Authors: Ogbonna Nnamuchi
Abstract: This paper introduces a formal framework utilizing mathematical functional analysis to bridge the gap between empirical sleep science and jurisprudence. By treating fatigue trajectories as functions within infinite-dimensional Banach spaces, we formalize how biomathematical fatigue inputs intersect with duty-of-care allocations within tort and regulatory systems, shifting the legal focus from rigid shift-hour compliance to systemic accountability.
Authors: Shravani Phalke, Rajit Joshi, Raj Lohar, Bharti Dhote
Abstract: By facilitating natural, flexible, and context-aware communication across a variety of languages and cultural contexts, artificial intelligence (AI) has revolutionized human-computer interaction. Large language models have advanced, but chatbots still have difficulty identifying, interpreting, and reacting sympathetically to users' emotional states. As a result, they frequently provide generic responses that lack genuine resonance. This paper introduces Novachat, a full-stack AI chatbot designed to close this gap by combining multilingualism and sophisticated emotion intelligence into a scalable MERN-stack architecture. In order to provide human-like, contextually nuanced conversations in English, Hindi, Marathi, and other languages, Novachat's modular framework integrates sentiment analysis, emotion-adaptive response generation, and language detection. To ensure smooth real-time adaptability, each module functions as a microservice and communicates via orchestration driven by APIs. The study describes the system's overall architecture, emotional classification model, dataset organization, and quantitative performance assessment using metrics like System Usability Scale (SUS), emotion recognition accuracy, response relevancy, and user engagement latency. According to experimental results, Novachat generates sympathetic responses and detects emotions with high accuracy; a SUS score indicates strong user acceptance. The field is moving closer to AI systems that genuinely recognize and value the user's emotional experience as a result of these results, which validate Novachat's function as an efficient, inclusive, and emotionally engaging conversational platform.
Authors: Narasimman S, Jayavarman V, Parandhaman P, Vasanth V, Umavathi. V
Abstract: Rising storage and computational capacities have led to the accumulation of voluminous datasets. These datasets contain insights that describe natural phenomena, usage patterns, trends, and other aspects of complex, real-world systems. We propose greedy K-NN (K-Nearest Neighbor) data allocation strategies (across the agents) that improve the probability of identifying data leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party. Mining large data requires intensive computing resources and data mining expertise, which might be inaccessible to most of the users. With the regularly obtainable cloud computing resources, data mining tasks cannot be stimulated to the cloud or outsourced to the third party to save cost. In this new pattern, data and model confidentiality becomes the major unease to the data owner. Data owners have to understand the possible trade-offs among client-side costs, model quality, and confidentiality to justify outsourcing solutions. In this paper, we propose the RASP Boost framework to address these problems in confidential cloud-based learning. The RASP-Boost approach works with our previous developed Random Space Data Perturbation (RASP) method to protect data confidentiality and uses the boosting framework to conquer the complexity of learning high-class classifiers as of RASP disconcerted data. So, we have to build upsome cloud-client combined boosting algorithms. These algorithms need low client-side calculation and communication expenses. The client does not call for to stay online in the progression of learning models. So, we have methodically studied the confidentiality of data, model, and learning process under a realistic security model.
| Vinod Kumar Jangala | |
| Affiliation | Sr Java Developer EXPERIENCE Scadea Software Solutions,Texas |
| Email-Id: | vinodkumarjangala01@gmail.com |
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| Sravika Koukuntla | |
| Affiliation | Full stack Developer, Richardson, Texas. |
| Email-Id: | sravikakoukuntla01@gmail.com |
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Authors: Samarth Kumbhar, Viraj Rajendra Patil, Hemant Prashant Chandegave, Vivek Nagargoje
Abstract: For many years beginners tend to invest in stocks and face loss due to volatile nature of markets, or lack of informed decisions like trusting investment through word of mouth, this leads to discouragement from investment in stock market. InvestAi is a platform designed for beginners who are looking to enter the world of Stocks, platform is AI driven forecasting and analysis system designed to help users understand stocks and predictions using “explainable” machine learning techniques. The system aims to increase financial literacy and increase Informed investment decisions via explainable Ai (X AI) and interactive visuals. It also features sentiment analysis of news and also explains how it links or affects a particular stock.
| Vinay Kumar Reddy Vangoor | |
| Affiliation | MetaSoftTech Solutions LLC, Chandler, AZ, USA Client: American Express, Phoenix, AZ, USA Role: System Administrator. |
| Email-Id: | vinaykumarreddyvangoor@gmail.com |
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Authors: Vivek Nagargoje, Hemant Chandegave, Samarth Kumbhar, Viraj Patil
Abstract: Predicting stock prices accurately is a complex challenge that must combine financial theory and applied machine learning. It involves issues like market non-stationarity, sensitivity to real-world events, and ways investor psychology impacts price movements. In this paper, we present Invest AI, a hybrid framework for prediction and analysis that combines three powerful models: XGBoost-based learning for processing structured features, stacked Long Short-Term Memory (LSTM) networks for capturing sequential patterns, and FinBERT-based sentiment analysis of financial news. Invest AI integrates these models’ outputs using a Loopy Belief Propagation-inspired weighting system that adjusts predictions based on the confidence of each model. The system was trained and tested on historical data sourced from the yfinance API. It has expanding window validation to prevent data leakage. Other than just making predictions, InvestAI includes SHAP-based explainability, anomaly detection, and financial performance backtesting through Sharpe ratio and maximum drawdown metrics. Over a year of out-of-sample data evaluation, this hybrid approach achieves a reduction in MAPE by 14.2% compared to other single-model performances. It also had a Sharpe ratio of 1.47 in simulated trading. This system combines temporal, relational, and sentiment-driven metrics to produce better results in financial forecasting.
Authors: Apali, Najul, Satyendra, Shubham Rahangdale, Tarachand, Praveen Choudhary
Abstract: Electric Vehicles (EVs) are rapidly transforming the transportation sector by reducing dependence on fossil fuels and minimizing environmental pollution. This paper discusses the history, working principles, battery technologies, charging infrastructure, advantages, limitations, environmental impacts, and future scope of electric vehicles. The proposed system uses sensors, microcontrollers, relays, and wireless communication technology to control household appliances such as lights, fans, doors, and emergency alarms. The system can be operated using mobile applications, voice commands, or simple switches depending on the user’s capability. The project aims to improve the quality of life of disabled villagers by reducing physical effort, increasing safety, and promoting independent living. The system is designed to be affordable, energy efficient, and easy to install rural homes.
DOI: http://doi.org/10.5281/zenodo.20715066
Authors: Ar. Yashika Garg, Major Soni
Abstract: Indian cities are projected to contribute to 70% of the total GDP by 2030. But rapid urbanization and increase in urban migrants are exerting huge pressure on the environment. Despite the complexities of meeting the housing demand, sustainable affordable housing is a challenge. Indian Government has tried to boost the supply of housing stock from the first 5-year plans (1951) to the recent initiatives of “Housing for all”. The six Light House Projects (LHPs) initiated under the Global Housing Technology Challenge in India, are a step closer to meeting the demand. As LHPs near completion, the paper attempts to critically analyze the projects by comparative analysis. The analysis is broadly divided into site/masterplan level, block level, and unit level. The study revealed that the LHP is innovative in terms of technological advancement but lacks consideration in socio-cultural aspects and quality affordable housing which is required for diverse Indian households.
