Authors - Amulya Naik, Pallavi Dhaded, Shireesh Hakki, Satish Chikkamath, Suneeta V Budihal, Sujata Kotabagi Abstract - A text processing framework that applies Encoder-Decoder architecture with attention mechanism functions as the main focus of this research for resolving Natural Language Processing predictive problems. The research first outlines base technologies along with methodologies and frameworks required to build the system design. Detailed analysis of the dataset happens at this phase through observing dataset structure and calculating statistical summaries to detect missing or duplicate values. Better understanding of the dataset by using descriptive analytics to identify potential problems which leads them to improve the dataset. Data cleaning serves multiple functions during the process by eliminating unneeded columns together with missing value management and text normalization methods. The normalization procedure entails converting text into either upper or lowercase format and executes tag stripping alongside URL replacement and shorthand elimination and emoji and contraction removal. The processing begins after tokenization divides the text into segments. After cleaning the data the input and output components get separated while padding is used to maintain consistent dimensional structure.The model base incorporates an Encoder-Decoder framework combined with attention functionality while implementing a BiLSTM network. Through this specific model configuration both past and future inputs can be read contextually which boosts the prediction accuracy. The designed model produces 90.23 percent achievement in accuracy which highlights its strong capability in processing intricate NLP operations.
Authors - Abirami K, Megha Nayanar, Shobhana Palat Madhavan, Deepak Gupta Abstract - This study explores the impact of AI anxiety on career anxiety and career uncertainty, using Self-Determination Theory as its theoretical framework. As Artificial Intelligence (AI) continues to reshape industries, it presents both opportunities and challenges in career related factors. AI anxiety, driven by concerns over job security and skill obsolescence, affects individuals' confidence in their career choices and decision-making processes. As a result, many individuals may experience uncertainty about their professional future. By analyzing data from 237 respondents across India, this study identifies AI anxiety as a significant factor influencing career anxiety and career uncertainty. The findings reveal that individuals with higher AI anxiety are more likely to experience higher anxiety and indecisiveness in their career. However, the overall life satisfaction helps in lowering career anxiety by providing individuals with a higher sense of well-being that counteracts the anxiety. In contrast, factors such as relatedness, competence, and AI resilience do not show significant influence on either career anxiety or career uncertainty. This study enhances the understanding of how AI anxiety shapes career-related concerns, offering insights into how individuals navigate career decisions in an AI-driven world.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Shreya Ratagal, Parvati Navalur, Amruni Joshi, Satish Chikkamath, Sujata Kotabagi Abstract - With the instant advancement of generative artificial intelligence, this study investigates the domain of text-to-image generation, concentrating on the utilization of the Stable Diffusion model. The research examines the creation of visual content from written descriptions by leveraging sophisticated neural network architectures and underscores the importance of Natural Language Processing (NLP) in producing high-quality results. An extensive analysis was performed, integrating both qualitative and quantitative assessments to evaluate the model’s performance, scalability, and ability to adapt to various inputs. The study emphasizes potential uses in creative content production, virtual environments, and educational resources while tackling ethical issues to promote responsible AI practices. The results highlight the revolutionary effects of text-to-image generation in transforming the process of visual content creation.
Authors - Nishant Sharma, Mohit Mahlawat, Mohit Sharma, Gagandeep Singh, Ayush Kumar Singh, Kamlesh Sharma Abstract - An Early Warning System (EWS) utilizing Internet of Things (IoT) technology represents a transformative approach to disaster prevention and management. By leveraging interconnected devices, sensors, and real-time data transmission, IoT-based EWS enhances the ability to detect potential hazards—such as natural disasters, industrial failures, or environmental threats—at their earliest stages. These systems enable timely alerts and response strategies, minimizing risks to human life, infrastructure, and ecosystems. IoT technology plays a crucial role in gathering precise, real-time data from various sources, including seismic sensors, weather stations, water levels, and air quality monitors. This data is then transmitted to centralized platforms for analysis, allowing authorities and stakeholders to predict, assess, and act swiftly before a disaster strikes. With cloud computing and AI integration, IoT-enabled EWS can also deliver highly accurate forecasts and automated decision-making, further enhancing disaster resilience. As the world faces increasing threats from climate change, environmental degradation, and urbanization, IoT-based Early Warning Systems are becoming essential tools for safeguarding communities, enhancing preparedness, and ensuring a more resilient future
Authors - Jigme Nidup, Adithya Gattadi, Naresh K Abstract - Many serious health conditions, such as atrial fibrillation (AF), neuropathy, muscle disorders, and sleep-related neurological diseases, often go undiagnosed until complications arise. To address these challenges, this paper presents an advanced health monitoring system that integrates a multifunctional 4-in-1 electrogram sensor capable of measuring muscle activity using Electromyography (EMG), eye movement using Electrooculography (EOG), brain activity using Electroencephalography (EEG), and heart rhythm using Electrocardiography (ECG), along with a body temperature sensor, into a compact and wearable device at low cost. The device leverages the ESP32 Wi-Fi module to process and enable seamless data transmission to a Message Queuing Telemetry Transport (MQTT) cloud platform, ensuring secure, efficient, and scalable storage and analysis of collected health data. The system uses multiple pre-trained CNN models, each specialized in detecting specific diseases. Tests show an average accuracy of 90.3% making it a cost effective and efficient solution.
Authors - Harsha M R, Jyotiradhitya Kallimani, Nitishgouda Patil, Tohid Bijalikhan, Satish Chikkamath, Suneeta V. Budihal, Sujata S. Kotabagi Abstract - In India what does it take to go PRO in football? Players from their childhood shred sweat,blood and their precious time. And in sport we know that the next-gen superstar is guaranteed to start off his career from local and youth leagues. And honestly speaking these leagues do not offer the resources to invest. Resources in the sense that include technical skills, opponent player data, and event data(player stats). Currently, event data is mostly collected manually by human individuals, who gather data in several steps and through numerous persons involved. And this manually collecting data requires a lot of human resources and requires multiple checks and for that reason collection data is not practical in local or youth leagues. And this process takes a lot of time. So Automatic event detection could provide event data faster. which players can take use to analyse players and their own performance. And through which scouts would be able to ensure no player is missed or overlooked.
Authors - Arushi Madaan, Sunita Garhwal, Anu Bajaj Abstract - Women today are most likely to be experiencing Polycystic Ovary Syndrome (PCOS), a hormonal imbalance disorder. This disorder mostly affects women’s ovaries, where a large number of tiny fluid-filled sacs called cysts—also referred to as follicles—form around the ovary’s periphery. The exact root cause of PCOS is still unknown despite advances in science. Using ultrasound (US) scans to identify numerous follicles is an efficient way to diagnose PCOS early and schedule treatment. The primary purpose of this article is to determine whether or not a woman has PCOS or not without supervision from a physician. In this work, we provide a deep learning (DL) method based on transfer learning for PCOS classification using US ovarian images, with the goal of improving diagnostic efficiency and precision. InceptionV3 and ResNet50 models, which had accuracy rates of 99.68% and 97.5%, respectively, were used for this research. The study’s findings show that, as compared to conventional machine learning (ML) techniques, transfer learning-based classification performs better in PCOS variant classification. This study aims to accurately diagnose PCOS in patients and use our proposed model to treat PCOS. Gynaecologists and other medical professionals can benefit from our model’s ability to provide a prompt, reliable, and correct response.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room DGOA, India
Authors - Sushma Vispute Priya Surana, Shubhangi Vairagar, Sujit Shaha, Omkar Shinde, Sameer Sambhare, Krushna Salbande Abstract - Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition affecting social interaction, communication, and behavior. Autism is the third most common developmental disorder in the world. In India, the prevalence of autism is increasing and is estimated to be around 1 in 68 children. With its rising prevalence, early detection is crucial for timely intervention. This paper serves as both a review and a research study. The review explores existing ASD detection methodologies, highlighting machine learning approaches such as multinomial logistic regression (MLR), support vector machines (SVM), and convolutional neural networks (CNN), along with tools like eye-tracking and EEG analysis. The research component applies machine learning models—including Logistic Regression, Decision Tree, Random Forest, SVM, KNeighbors, Naive Bayes, and Neural Networks—on AQ10 survey data (1054 samples, 19 features) to evaluate their effectiveness. SVM achieved the highest accuracy. Further analysis examined the necessity of all 10 AQ10 questions, revealing that AQ4 and AQ10 contribute the least to predictive accuracy. Heatmap analysis confirmed weak correlations with the total ASD score. These findings suggest that refining ASD screening tools by removing less informative questions can improve efficiency while maintaining diagnostic reliability.
Authors - Vijayalakshmi S Katti, Usha J Abstract - Pests, pose a pervasive threat to agriculture on a national scale. Their voracious feeding on plant roots results in diminished crop yields, leading to economic losses for farmers and potential food security challenges. The integration of advanced sensor technologies and deep learning offers a promising avenue to address the impact of Pests, enabling timely and accurate detection. This, in turn, allows for targeted and efficient pest management strategies, mitigating the widespread repercussions of infestations and fostering sustainable agricultural practices on a national level. This paper explores the transformative synergy between sensor technologies and deep learning techniques for the identification and density detection of pests in agriculture. Traditional methods face limitations, prompting a shift towards advanced technologies. We survey the landscape of sensor technologies, including image sensors, acoustic sensors, and soil sensors, highlighting their real-time, high-dimensional data contribution. Integration with deep learning models, such as Convolutional Neural Networks and Recurrent Neural Networks, offers a precise and adaptive approach to pest management. The potential impact of this integration is substantial, promising increased crop yields, reduced reliance on broad-spectrum pesticides, and improved environmental sustainability. The review underscores the importance of continuous adaptation and scalability, setting the stage for a future where technology plays a pivotal role in ensuring the health and productivity of agricultural landscapes.
Authors - Ashwini Jarali, Sanskruti Lad, Snehal Kavathekar, Prajwal Lalpotu, Shreya Jadhav Abstract - Potholes on roads significantly impact safety and road infrastructure, leading to accidents and increased vehicle damage. Timely detection and repair are crucial to address these issues effectively. This paper presents an AI-based system for automated pothole detection and reporting, aimed at improving pothole management for road maintenance authorities. The system uses the YOLO (You Only Look Once) object detection model to accurately identify potholes in real-time road imagery, combined with GPS for precise localization. Detected potholes are automatically reported to the relevant authorities via email, ensuring swift corrective action. The YOLO model is trained on a diverse dataset of pothole images, achieving high detection accuracy across various pothole sizes and shapes. Additionally, the system tracks the status of reported potholes to ensure repairs are completed. This solution enhances road safety and reduces manual effort, providing a comprehensive approach to modern road maintenance.
