Authors - Selvamani K, Saranraj S, Muthusundar SK, Kanimozhi S, Mohana Suganthi N Abstract - The phishing attack through email remains a significant threat to cybersecurity because the attack has become highly advanced, flexible, and widely spread among individuals and organizations. The phishing tricks, such as personalized social engineering, impersonated identities, and malicious links, have evolved fast and made the traditional email security measures less useful. As such, numerous schemes of email phishing attack detection and prevention have been suggested, combining rule-based approaches with machine learning, deep learning, natural language processing, and sophisticated artificial intelligence systems. This review paper provides a detailed discussion of the currently existing email phishing detection and prevention frameworks, their architectural elements, detection schemes, and preventive schemes. The paper systematically evaluates the conventional, machine learning, and more advanced AI-driven methods with their advantages, weaknesses, and flexibility to the changing phishing threats. The synthesis of existing research trends and unaddressed issues makes the review valuable to researchers and cybersecurity practitioners and will allow building solid, scalable, and intelligent email phishing defense systems.
Authors - Mohanad A. Deif, Mohamed A. Hafez, Samar Mouakket, Mohamed Abstract - Polypharmacy and multiple chronic conditions in older adults increase the likelihood of adverse drug events caused by drug–drug interactions (DDIs) and contraindications. Many clinical decision support systems still have limited ability to use patient context and to exchange knowledge in a consistent semantic form. This study presents a hybrid semantic–linguistic framework for automated DDI detection by combining biomedical natural language processing, ontology-based reasoning, and risk scoring. The framework uses BioBERT to extract relevant information and represents it using RDF knowledge graphs, OWL 2 DL ontologies, and SWRL rules. In an evaluation with 1,000 synthetic patient profiles containing RxNorm-coded medications and SNOMED CTencoded diagnoses, the system identified a wide range of clinically important interaction patterns. Statistical testing showed that age and the number of medications were strongly associated with alert frequency (p < 0.001). These findings suggest that the proposed approach can improve medication safety by providing explainable clinical decision support.
Authors - Selvamani K, Kanimozhi S, Muthusundar S K, Saranraj S, Jagadeesh K Abstract - Multi-object tracking (MOT) is a pillar of many computer vision applications such as video surveillance, self-driving and crowd analysis [1]. The main difficulty does not only exist in correct identification of objects but also in consistent identities of objects in different frames when there is occlusion, camera motion and changes in scene density [14]. The paper introduces a highly advanced MOT system, combining the latest YOLOv8x detector with a modified and improved version of the original ByteTrack association system, which is called RobustBoTSORTTracker [14]. With the new detection quality of YOLOv8x and the robustness of low-confidence detections in ByteTrack, augmented with selective improvements of BoT-SORT including camera motion compensation and exponential moving average smoothing, the proposed system demonstrates significant gains on the MOT15 benchmark [7]. Experimental findings indicate a MOTA of 55.6, IDF1 of 72.2, precision of 74.3 and a recall of 95.7, which is significantly higher than the previous baselines under similar conditions.
Authors - Abhay Saxena, Ankit Kumar, Prasant Kumar Sahu Abstract - In this paper, we address the problem of rainy condition classification in order to allow autonomous systems to ensure safe operation in different weather conditions of rain, especially for drones. The earlier weather condition classification methods are inclined towards using big and computationally costly models and cannot thus be employed in real-time on resource-constrained platforms such as drones and edge devices. The motivation behind this work is to introduce a light-weight, efficient deep model which would be able to classify various rain conditions with low computational cost so that it may be deployed efficiently on low-resource devices. We present a novel CNN architecture and evaluate its performance on a collection of seven distinct rain conditions. The models are bench marked against some of the state-of-the-art pretrained models to demonstrate the compromise between efficiency and accuracy. Performance is evaluated using accuracy, inference time, and model size. The model has accuracy 95.93% with least model size 89.09 KB with inference time of 32.664 ms bridging the gap in lightweight and real-time classification.
Authors - Arjun Verma, D.K. Chaturvedi Abstract - Ethylene and vinyl acetate or EVA is a co-polymer used as a substitute for a lot of materials. EVA is a versatile material and it has a lot of applications ranging from electronics, healthcare, footwear, building applications etc. It is mainly used in sport shoes due to its property to absorb shock impact and insulation properties. In addition, EVA is very cost-friendly, produces no odor, and light in weight material. But with overuse of it, the cellular structure chang-es and can affect the shoes' quality and insulation properties. In addition to the cellular structure, the air molecules present in it also collapse. This paper focus-es on the bonding properties of EVA at different temperatures and its dielectric properties under different operating and manufacturing conditions. The upper, bottom, and sides of EVA shoes are exposed to high voltage till the breakdown. The experimentation was done at Electrical HV laboratory on the university campus where a 100kV HVAC testing system is available. This paper presents the tabulated results on the dielectric strength of EVA shoes under varying operating conditions. Additionally, it examines the bonding properties of EVA shoes at different manufacturing temperatures, aiming to predict their lifespan, quality, and finish. The results of these studies are thoroughly discussed within the document.
Authors - Nasika Ijaz, Farooque Azam, Saliha Ejaz, Muhammad Waseem Anwar Abstract - Anomaly detection in dynamic cybersecurity networks has been a promising problem that has been addressed using Graph Neural Networks (GNNs). Today’s network topologies are too difficult to handle for traditional methods; the topologies are too dynamic and complex. The main contribution of this study is the evaluation of three GNN models, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and RepographGAN, in terms of effectiveness to detect anomalies in dynamic network environments. Conventional anomaly detection techniques such as logistic regression, support vectors machines (SVM) and decision trees are compared against the models. The results demonstrate that RegraphGAN is superior to the other models in terms of accuracy, precision, recall, F1 score, and AUC-ROC, and is thus very effective at identifying anomalies. However, as computing resources are required for it, a compromise between performance and computing resources is found. Despite the lower accuracy of GCN and GAT, these provide more computationally efficient solutions that are appropriate for real time deployment constraints in such resource constrained environments. The findings provide a basis for future research that can optimize scalability and computational efficiency for large scale applications and in the context suggest the use of GNNs for improving cybersecurity systems.
Authors - Aaqib Hakeem, Akshay V, Parthav Mathu, Kotnada Yogesh, Gokul Kannan Sadasivam Abstract - Passwords remain one of the most widely deployed authentication mechanisms despite well-documented vulnerabilities to guessing attacks. Recent deep learning approaches, including Password Guessing using Temporal Convolutional Networks (PGTCN), have demonstrated that sequence modeling can effectively capture structural regularities in leaked password corpora. However, practical performance often depends not only on model architecture but also on training stability, batching strategy, and decoding configuration. In this work, we investigate a partition-aware training and generation pipeline built around a single Temporal Convolutional Network (TCN). Rather than introducing additional architectural complexity, the proposed framework emphasizes standardized preprocessing, balanced data partitioning for stable batching, optimized training procedures, and large-batch probabilistic decoding. A lightweight buffering layer is incorporated to decouple generation from evaluation and improve throughput without requiring distributed training infrastructure. Experiments on multiple real-world leaked password datasets show consistent, though modest, improvements in match rate compared to the PGTCN baseline under same-site evaluation. The results suggest that careful optimization and pipeline-level design can yield measurable gains in candidate ordering while maintaining reproducibility and implementation simplicity.
Authors - Sushant Maji, Sachin B. Jadhav Abstract - The offline signature validation by means of hand written signature is also a significant consideration in the financial, legal and ad- ministrative authentication systems. However, this is particularly challenging because of the inaccessibility of dynamic data of handwriting such as pen-pressure and stroke-velocity, and small training samples. The paper describes a modified version of Siamese-Transformer model called SigNeura, which is also improved with Synthetic Pen Pressure Map Generation to refine the accuracy of the verification in the few-shot learning. The adaptive thresholding, and utilization of the stroke-width estimation is applied to obtain synthetic pressure maps and fill in the dynamic information of the synthetic grayscale signatures with the static grayscale signatures. The Siamese network is optimized on discriminative embeddings and Transformer encoders are optimized on triplet long range contextual dependencies. The analysis conducted on benchmarking data using experiments demonstrates that SigNeura is a significantly superior approach than conventional CNN and Siamese-based approaches with a high level of accuracy and resistance to skilled forgeries.
Authors - Siddharth Joshi, Deepti Kiran, Dev Kumar Yadav, Harshit Sinha, Abhishek Kukreti Abstract - Artificial Intelligence (AI), as a technology, has the potential to change the manner in which organizations are run in the world. However, small and medium-sized enterprises (SMEs) in the Philippines have unique limitations in the use of AI in running the business. The study aims to explore the perceptions of SME managers in the Philippines on the use of AI, with particular reference to the limitations and facilitators in the use of the technology in the business environment. In this study, the researcher interviewed five SME managers from different sectors, including retail, manufacturing, and service sectors. The researcher used thematic analysis to identify the commonalities in the decisions made by the SME managers on the use of AI in the business environment. The study revealed the perceptions of the SME managers on the use of AI in the business environment in the Philippines, with the limitations and facilitators in the use of the technology in the business environment. The study provides practical insights that can guide strategies aimed at strengthening AI readiness and responsible adoption among SMEs in the Philippines.
Authors - Ambrish Kumar Sharma, Swati Namdev Abstract - The volume of data is growing gradually in all around by various sec-tors like e-commerce, stock market, medical, banking, education, social networks (Facebook, Twitter, WhatsApp) and also because of the utilization of the internet and mobile apps. Privacy and security have always been important issues with big datasets. Big datasets may be a collection of facts that has huge and multiplex structure like sensors, emails, weblogs and images. Sensitive information about individuals, which is usually evident or hidden in data, is susceptible to various privacy attacks and high risks of privacy disclosure. Constructing a secure and reliable environment for big dataset requires a distinction between existing approaches so that we can develop a unique solution in future for this that maximizes data privacy. This paper offers insights into the overview of big datasets, big dataset privacy problems and various privacy preservation techniques with comparative study used in big datasets.
