Glaucoma is a chronic disease often called “silent thief of sight” as it has no symptoms and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural changes in the retina which aid ophthalmologists to detect glaucoma at an early stage and stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Our proposed technique provides a novel algorithm to detect glaucoma from digital fundus image using a hybrid feature set. This paper proposes a novel combination of structural (cup to disc ratio) and non-structural (texture and intensity) features to improve the accuracy of automated diagnosis of glaucoma. The proposed method introduces a suspect class in automated diagnosis in case of any conflict in decision from structural and non-structural features. The evaluation of proposed algorithm is performed using a local database containing fundus images from 100 patients. This system is designed to refer glaucoma cases from rural areas to specialists and the motivation behind introducing suspect class is to ensure high sensitivity of proposed system. The average sensitivity and specificity of proposed system are 100 and 87 % respectively.

Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset.

The increasing complexity and scale of cyber threats demand intelligent and adaptive defense mechanisms that extend beyond traditional approaches. Artificial Intelligence (AI) has emerged as a key enabler for enhancing cyber security through automated detection, analysis, and response. This paper presents a comprehensive survey of AI applications in cyber security across five major domains: malware detection, intrusion detection, phishing and spam detection, botnet detection, and cyber forensics. A systematic methodology based on data and methodological triangulation is employed to analyze 75 studies published between 2021 and 2025. The paper introduces a multi-layer taxonomy that maps cyber threats to application domains, analysis methods, AI approaches, and their associated capabilities and limitations. In addition, a cross-domain meta-analysis is conducted to identify recurring trends and assess the adoption of AI across different cyber security scenarios. The analysis reveals that deep learning and transformer-based models dominate data-intensive domains such as intrusion detection and malware analysis, whereas traditional machine learning techniques remain effective in structured and resource-constrained settings, particularly for phishing detection. Key challenges include dataset limitations, limited explainability, adversarial vulnerabilities, and computational constraints. Unlike existing surveys that focus on specific techniques or individual cyber security domains, this work provides a unified, application-oriented perspective on AI-driven cyber security. It further highlights emerging trends, open challenges, and future research directions toward more robust, scalable, and trustworthy cyber security systems.

Android malware continues to evolve in sophistication, creating a need for accurate, efficient, and interpretable detection mechanisms. This paper proposes a novel Android malware detection framework that integrates graph-based software engineering analysis, explainable artificial intelligence (XAI), and heterogeneous ensemble learning. The framework decompiles Android applications, constructs call graphs and control flow graphs, and extracts structural, functional, and behavioral software engineering metrics that are transformed into statistical feature representations. To improve feature quality and reduce dimensionality, LIME and SHAP are engineered into a threshold-based feature relevance mechanism, reducing the original 89 features to 65 and 61 features, respectively. A new heterogeneous ensemble classifier, RF–LNN–GRU, is then introduced to combine the complementary strengths of random forest, lightweight neural networks, and gated recurrent units through asymmetric probabilistic voting. Experiments conducted on 10,365 Android applications demonstrate that the proposed framework consistently outperforms conventional machine learning and ensemble baselines, achieving a detection rate of 98.98%, an F1-score of 96.2%, and an AUC of 98.1% while maintaining an inference latency below 0.4 ms per sample. These results demonstrate that the proposed combination of graph-derived software engineering metrics, XAI-guided feature engineering, and heterogeneous ensemble learning provides an effective and computationally efficient solution for Android malware detection.

In contemporary education, learners face the challenge of navigating an overwhelming abundance of information. Traditional search methods, often limited to keyword matching, fail to capture the nuanced meaning and relationships within educational materials. Our multimodal approach combines Sentence Transformer for text and Inception V3 for images to generate vector embeddings for textbooks which are stored in an Elasticsearch database. Learners’ queries again are converted to vector embeddings which are matched through cosine similarity with stored embeddings, resulting in retrieval of relevant material which is ranked and then synthesized using large language model (LLM) APIs. The approach retrieves answers based on semantic search rather than keywords. The system also integrates GenAI capabilities separately, specifically leveraging LLM APIs, to generate context-aware answers to user-posed questions at varying levels of complexity, e.g., beginner, intermediate, and advanced. Through comprehensive evaluation, we demonstrate the system’s ability to retrieve coherent answers across multiple sources, offering significant advancements in cross-text and cross-modal retrieval tasks. This work also contributes to the international discourse on ethical GenAI integration in curricula and fosters a collaborative human–AI learning ecosystem.