Authors - Meena Rani, Randeep Singh Abstract - The exponential growth of smart devices and advanced image editing tools has made detecting and localizing image forgeries critical for ensuring digital content integrity. This paper focuses on developing a robust and scalable model for passive image forgery detection using convolutional neural networks (CNNs). Leveraging datasets like CASIA1 and MICC-F220, the study aims to identify tampered regions in digital images by analysing noise patterns, pixel-level anomalies, and compression artefacts. The proposed methodology integrates preprocessing, model training, and validation using diverse datasets to enhance detection accuracy and scalability. Compared to traditional techniques, the deep learning-based approach shows significant improvements in detecting complex forgeries, including splicing and copy-move manipulations. Applications of this research extend to digital forensics, media authentication, and cybersecurity. The findings underscore Deep learning systems' show promise to tackle new issues in picture forgery detection and localization
Authors - Kritika Benjwal, Rishika Agrawal, Rashi Gupta, Dinesh Kumar Saini Abstract - The ublic distribution system (PDS) plays a vital role in eradicating hunger and ensuring food security across the world[1]. However, there are certain challenges like beneficiary identification, inconsistent transaction, diversion of grains during procurement at different stages to the open market, ration shop owners selling subsidized goods at higher prices and most importantly the paper focusses on supply chain leakages.[2] This paper proposes a conceptual model for implementing blockchain in PDS and eradicating all the supply chain leakages and making PDS fair[3]. It first focusses on operations of PDS, then assessing all the possible loopholes and implementing blockchain for removing these loopholes. It proposes the idea in which it leverages the benefits of using smart contract and consortium-based ecosystem that can bring efficiencies in PDS.
Authors - Sandhya Borkar, Shital Patil Abstract - The rapid development of the Internet of Things (IoT) has made the provision for innovative solutions in various sectors, including fuel management. The research presents the design and analysis of a smart IoT-based system for fuel dispensing, aimed at improving the efficiency, transparency and security of fuel distribution. The proposed system uses microcontrollers, sensors and real-time data communication technologies to automate fuel dispensing, monitor fuel levels and prevent theft or misuse. Key components include flow sensors to measure fuel output, RFID modules for secure user authentication and cloud-based platforms for remote monitoring and control. Additionally, mobile applications provide users with instant transaction records, fueling history and alerts. The system undergoes performance evaluation to ensure precise fuel measurement and seamless data synchronization. The results demonstrate significant improvements in operational efficiency and customer satisfaction, reducing manual errors, fuel theft and fraud, operational downtime, high maintenance and labor cost. This smart fuel dispensing system offers a scalable and cost-effective solution suitable for fuel stations, logistics companies and industrial applications, contributing towards smarter resource management and enhanced energy distribution practices.
Authors - Pravin Game, Shubham Bhingardive Abstract - Deepfake technology, which enables manipulation of images and videos, poses serious threat to media integrity and cyber security. Existing detection models often struggle with accuracy due to data complexity and variability. This study introduces a hybrid deepfake detection model that combines MobileNetV2, EfficientNetB7 and Vision Transformer (ViT) to enhance feature extraction and classification. ViT provides strong pattern recognition, EfficientNetB7 offers scalable accuracy and MobileNetV2 ensures lightweight processing. The model is trained on a publicly available datastet from Yonsei University, consisting of real and fake facial images. Techniques such as data augmentation and image resizing improve generalization. Experimental results show that the proposed model achieves 94.64% accuracy, 93.55% precision, 96.67% sensitivity, 92.31% specificity and 95.08% F1 score outperforming precious methods. These improvements ase statistically significant (p < 0.05). The results highlight the effectiveness of multi-model fusion for robust deepfake detection offering a scalable and reliable solution for applications in digital forensics, information verification and cybersecurity.
Authors - Pravin Game, Shubham Bhingardive Abstract - The correct identification of heart disease is essential for successful treatment and management. In this work, we assess different machine learning algorithms' predictive power for diagnosing heart disease. On the dataset, we employed the method of principal component analysis (PCA) to choose features, we got top 9 principal components out of 13 features. Then, applied the hippopotamus optimization algorithm on that 9 principal components then trained and tested the model on eight different algorithms: Bagging, Boosting, Naive Bayes, K - Nearest Neighbors (KNN), Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression(LR). The algorithm’s accuracy ranged from 86.81% to 94.53%, The most accurate methods were SVM, KNN and random forest. These findings show that machine learning algorithms may be able to help with heart disease and focus on the need of choosing suitable algorithms for exact and trustworthy clinical decision-making. Future research will concentrate on using sophisticated on feature selection and ensemble learning strategies to further increase model accuracy.
Authors - Ganesh Shivaji Pise, A D Londhe, Hrushikesh Jaivant Joshi, Bhagwan Dinkar Thorat, Yashita Parikshit Mahalle, Pankaj Chandre Abstract - Data leakage poses a significant threat to modern security systems, leading to unauthorized access and privacy breaches. Deep learning models have shown promise in detecting such anomalies; however, their black-box nature raises concerns regarding trust and interpretability. This paper explores the role of Explainable AI (XAI) in enhancing transparency and trust in deep learning-based data leakage detection. Various explainability techniques, including SHAP, LIME, and Grad-CAM, are integrated into a security framework to provide interpretability while maintaining detection accuracy. The proposed architecture bridges the gap between AI-driven security solutions and human decision-making, enabling security analysts and compliance officers to make informed assessments. Additionally, the study evaluates different XAI approaches based on accuracy, interpretability, and scalability to identify optimal techniques for real-world security applications. The findings highlight the importance of balancing explainability with performance to ensure robust and trustworthy cybersecurity solutions.
Authors - Priya Surana, Soham Jadhav, Janhvi Jathot, Arnav Joshi, Roshani Kadam Abstract - Landslides are natural disasters posing great risks to life, infrastructure, and the environment. Timely and accurate predictions are highly beneficial to reduce such impact. The advent of machine learning (ML) and deep learning (DL) has significantly improved the state of landslide prediction models. The review outlines the various ML and DL techniques adopted for landslide prediction and gives a brief account of methodologies, applications, benefits, and limitations. This is mainly the melding of ML and DL techniques, such as Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, for the enhancement of the predictive ability of such models. Key challenges in landslide prediction, such as data availability, model interpretability, and computational complexity, alongside future directions, will be discussed to contribute to the robustness of landslide prediction models. Finally, inferences will be drawn as to the significance of hybrid ML-DL approaches in pushing forth landslide prediction models into better accuracies and reliability (Khuc, T.D., et al, 2023)(Wu, X., et al, 2023).
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - A.Punidha, E.Arul, E.Yuvarani, S.Rajasakaran Abstract - With the rapid expansion of Internet of Things (IoT) and smart device ecosystems, security threats such as malware attacks have become a critical concern. Traditional signature-based malware detection methods struggle to detect evolving and polymorphic threats, necessitating the development of intelligent, data-driven cybersecurity mechanisms. This study proposes a novel malware detection framework that integrates optimized feature engineering and deep neural networks (DNNs) to classify malware in smart devices with high precision. The approach focuses on behavioral feature extraction, including API call sequences, network activity logs, and application permissions, followed by feature selection techniques to reduce dimensionality while retaining key discriminative attributes. A comparative analysis of various machine learning (ML) models, including Random Forest, Support Vector Machine (SVM), and Deep Learning models, demonstrates that the proposed feature engineering-enhanced DNN model achieves 96.1% accuracy, outperforming conventional methods. Extensive experimentation on a real-world dataset of 10,000 smart device applications showcases the robustness and scalability of our framework. This research contributes to enhancing security in smart environments by providing an adaptive and computationally efficient malware detection system.
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - Jolou Vincent M. Jala, Everly A. Nacalaban, Nenon Roy A. Sandinao, Ryan Boyd D. Origines, Randy Joy M. Ventayen, Neilson D. Bation Abstract - Artificial intelligence (AI) has completely transformed enterprises and organizations all over the world due to its propensity to spur innovation and mimic operational efficiency (Jala, J.V.M. et al., 2024). Through its capability to boost learning outcomes, promote inclusion, and streamline operations, artificial intelligence (AI) is revolutionizing higher education. This study investigates the influence of artificial intelligence in higher education in the Philippines. The study specifically seeks to understand how artificial intelligence (AI) can be used in higher education institution in terms of personalized learning amidst large class sizes, access to education in rural areas, solving job skills mismatch, modernizing administrative procedures in inadequate resources institutions, artificial intelligence powered innovation and research, challenges of embracing artificial intelligence such as digital literacy and infrastructure, social and ethical implications and resistance to change and faculty development Moreover, this work also examines the ethical considerations in employing arti-ficial intelligence in higher education in the Philippines, precisely in terms of security and data privacy, fairness and algorithmic bias, digital divide, human supervision and accountability, autonomy and consent, influence on staff and faculty roles and intellectual property and academic integrity. To realize this objective, the proponents essentially examined 170 publications in the literature that were indexed by Scopus to look at artificial intelligence in the context of higher education. This finding highlights artificial intelligence’s essential role in embracing challenges and improving higher education in the Philippines while emphasizing ethical considerations such as fairness and data privacy. (Jala, D.J.V., 2025).
Authors - Kishan Raj, Samyuktha Vimal, V Shini Abstract - In recent years, the Indian online fashion e-commerce industry has experienced significant transformations due to fast-paced technology, consumer behavior changes, and a growing e-commerce landscape. Competition is fierce, especially in the e-lifestyle market as its payoff will exceed $30 billion by 2025, developing brand equity understandably becomes a key factor in maintaining long-term success. It is understood that brand equity is a significant factor as it directly affects consumers' trust, buying decisions, and consumer retention, which are also vital elements in preserving brand equity in an industry where distinctions matter. This study's goal is to examine the impact of marketing mix variables 7Ps including Personalization, which has just recently emerged as a particular critical factor in the e-commerce business, on brand equity in the Indian online fashion e-commerce industry. Quantitative research methods were adopted to analyze data provided by consumers regarding the influence of such factors. The analyses indicate that Personalization and Place affect brand equity substantially, whereas traditional elements such as Price and Promotion do not exert such influence in online fashion retailing. Indian consumers seem increasingly to make a shift toward a digital-first shopping experience, the marketing strategies of brands must follow suit by creating engaging, personalized, and innovative interactions. These research findings will provide some strategic recommendations to online fashion retailers, marketers, and industry gurus seeking to consolidate their brand positioning in this ever-evolving and highly competitive marketplace.
