Volume 13 | Issue 5 |

 Abstract

The rapid urbanization and surge in the vehicle usage have led to increasing traffic congestion, posing significant challenges to city infrastructure, safety, and efficiency. Traditional traffic monitoring systems rely heavily on manual effort or static sensors, which are costly, error-prone, and limited in coverage. This paper proposes a smart traffic analysis system leveraging deep learning, specifically Convolutional Neural Networks (CNNs), for real-time vehicle detection and dynamic traffic analysis from video footage. The system continuously detects and counts vehicles within live or recorded traffic video streams, updating counts as vehicles move forward or disappear from view. When traffic density exceeds a pre-defined threshold, the system generates a congestion alert via a beep sound.

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 Keywords

Traffic Analysis, Deep Learning, Convolutional Neural Network(CNN), Image Processing, Object Detection

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 Abstract

With the rapid growth of the technology worldwide, AI has become increasingly important as it has revolutionized various aspects of our lives as it has had a great impact on various sectors like Healthcare, education, business etc. and along with these sectors, AI has played a significant role in the growth of the law sector of India. In Corporate law, AI has emerged as a transformative force in corporate law, and has revolutionized the processes of decision making and reshaping the lifecycle of business contracts. This dissertation seeks to examine the regulatory challenges in AI driven decision making and focus on the implications of AI in drafting of business contracts. Businesses have now started relying on AI due to its efficiency and reduced human error, the integration of AI and corporate law leads to an array of regulatory, ethical, and legal challenges that this paper would address. The primary aim of this dissertation is to investigate the capabilities of the existing legal frameworks in our country in addressing these challenges, particularly under statutes like the Companies Act 2013, and the Indian Contract Act 1872. With the rapid growth of AI and the constant development of its generative capabilities, it's still not invincible and one would face many challenges. This dissertation aims to analyze those challenges and work towards finding possible solutions to overcome them. This dissertation also focuses on the probable changes that would be witnessed after the integration of AI with corporate law, analyzing the advantages as well as the disadvantages of it. Another focal point of this dissertation is the challenges that come with the integration of AI with law specifically in reference to contract drafting by the AI. And in order to address such issues this dissertation provides recommendations to overcome such challenges through regulatory reforms and strategies. Using a comparative approach and analyzing the regulations of various other countries like US, UK, China and Japan, in relation to the integration of AI with corporate law, this dissertation deals with looking for recommendations on adoption of global best practices for regulating AI-driven decision-making and contracts. This dissertation is based on the methodology of doctrinal research using both primary and secondary legal source along with comparative analysis with international AI regulations from different countries. Also another methodology of this dissertations is through various case studies focussing on AI implementation in corporate scenarios of India. In conclusion, this dissertation underscores the critical importance of addressing the regulatory challenges of AI-driven decision-making in corporate law to foster a legal environment that promotes innovation while safeguarding the rights and interests of all stakeholders. It provides a roadmap for future reforms, aiming to align Indian corporate law with the evolving dynamics of AI integration in business and corporate processes. By doing so, it contributes to the broader discourse on AI regulation, offering insights that are both contextually relevant to India and globally significant.

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 Keywords

Artificial Intelligence, Corporate Law, AI-driven Decision Making, Contract Drafting, Regulatory Challenges, Companies Act 2013, Indian Contract Act 1872 , Legal Frameworks, Comparative Analysis

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This study examines the impact of AI-powered Virtual Try-On (VTO) technologies on consumer purchasing intent and behaviour across the fashion and beauty industry. As online shopping becomes increasingly experiential, AI VTO tools offer personalized, interactive, and immersive experiences that reduce product uncertainty and enhance engagement. The research highlights key influencing factors such as perceived usefulness, enjoyment, body esteem, fit confidence, and self-congruity. While AI VTO improves consumer trust and purchase likelihood, barriers like privacy concerns and perceived intrusiveness remain. As AI continues to improve VTO features, its ability to deliver immersive, inclusive, and confidence-boosting shopping experiences will be pivotal in influencing consumer behaviour and driving e-commerce growth. Overall, AI-driven VTO plays a dual role enhancing both utility and emotional value reshaping how consumers explore and evaluate products in digital environments.

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 Keywords

AI Virtual Try-On (AI VTO), Consumer purchase intent, online shopping behaviour, Self- congruity, augmented reality (AR), buying experience

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder that impacts millions of women globally, especially during their reproductive years. It is marked by irregular menstrual cycles, elevated male hormone levels, and the presence of multiple cysts on the ovaries. If left unmanaged, PCOS can lead to serious health issues, including infertility, obesity, insulin resistance, and an increased risk of conditions like type 2 diabetes, heart disease, and mental health disorders such as depression and anxiety. The complexity of PCOS arises from the combination of genetic, environmental, and lifestyle factors that contribute to its development. Unfortunately, it is often underdiagnosed or misdiagnosed due to the wide range of symptoms and the absence of a single, conclusive test. This highlights the importance of accurate and early detection to prevent long-term complications. To overcome these challenges, a new diagnostic approach is necessary. Machine learning can play a key role--by using advanced feature selection techniques, a machine learning model can be developed to pinpoint the most significant risk factors for PCOS, improving diagnostic accuracy. This innovative method has the potential to transform the way PCOS is diagnosed and managed, allowing healthcare providers to offer more effective treatment and care for women with the condition.

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Polycystic Ovary Syndrome (PCOS), Hormonal disorder, Insulin resistance, Machine learning, Diagnosis

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Creative Commons Attribution 4.0 and The Open Definition

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This paper provides an in-depth analysis of microcontrollers (MCUs), covering their internal architecture, categories, primary components, and diverse applications. It explores widely used microcontroller families such as ARM, AVR, and PIC, offering comparisons based on features and typical use scenarios. To assist in selecting a suitable MCU for embedded and IoT-based systems, the paper introduces structured decision-making frameworks including the Kano model, specification matrices, and pin compatibility evaluations. It further outlines essential selection criteria such as memory configuration, energy efficiency, development tool support, and hardware compatibility. Common challenges like timing errors, thermal issues, and interface mismatches are also discussed, with suggestions for mitigation. The insights presented aim to guide engineers, students, and developers in making strategic microcontroller choices tailored to system demands and performance requirements.

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 Keywords

Microcontroller (MCU), Embedded Systems, Internet of Things (IoT), MCU Architecture, MCU Selection Criteria, AVR / ARM / PIC Microcontrollers, Kano Model

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Creative Commons Attribution 4.0 and The Open Definition

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This study explores the impact of macroeconomic variables such as inflation, interest rates, and exchange rates on the performance of selected mutual funds in India. By analyzing Net Asset Value (NAV) data using regression and correlation techniques, the research identifies how economic fluctuations influence fund returns. The findings provide useful insights for investors and fund managers in making informed decisions.This study examines the impact of key macroeconomic variables such as inflation, interest rates, GDP growth, and unemployment on the performance of selected mutual funds. It aims to understand how these factors influence NAV, AUM, risk, return, and investor behavior. The findings provide valuable insights for investors and fund managers to make informed decisions. However, the study is limited by its reliance on historical data and the scope of selected variables and funds. This study investigates the impact of key macroeconomic variables such as interest rates, inflation, and exchange rates on the performance of selected mutual funds in India over a five-year period (2020-2025). Using secondary data and a combination of descriptive and analytical research designs, the study employs tools like Net Asset Value (NAV), Asset Under Management (AUM), ratio analysis (Sharpe Ratio, Beta, Treynor's Ratio, etc.), and regression techniques including ANOVA and regression coefficients. The analysis aims to understand how economic conditions influence mutual fund returns, offering insights for investors and financial analysts in making informed investment decisions.

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Asset Under Management (AUM), Mutual Funds, Macroeconomic Variables

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This study has been undertaken to investigate the indepth subjective interpretations of Marathi padas particularly in the category of sangeeta and nirtya padas performed during the period of Tanjore kings. It consists of around 60 compositions popularly used by the the dance artists in tune with the rich traditions of Royal elegance. The proposed study is based oncompositional varieties from a book preserved in Saraswati Mahal library composed by different authors. The compositions are examined with the help of their theme, characters, and the writing style.

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 Keywords

Sangeeta Pada, Nirtya Pada, Tanjore kings, Themes of Padas

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 Abstract

The increasing complexity of service operations in the medical device industry demands advanced analytical tools capable of delivering real-time, actionable insights. This article presents a comprehensive framework for developing Tableau dashboards to measure and improve service efficiency metrics using data from Salesforce. It reviews current technologies and dashboard methodologies, identifies key challenges in integration and KPI standardization, and introduces a four-tier theoretical model encompassing data acquisition, transformation, visualization, and decision support. A real-world case study of a mid-sized medical device firm demonstrates measurable improvements in Mean Time to Repair (MTTR), SLA compliance, and technician productivity following dashboard implementation. Despite limitations such as data quality and real-time lag issues, the proposed model provides a scalable and practical foundation for healthcare service analytics. Future research directions include enhancing dashboards with machine learning, natural language interfaces, and cross-platform integration.

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 Keywords

Tableau dashboards; Salesforce; Medical device service; KPI analytics; Service efficiency; Business intelligence; Healthcare technology; CRM data integration; Predictive maintenance; SLA compliance

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In the fields of architecture and product development, parametric design and generative algorithms are innovative tools. In order to demonstrate how these technologies affect design methodologies, this paper looks at the basic ideas, procedures, and examples. We explore how parametric design and generative algorithms are changing the design environment and encouraging creative and effective solutions by closely examining recent academic publications and analyzing specific case studies.

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 Keywords

Architecture, product design, computational design, parametric design, generative algorithms, and design optimization.

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Creative Commons Attribution 4.0 and The Open Definition