Department of Commerce

Patient satisfaction is an important measure of the quality of care provided by health care organisations. It is not only important for gaining insights into the perception of the patient’s on the delivery of the health care service, but also a key outcome of care. The present study was undertaken to identify the factors in which the patients are satisfied by the services offered by the multi- speciality hospitals in Coimbatore city. The objective of the study states to examine the level of patient satisfaction towards hospital services. primary data was collected from 120 respondents using a well-structured questionnaire through google form. The data collected through questionnaire were analysed. The research methodology employed in this study includes percentage analysis, Garrett ranking and Chi- square test. It was found that the majority of the respondents choose Kovai medical center and hospital to undergo the treatment towards multi- speciality hospitals. Most of the respondent’s nature of treatment is towards medical. It was suggested that the hospital management should increase the physician referral centers, complaint handling and service recovery systems should be strengthened to enhance effectiveness of multi- speciality hospital services. It was concluded as the competition is increasing, the hospitals are making their best efforts to provide quality healthcare services to its customers.

This study investigates the level of political awareness, knowledge, and perceptions held by young voters —specifically college students in Coimbatore city, Tamil Nadu — regarding India's political and electoral system. With 150 respondents selected through convenience sampling, the research employs a structured questionnaire and four analytical tools: Simple Percentage Analysis, Weighted Average Score, Chi-Square Analysis, and ANOVA. Findings indicate that social media platforms are the dominant source of political information among youth, while national development remains the foremost motivator for political interest. Respondents demonstrate a moderate level of overall electoral awareness (mean score 1.58/2), with relatively stronger familiarity with local body elections thanparliamentary processes. Perception analysis reveals that young voters hold a moderately high and critical view of political leadership (mean score 4.19/5), acknowledging both desired leadership qualities and persistent systemic problems such as corruption, nepotism, and unfulfilled promises. Chi-Square tests confirm that demographic variables do not significantly affect awareness or perceptions, while ANOVA identifies significant variation in opinions concerning corruption, the VVPAT system, educational qualifications for leaders, online voting, and biometric verification. The study calls for stronger civic education programs, media literacy initiatives, and institutional reforms to bridge the gap between political exposure and informed democratic engagement among youth. Keywords: Political Awareness, Electoral System, Youth Perception, Civic Engagement, Social Media,Democratic Participation

Fe-doped manganese oxide nanoparticles were successfully produced using the chemical precipitation method. The generated nanoparticles were characterized by XRD, SEM, EDAX, FTIR, and UV. The average crystallite size of the sample was examined using the XRD method. The Fe-doped MnO2 NPs crystallite size was determined to be 11.56 nm. The morphology and grain size were ascertained using SEM images. They exhibit tiny granules that range in size from 10 to 40 nm. The elemental composition of Fe-doped MnO2 NPs was confirmed by EDAX to be 77.20 Fe, 20.60 Mn, and 2.20 O₂. The FTIR tests reveal high absorption peaks for many functional groups, including C=O, CO₂, NH+, O-H, and MnO₂. In the UV spectra, the optical band gap was calculated between 1.5 and 5.0 eV. XRD examination revealed that the average particle size of Fe-doped MnO2 nanoparticles was 11.56 nm. The produced nanoparticles were appropriate for the photo-catalytic degradation of the colour indigo blue.

Abstract E- Commerce is a latest trend in modern business world. electronic commerce or e-commerce refers to any sort of business transaction that involves the transfer of information through the internet. It covers a variety of business activities, which use internet as a platform for either information exchange or monetary transaction or both at times. It refers to a wide range of online business activities. It is leading to a great change in traditional way of doing business. It is like a revolution in the modern business world. It allows customers a wide range of products. The convenient and quick delivery of products at the desired place also makes it very popular. In this paper an attempt is made to understand the recent trends, opportunities and challenges of E-Commerce in India. Further the paper also discusses different components of E-Commerce, and its future in India. Key words: E Commerce. Trends, Challenges. and Opportunities.

Abstract Loan approval prediction is a critical task in the financial sector, enabling institutions to assess credit risk and make informed lending decisions. This research presents a comprehensive comparative study of various machine learning approaches for loan approval prediction, evaluating both traditional algorithms and deep learning architectures. We implemented and compared five traditional machine learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and SVM) against three neural network architectures (Simple Feed-Forward Neural Network, Deep Neural Network with Batch Normalization, and Wide & Deep Network). Using a synthetic dataset of 1,000 loan applications with features including income, credit score, and employment status, we achieved the highest accuracy of 93.5% with Gradient Boosting, closely followed by the Wide & Deep Neural Network at 93.5%. Feature importance analysis revealed credit score (45%) as the most influential factor, followed by income (40%) and employment status (15%). Additionally, we developed a professional Tkinter-based graphical user interface that enables real-time predictions, batch processing, and interactive visualizations. This system demonstrates the practical application of machine learning in financial decision-making and provides a framework for deploying predictive models in production environments. Keywords — Loan Approval, Machine Learning, Deep Learning, Neural Networks, Gradient Boosting, Tkinter GUI, Credit Risk Assessment

Artificial Intelligence (AI) is transforming societies by reshaping economies, democracies, and public services. This paper explores AI's dual role as both an enabler of progress and a source of new challenges. It examines its impact on labor markets, governance, healthcare, education, and urban development, highlighting both opportunities and risks such as bias, inequality, and misinformation. Through international cooperation, ethical frameworks, and inclusive policies, AI can be guided toward enhancing human well-being. Real-world case studies from Estonia and India demonstrate how AI can strengthen democracy and public services when developed responsibly.

This study examines the role of social media marketing in shaping women’s skincare buying behavior. With the rapid growth of digital platforms such as Instagram, TikTok, and YouTube, marketing strategies in the skincare industry have shifted from traditional advertising to influencer-driven and content-based engagement. Women increasingly rely on social media for product reviews, tutorials, ingredient information, and peer recommendations before making purchasing decisions. The study adopts a quantitative research design using a structured questionnaire distributed to women who actively use social media and purchase skincare products. Key variables examined include influencer marketing, online reviews, social media advertisements, content quality, and customer engagement, while purchase intention and brand loyalty represent buying behavior outcomes. Data were analyzed using descriptive statistics, reliability analysis (Cronbach’s Alpha), and inferential statistical techniques. The findings indicate that social media marketing significantly influences women’s skincare purchase intentions and brand loyalty. Influencer credibility, user-generated content, and positive online reviews emerged as strong determinants of buying behavior. The study concludes that effective and trust-based social media strategies are essential for skincare brands seeking competitive advantage. The research contributes to consumer behavior literature and provides practical implications for digital marketing strategies within the skincare industry.

Abstract: This study examines the role of startups in driving economic growth in India, particularly within the framework of the Startup India initiative. The global rise of the startup economy, supported by technological innovation and venture capital, has transformed startups into major contributors to job creation, innovation, and economic diversification. India’s demographic advantage, with a large young population, provides a strong foundation for entrepreneurial development and innovation-led growth. The Startup India programme, launched in 2016, aims to build a supportive ecosystem by simplifying regulations, providing funding support, and promoting incubation and mentorship. The study reviews existing literature to understand how startups influence economic development through innovation, employment generation, and sectoral expansion. It also analyses the impact of startups on macroeconomic indicators such as GDP growth, foreign investment, and export potential. Despite significant progress, startups continue to face challenges including funding constraints, regulatory complexities, skill gaps, and the urban–rural divide. The study concludes by suggesting policy measures such as strengthening financing mechanisms, improving infrastructure, enhancing entrepreneurship education, and promoting public–private partnerships to ensure sustainable startup-led economic growth in India.

Blockchain technology is a somewhat new approach to finding the integrity and chain of digital evidence in various industries, including law enforcement, forensic investigations, supply chain management, and judicial proceedings. Although traditional evidence-keeping systems are prone to manipulation, loss, and inefficiency, blockchain offers an immutable, transparent, and decentralized ledger that securely records and validates every evidence-related transaction. Blockchain technology increases reliability in handling both physical and digital evidence. It uses distributed consensus, intelligent contracts, and cryptographic hashing to eliminate human error and backdoor intervention by assuring immutability, accountability, and automation. This study offers a model blockchain (Chain of Digital Evidence) based on the Ethereum blockchain to guarantee integrity and authenticity in the chain of digital evidence. Ethereum's decentralization ensures that digital evidence is free from manipulation, transparent, and easily verifiable. The study discusses other challenges and prospects for integrating the Ethereum blockchain into the digital evidence chain.

The increasing complexity and uncertainty in global supply chains have intensified the need for effective risk management strategies. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of enhancing the identification, prediction, and mitigation of supply chain risks. This study conducts a systematic review of existing literature to examine the role of AI in supply chain risk management. Using a structured review approach, relevant research articles published between 2015 and 2025 were collected from major academic databases and screened based on predefined inclusion and exclusion criteria. The review aims to synthesize the existing body of knowledge on how AI technologies such as machine learning, predictive analytics, and big data analytics are applied to manage disruptions and uncertainties in supply chains. Furthermore, the study identifies the dominant research themes, technological applications, and methodological approaches adopted in previous studies. The findings highlight the growing integration of AI-driven tools in areas such as demand forecasting, disruption prediction, supplier risk evaluation, and decision support systems. Despite the increasing attention to AI-based solutions, the review also reveals several research gaps related to implementation challenges, data quality issues, and integration with traditional risk management practices. The study provides a comprehensive understanding of the current research landscape and offers directions for future studies in AI-enabled supply chain risk management.