Authors - Akshar Sodankoor, Avanish Shenoy, B Monish Moger, Mohnish Gowda, Prafullata Kiran Auradkar, Subramaniam Kalambur Abstract - Virtualization is essential for efficient resource utilization in cloud and development environments. With the growing adoption of gRPC as a Remote Procedure Call (RPC) framework, evaluating its performance across different virtualization technologies has become crucial. This work benchmarks the performance of four gRPC call types: unary, client-streaming, server-streaming and bi-streaming, across four lightweight virtualization technologies. Docker, gVisor, Firecracker and nanos unikernel. The analysis examines CPU utilization, memory utilization and network capabilities to provide a comprehensive comparison. The results show that docker delivers the best performance across all metrics. Firecracker shows comparable latency performance to docker, but consumes higher memory. Nanos unikernel exhibits CPU utilization similar to that of docker, but has the highest latencies in all cases except unary gRPC call. gVisor exhibits the lowest CPU utilization under heavier workloads and also has the lowest latencies for client-streaming and server-streaming gRPC calls.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Aryan Goyat, Aditya Maan, Vimmi Malhotra Abstract - Deep learning has transformed artificial intelligence and enabled major breakthroughs in applications like computer vision, natural language processing, medicine, cybersecurity, and robotics. Through the use of deep neural networks, it enables automatic feature learning and exceeds machine learning-based methods in accuracy and flexibility. Challenges including excessive computational expense, uninterpretable nature, and ethics are still major hurdles to its widespread application. This article discusses the development and applications of deep learning and presents new research directions that seek to overcome its limitations. Federated and decentralized learning methods improve security and privacy by enabling collaborative model training without raw data sharing. Explainable AI (XAI) techniques, including SHAP and LIME, enhance the interpretability of deep learning models, making their decision-making more transparent. In addition, energy-efficient deep learning methods, such as model pruning, quantization, and neural architecture search (NAS), are being designed to minimize computational and environmental expenses. The emergence of self supervised learning further minimizes dependence on labeled data, making deep learning more feasible across domains. Future developments will center on the fusion of deep learning with reinforcement learning, symbolic AI, and evolutionary algorithms to build more generalizable and efficient systems. These technologies will power the next wave of intelligent, ethical, and sustainable AI solutions.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room DGOA, India
Authors - Anita Agrawal, Ruhi Panjwani, Aditya Mallik Abstract - This paper explores the design and implementation of a multi-user, multi-access web application tailored specifically for automated weather stations (AWS). By examining real-world scenarios and user interactions, we identify key design considerations, including system performance, security, and scalability. The study aims to provide practical insights for developers to create efficient and user-friendly web applications that effectively handle large-scale weather data and cater to various user access levels.
Authors - Krishna Shirsath, Abdullah Ansari, Riyaz Memon, Phiroj Shaikh Abstract - Real-time collaboration is essential for modern software development, enabling developers to work together seamlessly from different locations. This paper presents DevTogether, a collaborative coding platform that facilitates efficient teamwork through live code editing, customizable collaboration sessions, integrated chat, a collaborative drawing board for design prototyping, and real-time video meetings powered by WebRTC. The platform incorporates an intelligent code assistant using the Gemini API, along with an autosave mechanism to ensure workflow continuity. Built using the MERN stack, DevTogether emphasizes scalability, low latency, and responsive performance while addressing synchronization conflicts and communication challenges. Experimental evaluations and simulated performance metrics underscore its effectiveness compared to similar platforms.
Authors - Arnav Shukla, Subhashree Choudhury, Logeshwaran R. Abstract - One of the key limitations of any crowdfunding platform that uses blockchain technology is the lack of transparency regarding fund usage by campaign creators after receiving donations. The current paper addresses this issue. For this investigation, we conducted a detailed examination of the relevant literature on decentralized applications (DApps) and the use of smart contracts to automate administrative tasks. We identified a significant gap in existing platforms: the ambiguity surrounding post-donation and how fund management can be unfair, which blockchain alone does not address effectively. Our findings highlight the strengths of blockchain security and automation capabilities, which ensure a safe and trustworthy environment for users. Our proposed solution integrates a decentralized voting model and a collusion prevention algorithm called EigenTrust to empower donors with a participatory role in decision-making processes, eliminating the need for centralized authority. In this study, we implement and evaluate a decentralized voting model that uses specific techniques to prevent any attacks or chances of misuse of powers that would then empower donors to participate in critical campaign decisions, enhancing trust and satisfaction by allowing them to verify the responsible use of their contributions. By reducing uncertainty around fund allocation, our model increases a more engaging and end-to-end secured donor experience, encouraging donations and supporting the long-term success of social crowdfunding projects. In all, this paper presents a novel approach to increase crowdfunding platforms using blockchain technology with a voting model paired with collusion prevention to address the issue.
Authors - P S Sree Harsha, Harshitha S, Ayush Sisodiya, M P Deepti, Sarasvathi V Abstract - Concerning the Conventional recruitment methods, which has many challenges such as the poor matching of the candidates to the right requirements, issues of transparency and issues of privacy in dealing with sensitive information. This leads to the hiring of candidates who are not qualified to meet their requirements and compromise the organizational data. To solve these problems, Human Resource Blockchain Intelligence Recommendation System (HRBIRS) is proposed to use Blockchain and Recommendation system technologies for getting an efficient recruitment process. Blockchain is evolving technology providing transparent and secure data. Privacy issues are resolved by decentralized architecture. The Hybrid recommendation system makes recruitment effective by determining the qualifications, skills and experience of the job seekers relevant to certain job recruitments posted by the HR. One of the most attractive features of HRBIRS is the peer approval and endorsement systems within the organization without bias to a particular candidate. This paper provides detailed information on the architectural design, implementation and its performance features and demonstrates how this system could be beneficial for recruitment processes throughout the organization by providing data security and enhancing the effectiveness of selecting the right candidate.
Authors - Arunangshu Giri, Dipanwita Chakrabarty, Manash Routray Abstract - The present study emphasized on how enrollment of loyalty programs at fuel retail outlets get enhanced through digital communication and intelligent promotional strategies. The study has evaluated the three major dimensions like participation intention and promotional efficiency to understand the efficacy of the loyalty programs organized by different fuel retail outlets. 454 Indian customers were interviewed over a three months period using a structured questionnaire. Cross-sectional descriptive research design was followed for the study. Both qualitative and quantitative analysis was done using NVivo and SPSS-28 software. The study revealed that customer participation in loyalty programs was highly influenced by flexible redemption options, digital reward system and exclusive benefits. Again, the study has shown how staff knowledge, promotional policies, customer loyalty and digital engagement influence promotional effectiveness. The study acknowledged the pivotal role of intelligent communication strategies in enrichment of customer adaptability towards digitized loyalty programs. Establishing a seamless communication between the customers and digital platforms along with effective staff training can be prudential for optimal customer engagement, sustainability and loyalty.
Authors - Lalithya Govardhan, Shalini M S, Gagan Deep P S Abstract - This work shows an extensive multimodal system of mood detection and customized playlist recommendation based on EEG, GSR, and face recognition. Brainwave activity for emotional evaluation is sensed by EEG electrodes, while GSR sensors provide skin conductance, heart rate variability, and temperature values for physiological behavior. Facial behavior with emotional facial expressions is determined through facial landmarks. Preprocessing entails Butterworth filters for EEG frequency bands, GSR data normalization, and facial feature extraction from a pretrained model (FER) to track eyebrow position, mouth curvature, and eye openness. EEG features are examined using frequency domain analysis, whereas GSR and facial features are classified using Random Forest. To increase precision, a fusion model aggregates predictions by weighted averaging or majority voting, with EEG assigned the greatest weight due to its high correlation with mood. After determining the emotional state, a suitable playlist is suggested: energetic songs for happiness, relaxing music for stress, comforting songs for sadness, and relaxing music for relaxation. This feature-based recommendation system enhances personalization through the use of features like tempo, genre, and mood to provide a dynamic and interactive listening experience for the user
Authors - A. Revathi, A. Sunidhar Reddy, Geetika Alapati, R. Pranay Abstract - This paper presents the performance of the grocery identification system concerning Telugu grocery items, considering both native and non-native speakers. Speech recognition for Telugu groceries presents a unique challenge due to variations in pronunciation, accent, and noise conditions. This study explores the implementation of a Gaussian mixture model (GMM) classifier in conjunction with rasta-perceptual linear prediction (RASTA-PLP) features to enhance the accuracy of Telugu grocery identification. Rasta-PLP effectively captures robust speech features by suppressing unwanted spectral variations, while GMM provides a probabilistic framework for classification. The proposed system is trained on a dataset comprising commonly used Telugu grocery names and evaluated under diverse acoustic environments. Experimental results demonstrate improved recognition performance, showcasing the effectiveness of RASTA-PLP in feature extraction and GMM in classification. This work contributes to developing efficient speech-based interfaces for regional language applications, facilitating voice-driven grocery identification systems. The recognition accuracy of the proposed system is approximately 99%, ensuring high reliability in real-world applications. This technology benefits society by aiding visually impaired individuals and non-Telugu speakers in grocery identification, enhancing accessibility and convenience. By enabling seamless voice-based interaction, promotes inclusivity and improves social equity through technological advancement.
Authors - A.Revathi, Reethikaa Vallinayagam, S. Sivaranjani, A. Deepthi Abstract - This research work introduces a system for identifying genuine speech and recorded (replay) speech through Mel-Frequency Cepstral Coefficients extraction and uses k-means clustering for classification purposes. Speech features obtained from various speakers undergo normalization procedures before receiving cluster assignment during training sessions. During the testing phase speaker identification depends on measuring the distance between input features against cluster centroids. The confusion matrix indicates system performance by showing correct genuine speech detection through high diagonal values yet exhibiting lower off-diagonal values to indicate possible attacks based on recorded speech. Auto-correlation together with cross-correlation serve to evaluate the similarities between speakers. Strong recognition of the same speaker is indicated by high auto-correlation values but weak cross-correlation values demonstrate effective differentiation between different speakers. The AVSpoof dataset serves as the experimental foundation because it includes ten recording subjects who are distributed between five male and five female speakers. The acceptance and accuracy evaluation for the system happens through testing samples which proves its ability to recognize genuine speech from recordings as well as identify distinct speakers properly.
