Authors - Navneet S Patil, Shashidhar Kumbar, Sakshi Bhantanur, Arjav Jain, Satish Chikkamath, Sujata Kotabagi Abstract - Human movement prediction is a key machine learning domain whose purpose is to predict future movement from previous motion patterns and context information, with usage in autonomous vehicles, virtual reality, video games, and health care. In this study, the goal is to apply Convolutional Neural Networks (CNNs) for predicting human movement from the UCF50 dataset, whose collection contains action videos with a wide variety of actions. CNNs excel at discovering spatial and temporal patterns from video data and, thus, can be used in understanding motion complexities. In this work, a CNN-based approach is developed using a CNN architecture to assess motion dynamics and make accurate forecasts about future moves. By systematically preprocessing the dataset and optimizing the model’s architecture, the study achieved an accuracy of 99.09demonstrating the reliability and efficiency of CNNs in motion prediction tasks. Furthermore, the paper discusses existing methodologies in human motion prediction, comparing their performance and highlighting the advantages of CNNbased models in processing visual data. The results here bring out the potential of CNNs for real-world applications and set the foundation for future advancements in human activity recognition. The current study adds insight into machine learning methods and how they can be used to enhance motion prediction, with implications toward innovations in those fields that rely on precise modeling of human activities
Authors - V.Sudeep, V.Nishant, MM.Mohamed jasir Faiez, T.Monish, Yuvaraj kumar.GP, Akhil K J, Praveen.K Abstract - Docker containers are central to modern software development and deployment due to their portability, efficiency, and scalability. By isolating applications and dependencies, they provide a lightweight alternative to virtual machines, enabling consistent environments across platforms. However, Docker containers pose security challenges, including shared kernel risks, vulnerabilities in container images, and misconfigurations, which can lead to breaches.This paper examines security concerns in Docker containers and proposes a framework to identify and address vulnerabilities. The framework helps detect issues like outdated components and misconfigurations, offering insights to enhance security. Through practical use cases, it highlights its effectiveness in closing security gaps and equipping developers with tools to protect containers. The study emphasizes the need for proactive security measures and continuous vigilance in securing containerized systems.
Authors - Chaithra S Raju, Nimisha S, Arathi A N Abstract - The Digital payment landscape in India has seen rapid progress, driven by technological advancements, government initiatives and increased smartphone penetration. Digital wallets are becoming a payment method as they are convenient, secure and seamlessly integrate with financial services. Although Generation Z known for a Digital-first approach, inconsistency in the adoption of Digital wallets can be observed among this segment. This research will look at the reasons why Generation Z may adopt or not adopt Digital wallets, namely perceived ease of use, perceived usefulness and perceived security.A cross-sectional survey was undertaken for the 220 Gen Z respondents using a structured questionnaire. The statistical analysis was done by using SPSS analysis of variance to examine the impact of these factors on adoption behaviour. . The findings highlight that while convenience and utility drive adoption, security concerns remain a critical barrier.This study provides valuable insights for fintech companies, policymakers, and firms looking to bolster the digital payment infrastructure and build trust in Digital wallet services. Overcoming security concerns and improving the user experience can accelerate the transition towards a cashless economy.
Authors - S.Asha, Siddharth M Nair Abstract - According to a study, one out of every 20 people above the age of 65 are suffering from Alzheimer's. People with such neurological conditions have poor navigation skills and often wander around without having knowledge of where and what they are doing. In such situations, tracking them down is extremely important as it is life threatening to themselves and the people around them. It is also important to monitor elderly individuals' vitals like heart rate and steps along with detecting an impact (fall) so that necessary actions can be taken. Other than the strong personal motivation the current market needs a product through which people suffering from such neurological conditions can be supported. But not many are present in the current market and the ones that are, require the patient to wear some dedicated device like a neck ring or other uncomfortable devices. Often, people, especially elderly individuals lose their lives because 'it was too late'. There is a major requirement in today's market for a system which would send alerts and concerned individuals in case of any abnormality in detected data so that it would not be 'too late' to act. The sensors that are incorporated within the Apple Watch provide an ocean of valuable data which can be harnessed by caretakers and other concerned individuals. Now-a-days, people are too involved and busy with their work to stay at home and be there for elderly individuals at all times. Through this data, people can take care of their loved ones even when they are not around. By receiving timely notifications in case of any emergencies, the world would become a safer, more reliable place for all elderly individuals, especially those who suffer from Alzheimer’s and other neurological conditions.
Authors - Anitha D, Swetanshu Agrawal, Samudra Banerjee Abstract - Particularly affecting patient response to alkylating treatment, the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a well-established prognostic and predictive biomarker in gliomas. Conventional evaluation techniques are prone to limits including sampling mistakes and intratumoral heterogeneity and call for invasive tissue biopsies. In this work, we present a non-invasive, deep learning-based system for multi-modal magnetic resonance imaging (MRI) based MGMT promoter methylation prediction. The method combines improved preprocessing, automated tumor segmentation, and a customized EfficientNet-based classification architecture with structural MRI sequences including T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR imaging. Our model achieves strong performance, high accuracy and generalizability in methylation status prediction. Comparative study including current literature shows either better or equivalent prediction performance, so highlighting the clinical possibilities of this technique. The suggested pipeline advances the function of virtual biopsy in neuro-oncology by providing a scalable, dependable, radiation-free substitute for MGMT methylation testing, therefore enabling individualized therapy planning.
Authors - Richa Goenka, Meenu Chawla, Namita Tiwari Abstract - In recent years, phishing attacks have emerged as a substantial hazard, endangering online businesses and security by exploiting users to divulge sensitive financial information through fraudulent websites. Despite various proposed methods, accurately distinguishing between legitimate and fraudulent sites in real-time remains challenging. This paper provides a new approach to identifying phishing URLs by employing a feature selection approach that integrates Genetic Algorithm and Particle Swarm optimization. This system optimizes feature selection through population initialisation, fitness evaluation, GA operations, and PSO integration, dynamically balancing exploration and exploitation. The objective is to identify significant features for supervised machine learning techniques, enabling precise phishing URL detection. For classification, multiple machine learning classifiers are employed among which XGBoost provided the best results. Experimental results using the hybrid feature selection prove that the machine learning classifier works much better than the prevailing feature selection approaches. This comprehensive approach provides a reliable method for detecting phishing URLs, improving internet security, and reducing the threats associated with phishing attacks.
Authors - Soumitra De, Jaydev Mishra Abstract - In this paper, a new method is focused to handle indeterminacy part of an imprecise data using neutrosophic set to generate proper constructive message. This method is capable to handle imprecise part of a neutrosophic data. Earlier no uncertain data set was handled this indeterminacy part of any uncertain data. We have drawn an output using this new method of any patient related data set that has suffering from disease. Vague logic is unable to process indeterminacy part. So only neutrosophic set is handled indeterminacy part of a imprecise data to outcome.
Authors - D.K. Chaturvedi, Nisha Verma Abstract - The technological intervention in our day-to-day life, impacted our social, physical, psychological and spiritual domains. The shoes are not untouchable from the latest innovations. The footwear is an essential wear in present time. The technology is completely changed the footwear industry and the customer flavour. Now the customer is looking for customized, smart footwear, which is environment friendly. The present footwear is using polymer soles (i.e. PVC, PU, EVA or Rubber), chemical based adhesives and animal leather upper material, which are not eco-friendly. A lot of research is going on to make sustainable and eco-friendly shoes with different biodegradable materials. The footwear industry is embracing both smartness and sustainability, blending technological innovation with eco-conscious practices. The smart footwear uses many types of sensors/IoTs to include different features of smartness. This paper discusses some innovations in footwear technology, important issues, challenges and their remedies related to design and development of smart sustainable footwear.
Authors - Kashinadh.S, Dhanush Devaraj, Yedukrishnan VS Abstract - Food safety and nutritional transparency are essential for public health, particularly as diet-related illnesses like obesity and diabetes rise. The Food Safety and Standards Authority of India (FSSAI) introduced a menu labeling policy in 2020, requiring restaurant chains to display calorie counts and nutritional information.The consumer awareness on the menu labelling is poor, and compliance is still low among restaurants. FSSAI Food Safety Connect app, which is designed to help with grievance redressal was having some negetive shades because of the complaints registered and reviews posted . This study employs stakeholder interviews, compliance audits, and sentiment analysis to evaluate how effective the policy is. The findings indicate that the compliance is lacking because of financial barriers and enforcement is also lacking. This study recommends implementing chatbot-driven grievance resolution, using QR codes for digital menus, and leveraging AI for compliance tracking as strategies to boost adherence. These solutions leads to the Sustainable Development Goals (SDGs 3 and 12) by enabling customers to make informed decisions while also promoting food safety. Menu labeling will become a more effective public health tool if we can improve digital enforcement in food industry.
Authors - Vanishree Pabalkar, Anuja Bokhare, Reena (Mahapatra) Lenka, Jaya Chitranshi Abstract - [1] Crime is one of the most worrying and widespread issues of our society. Criminal deterrence is essential for people safety. The overall crime inference when assessed, does help to keep a record of crime and assist in avoiding adversities. The aim of the study is to examine patterns in data acquired over time. Criminal violations offend humanity, and it should be prosecuted as soon as possible. Criminology is the scientific method of understanding crime and the motives behind the act. Criminology is an interdisciplinary area which gathers data and conducts further study into such offenses. While there is such a large amount of data on criminal activities, identifying and preventing crimes is one of the most difficult tasks. It is imperative to develop approaches and procedures for predicting future crimes and taking appropriate preventative steps. Cluster analysis includes breaking down huge data to minute groups with similar or identical characteristics. We can evaluate and assess methods, structures, layouts and interactions that are present in the data using visualization tools, so as to help uncover interesting areas and acceptable parameters for future analysis.
Authors - Ajay J, Kavitha R, V S Ashwin, Dhanya M Abstract - The exchange-traded funds have seen greater condition as an entertainment choice in modern financial markets as they were designed for providing diversified exposure in equities as well as bonds and commodities types of asset classes. Despite the steadily increasing attractiveness and usage of such products, there still exists a gaping research gap in predicting their performance relative to sectors, especially in a comparatively emerging market such as India. This work intends to fill that gap by comparing Machine Learning models such as Random Forest and SVM with traditional models for sector-wise performance forecasting like ARIMA and Holt-Winters. Based on data available in investing.com, the work analyzes daily ETF prices across seven key sectors—Pharmaceuticals, FMCG, Banking, IT, Infrastructure, Consumption, and Healthcare—from 2021 to 2024. Performance of the model is evaluated by overall fit criterion: R² (coefficient of determination), Mean Absolute Error (MAE), Difference in Square Errors (DSE). Machine learning techniques have been found to considerably out-perform classical statical models in capturing complicated market activities, especially in volatile sectors like Infrastructure and Banking. Hill-Winters and ARIMA models reliably forecast stable sectors, such as Pharmaceuticals and Healthcare, while their kings fade away in overly dynamic markets. These research observations offer information to assist investors, portfolio managers, and policymakers as an illustration of the possibilities that exist for machine learning applications in financial forecasting. The integration of machine learning approaches should thus be magnified to improve on ETF price forecasting and investment strategies.
Authors - S.Prince Samuel, R.kiruba, P.Kingston Stanley, R.Karthick Abstract - The Internet of Things (IoT) has seen an increase in cyber attacks, especially botnet attacks, mainly brought on by weak security on networks. As a result of the rise in IoT users, the requirement for electronic data interchange, and the desire for virtual services, the frequency of cyberattacks to gain access to private data has increased in recent years. As a result, industry and researchers have given the security of IoT applications and particular data attention. A botnet is a formally organized group of infected, internet-connected devices managed by cybercriminals. Attacks from botnets, which spread spam and viruses and are no longer under the control of authorized users, can damage IoT devices. To effectively detect botnet attacks, proposed a botnet attacks detection system based on Transfer Learning (TL). The transfer learning (TL) model is built upon convolutional neural networks (CNNs), which are widely used for their effectiveness in feature extraction and pattern recognition in complex datasets. For the existing model achieved 91.93%, the proposed botnet attacks detection model performed with over 99.54% accuracy on two well-known public benchmark IoT security datasets: CICIDS2017 and UNSW-NB 15. This shows the proposed model’s effectiveness in predicting botnet attacks in an IoT environment.
Authors - Aaron Mendonca, Arya Gawde, Nikki Mehta, Nilay Koul, Rohit Parmar, Nikita Raichada Abstract - Communication barriers have a major influence on the deaf and mute society in India, resulting in social isolation and restricted access to education, employment, and everyday interactions. Indian Sign Language (ISL) is the primary mode of communication, but its lack of widespread understanding restricts integration with the larger society. This study presents a real-time ISL recognition and translation system that integrates deep learning, spatio-temporal analysis, and natural language processing (NLP) to overcome this communication barrier. This study proposes a real-time ISL gesture recognition and translation system utilizing Long Short-Term Memory (LSTM) networks, which are ideal for sequential gesture recognition so that accurate mapping of static and dynamic ISL gestures into text and speech can be done. A spatiotemporal feature extraction pipeline is incorporated using MediaPipe-based skeletal keypoint detection to guarantee strong recognition through capturing hand, facial, and body landmarks. The dataset, created with deaf and mute people’s inputs, provides regional gesture diversity and sign diversity. It has been engineered to operate effectively in real-world environments, with adaptations to lighting changes, background noise, and the complexity of gestures. This work contributes to assistive technology, accessibility, and human computer interaction, fostering social inclusion through facilitating effective communication between the hearing and non-hearing populations. This paper is a step towards a more inclusive digital communication environment, empowering the deaf community in various aspects of life.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Mohit Matte, Sandeep M.Chaware, Pratik Dahagaonkar, Anurag Deotale, Laukik Pagar, Jayesh Sarwade Abstract - Agriculture, in particular, has drawn a lot of attention lately due to the introduction of innovations like machine learning and smart computing. It is becoming increasingly challenging for farmers to effectively manage land and optimize profit in a particular terrain due to the changing economics of agri-produce. Crop yield forecast is heavily reliant on environmental parameters such soil composition, rainfall, humidity, and cultivable area, among other crucial indicators. Because they don't adequately account for a variety of environmental factors, traditional Crop Yield Prediction approaches like historical averages frequently don't yield reliable results. Furthermore, farmers find it challenging to choose crops and cultivate them effectively due to shifting market patterns in supply and demand. While a shortage of a certain crop could result in lost profit chances, a surplus production could result in reduced market pricing. Thus, combining yield prediction models with demand and supply research can assist farmers in improving crop planning for increased profitability. These challenges are addressed and accurate forecasts are generated using a machine learning-based approach. Crop prediction is done with classification models, whereas yield prediction is done with regression models trained on both historical and present data. To identify best course actions, these models examine a number of performance indicators. For practical use, the top-performing model is integrated into the backend. With a MAE of .64 , an R-squared mark of .96, Random Forest Regression outperforms the other models employed for yield prediction. At 99.39%, the Naïve Bayes classifier has the best accuracy for crop prediction. Predictions are further improved by adding market data to these models, such as price swings, customer demand, and past sales patterns. Farmers can improve profitability and minimize waste by matching their agricultural techniques with market demands through the integration of demand and supply analytics. This study demonstrates how machine learning may transform crop management by assisting farmers in making data-driven decisions to match their output with supply and demand in the market, as well as by optimizing resource allocation and raising total yield.
Authors - Vanishree Pabalkar, Reena Lenka, Jaya Chitranshi, Kalpesh Bhave Abstract - Barrier coating refers to a type of coating applied to the surface of a material, such as paper, cardboard, or plastic, to create a protective layer that prevents the penetration of liquids, gases, oils, or other substances. The primary purpose of barrier coatings is to enhance the material's resistance to moisture, oxygen, grease, and other environmental factors, thereby improving its functionality and extending its durability. In the context of the paper industry, barrier coatings are often used to make paper and paperboard suitable for packaging applications, particularly for food products, where protection from moisture and grease is essential. These coatings can be made from a variety of materials, including polymers, waxes, biopolymers, and even certain types of natural and sustainable compounds, depending on the desired properties and environmental considerations. Barrier coatings are crucial in the development of sustainable packaging solutions, as they allow paper-based materials to replace plastics and other non-renewable materials in various packaging applications. End Use of barrier chemical coated paper: Pizza Boxes, Pet food Bags/Boxes, Ice cream Frozen food, Fish Trays, Meat Packaging, Paper Cups & Plates, Cakes / Cookies, Wet Vegetables.
Authors - Archita Bhattacharyya, Ayan Bhaumik, Mrinal Kanti Deb Barma Abstract - The rapid expansion of the Internet of Medical Things (IoMT), a healthcare-driven subset of the Internet of Things (IoT), has introduced significant cybersecurity threats, underscoring the need for effective and privacy-preserving anomaly detection systems. In this study, we present an anomaly detection framework for IoMT data using autoencoder-based reconstruction loss analysis and feature space visualization. The reconstruction loss distribution enables the identification of anomalous samples using a predefined threshold. In addition, anomaly scores plotted against sample indices help visualize deviations in model behavior, distinguishing normal from suspicious activities. To better understand the latent feature space, the t-SNE visualization provides clear clustering of encoded representations, highlighting the separation between normal and anomalous patterns. This integrated approach offers an interpretable and effective means of detecting anomalies in IoMT environments.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Naman Yadav, Preety Sharma, Ayush Singh, Atharva Deshmukh, Aditya Thakur, Akshat Gora Abstract - This paper studies handwritten digit recognition methods with Convolutional Neural Networks (CNN) while performing a performance comparison with EfficientNetV2. The investigators applied the EMNIST dataset for model education and performance testing before using it to examine the model generalization characteristics through HASYv2 dataset analyses. The research examines key obstacles in handwritten digit recognition through multiple aspects such as different writing styles and diverse dataset characteristics as well as inefficient computing capabilities. The research evaluates enhanced accuracy through preprocessing methods along with model optimization methods. The research data reveals CNN provides excellent performance on EMNIST although it falls short on HASYv2 whereas EfficientNetV2 extracts superior features yet requires more computation power. The evaluation reveals the effective features and challenging aspects of both models so researchers can focus on developing hybrid structures and growing datasets for actual handwriting recognition systems in OCR applications and banking and automated document processing fields.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Anupama K, Kalyani Suresh Abstract - As digital tools become integral to parenting, understanding the psychological and practical factors influencing app adoption is crucial. Parenting apps are leaning progressively more towards integrating AI for personalization and societal benefits, which is an emerging area of study in the Indian context. While research points towards parental attitudes being significantly affected by AI mediated technologies, AI research culture is poised to draw on the experience and theory related to parenting. Drawing from Human-AI interaction theories, this study explores the hedonic and utilitarian motivations driving the use of AI-powered parenting apps among young Indian parents. The study uses a quantitative approach, to assess the extent to which young parents are motivated to use the AI-driven apps within the different levels of family support scenarios. Cluster analysis revealed the presence of four clusters based on their levels of hedonic or utilitarian motivations. Findings suggest that young Indian parents who use AI powered parenting apps are mostly Beta users – moderately engaging with selective feature usage - with both hedonic and utilitarian motivations playing crucial roles. Family support is found to improve hedonic and utilitarian motivations to use AI driven parenting apps. This study provides initial insights into the complex interplay between pleasure and practicality in technology adoption, setting the stage for larger-scale research on the impact of AI in parenting practices in India.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Prabira Kumar Sethy, Sachin Sharma, Ajit Behera, Satyaprakash Barik, Amresh Bhuyan Abstract - Signature verification constitutes a fundamental component of biometric authentication methods used in financial and legal identity verification systems. The research presents an offline signature verification method that examines geometric and morphological region-based features to authenticate test signatures. The methodology analyzes binarized signature images to extract important attributes such as area, perimeter, centroid, eccentricity, solidity, extent, major and minor axis lengths, orientation, convex area, Euler number, and equivalent diameter. After analyzing the reference signature collection, the most prominent image region gets processed for feature extraction. The test signature is evaluated through feature-wise similarity calculations while undergoing pre-processing identical to reference images. The normalization process for each feature difference allows comparison against specific thresholds to determine cumulative similarity scores. Authentication confirmation for a signature occurs when its score level exceeds the 95% predetermined acceptance benchmark. Experimental results demonstrate that our method achieves optimal computational efficiency while providing high verification accuracy and clear distinction between real signatures and forgeries. The framework merges reliable performance with simple operation and quick processing abilities making it ideal for lightweight biometric systems.
Authors - Atul Kumar, Devendra Kumar, Niranjan Kumar Abstract - The Internet of Things (IoT) has transformed the digital environment, but its fast expansion raises substantial cybersecurity concerns. IoT devices are naturally vulnerable to a variety of assaults, and the data they manage can be used by malevolent or unauthorized service providers. The introduction of IoT into cloud-based systems creates new security vulnerabilities. Cloud-based IoT solutions provide flexibility and scalability, but they also increase security vulnerabilities. The complicated interconnections between these traditional devices and systems demand strong measures to ensure privacy and integrity. This article tackles important security problems in IoT adoption by strategies to suggest in bridging present gaps and prepare for future difficulties. Its goal is to improve service security systems and device and strengthen IoT ecosystems through proactive approaches.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room CGOA, India
Authors - Nisha Fulzele, Chetan Parlikar Abstract - The development of financial instruments has greatly changed consumer expenditure patterns, and credit cards have been central in contemporary economies This paper analyzes the expenditure behavior of credit card customers in Wardha City, with reference to priority drivers of expenditure patterns. Employing a descriptive research method, primary data were gathered from 140 participants using a systematic questionnaire. Analysis proves that young professional salaried individuals constitute the maximum segment of credit card customers, who prefer online payment and high-end transactions. Whereas convenience and payment flexibility come with credit cards, their use in everyday consumption is still limited. Correlation analysis indicates that rewards, cashback, impulse buying, and financial security drive spending most, compared to peer influence and promotional offers, which have lesser impacts. The research indicates that credit card use in Wardha City is increasing, driven mostly by electronic payment behavior and financial stability. By comprehending these behavior patterns, financial institutions can make strategies to encourage prudent use of credit and financial literacy among consumers.
Authors - Radhika V. Kulkarni, Vaibhav Aher, Harsh Ukey, Sujal Dubey, Aarya Labhshetwar, Manjiri Kulkarni Abstract - The majority of community in globe use sign language as the most basic way of interaction with Deaf and speech-impaired people. In most instances, a person finds it difficult to learn sign language for communicating with deaf and dump people, which leads to isolation among those individuals. Most people are unaware of the interpretations made in sign language. Hence, this paper presents an intelligent sign recognition system for translation of dynamic sign language for easy communication among people with hearing and speech impairments. The intelligent system takes advantage of advanced computer vision and deep learning techniques to identify dynamic hand signs accurately. This approach includes video data capture, preprocessing, feature extraction, and real-time gesture recognition. Hand movements are captured from webcam video streams, and the MediaPipe library is used to capture key points over the hand. A sequential model based on deep learning maps the relationships in hand gestures, which ensures high recognition accuracy. Extensive testing on different hand gesture recognition datasets shows that they perform efficiently and reliably in real-world situations. This technology facilitates greater accessibility through the ability to quickly and accurately translate sign language, thereby helping create inclusive communication technologies.
Authors - Molly Goel, Prince Kumar Sharma, Nainshi Singh, Madhvi Gaur Abstract - A secure and dignified electronic voting system is needed to provide the security and decency of a traditional one. While still allowing for flexibility and accuracy, this system has been tested for a long time. The use of blockchain technology can be utilized to actualize distributed voting structures. Despite the technological advancements that have occurred in the past few years, the traditional balloting system still remains unsuited for the modern era. There are numerous issues that prevent the integrity of the elections, such as the lack of transparency and the use of bribes. Besides these, the time it takes to check the vote's integrity is also very long. Current technology has to be used to improve the voting system. One of the most important factors that needs to be considered is the development of blockchain technology. This type of innovation eliminates the character flaw in the voting process and ensures that the correct votes are sent out. The development of blockchain technology is carried out through a stable set of rules that are designed to solve the problems related to the voting process. This type of innovation will help to ensure that the public can easily remember the individuals who participated in the process. The development of a voting poll programming application can help the political selection executives and citizens get the most out of it. However, it can also expose them to various risks. For instance, e-voting can lead to political race safety issues and fraud. Despite the advantages of this type of innovation, it is still not ideal for the people who are interested in maintaining a transparent and honest political selection process.
Authors - Manisha Mane, Saurav Bedse, Vikrant Patil, Pruthviraj Dhande, Om Darekar Abstract - The fast advances in deep learning and computer vision have dramatically improved the ability to detect objects, with applications in surveillance, driverless cars, and smart traffic management. The current paper describes an implementation of the YOLOv8 model for real-time object detection on different categories such as persons, cars, and bicycles. We trained the model on a customized dataset of annotated images, fine-tuning it through extensive hyperparameter tuning and multiple training epochs. Our training setup consisted of 75 epochs, utilizing a Tesla T4 GPU for computation. The model recorded a mean Average Precision (mAP@50) of 76.5% over all classes, with class performance highlighting high precision and recall rates for classes like cars (98.2%) and bicycles (87.8%). To further improve accuracy, we utilized data augmentation methods, batch normalization, and optimizer tuning. After training, the model was subjected to extensive validation, with an inference speed of 8.5ms per image, making it viable for real-time performance. We also incorporated the model into a realistic deployment pipeline, showcasing its efficacy in real-world applications. This paper presents a thorough analysis of the trained model, such as performance metrics, comparison with other versions of YOLO, and discussion of future improvements. Our results emphasize the model’s ability to achieve speed and accuracy balance, rendering it an appropriate choice for object detection in real-time applications. Future research will investigate additional optimizations such as light-weight model variants and domain-specific dataset adaptation.
Authors - Abhay Shinde, Ketal Patil, Nirmitee Chaudhari, Samrudhi Bachhav, Kavita Moholkar Abstract - The energy that never goes out of style is solar energy that is readily available and produces no pollution; its use has increased over the years. It is an endless supply of energy. Optimising solar radiation absorption for power generation is still a major challenge. A solar panel's best position for collecting sunlight is orthogonal to the trajectory of the sun's rays, but throughout time, the sun's rays direction varies. Even though a solar tracking system does a good job of recording the sun's motion during the day, it suffers when adverse weather conditions cause the sun's intensity to decrease. A passive tracking system, which can handle such circumstances and yield better results, can therefore be employed to overcome them. The design and functionality of a solar tracking system are the topics of this research. By aligning the solar panel with the sun's position, which is grounded on a fluid medium, the suggested outcome offers the best possible conversion of solar energy into electrical power.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room CGOA, India
Authors - Trupti Chetan Kherde, Dhiraj Jitendra Marathe, Prathamesh Shivaji Kadam, Sanskar Dipak Shinde, Chetan Balaji Phulmante Abstract - Agriculture is one of the fundamental pillars of human civilization. In addition to providing food, it boosts the economy. Crops and plant leaves are susceptible to several diseases during agricultural production. Diseases prevent each species from growing. Early and accurate plant leaves disease diagnosis helps to minimize major damages to plants. Plant leaves disease classification and detection has grown to be major issues. Failure to promptly identify and categorize plant diseases could lead to agricultural plant loss and a sharp decrease in product. Utilizing digital image processing techniques in their fields can help farmers enhance output and decrease losses. Various techniques have been developed and implemented to identify and classify plant diseases. Over the years, considerable advancements have been made in finding different disease by exploring and applying different methodologies. However, because of new developments, and conversations, improvements are needed. Globally, crop production can be greatly increased with the application of technology. Conventional techniques, such as laboratory-based diagnostics and manual inspection, are still dependable but time-consuming and labor-intensive. Emerging technologies, such as Machine learning (ML) and deep learning (DL) techniques have revolutionized automated disease detection, offering robust solutions for analyzing complex patterns in plant images. This survey highlights recent advancements in these areas.
Authors - Jayasmita Kuanr, Deepanjali Mishra Abstract - Dalkhai is conventionally a female-centric folk tradition; nonetheless, patriarchal frameworks have frequently influenced its performance and distribution. Grounded in Judith Butler's theory of gender performativity, which analyses how Dalkhai's lyrical narratives and physical expressions formulate, contest, and navigate gender identities. The emergence of digital media has allowed Dalkhai to explore new avenues of representation, enhancing reinterpretations of old themes and promoting wider interaction. Digital media and technology-enhanced performances have elevated female voices, but they may also commodify or alter traditional expressions to conform to modern cultural norms. This study contends that although digital technology provides opportunities for transformation and inclusivity, it also requires critical awareness about the recontextualization of traditional folk narratives in virtual environments. The study indicates that the convergence of gender performativity and digital media is transforming Dalkhai’s cultural relevance, establishing a dynamic arena for both continuity and transformation. The technology integration and folk traditions such as Dalkhai can transform while preserving their artistic integrity, providing novel opportunities for female representation in the digital era. Therefore, the study examines the changing performance of Odisha’s Dalkhai folk music via the perspectives of gender performativity and digital transformation. It proposes a critical textual and performative examination of Dalkhai's lyrics, gestures, and vocal expressions to elucidate how the folk tradition both reinforces and subverts gender stereotypes.
Authors - G.B.Sambare, Prajwal Solase, Raj Lokhande, Chaitanya Shinde, Sujit Aher Abstract - Heterogeneous wireless networks face challenges in ensuring smooth mobility between WLAN and LTE, as traditional handover decisions based on signal strength often degrade service quality. A more advanced approach incorporates multiple network parameters like signal power, link speed, system delay, and user mobility for optimized vertical handover. Real-time throughput calculations and dynamic network ranking enhance selection, while MCDA techniques improve transfer continuity, reduce delays, and minimize packet loss. Simulation results confirm that this strategy outperforms conventional methods by reducing handover failures and improving network selection. Additionally, advanced techniques like FSHO and SSHO are explored for seamless multimedia services in 5G networks.
Authors - Khushi Ingalalli, Vanshika Kavi, Sainath Walthati, Satish Chikkamath, Suneeta Budihal, Sujata Kotabagi Abstract - With the millions of tweets per day, Twitter is a rich and large database of information on public sentiment on a wide range of issues, including events, products, politics, and social issues. The purpose of this research is to create an automated system that can analyze tweet sentiments to determine attitudes as positive or negative. Through Natural Language Processing (NLP) methods and machine learning algorithms, the system efficiently handles high quantities of unstructured data, making sentiment classification possible in real time. The model begins the analysis by gathering various tweets from various sources, such as hashtags, user mentions, and trends. The tweets are then subjected to preprocessing techniques like removing stop words and treating misspellings, emojis, and special characters. Various classification models, like Naive Bayes, Support Vector Machines (SVM), Logistic Regression (LR) were experimented with to see which was most efficient in sentiment classification. Of these, Logistic Regression (LR) showed the best performance with an F1 score of 0.833 and accuracy of 83%. The efficiency of various feature extraction methods, such as Term Frequency- Inverse Document Frequency (TF-IDF) and word embeddings, was also examined to try and improve model performance. This work emphasizes the increasing importance of Twitter Sentiment Analysis across different fields, such as market research, event tracking, and social research. Sentiment analysis is employed by companies to know customer views and enhance services, whereas policymakers utilize it for measuring public reaction. By combining NLP and machine learning, the suggested system provides better and scalable method for sentiment analysis[1].
Authors - Gopal D. Upadhye, Deepak T. Mane, Devang Gentyal, Chetan Channa, Shubham Landge, Radhika Gadewar Abstract - Toxic comment identification in Hinglish (a combination of Hindi and English) is a difficult task because of code-switching, transliteration, and class imbalance. This paper suggests a machine learning based method for identifying toxic Hinglish comments based on TF-IDF feature extraction along with an ensemble model. In order to mitigate class imbalance, Random Oversampling was utilized, and model interpretability was facilitated using SHAP (Shapley Additive Explanations). The suggested model was trained on publicly released datasets, with 90.0% accuracy compared to individual classifiers. This work contributes to content moderation system for code-mixed languages and offer an extensible solution for social media toxicity detection.
