Authors - Moushmee Milind Kuri, Ganesh Pathak Abstract - Cyberbullying is a growing concern across social media platforms, necessitating advanced detection mechanisms to mitigate its impact. Traditional machine learning models often struggle with understanding contextual dependencies and ensuring model interpretability. This paper proposes a hybrid deep learning approach that combines BERT and RoBERTA for feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for sequential dependency learning. To enhance interpretability, attention mechanisms such as Self-Attention and Bahdanau Attention are integrated, allowing the model to focus on crucial words contributing to classification. The proposed system aims to improve accuracy, scalability, and explainability while addressing key challenges in cyberbullying detection. This research lays the groundwork for developing more transparent and effective AI-driven moderation systems for online safety.
Authors - Devi V S, Durgalashmi C V Abstract - This study assesses the effectiveness of income tax deductions and exemptions in promoting savings and investments in India. The Indian government has implemented various tax incentives to encourage individuals to save and invest, including provisions under sections 80C, 80D, and others. These deductions and exemptions are designed to stimulate economic growth by fostering long-term financial planning among individuals. The research examines the impact of these provisions on individual taxpayers' behavior and their overall influence on savings and investment patterns. Through a comprehensive analysis of available data, the study identifies the key tax incentives that have led to increased savings in instruments such as Provident Funds, National Savings Certificates, and insurance products. Additionally, the research evaluates the extent to which these tax benefits contribute to fostering a culture of investment and financial security. The study concludes that while tax deductions and exemptions have provided some incentives for savings, their effectiveness is often limited by lack of awareness and financial literacy. To further promote savings and investments, the study recommends improvements in policy communication, accessibility, and the alignment of tax incentives with broader economic goals.
Authors - Akash Tibeli, Saroja V Siddamal, Suneeta V Budihal Abstract - The AHB to APB Bridge is crucial component in System-on-Chip (SoC) designs, Achieving efficient communication between the pipelined AHB bus and the non-pipelined APB bus. In the proposed work a AHB to APB bridge is built using a bridge architecture which enables to translate pipelined, burst-oriented, high speed AHB transactions into sequential, low-power APB transactions by maintaining synchronization and data integrity. It was developed with a FSM to manage transactions and pipelining to maintain efficiency. Verification was performed using a Universal Verification Methodology testbench environment through direct and random testcases of burst, single, sequential, non-sequential transactions. 80 testcases were tested to obtain a functional coverage of 88%.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Judy K George, Elizabeth Sherly Abstract - Convolutional Neural Networks are extensively employed in critical domains such as computer vision, medical imaging, and autonomous systems. Enhancing model interpretability by providing users with concise and context-relevant explanations of CNN decision making such as visualizing feature maps or saliency regions, enables a deeper understanding of the model’s internal representations and inference process. The proposed work presents a deep learning framework integrating a ResNet-based U-Net architecture with a Fixation Point Generator (FPG) to perform classification and saliency aware reconstruction on the hand-written dataset. The model leverages transfer learning by employing a pre-trained ResNet-18 as the encoder backbone, enabling robust feature extraction. A custom decoder reconstructs input images while a classification head predicts digit labels. To enhance model interpretability, a Fixation Point Generator predicts spatial attention maps (saliency maps) from high-level global features, highlighting regions of interest that influence model decisions. This implementation aims to bridge the gap between classification performance and model explainability, offering insights into the model’s focus areas through learned attention. The model got an accuracy of 98.44 on the Malayalam handwritten dataset, 97.81 on English handwritten dataset, and 99.56 on the MNIST dataset.
Authors - Meghali Kalyankar, Om Pratap Gajra, Prathamesh Vilas Sagvekar, Mehul lalit Sharma, Zoheir Shahid Shaikh Abstract - Deepfakes are an emerging threat to digital authenticity and security, hence a proper detection technique needs to be created in order to establish public study confidence. A thorough roadmap to the development of deepfake detection software has been provided in this paper, reviewing the state-of-the-art algorithms, such as XceptionNet, EfficientNet, and hybrid models integrating spatial and temporal analysis. It provides methodologies for implementation, data preprocessing, and software pipeline development, serving as a practical guide to researchers and developers. Theoretical study to application-oriented practice closes the gap in terms of bottom line development and adaptive detection systems addressing the growing menace of deepfake media.
Authors - PRANAY SAMAL, K R LOKESH KUMAR, CHAKILELA SAIRAJ, G VIDYA SRI, SUSHAMA RANI DUTTA, SUKLA SATAPATHY Abstract - Music plays a significant role in our day-to-day life, and selecting appropriate songs can enhance the experience. This paper describes an intelligent music recommendation system that applies machine learning to recognize what users prefer and recommend music that they will like. It incorporates various approaches, including considering user decisions and music attributes, to enhance suggestions. The system adapts based on user actions and refines recommendations over time. Findings indicate that this method provides easier and more precise music discovery. This paper emphasizes how technology can assist in providing a higher quality and better personalized music experience.
Authors - Arya Tripathi, Akash Mecwan Abstract - In recent years, the RISC-V architecture has emerged as a promising platform for embedded systems, offering flexibility and open-source accessibility. Consequently, the demand for secure communication in embedded devices, particularly within the Internet of Things (IoT) ecosystem, has driven the adoption of cryptographic algorithms. Integrating cryptographic functionalities into RISC-V architecture presents unique challenges, requiring innovative solutions to optimize performance and security. In response, the proposed design introduces an approach to address these challenges by incorporating a dedicated cryptoprocessor module into the RISC- V architecture specifically designed to handle encryption and decryption tasks efficiently. The cryptoprocessor module employs the Blowfish-64 algorithm to ensure robust security while lowering the computational overhead. Blowfish is a well-established symmetric-key block cipher known for its simplicity and efficiency. The compact design of the cryptoprocessor module significantly reduces resource utilization and execution time compared to existing implementations while preserving security and functionality. The design emphasizes low resource utilization, achieving a utilization rate of 34% (11,340 out of 33,216 available units) with an execution time of 160 ns. The implementation is carried out using Verilog HDL for the Cyclone II EP2C35F672C6 based FPGA.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Kirti Karande, Sujata Kadu, Deven Shah Abstract - Breadth-First Search (BFS) is a foundational graph traversal algorithm, it’s systematic layer-by-layer exploration of nodes makes it invaluable for a variety of domains, including transportation networks, social network analysis, and artificial intelligence. However, traditional BFS implementations face challenges when dealing with large-scale graphs due to memory limitations and inefficiencies in handling massive datasets. This project addresses these challenges by integrating BFS with a CSV-based data storage system, enabling efficient traversal of large graphs without relying on a traditional SQL database or requiring the entire graph to be loaded into memory. The graph data, comprising nodes and edges, is stored in CSV files, which act as lightweight and accessible storage. The implementation is memory-efficient due to the use of Pandas DataFrames for handling CSV data and NetworkX graphs for traversal. Additionally, the integration of a machine learning model from Scikit-learn, a memory-efficient library, ensures effective prioritization of edges without excessive computational overhead. In this project, we address a key limitation of the traditional Breadth-First Search (BFS) algorithm: its inability to consider edge weights during traversal. It is unsuitable for scenarios where varying edge weights significantly impact the traversal outcome, such as in shortest-path calculations for weighted graphs. To overcome this drawback, our project integrates a machine learning (ML) model to analyze and prioritize edges based on their weights, effectively augmenting BFS for weighted graphs. By leveraging CSV-based storage and combining it with an ML-driven edge prioritization mechanism. this project offers a scalable solution for managing and analyzing large, weighted graphs. This combination ensures that the navigation system not only computes the shortest path but also suggests the most practical and efficient routes tailored to user preferences or constraints.
Authors - Dipak Ligade, Chiranjit Das, Rupali Parte, Masira Kulkarni, Shivraj Jadhav, Abhishek Mohite Abstract - —This project aims to automate tree counting and forest land diversion assessment through satellite image combined with advanced computing techniques. The treatment of forest re sources needs accurate monitoring because growing environmental challenges such as deforestation, biodiversity loss, and climate change require it for sustainable land management. The research uses satellite imagery along with machine learning and deep learning tools, specifically convolutional neural networks (CNNs), to precisely detect and count trees across expansive territories. The study demonstrates how satellite analytics technologies will enhance forestry applications with their capabilities for better tree enumeration at higher efficiency and greater accuracy.
Authors - Suchanta Ravan, Prashant Dhotre Abstract - Conventional identification techniques that depend on privacy concerns and credentials are becoming more vulnerable to web-based risks like hacking and data breaches. The need for sophisticated authentication techniques has grown dramatically because of identity theft, cyberthreats, and illegal access. Traditional security methods, such as PINs and passwords, are insufficient for high-security applications since they are vulnerable to phishing, brute-force assaults, and credential breaches. To improve safety and tackle problems like privacy threats, spoofing, and accessibility problems, this study suggests a strong adaptive authentication mechanism that integrates biometric along with behavioral assessment. The multimodal authentication framework guarantees a smooth and easy verification process while also enhancing security. This structure guarantees a smooth and safe authenticating process by utilizing cutting-edge security methods like encryption, machine learning, and multifaceted biometrics in conjunction with a user-centric architecture. By combining behavioral biometrics with conventional authentication techniques, total authentication reliability is increased, and cyber risk is mitigated. The effectiveness of the suggested approach in lowering susceptibility to cyberattacks while preserving superior usability and consumer satisfaction is demonstrated by experimental findings, Highlighting the importance of two-way authentication.
