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
