Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - Artificial Intelligence (AI) has undoubtedly emerged as an extremely dynamic and powerful tool in the area of IT. It is currently handling complex-work in data-analysis, working through predictive modeling, showcasing high level of capabilities and supporting the function of strategic decision-making. AI is dependent on real-time data to identify patterns of attrition, understand dissatisfaction and predict future exits. An AI system in the area of HRM, would thus help the organization in filtering staff-retentions and impending attrition. Meaningful insights can be developed with the use of AI that will help organizations work on reducing employee-turnover on one hand and engaging with their workforce in the long-run, on the other. The innovation elaborated in the study, connects with forecasting the employee-attrition and retention strategies in human resource management. It makes use of AI (Artificial Intelligence) through which HR data sources are incorporated to analyze performance evaluations, engagement surveys, attendance records and demographics in real time and historical context. The machine learning embedded in the system will detect patterns that show evidence of turnover and associate attrition scores to each employee, then recommend targeted retention strategies through personalized career guidance, modification of the workload, and incentives. The system incorporates attrition feedback loops to heighten prediction accuracy and refine strategies over time, enabling the organization to control and reduce turnover rates, boost workforce stability, and achieve cost savings. It is scalable across verticals like corporate HR, healthcare, education, and retail, where talent retention is imperative.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - A person's ‘perception’ and ‘consumer behaviour’ regarding mutual funds both are influenced by a variety of factors, including socioeconomic characteristics of a given society, financial awareness, risk tolerance, and prior investing experiences. As a professionally managed investment choice and a convenient investing tool, mutual funds are particularly well-liked among middle-class and urban individuals. In contrast to traditional savings instruments, investors typically connect mutual funds with advantages such as access to liquidity, diversification opportunities, and the potential for higher returns. Additionally, perceived dangers, market volatility, and a lack of thorough understanding of financial instruments all have a significant impact on consumer behaviour. A mutual fund is a professional system that collects funds from different investors for investment and protection. Since shared reserves have no legal meaning, the term applies as ambiguously as possible to aggregate speculation that is controlled, accessible, and open to investors. Mutual funds enjoy strengths and weaknesses instead of putting resources directly into personal protection. Today, they represent a significant portion of household budgets. Therefore, the current review focuses on general asset-related buyer behavior and preeminent mutual fund companies. Information was gathered from important resource sources. Important information was collected through systematic research. An opportunity-sampling method was used to collect responses, and the process was targeted toward major Indian cities. This review provides information on donor mindfulness of communal property, donor knowledge, donor propensity, and communal property sufficiency. Ideas were also developed to enhance mindfulness of joint assets and measures to select appropriate common assets to increase profit.
Authors - Jeevesh Sharma Abstract - Blockchain technology is largely used in banking, but it also has applications in gaming, real estate, supply chain management, and healthcare. By 2023, digital money will be the most widely discussed blockchain application. The potential uses of blockchain technology concerning various facets of any sector, market, agency, or governmental organization have gained attention in recent years. Blockchain scalability analyzes the effects on the security of scaling blockchain networks to support more transactions per second. This innovative distributed peer-to-peer architecture drew the interest of companies and communities outside and inside the financial sector. Furthermore, the system it operates in has been created around numerous scenarios that address the trust issue in open networks without the requirement for a trustworthy third party. Even though its decentralized structure allows for a wide range of potential applications, scalability remains a hurdle. Function extension, excessive delay in confirmation, and performance inefficiency are three important areas where blockchain scalability has been hindered. This research paper provides a thorough summary of previous research on scalable blockchain systems.
Authors - Ajidhashini Thulasidass, M. Suresh Abstract - Climate change is making agriculture more challenging in many parts of the country. This is notably true in developing nations where small farmers depend on systems that obtain water from rain to keep their land open. Numerous studies were conducted from 2015 to 2025 to discover how climate change is transforming farming systems in rural regions, how people are responding, and what policies are in place to make these systems safer and more resilient. It looks at significant challenges, including rising temperatures, unclear rain, soil depletion, and more pests that consume food. There is less food, which contributes to reduced food production and declining market stability. One method to make things better is to employ local expertise. Another is to employ agroforestry. Most individuals have problems agreeing because they don't have enough money, technology, or aid from the government. There are tips for extra reading at the end of the essay. To enhance farming, we may employ both new and ancient equipment and processes, as well as long-term research and approaches that engage both men and women.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room CGOA, India
Authors - Reena (Mahapatra) Lenka, Ronak Gupta, Vanishree Pabalkar, JayaChitranshi Abstract - Organizations in the contemporary global economy encounter substantial challenges in managing payroll operations, particularly concerning data security, transparency, and compliance with diverse regulatory standards. Traditional payroll systems rely on centralized infrastructures and are vulnerable to fraud, inefficiencies, and elevated operational costs. These centralized systems pose significant risks, including unauthorized data access and single points of failure, leading to potential data breaches and financial losses. Organizations handling payroll operations in today's international market confront several obstacles, such as protecting data, upholding openness, and adhering to various legal standards. Because they frequently rely on centralized infrastructures, traditional payroll systems are vulnerable to fraud, inefficiency, and excessive operating expenses. In order to solve these problems, this article investigates the integration of blockchain technology into payroll management. We suggest a multi-layered architecture that consists of (1) an off-chain Human Resources (HR) system for payroll and employee management, (2) a distributed storage layer that uses technologies such as the Interplanetary File System (IPFS) for safe data storage, and (3) an on-chain blockchain layer that uses smart contracts to guarantee immutable transaction records and automate payroll processing. This decentralized approach enhances transparency, bolsters security through encryption and consensus mechanisms, and streamlines payroll operations by reducing manual dependencies. Furthermore, the system facilitates real-time cross-border payments and integrates with Decentralized Finance (DeFi) platforms, offering employees innovative financial services. By leveraging blockchain for payroll, organizations can enhance trust, reduce operational costs, and eliminate redundancies, making it a promising use case for HR departments worldwide. This framework demonstrates how blockchain technology can revolutionize payroll management, increasing organizations' efficiency, cost-effectiveness, and trust.
Authors - Shripada Rao, Aadithya Mahesh, Navya Jaideep, Rajeshwari Hegde, Vinay Rao, Saurabh Suman Choudhuri Abstract - This paper introduces a novel approach to integrate LLM capabilities directly on mobile devices to enhance chat applications. By implementing a tonality-driven paraphrasing feature, our system can rephrase poorly written messages into clear, professional text while preserving the intended tone. Unlike conventional server-side AI solutions that raise privacy concerns, our approach processes data locally using fine-tuned models (TinyLlama Instruct 1.1B and Qwen2 0.5B) with parameter-efficient techniques such as LoRA and QLoRA. Experimental evaluations demonstrate competitive paraphrasing quality, improved inference speed, and reduced resource consumption on mobile devices, making this work a promising step toward privacy-preserving on-device conversational assistance.
Authors - Anusha.S.Pujar, Gourishankari.S.P, Saniya.G, Pooja.B.L, Sanchit.H, Amit.N, Suneetha.V.B Abstract - For the purpose of controlling traffic flow, identifying congestion, and averting accidents, contemporary urban traffic monitoring is essential. An enhanced YOLOv5 model is presented in this study for precise vehicle tracking and identification under a variety of circumstances, including day and night. A multi-scale feature detection layer for seeing cars of all sizes in congested regions and an improved pixel-to-real-world distance calibration for accurate speed and distance estimation are two important improvements. Real-time traffic management is improved by integrated collision warning and congestion identification algorithms. Experimental results demonstrate improved detection reliability and mean Average Precision (mAP), making this approach suitable for scalable urban traffic control systems.
Authors - Pratiksha Kulkarni, Rakshita Patil, Veena S Kulkarni, Satish Chikkamath, Suneeta V Budihal, Sujatha Kotabagi Abstract - The major reason for deaths across is due to alarming increase in Heart Disease. There are many factors which elevates the risk of cardiovascular diseases which includes high blood pressure, obesity, cholestrol, smoking habits , lack of physical activities and heavy work pressure. Diagonising and identifying the Heart disease in prior is a challenging task.Which can be overcome by Machine learning methods based on huge dataset of patient traits and medical indicators that help in prediction of heart diseases.
Authors - Dipti Varpe, Gouri Kulkarni, Nishant Thakare, Mitesh More, Suyog Shinde, Navanath Patil Abstract - Traditional farming methods often require extensive manual labour, leading to inefficiencies and increased costs. Recent advancements in agricultural robotics provide innovative solutions to automate essential tasks. The Node-MCU based Solar Powered Multipurpose Farming Robot is an autonomous system designed to enhance farming operations, including ploughing, weeding, and harvesting. Powered by solar energy, it offers a sustainable and energy-efficient alternative to conventional farming practices. The robot operates using a Node-MCU microcontroller, ensuring precise navigation and task execution. Integrated soil moisture and temperature sensors enable real-time environmental monitoring, optimizing farming decisions. Programmed via Node-MCU IDE, the robot is customizable and supports various smart farming features. With its modular design, multiple farming tools can be attached, reducing labour demands and improving efficiency. Additionally, IoT connectivity enables remote monitoring and control through cloud-based platforms. By integrating renewable energy, automation, and IoT-driven sensing technologies, this project enhances agricultural productivity while promoting sustainability. The system lays the groundwork for intelligent robotic solutions in modern farming.
Authors - Harie Sanker V, Gayathri E , Akshay R, Vandana Madhavan Abstract - The human resource management practices have developed alongside technological advancements. AI-based coaching and engagement chatbots are new tools with a great potential to improve employee engagement. Conventional coaching processes have certainly proven their effectiveness but could never attain the scalability or cost-efficiency needed for widespread implementation. Machine learning- and natural language processing-adapted AI solutions can assist by providing real-time feedback, setting goals, and automating HR services based on specific individual need assessments. This research is aimed at understanding the influence of the AI coaching and chatbots on motivation, active engagement, and general employee satisfaction within technological organizations. This study employed primary data collected from questionnaires, finding that AI coaching promotes motivation, satisfaction, and expansion of remote working opportunities. However, lack of trust in AI and perceived ethical absence of transparency on the part of recommendations made by the AI can be seen as significant drawbacks. To overcome these challenges, a hybrid form of human-AI coaching is suggested where AI maximum benefits are retained without missing out on the human touch and consideration that defines coaching. This may facilitate the understanding of the substantial influence that AI has on employee engagement and ultimately on the future of human resource management. The study also suggests how organizations can optimally leverage AI coaching and engagement chatbots while minimizing associated risks.
Authors - Vanishree Pabalkar, Anuja Bokhare Abstract - Sentiment analysis in multilingual social media data is a challenging and critical task due to the diversity of languages and sentiments expressed by users worldwide. In this study, we focus on conducting sentiment analysis on the Twitter US Airline Sentiment dataset, which includes tweets in English from users expressing their opinions about various US airlines. We address the research gap of multilingual sentiment analysis by leveraging advanced NLP techniques and machine learning algorithms. Count Vectorization and TF-IDF Vectorization is used during the study to extract features after cleaning up the data and processing the text. To categorize tweets as having positive or negative sentiment, we assess the effectiveness of three classifiers: Multinomial Naive Bayes, Bernoulli Naive Bayes, and Logistic Regression. We examine these classifiers' accuracy on a different collection of unlabeled tweets without ratings in more detail. The work provides valuable insights into the opinions posted by individuals on Twitter about US airlines and intends to develop multilingual sentiment analysis, especially for social media data. The findings serve as a basis for creating sentiment analysis algorithms that are more precise and reliable and that can be used to various language groups on social media platforms.
Authors - Tanuja R. Patil, Samiksha Dandgall, Vishwanath P. Baligar Abstract - Early diagnosis of brain diseases is very important and challenging nowadays. Detecting neurological disorders such as brain tumors using magnetic resonance imaging (MRI) has become an important research topic. Recently many machine learning and deep learning models have been proposed to detect and classify the brain abnormalities. Many of these models have high time complexity and still efficient models are required. The proposed model makes use of a Novel and Low Complexity Approach to solve the problem of classification of brain images. This approach is a less complexity deep learning model which uses novel methods for Denoising, Segmentation, Feature extraction and Classification of brain tumors. Here, it makes use of the advantages of bit plane approach and a unique feature extraction method. The proposed model makes use of the data set from Kaggle in which, size of the training data set is 2870 with four classes namely No Tumor, Glioma Tumor, Meningioma Tumor and Pituitary Tumor. The size of the testing data set is 394. A feature vector which matches most with the feature vector of the input image is considered as the class of the input image. The proposed method makes use of advantages of time domain and able to give good results. The overall performance of the proposed algorithm considering both training and testing data set is 97.34%. The proposed idea is comparable with the many existing models and the results are compared with three models CNN, VGG19 and Inception-V3 models and found to be promising.
Authors - Arnav Rahul Jade, Jatin Santosh Jaiswal, Nishad Sachin Kamat, Vedant Mahesh Kandarkar, Amruta Pabarekar Abstract - The Cloud-Based Plant Health Monitoring System is designed to help farmers and agricultural experts precisely identify plant diseases using artificial intelligence and cloud technology. Traditional plant health assessments rely on manual inspection, which can be time-consuming and prone to errors. This project automates the process by allowing users to upload images of plant leaves, analyzed by a machine learning model hosted on a cloud platform. The system identifies whether the plant is healthy or has a disease, providing instant results through a simple mobile or web application. To achieve this, the system uses a Convolutional Neural Network (CNN) trained on a dataset of plant leaf images, covering both healthy and diseased conditions. The application is designed to be user-friendly, allowing even non experts to access plant health information easily. This approach reduces the need for excessive pesticide use, saves time, and supports sustainable farming practices by helping users respond to plant health issues promptly. Keywords-plant health monitoring, artificial intelligence, convolutional neural network (CNN), plant disease detection, cloud computing, mobile application and machine learning.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Arunabh Barooah, S. Saranya Rubini Abstract - In the context of the digital music industry, accurate classification of music into genres and the ability to recommend appropriate genres for a user greatly improves usability of various streaming platforms. Several traditional machine learning approaches that rely on metadata face significant limitations, such as inconsistencies in the data, the growing diversity of musical styles, and a lack of focus on the actual musical content of songs. These shortcomings often result in suboptimal performance, particularly in recommendation systems. In response to these issues, this work proposes Deep Tune Network (DTN), a deep learning system for automated genre analysis and discovery of music based on similar acoustic patterns. This system uses Convolutional Neural Networks (CNNs) and Mel-frequency cepstral coefficients (MFCCs) to identify the repetitive patterns inside audio signal needed to classify music into different genre. The model achieves a maximum test accuracy of 93.01%, demonstrating its reliability in real-world applications. Additionally, a cosine similarity-based recommendation system is implemented to suggest acoustically similar songs, bridging accurate genre classification with personalized playlist curation.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Om Gadhvi, Srushti Pawar, Shravan Gadhvi, Mansi Jadhav, Manya Gidwani Abstract - Farmers in Maharashtra, India face substantial costs and low utilization rates on equipment, leading to significant financial strain. We suggest implementing an AI-powered Dynamic Machine Price Prediction Model that we can use for a digital platform that enables renting out equipment. This model uses Linear Regression, Random Forest, and Gradient Boosting to output what prices equipment should sell for, given the age, how often farmers use it, when they use it, and market demand. Gradient Boosting turned out to be the most accurate model in our exams, giving us a 94% R² score so that our model predictions are trustworthy. The website is built using the MERN stack, and it employs secure transactions through PayPal and a feature that enables farmers to search for equipment based on their location. By adjusting equipment prices on the go, we provide insights to farmers into how much they should charge for renting out their equipment in all conditions The proposed system enhances resource utilization, sustainability, and economic resilience in the agricultural sector.
Authors - Elakkiya R, Sagunthala G, Tanisha Sinha, Gugapriya G Abstract - This project presents an IoT-based predictive maintenance system based on machine learning algorithms—Random Forest, Logistic Regression, SVM, and LSTM—to identify motor faults precisely. Realtime data such as sound, vibration, and RPM are recorded through hardware prototyping, while Simulink simulates speed and torque. Data is transmitted to Firebase for real-time monitoring, prompting automated fault notifications. This system improves industrial efficiency by minimizing sudden failures, reducing maintenance expenses, and increasing machinery lifespan through prompt, data-driven interventions.
Authors - Atharva. Makode, Yash. Kasar, Yatish. Gharat, Yuvraj. Gage, Mandar Ganjapurkar, Kiran Deshpande Abstract - This paper proposes MediLink, a blockchain-based platform to store, share, and manage secure medical records. Patients and care providers in healthcare systems today typically face problems of data privacy compromises, system-to-system noninteroperability, and bureaucratic administration. These present risks to compromised patient care as well as expose data security gaps. MediLink addresses these challenges head-on by developing a decentralized, transparent system that places patients in control, with full control over their medical information while ensuring the integrity and confidentiality of the same. The platform employs smart contracts to carry out important functions such as processing insurance claims, handling patient consent, and keeping track of audit trails. Not only does this reduce human errors between humans but also saves administrative burdens and costs as well. Utilizing the strength of blockchain technology, MediLink not only secures data—yet also streamlines the easy exchange of patient records between healthcare providers. The end result? Seamless care coordination, enhanced patient outcomes, and a smoother experience for all
Authors - D.V.N. Bharathi, K.P.K. Lalitha Vitala, K. Bala Sindhuri, Sai Nikilesh Kudapa, M.L. Hiranya, Lingam Swamy Surendra, Manoj Kumar Juttuka, Kishore Varma Manthena Abstract - This paper explores the design and efficiency of optimized Wallace multipliers integrated with approximate adders to enhance energy efficiency, reduce delay, and minimize hardware complexity in the first address. Five distinct departments are introduced: architectures (AA1–AA5), each offering unique trade-offs regarding power consumption, processing speed, and circuit area. These adders are incorporated into Wallace multipliers, which improve computational speed while lowering energy requirements and design complexity. The proposed designs are evaluated using 90 nm technology to determine their applicability in resource-constrained and error-tolerant domains, such as image processing, machine learning, and IoT applications. The findings demonstrate a versatile balance between accuracy and resource efficiency, making these designs well-suited for real-time systems with different performance demands.
Authors - Deepa Unni, Murale Venugopalan, Sanju Kaladharan Abstract - Community Health Workers (CHWs) does an important role in delivering health to the public. CHW programmes often fail due to the impracticable expectations, lack of proper planning and also the efforts required to implement these activities are often underestimated. Prior to the COVID-19 pandemic, many of the developing countries used digital health technologies to address a range of health issues. Limited research has been held so far to discover the role of e-governance in addressing sustainable community health workforce. This paper bridges this gap by proposing an E-Governance Enabled Sustainability Model for CHWs.
Authors - Aparna Joshi, Moreshwar A. Mahale Abstract - Crop disease diagnosis forms one of the most significant facets of precision farming, which owes a great boost to the power of machine learning. This work presents a new method through which the use of the Tomato Leaf Disease Dataset to classify the tomato leaf's disease is achieved. The database contains 1609 images for ten disease classes: Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites, Target Spot, Tomato Yellow Leaf Curl Virus, Tomato Mosaic Virus, and Healthy Leaves. The features are extracted through the combination of Thepade's Sorted Block Truncation Coding (TSBTC) and Haralick Moments (GLCM) to improve texture and intensity description. Extracted features are categorized into multi-level classification (2-ary, 3-ary, 4-ary, 5-ary). The results achieved are stored in an Excel file and re-run using the support of Weka tool where classifiers like Naïve Bayes, Logistic Regression, Sequential Minimal Optimization (SMO), J48, Random Forest, and Random Tree. are employed. The study identifies the optimal model so that accurate and automated diagnosis of crop disease can be performed.
Authors - Prema Sahane, Shreyas Borse, Kartik Narkhede, Manasi Choudhari, Pradnya GaikWad, Ashwini Bhosale, Rutuja Khedkar Abstract - An innovative strategy to address one of the main causes of blindness in diabetic patients is presented in AI-Vision: Forecasting Diabetic Retinopathy for Preventive Care. Through sophisticated predictive modelling, this study uses artificial intelligence (AI) to transform the diagnosis and treatment of diabetic retinopathy (DR). Our approach improves the accuracy of DR diagnosis and makes it easier to identify risk factors that contribute to the progression of the disease by combining cutting-edge Convolutional Neural Networks (CNNs) with extensive medical datasets. Healthcare practitioners may now use individualized preventative tactics based on patient profiles thanks to our cutting-edge model, which uses real-time data analytics to deliver actionable insights. By empowering doctors to intervene promptly, this proactive approach not only seeks to identify DR in its early stages but also lowers the likelihood of serious sequelae .Our research also shows how AI can be used to streamline automated screening processes. AI-Vision hopes to establish a new benchmark in preventative healthcare by bridging the gap between ophthalmology and AI, with the ultimate goal of eradicating avoidable blindness in diabetic populations worldwide.
Authors - Jalindar Gandal, Nilesh Gorade, Kunal Sonawane, Khushal Patil Abstract - Cattle health and productivity are fundamental to the livelihoods of agriculturalists and the overall agricultural economy. Traditional cattle health monitoring methods are often time-consuming and lack the capacity for real-time assessment of cattle health, resulting in reduced milk productivity and economic losses. To address these challenges, we propose the implementation of an Internet of Things (IoT) technology-based, low-cost, real-time cattle health monitoring system. The system comprises wearable sensors for continuous monitoring of vital parameters such as body temperature, heart rate, and activity level. These sensor values are relayed wirelessly to a cloud server, where data is processed and analyzed to identify anomalies indicative of potential health-related problems. This information is presented to the farmer through a user-friendly mobile application, which displays real-time alerts and suggests preventive or remedial actions. The system facilitates early disease detection, leading to improved cattle health, enhanced milk production, and enhanced farm profitability. The system emphasizes cost-effectiveness by utilizing readily available hardware, thereby increasing accessibility for smallholder farmers. The system offers a long-term solution for cattle health management. This paper aims to demonstrate the transformative potential of integrating low-cost IoT technologies with livestock farming to establish precision agriculture and enhance the prosperity of rural farming communities.
Authors - Samadhan Pujari, Hetvi Saroliya, Vedika Gawde, Ekansh Manral, Jalpa Mehta, Deepika Patil, Rashmi Malvankar Abstract - Mortality prediction is crucial for public health, aiding resource allocation, policy-making, and preventive strategies. This study applies ARIMA and SARIMA models to analyze mortality trends in India (1990–2022) using data on infectious diseases such as Malaria, HIV/AIDS, Tuberculosis, as well as non-communicable diseases like Nutritional deficiencies and neonatal disorders. ARIMA [1, 3, 4] captures non-seasonal trends, while SARIMA, incorporating seasonality, proves more accurate. Implemented using Python libraries like pandas, stats models, and scikit-learn, their accuracy is assessed using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Findings indicate that SARIMA outperforms ARIMA, emphasizing the role of seasonality in mortality patterns.[6,7,16] A significant decline in deaths from infectious diseases like Malaria and Measles is observed, attributed to public health initiatives, immunization programs, and improved healthcare facilities while neonatal and non-communicable diseases remain pressing concerns. Accurate data collection is essential for improving predictive modeling, and ARIMA/SARIMA provide critical insights for public health planning. Future research could integrate factors such as climate change, economic conditions, and machine learning techniques to refine forecasting models further. This study reinforces the significance of time series forecasting in public health decision-making and strategic healthcare interventions.
Authors - Vaishali S. Katti, Kailash J. Karande Abstract - This paper reviews significant advancements in sentiment analysis, emphasizing various innovative applications in fields such as poetry analysis, human resources, customer feedback, and mental health monitoring. The study systematically examines methodologies adopted in recent research, elucidating their contributions to the field while presenting diagrams to enhance understanding. This overview not only highlights the evolution of sentiment analysis but also explores its implications across diverse sectors.
Authors - Manjunath Inamati, Goutami Naragund, Chetan Paranatti, Saroja Siddamal Abstract - Electrocardiogram (ECG) signals are vital for diagnosing cardiovascular conditions. However, they are often contaminated by noise, hindering accurate analysis. This paper presents the FPGA implementation of Infinite Impulse Response (IIR) elliptic filters for denoising ECG signals. Elliptic filters were chosen for their sharp roll-off and computational efficiency, while an FPGA platform was utilized for real-time, low-latency processing. The design leveraged MATLAB as the primary tool for filter parameterization and hardware-oriented signal processing due to its comprehensive functionality, ease of use, and seamless integration with HDL Coder for Verilog code generation. Synthesis and hardware deployment were performed using Xilinx Vivado. The system was validated using both synthetic and real ECG signals, demonstrating effective noise suppression while preserving diagnostic features. Results indicate the potential of FPGA-based digital filters, designed with MATLAB, for portable and efficient biomedical applications.
Authors - Aswin Sreerag, Meenakshy P, Akhil A, Anargha Ranjit, Anoop S. Babu Abstract - The field of text-to-video generation is going through a transformation, by the advancements in Artificial Intelligence (AI) and deep learning. AI-powered video generation models helps us in the conversion of textual descriptions into visual content, unlocking new possibilities in the fields of education, entertainment, and multimedia content creation. But the existing systems face challenges such as maintaining temporal coherence, such as ensuring smooth transitions, and correctly aligning video sequences with advanced textual inputs. This research combines Large Language Models (LLMs) and Generative Adversarial Networks (GANs) to develop a high-quality, temporally consistent text-to-video generation framework. The system uses the Gemini model to change textual prompts into detailed and structured descriptions. These descriptions are initially given to the Stable Diffusion model to get the corresponding text-image before being input into a fine-tuned MoCoGAN model for video synthesis. The VATEX dataset, which has extensive video and text pairs, is the primary training resource, this ensures meaningful alignment between textual descriptions and generated visuals. Apart from that, the research also explores the DAMO ViLab which is a diffusion model, that operates without additional training, so that it provides a comparative analysis of different generative approaches. The results shows enhanced video smoothness, improved scene consistency, and stronger semantic alignment with the textual prompts. This work advances text-to-video generation by addressing key limitations, using AI driven storytelling and visualization applications.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room CGOA, India
Authors - Jyoti Patil Devaji, P. C. Nissimagoudar, Prerana Savant, V.S.Nandana, Vaishnavi Harlapur, Vidhi Agarwal Abstract - The project aims to focus on improving object detection and safety measures for autonomous vehicles by combining LiDAR and camera data. The main goal is to increase object detection accuracy and resilience by combining LiDAR data with the advanced object detection model YOLOv5. The system detects objects, recognizes and tracks cars, and uses a simple depth estimation technique based on bounding box width to determine how far away vehicles stand. To provide more accurate object localization in 3D space, bounding boxes are constructed around identified objects, and the related depth information is computed using the LiDAR data. A safety function that improves the situational awareness of the autonomous vehicle by generating an audio alert when a vehicle is spotted too close is also included in the system.
Authors - Vismay Tank, Khush Sanghavi, Vyom Gandhi, Pramila Shinde, Jalpa Mehta, Vaishali Korade Abstract - Everyone in the current world hopes that his/her personal information won’t be revealed in any way. Security insurance is essential for protecting personal information from prying eyes. The information may be extensive, and it is important to minimize risk and ensure sensitive information is protected. This analysis addresses the drawbacks of previous customized security and other anonymization techniques by implementing a progressive modified protection saving technique. The core of the suggested technique is composed of two main components. Two additional states that are used in the report table but are hidden in the primary segment are sensitive data and fragile weight. The Fragile Data (DI) of the record holder determines whether the mystery should be retained or, conversely, whether it should be disclosed. Sensitive weight (DW) illustrates how brittle a characteristic's value is in comparison to the others. The following section discusses the Recurrence Circulation Block (FDB) and Semi Identifier Dispersion Block (QIDB), two other portrayals used for anonymization. Exploratory findings show that the suggested framework performs faster and loses less information than existing approaches.
Authors - Manas Tiwari, Rohit Sharma, Nyasa Singh, Sandhya Avasthi Abstract - Although packaged foods are generally considered safe and hygienic, many consumers are unaware of the additives they contain, which can pose serious health risks. This lack of awareness has contributed to a rise in health issues such as allergies, asthma, diabetes, and other chronic illnesses. To address this problem, a mobile application is proposed that helps users make healthier food choices by analyzing packaged food ingredients. The application enables users to scan or upload images of ingredient labels, utilizing image preprocessing techniques (grayscale conversion, Gaussian blur) and Optical Character Recognition (OCR) to extract the ingredients. Users can also input personal health conditions like diabetes, asthma, or allergies, allowing the system to tailor its analysis. By referencing a comprehensive database such as OpenFoodFacts, the application provides immediate health-related insights on the detected ingredients. For ease of understanding, ingredients are classified using a color-coded system: red for highly harmful substances, orange for moderately harmful ones to be consumed in moderation, and green for safe ingredients. This approach empowers consumers to make informed, health-conscious decisions regarding packaged food consumption.
Authors - Nivedya Krishnan M T, Mariya P Jose, Amrita V S Abstract - The Financial Independence, Retire Early (FIRE) movement is primarily focused on giving people financial security through saving and investing in such a way that they become less dependent on regular jobs. This empowers them to stop working earlier than regular retirement ages. The study explored major motivations for adopting FIRE through work-life attitudes, desire for freedom, financial well-being, frugality and minimalism, social influence, and spousal/family support. A survey was administered to collect primary data from students and professionals from rural, urban, and semi-urban areas in India, to reach a sample size of 400 respondents. The structured questionnaire includes a 5-point Likert scale, binary, and frequency-based questions. Data were coded and analyzed using SPSS, with regression analysis employed to test the impact of independent variables on motivation to FIRE. Regression results showed that by far the best predictors of motivation for FIRE were frugality and minimalism, desire for freedom, and spousal support. The model was statistically very highly significant (p
