Advancement of the Internet has increased security risks associated with data protection and online shopping. Several techniques compromise Internet security, including hacking, SQL injection, phishing attacks, and DNS tunneling. Phishing attacks are particularly significant among web phishing techniques. In a phishing attack, the attacker creates a fake website that closely resembles a legitimate one to deceive users into providing sensitive information. These attacks can be detected using both traditional and modern AI-based models. However, even with state-of-the-art methods, accurately classifying newly emerged links as phishing or legitimate remains a challenge. This study conducts a comparative analysis of more than 130 articles published between 2020 and 2024, identifying challenges and gaps in the literature and comparing the findings of various authors. The novelty of this research lies in providing a roadmap for researchers, practitioners, and cybersecurity experts to navigate the landscape of machine learning (ML) and deep learning (DL) models for phishing detection. The study reviews traditional phishing detection methods, ML and DL models, phishing datasets, and the step-by-step phishing process. It highlights limitations, research gaps, weaknesses, and potential improvements. Accuracy measures are used to compare model performance. In conclusion, this research provides a comprehensive survey of website phishing detection using AI models, offering a new roadmap for future studies.

Micro-Expression Recognition is crucial in various fields such as behavioral analysis, security, and psychological studies, offering valuable insights into subtle and often concealed emotional states. Despite significant advancements in deep learning models, challenges persist in accurately handling the nuanced and fleeting nature of micro-expressions, particularly when applied across diverse datasets with varied expressions. Existing models often struggle with precision and adaptability, leading to inconsistent recognition performance. To address these limitations, we propose the Convolutional Variational Attention Transformer (ConVAT), a novel model that leverages a multi-head attention mechanism integrated with convolutional networks, optimized specifically for detailed micro-expression analysis. Our methodology employs the Leave-One-Subject-Out (LOSO) cross-validation technique across three widely used datasets: SAMM, CASME II, and SMIC. The results demonstrate the effectiveness of ConVAT, achieving impressive performance with 98.73% accuracy on the SAMM dataset, 97.95% on the SMIC dataset, and 97.65% on CASME II. These outcomes not only surpass current state-of-the-art benchmarks but also highlight ConVAT’s robustness and reliability in capturing micro-expressions, marking a significant advancement toward developing sophisticated automated systems for real-world applications in micro-expression recognition.

Wheat is an essential crop that plays a vital role in global food security, but is susceptible to a variety of diseases, which have the potential to drastically decrease crop productivity. Detection of disease at an early stage and in an accurate manner is crucial to minimize crop losses. This research presents a deep learning technique based on the Shifted Window (Swin) Transformer, a powerful attention-based model that effectively captures both local and global information for enhanced classification output. Unlike conventional CNN-based methods, which often face limitations in feature extraction, the Swin Transformer utilizes hierarchical attention mechanisms to improve disease detection accuracy. The proposed modelis trained on a dataset of 9,346 wheat leaf images, categorized into eight disease classes and one healthy class. Using Bayesian hyperparameter optimization, we tuned key parameters such as learning rates, batch sizes, and dropout rates, and achieved an accuracy of 99.3%. We also conducted comparative analyses with baseline self-attention, CNN-based feature extraction, and hybrid attention layers. To enhance interpretability, Grad-CAM visualization techniques were applied, confirming model reliability. This research advances precision agriculture by improving the efficiency of wheat disease identification and supporting sustainable farming through state-of-the-art deep learning methodologies.

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis.

Unanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase identification. Previous studies had challenges recognizing ME due to complicated spatiotemporal linkage in video data. Using the ConvMixer architecture, we Proposed a novel technique for facial microexpression identification based on convolutional attention mechanism. The research uses SAMM, SMIC, and CASME-II are benchmark datasets used to perform experiments. ConvMixer deployed to analyze the SAMM dataset where ConvMixer achieved an amazing 99.73% accuracy, 97.3% precision, 96.5% recall, and 99% F1-Score while 10-fold cross-validation. In addition, we extended our analysis to the CASME-II dataset, where ConvMixer attained an F1-Score of 99.4%, an accuracy of 99.12%, a precision of 98.3%, and a recall of 98.7%. These findings indicate that ConvMixer regularly outperforms other MER architectures, while capturing video specific and dynamic characteristics. ConvMixer architecture are good in capturing both spatial and temporal correlations and extracts spatial information using depthwise convolutions and channel mixing processes. High F1-Score, recall, precision, and accuracy across several datasets demonstrate the robustness and adaptability of the ConvMixer architecture. Finally, our findings show that the Convolutional Attention-Based Mechanism for facial microexpression recognition (CABM-FMER) works effectively for identifying facial MEs.

The use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs), also known as drones, is changing the way future communication and networking systems are designed. UAVs can collect data, support wireless networks, and help deliver services from the sky, which makes them an important part of modern technology. To understand these developments, we reviewed almost 250 research papers published between 2015 and 2024. Our review focuses on UAV network design, communication methods, energy management, AI-based optimization, and future challenges. Unlike previous surveys that mainly summarize individual technical domains, this work introduces a new AI-driven UAV classification framework that connects these aspects under one structure. The framework organizes UAV systems across five dimensions–mission adaptability, autonomy level, communication intelligence, scalability, and deployment context–providing a unified way to compare current and future UAV technologies. This analytical structure highlights how artificial intelligence enables UAVs to move from static, pre-defined operations toward dynamic, real-time decision-making and mission-specific adaptation. We found that deep learning and reinforcement learning are the most common AI methods used to improve routing, flight planning, resource use, and network performance. These techniques help UAV networks adapt to changing conditions and reduce communication delays. However, we also found several open challenges, such as improving real-time energy efficiency, increasing security and privacy, managing large drone groups (swarms), and dealing with regulatory and policy issues. By combining this new framework with an extensive literature review, the paper offers a holistic view that not only summarizes past progress but also maps existing gaps and trends for future research. This paper provides a clear summary of current research, explains key trends, and points out gaps such as the need for lightweight AI models and better swarm coordination. The insights from this review can help researchers and engineers build smarter, safer, and more efficient UAV networks in the future.