing 95% accuracy. Challenges such as data availability, model generalization, and computational costs are critically examined, alongside potential future directions for integrating emerging technologies to enhance agricultural productivity and sustainability. This survey aims to provide researchers and practitioners with a comprehensive understanding of the landscape of deep learning applications in agriculture, highlighting areas ripe for future research.