Category Archives: Uncategorized

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.

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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
Publication:  Books: Intelligent Autonomous Infrastructure: AI-Driven Self-Evolving Enterprise Systems and DevOps Intelligence. Publications: Vangoor, V. K. R.  Next-gen access control: Blockchain-powered biometric authentication 2025. Vangoor, V. K. R. Predictive cybersecurity for quantum-era data centers using artificial intelligence analytics. International Journal of Scientific Development and Research, 10(9), 16 2025. Madunuri, R., Ravi, C. S., Chitta, S., Bonam, V. S. M., & Vangoor, V. K. R. Machine learning-based anomaly detection for enhancing cybersecurity in financial institutions. In Proceedings of the Asian Conference on Intelligent Technologies (ACOIT) (pp. 1–8) 2024. Vangoor, V. K. R. Intelligent post-quantum cryptography deployment in enterprise Linux infrastructure using machine learning. South Asian Journal of Engineering and Technology, 14(6), 9 2024 Vangoor, V. K. R. Reinforcement learning-based virtual machine orchestration for hybrid OpenStackVMware cloud environments. International Journal of Economy and Innovation, 41, 10 2023

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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.

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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

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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.

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Authors: Minocha Dr. Pramodkumar, Kothwala Dr. Deveshkumar, Pandya Kamna, Shinde Divya, Sharma Rahul, Chauhan Sargam, Ladumor Rahul, Kadam Aniket

Abstract: Biocompatibility evaluation is a critical regulatory requirement for establishing the preclinical safety of medical devices in accordance with the ISO 10993 series of standards. The present study aimed to comprehensively assess the biological safety of the OCT Imaging Catheter, in-vitro and in-vivo tests selected based on its intended use and blood-contacting nature. In-vitro cytotoxicity was evaluated using L929 mouse fibroblast cells by qualitative morphological assessment and quantitative MTT assay, followed by in-vivo assessments including skin sensitization, intracutaneous irritation, acute systemic toxicity, and material-mediated pyrogenicity. Hemocompatibility was investigated through hemolysis, platelet activation, coagulation parameters, leukocyte activation, and complement activation studies. Genotoxic potential was assessed using the bacterial reverse mutation (AMES) assay and an in-vitro mammalian chromosomal aberration test in human lymphocytes. The test item demonstrated no cytotoxic effects, with cell viability exceeding ISO acceptance criteria at all extract concentrations. In-vivo studies revealed no evidence of skin sensitization, irritation, systemic toxicity, or pyrogenic response. Hemocompatibility testing confirmed the non-hemolytic nature of the device and showed no adverse effects on platelet function, coagulation pathways, leukocyte activation, or complement system activation. Genotoxicity assessments indicated that the test item was non-mutagenic and non-clastogenic under all test conditions. Collectively, the results demonstrate that the OCT Imaging Catheter exhibits an acceptable biocompatibility profile and is biologically safe for its intended clinical application. These findings support its preclinical risk assessment and provide robust evidence for regulatory submissions in compliance with ISO 10993 requirements.

DOI: http://doi.org/10.5281/zenodo.20708577

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Authors: Prof. Abhishek Dubey, Akshada Kale, Kashish Mahobiya, Kirti Thakur, Nikita Raj

Abstract: Clinical Decision Support Systems (CDSS) play a crucial role in helping healthcare professionals make accurate, timely, and evidence-driven decisions. However, the growing scale, speed, and diversity of healthcare data have revealed the limitations of traditional rule-based CDSS, especially when dealing with multimorbidity and personalized treatment. Recent advancements in artificial intelligence (AI)—including machine learning, deep learning, and natural language processing (NLP)—have enabled the development of intelligent CDSS that support adaptive learning, predictive analytics, and patient stratification. This paper provides a comprehensive, system-level review of AI-powered CDSS, examining their historical development, underlying technologies, architectural frameworks, and clinical applications. Unlike earlier surveys that focused mainly on individual algorithms, this review integrates AI methods with system architecture, clinical workflows, and ethical considerations. It explores key AI techniques for patient stratification, deep learning models for diagnosis and prognosis, and NLP-driven early warning systems. The paper also addresses critical challenges related to ethics, legal concerns, and explainability, while highlighting emerging trends such as federated learning, digital twins, and genomic-based CDSS. Overall, it aims to offer researchers and clinicians a thorough understanding of AI-CDSS design principles and their future potential.

DOI: http://doi.org/10.5281/zenodo.20706812

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Authors: Sheri Srujan Reddy, Thota Ramanna Dora Prabhas, Mancholla Ranateja, Assistant Professor Dr. P Kiran Kumar

Abstract: Traditional control surfaces on aircraft have been based on rigidly hinged flap sections that create unavoidable geometric discontinuities. These generate early flow separation and parasite drag, hindering aerodynamic performance in different flight conditions. This study explores the application of camber morphing, a biological concept involving wing deformation similar to those seen in bird-like flying organisms. The main goal of this study was to develop an adaptable mechanism, capable of changing the average camber line of the airfoil while keeping its structural integrity intact. The focus is put on a “Fishbone Active Camber” (Fish BAC), or [add name of the mechanism used, for instance, SMA or Rib-Linkage] based structure which replaces a hinge mechanism at the rear-spar position of the wing with a continuous flexible skin allowing for an even pressure distribution along the wingspan. The method involved a two-step approach. First, numerical simulations were carried out using an omega SST turbulence model to compare the aerodynamic parameters of the standard NACA 2412 airfoil with a morphing one. It is evident that the morphing wing has successfully reduced pressure drag significantly by removing the "hinge-gap" problem. More precisely, when the Angle of Attack (AOA) is 6 degrees, the morphing wing has shown a Lift-to-Drag ratio improvement of about 12 to 15 percent over the conventional flaps wing system. Also, flow visualization proved that the onset of turbulence occurred much later, thus broadening the aircraft's range of efficient flight.

DOI: https://doi.org/10.5281/zenodo.20706107

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Authors: Mr. Hemant Vanjari, Mr. Jay Deshmukh, Assistant Professor Ms.Pratibha Makar, Dr.Vijaykumar Kale, Dr.Mahesh Thakre

Abstract: Lately, more people pay notice to plant-based beauty products – main reason being they tend to be gentler, work well, rarely cause trouble unlike lab-made versions. This research zeroes in on crafting and testing a multi-plant shampoo made entirely from nature's lineup: Amla joins shikakai, those mix with soap nuts while bhringraj slips in beside hibiscus; fenugreek seeds blend with rice extract, stick amaltas pairs up with flaxseeds, then rosemary teams with aloe vera plus curry leaves tag along too. Long before labs existed, these plants earned rep for helping hair grow stronger, cutting down flakes, keeping strands from dropping, boosting scalp condition, adding glow to locks. Put together with earth-friendly carriers, the mix faced checks on looks, acidity level, thickness, how rich the bubbles get, if gunk spreads out when washed, how fast water soaks into fabric, pull at liquid surfaces, even how steady it stays over time. Results? Cleans thoroughly, makes foam just fine, hits the right acid balance, conditions like a charm – all without making scalps itch. From roots up, plant-based mix fed each strand what it needed. Hair grew stronger, smoother – no harsh stuff involved. Results showed this blend worked just as well as lab-made options. Cost stayed low, safety held steady. Folks using it daily found fewer issues than expected. Not one person reported serious irritation. Science backed its role in regular

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Authors: R A Shasank

Abstract: In the rapidly evolving landscape of financial technology, the imperative for model interpretability often conflicts with the pursuit of predictive accuracy. Financial institutions heavily rely on automated credit scoring models; however, the lack of transparency in conventional "black-box" approaches—such as deep neural networks and complex ensemble methods—poses significant regulatory and ethical risks. This paper introduces a hybrid credit risk assessment framework that bridges the gap between performance and interpretability. By leveraging First-Order Inductive Learners (specifically the RIPPER algorithm), the proposed model transforms raw financial data into a structured set of human-auditable domain rules. Furthermore, we implement a novel "Abstention-Driven Human Audit" layer, which identifies cases with marginal prediction confidence and redirects them for manual expert review. The experimental analysis, conducted on standard benchmark datasets, demonstrates that this architecture maintains competitive predictive power while providing a clear, logical rationale for every automated decision. The results highlight that the integration of rule-based logic not only fosters regulatory compliance but also enhances stakeholder trust in automated financial systems. This study contributes a scalable, transparent, and robust alternative for modern credit risk management.

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