Authors - Sanchit Prashant Joshi, Parth Atul Gargate, Yash Prabhakar Apotikar, Rupesh C Jaiswal, Mousami V. Munot Abstract - Social media platforms operate at top speeds when transferring image-based data. The shared and posted images and videos on WhatsApp and Instagram consume the majority of network resources. Lossless compression techniques were applied to images while maintaining image quality throughout data storage and transmission processes because this fundamental method produces perfect information reconstruction after decompression. The research evaluates Predictive Coding (DPCM) and Context-Based Coding and Arithmetic Coding and Dictionary-Based Techniques (LZW) and Block-Based Compression through analyses of their efficiency metrics and computational complexity and practical usage. New developments in JPEG2000 and LZW compression have led to increase speed and efficiency through Parallel Symbol Encoding in Arithmetic Coding and Compression Ratio Prediction. The Optimized Run-Length Encoding (ORLE) system uses dynamic compression approach adaptation according to image orientation to enhance its flexibility. The speed of real-time applications increases remarkably when using FPGA implementations. This survey examines trade-offs among compression ratio together with computational expense and suitable data sets to perform an evaluation between classical and modern methods. Future development in lossless data and image compression relies on emerging trends such as AI-driven compression models as well as hardware-accelerated algorithms and hybrid frameworksDeflate is the fastest compression technique taking about 0.043 seconds, with a Maximum compression ratio of 26.66 given by WebP.
Authors - Anuj Sudhir Kulkarni, Rama Gaikwad, Prathamesh Zad, Sai Lahane, Shivam Shelke, Saurav Jadhav Abstract - The rapid development of artificial intelligence (AI) is changing the financial landscape. It offers innovative solutions to optimize personal financial management and advisory services. This research focuses on developing an AI-based platform to improve financial decision-making by analyzing users' investments to provide insights into financial health. Key features include Portfolio Visualizer, Risk Radar, Fundamental Analyst, Price Forecaster and Financial advisory services ensure a comprehensive view of financial planning, emphasizing AI frameworks and interpretable applications. To build user trust and transparency, challenges such as mitigating bias are explored. Real-time problem solving and fine-grained scalability with a commitment to accessibility and precision This research highlights the ability of AI to democratize financial advisory services. and overcome limitations in the current system. Future directions include real-time risk assessment. Advanced portfolio management and innovative AI integration for dynamic market simulation.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - Mohan S G, Abhilash K Raj, Nayana S A, Pradhaan S, Rajendra Bhat Abstract - This paper explores the application of the You Only Look Once (YOLO) v11 model for real-time object detection in Indian road conditions, addressing challenges posed by unconventional objects like animals, autorickshaws, carts, and tractors. A dataset from dashcam and mobile footage was annotated using the Computer Vision Annotation Tool (CVAT) tool and combined with COCO to train YOLO v11. The model significantly improved detection accuracy, increasing classes from 30 to 108. Its high accuracy and real-time performance make it suitable for autonomous vehicles and traffic monitoring in India.
Authors - Vani E S, Gourav Subnani, Prajwal Gupta, Shivee Jaiswal, Mihir Sahu Abstract - Plant species classification accuracy is crucial for biodiversity conservation and ecosystem monitoring. Traditional taxonomy-based methods, which rely heavily on expert analysis, can be inefficient and prone to errors, particularly when processing large datasets. This study leverages deep learning and machine learning techniques to automate plant species identification, with a strong focus on leaf vein morphology analysis. The proposed approach begins with preprocessing leaf images by converting them to grayscale, extracting significant structural features, and skeletonizing vein patterns. Key morphological characteristics, including vein distributions, textures, and geometric attributes, are then used as input for classification models. They use both sophisticated deep learning models like Convolutional Neural Networks (CNN) and more traditional machine learning approaches like Random Forest (RF), k-Nearest Neighbours (kNN), and Support Vector Machines (SVM). The Xception architecture, known for its depth wise separable convolutions, is particularly effective in capturing intricate vein structures, enhancing classification accuracy. This automated system reduces the dependency on manual identification efforts, making it scalable for large-scale biodiversity research. By integrating deep learning-driven analysis, the proposed framework provides a robust and efficient solution for plant species classification, aiding conservation initiatives and ecological studies.
Authors - Sangita Lade, Muhammad Parkar, Shreyas Nagarkar, Om Shintre, Shivam Padalkar Abstract - The rapid evolution of machine learning (ML) has transformed industries by enabling automation, prediction, and optimization for complex real-world problems. However, developing ML pipelines involves repetitive tasks such as data preparation, model building, and evaluation, which are time-consuming and prone to errors. This paper introduces an automated system for generating ML code using Jinja2 templating and supervised MLbased feature prediction. The system analyzes 5000 ML code templates to extract parameters like data type, preprocessing techniques, model architecture, and hyperparameters. A supervised ML model predicts missing parameters based on partial user input, enabling dynamic code generation. The framework supports diverse data formats (tabular, image, text) and ML tasks (classification, regression). Experimental results demonstrate high accuracy in parameter prediction and significant time savings (70-80% reduction in setup time). The system simplifies ML development, reduces errors, and accelerates experimentation, making it accessible to researchers, developers, and students.
Authors - Rohini T.V, Srikrishna Adiga G, Tejas C, Sunil Mashyale, Sunil Kumar C Abstract - DevLaunch is a cloud-native deployment platform purpose-built for MERN stack apps, using AWS Amplify to make hosting and configuration easy. The platform provides real-time monitoring, auto-resource provisioning, and a CDN-tuned Next.js frontend to abstract away deployment nuances. In addition, DevLaunch increases developer efficiency by reducing the need for manual setup and cutting deployment and debug times by 40% and 30%, respectively. The auto-scaling architecture and simplicity of the system make it a secure and highly scalable way to deploy contemporary web applications.
Authors - Namrata Jangam, Nipun Jadhav, Riya Chavan, Priya Chavan, Rutuja Surve Abstract - Time-honoured treatment has long relied on pharmaceutical plants as genuine remedies due to their bioactive compounds. With increasing demand for natural products and sustainable healthcare, accurately identifying and classifying these plants is crucial. However, distinguishing species is challenging due to similar physical traits and varying environmental conditions. Machine learning (ML) and deep learning (DL) have shown substantial ability in medicinal plant detection and classification by analysing large datasets and extracting subtle features. Image recognition techniques, particularly convolutional neural networks (CNNs), can identify morphological traits like leaf size, shape, and texture for classification. Studies have demonstrated that CNN models can achieve up to 90% accuracy in medicinal plant identification, enhancing the process for novel drug discovery and therapeutic applications.
Authors - Ananya Kini, Saranya Rubini Abstract - In recent times, there have been several advancements in computer vision and image processing, and when combined with machine learning models, is very helpful in posture recognition applications. Posture detection is a useful tool in sports and fitness, as it helps people avoid injuries caused by poor alignment and achieve optimal posture in order to stay healthy. This paper reports on ”PoseNet : A Novel YOLODriven Framework for Badminton Posture Detection and Correction”, which is a Python-based application that utilizes Roboflow for dataset construction, annotation and augmentation, YOLOv5 for custom training the model on the dataset, and MediaPipe for giving corrective suggestions to the user. The novelty of this framework lies in its dual-stage architecture, combining YOLOv5 for classification and MediaPipe for real-time correction, specifically tailored for badminton. Additionally, it leverages a badminton-specific dataset, ensuring domain relevance and precise analysis. It predicts the stance that the player is planning to achieve and then tells whether the stance is correct based on their key points. The model obtained a high classification accuracy, with mAP50 value of 96.2% and mAP50-95 value of 81.1%.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - K. V. Deshpande, Sanskruti Parkhe, Varad Pawar, Vaishnavi Thorat, Rutuja Bagad, Priti R. Kale Abstract - In today's digital world, cyberattacks targeting critical infrastructure pose a significant threat to government agencies and organizations. These attacks can disrupt essential services and compromise national security, making it crucial to identify and respond to them quickly. This survey paper discusses the challenges faced in monitoring cyber threats and presents a proposed solution: a real-time cyberattack monitoring tool. This tool uses machine learning and web scraping to gather data from various online sources, storing it in a structured format for easy access. By visualizing the collected data through an interactive dashboard, cybersecurity teams can quickly identify and understand the nature of ongoing attacks. Additionally, the system includes an alert mechanism that notifies teams of high-frequency attack patterns, enabling prompt action. Overall, this solution aims to enhance the ability of organizations to protect their critical infrastructure by providing timely insights and effective incident response strategies.
Authors - Garima Ratra, Akriti Kumari, Vimmi Malhotra Abstract - Blockchain has been widely adopted across numerous industries and applications to improve privacy and security factors. However, with the rapid expansion of this technology, its significant energy consumption has become a growing concern, particularly in mobile cryptographic applications. Traditional consensus mechanisms, such as Proof-of-Work (PoW), require substantial computational power, making them unsuitable for mobile environments. This paper reviews innovative blockchain protocols that prioritize energy efficiency while ensuring security and decentralization. By analyzing alternative consensus mechanisms including Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS) and energy optimization strategies, we assess their effectiveness in lowering power consumption. The study highlights the role of sustainable blockchain approaches in enhancing mobile application efficiency with minimized environmental impact. This will help in increasing the energy efficiency and understanding the impact and applicability of blockchain by switching to greener systems.
