Volume 13 | Issue 5 |

 Abstract

This study analyzes the financial performance of the company from the perspective of DuPont analysis and value-based financial ratios from FY2018 to FY2024. The results show a period of high performance from FY2018 to FY2022 with a high return on equity (ROE) of 38.22%, high return on assets (ROA) and net profit margin (NPM), and strong value creation as depicted through Economic Value Added (EVA) and Cash Value Added (CVA).The subsequent decline, with worsening ROE (8.44% in FY2024), ROA (2.97%), and NPM (2.98%), indicates a reduction in operating and financial efficiency. Value measures such as EVA and CVA depicted volatility, with CVA turning negative in FY2024 (-INR4,807 lakh), indicating cost overruns and erosion of shareholder value. Volatile Free Cash Flow to Equity (FCFE) and the Financial Leverage Index (FLI) reflect instability in cash flow management and rising solvency concerns. Although revenue growth has been evident since FY2019, profitability has declined, highlighting inefficiencies in scaling up operations. Overall, the analysis underscores the need for greater focus on cost control, capital management, and strategic use of debt to restore financial health and improve shareholder returns.

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 Keywords

DuPont Analysis, Value-Based Financial Ratios, Return on Equity (ROE), Return on Assets (ROA), Net Profit Margin (NPM), Economic Value Added (EVA), Cash Value Added (CVA), Free Cash Flow to Equity (FCFE), Financial Leverage Index (FLI), Operating Efficiency, Financial Efficiency, Revenue Growth.

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This study proposes a dual-layered stock selection methodology applied to the NSE 500 universe. The first layer involves a multifactor model combining quality, value, momentum, and low volatility factors. The top 50 multifactor scoring stocks are further filtered through correlation matrix analysis, eliminating stocks with high pairwise correlations. Among each correlated pair (r > 0.50), the stock with the lower Sortino Ratio is removed. The final 25-stock portfolio is constructed using equal weighting. The study evaluates the portfolio's performance against market benchmarks using risk-adjusted metric i.e., sharpe ratio. Results indicate superior diversification and downside risk optimization through this systematic selection process.

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 Keywords

Multifactor investing, NSE 500, stock selection, correlation filtering, Sortino ratio, portfolio construction, sharpe ratio.

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 Abstract

The increasing demand for energy and the environmental impact of traditional fossil fuels have led to a growing interest in renewable energy sources like solar power. However, solar energy is highly dependent on weather conditions, causing fluctuations in power output and instability in energy supply. This project aims to address this challenge by integrating supercapacitors as an energy storage solution in solar power systems. Supercapacitors offer advantages over conventional batteries, including high power density, fast charge-discharge cycles, and longer lifespans, making them ideal for handling fluctuations in solar power generation. The proposed system uses supercapacitors to store excess energy during periods of high solar radiation and discharge it when sunlight decreases, ensuring a stable power supply. A boost converter is integrated to regulate the voltage from the supercapacitor, maintaining consistent power delivery to the load. An energy management system (EMS) optimizes the charging and discharging cycles, while power electronics components such as MOSFETs and DC-DC converters enhance power conversion efficiency. This approach offers several benefits, including fast response times, high cycle life, environmental sustainability, and improved system reliability. By studying meteorological data, the project also examines the impact of solar radiation variations on energy storage, ultimately improving the performance of solar power systems for applications such as off-grid power solutions, electric vehicle charging stations, and rural electrification.

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 Keywords

Solar Power Optimization, Supercapacitor, Energy Storage.

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 Abstract

Assistant allows users to control their systems through spoken commands. The system becomes increasingly accurate and tailored to individual needs by learning from user behavior and preferences using machine learning algorithms. With more usage, it improves speech recognition, anticipates actions, and generates relevant responses, enhancing overall usability. Unlike conventional voice control systems, adaptive assistants use sophisticated technologies like Machine Learning (ML) and Natural Language Processing (NLP) to understand diverse speech patterns and user habits. This improves accessibility and productivity by allowing hands-free control of applications, file management, reminders, and system configurations.

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 Keywords

Machine Learning Algorithm(ML), Natural Language Processing (NLP)

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 Abstract

International pharmaceutical companies are responsible for the present change in marketing strategies. Leading companies use high-end development rather than adaptable development, new innovation-based businesses are expanding, and businesses that don't innovate their processes and products are finding it hard to compete with businesses that are adapting. inventiveness. The pharmaceutical sector is extremely complex. A good marketing strategy will help in survival of pharmaceutical companies in the stiff competition not only from the domestic companies but also from their foreign counterparts. This research paper will focus on development of appropriate marketing strategies. The responses have been collected from various stakeholders in pharmaceutical industry.

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 Keywords

Pharmaceutical, marketing strategies, innovation-based business

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Mobile phones are being increasingly targeted by advanced malware, making conventional detection techniques less efficient. This paper introduces a hybrid malware detection system in an Android Studio WebView-based app that incorporates the VirusTotal API, machine learning, and real-time threat intelligence. The system integrates signature-based, heuristic, and behavioral analysis to identify malicious behavior and adds IP/domain analysis for network threat evaluation. A machine learning model that learns on an ongoing basis improves detection of polymorphic and zero-day malware. The software allows users to scan files, inspect URLs, and inspect network traffic. Performance indicates enhanced detection accuracy, minimized false positives/negatives, and efficient real-time threat response. This paper proves the capability of AI-driven, hybrid strategies in improving mobile security.

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 Keywords

Malware Detection, Android Security, VirusTotal API, Machine Learning, Threat Intelligence, Hybrid Detection, Mobile Security.

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Radiology technologists are key contributors to modern healthcare, providing vital support in the diagnostic process through advanced imaging techniques. This paper explores their role in ensuring accurate diagnoses and promoting patient-centered care. Data were collected through literature review and observational analysis in clinical settings. Results indicate that well-trained radiologic technologists significantly improve diagnostic accuracy, patient safety, and overall workflow efficiency.

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 Keywords

1. Radiology 2. Radiation safety 3. Patient 4. Technologist 5. Patient care 6. Diagnostic

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 Abstract

Financial decision-making has become increasingly complex due to the vast amounts of information available, which often leads to challenges in extracting actionable insights. This paper explores the role of Artificial Intelligence (AI) in enhancing financial decision-making processes by leveraging technologies such as Natural Language Processing (NLP), machine learning, and deep learning models to analyse unstructured data, uncover patterns, and deliver timely, data-driven predictions. Applications like portfolio optimization, sentiment analysis, and earnings forecasting demonstrate AI's ability to improve decision accuracy, efficiency, and risk management. However, the study also highlights the limitations of AI, including its dependence on data quality, vulnerability to noise, and struggles to adapt during regime changes, emphasizing the critical role of human expertise in validating machine-driven insights and offering forward-looking judgment. By advocating a collaborative "man + machine" approach, the paper underscores the synergy between AI's computational power and human intuition, enabling organizations to balance automation with rational oversight. This integration not only enhances decision-making processes but also strengthens businesses' ability to navigate uncertainties, discover growth opportunities, and maintain long-term financial stability in a competitive and dynamic economic landscape.

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 Keywords

Artificial Intelligence, Financial Decision-Making, Machine Learning, Neural Networks, Decision Support Systems, Ethical AI.

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 Abstract

Abstract The paper analyses the various stages of the Dalit movement in Karnataka According to the author, though the Dalit movement in Karnataka is as old as the Veerashaiva movement the Dalit concern received very little attention in the context of the Non-Brahmin movement in the princely state of Mysore. The real Dalit movement in Karnataka started with the Bhusa Uproar of B. Basavalingappa. In the process the rise of the Dalit Sangharsh Samiti proved to be a non-parallel event in the history of the Dalit movement in Karnataka but this movement suffered from too many lacunas. The movement in Karnataka did not base its activities on a definite ideological stance the lack of ideological commitment in turn did not allow it to emerge as a 'self-help movement'. As the Dalits continued to fight for cursor? Benefits they lacked themselves into the captivity of concessions like reservations. Besides, they took their 'Dalit world' too much for granted. Which ultimately kept them bound by chains of submission to exploitative Hinduism? Strangely, in the process. The Dalits succumbed to the same evils against which they had been lighting. Also the lack of social cohesion and unity left the various Dalit organizations politically distorted.

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Key words: Dalit, Movement: DSS, Caste, Karnataka, Committee, Social, Bhusa uproar etc.

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 Abstract

Stock market trend analysis is a complex and challenging problem because of its highly volatile and nonlinear nature. Traditional models sometime fail to capture intricate patterns present in financial time series data. In this paper, we present a deep learning approach by implementing Long Short-Term Memory (LSTM) networks to estimate future fluctuations in stock value of Adani Group companies listed on the National stock exchange. This study uses a Kaggle sourced dataset including features like company names, current prices, stock dates, stock volume and stock open close. The data were preprocessed and normalized to improve model convergence. An LSTM model was trained to forecast stock prices for the upcoming financial year, and its predictions were integrated into a user-friendly web application. The platform provides real-time visualization of stock trends, including line charts and company information, enabling users to interactively explore historical and predicted prices. Experimental results demonstrate that the LSTM model captures underlying stock trends effectively, providing accurate predictions despite market volatility. This work highlights the ability of deep learning models linked with interactive web applications in enhancing financial decision-making

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 Keywords

LSTM (long short term memory) , deep learning , machine learning ,recurrent neural network

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Creative Commons Attribution 4.0 and The Open Definition