THE ROLE OF PERCEIVED ATTRACTIVENESS IN SHAPING CONSUMER PREFERENCES: A STUDY OF SPORTS BRAND T-SHIRT PURCHASING BEHAVIOR AMONG COLLEGE STUDENTS IN ZHENGZHOU, CHINA

The preference of customers is very impactful for the sports brand T-shirt making companies. The growth in the sportswear industry of China and preference of college students towards it, is significant, however, changes like change in trend and personality riots of consumers is considered significant. In this literature review chapter, previous research findings are presented about perceived attractiveness and its influence on purchasing behaviour of T-shirts with sports brands among college students in Zhengzhou, China. The review also investigates general theories like social identity theory as well as consumer buying behaviour theories where determinants like customer’s attitude, brand image and advertisement have been identified as determinants in the buying process. Brand reputation is identified in the literature to play a huge role in increasing perceived product attractiveness as well as how customer attitudes act as the mediating factor between perceived brand image and actual purchase behaviour. The quantitative method has been used by selecting 410 students to conduct surveys for using their purchasing behaviour and preferences. Along with this, SPSS and SmartPLS have also been used here. Some recommendation has been provided and some limitations have also been identified while conducting the study

FRAUD DETECTION IN FINANCIAL TRANSACTIONS THROUGH DATA SCIENCE FOR REAL-TIME MONITORING AND PREVENTION

This study presents a comprehensive review of the use of advanced technologies in credit card fraud detection, with a focus on machine learning, blockchain, and federated learning, to understand their transformative impact on the field. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 97 articles were systematically reviewed and analyzed. The findings reveal that machine learning models, such as decision trees, support vector machines, and neural networks, have significantly improved fraud detection accuracy, reducing false positives and enhancing the ability to detect complex fraud patterns in real-time. Blockchain technology also plays a critical role by providing a decentralized, secure, and transparent framework for fraud detection, ensuring the integrity of transaction records and making fraudulent activities harder to conceal. Federated learning offers a privacy-preserving solution, enabling institutions to collaborate on fraud detection without sharing sensitive data, which is increasingly important in light of stringent regulatory requirements. Additionally, the study highlights the growing use of predictive analytics in forecasting potential fraud, allowing financial institutions to proactively prevent fraud before it occurs. Moreover, feedback loops integrated into fraud detection models allow for continuous improvement, ensuring that detection systems can adapt to new and evolving fraud tactics. Overall, the review underscores the importance of adopting these advanced technologies to build more secure, efficient, and adaptive fraud detection systems capable of safeguarding financial transactions in the modern digital economy

DATA SECURITY IN IOT DEVICES AND SENSOR NETWORKS FOR ROBUST THREAT DETECTION AND PRIVACY PROTECTION

The rapid proliferation of Internet of Things (IoT) devices and sensor networks has revolutionized various industries by enhancing automation, connectivity, and operational efficiency. However, these advancements have also introduced significant security challenges due to the resource constraints and decentralized nature of IoT environments. This paper provides a systematic review of IoT security solutions, focusing on encryption techniques, authentication protocols, and machine learning-based anomaly detection methods. A total of 55 peer-reviewed articles were analyzed following the PRISMA guidelines. The findings reveal that while lightweight cryptographic algorithms, such as elliptic curve cryptography (ECC), offer robust security with low energy consumption, scalability across large IoT networks remains a challenge. Blockchain-based authentication has emerged as a promising decentralized solution, but issues related to energy consumption and latency hinder its widespread adoption. Machine learning techniques have shown high accuracy in detecting threats in real-time, but their resource-intensive nature limits their application in low-power IoT devices. This review underscores the need for multi-layered, integrated security frameworks and highlights gaps in research on quantum-resistant cryptography and interoperable security standards. Future research must focus on developing scalable, energy-efficient security solutions to ensure data integrity and privacy in expanding IoT ecosystems

FOUNDATIONS, THEMES, AND RESEARCH CLUSTERS IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN FINANCE: A BIBLIOMETRIC ANALYSIS

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the financial sector has brought about a profound transformation in decision-making processes, risk management, and predictive analytics. This comprehensive study aims to systematically identify and analyze the foundational theories, emerging themes, and research clusters within the extensive body of AI and ML finance literature through an in-depth bibliometric analysis. By meticulously examining a vast array of publications spanning over two decades, the study uncovers the intricate evolution of AI and ML applications in finance, mapping out key areas of research and providing valuable insights into future research directions. The findings reveal a significant and accelerating growth in the application of AI and ML across various financial domains, notably in fraud detection, portfolio management, and algorithmic trading, demonstrating the substantial impact and transformative potential of these technologies. This study not only charts the current landscape of AI and ML research in finance but also identifies critical gaps and opportunities for future exploration, underscoring the ongoing evolution and maturation of this dynamic field.   &nbsp

ENHANCING FASHION FORECASTING ACCURACY THROUGH CONSUMER DATA ANALYTICS: INSIGHTS FROM CURRENT LITERATURE

The fashion industry is characterized by its fast-paced nature and constant evolution of consumer preferences, making accurate fashion forecasting essential for brands to remain competitive. Traditional forecasting methods, which rely heavily on historical sales data and expert intuition, are increasingly being complemented or replaced by advanced consumer data analytics. This article explores the integration of consumer data analytics into fashion forecasting, drawing insights from recent literature. By examining methodologies such as machine learning, big data analytics, and AI, as well as utilizing diverse data sources including social media, online shopping behaviors, and mobile data, this study highlights the significant improvements in trend prediction accuracy and operational efficiency. Key findings indicate that data-driven approaches provide more precise and real-time insights into consumer preferences, enabling brands to better anticipate market demands and optimize inventory management. The discussion underscores the transformative potential of consumer data analytics in enhancing the overall effectiveness of fashion forecasting

DESIGNING EARTHQUAKE-RESISTANT FOUNDATIONS: A GEOTECHNICAL PERSPECTIVE ON SEISMIC LOAD DISTRIBUTION AND SOIL-STRUCTURE INTERACTION

The design of earthquake-resistant foundations is a critical aspect of geotechnical engineering, particularly in regions susceptible to seismic activity. This study explores the role of seismic load distribution and soil-structure interaction in the development of resilient foundation systems. By integrating advanced geotechnical analysis techniques, the research examines various soil types, foundation materials, and structural configurations to identify the optimal conditions for mitigating seismic impacts. Emphasis is placed on understanding the interaction between soil properties, foundation stiffness, and seismic forces, with the goal of improving the safety and durability of built environments. The findings contribute to better predictive models for designing foundations that can withstand seismic loads while ensuring long-term stability

The Future Of Human-Ai Collaboration In Software Development: A Narrative Exploration

Investigating how artificial intelligence technologies are changing conventional development methods and the role of human developers, this study investigates the changing dynamics of human-AI collaboration in software development. The research explores the opportunities and difficulties brought about by the incorporation of AI tools into software development processes using a thorough investigation of secondary data sources and the body of current literature. The results show that AI is going beyond simple automation in software development, creating a mutually beneficial partnership in which developers become strategic managers of AI-driven workflows and AI enhances human creativity and cognitive capacities. The transition from routine coding to higher-order problem-solving, the rise of new development models, and the increasing significance of ethical considerations in AI implementation are some of the major themes identified in the study. Important issues are also covered, such as the need for improved developer education in AI literacy, bias prevention, and transparency in AI decision-making. The study concludes that effective human-AI cooperation necessitates a well-rounded strategy that makes use of AI's processing capacity while preserving human supervision and originality. Among the suggestions include funding developer education, creating transparent AI systems, encouraging teamwork, and establishing moral standards for integrating AI. This study advances our knowledge of how artificial intelligence will influence software development in the future by indicating that the way forward is to establish an environment in which human knowledge and AI capabilities work in tandem to promote creativity and productivity in software development

BIG DATA ANALYTICS FOR ENHANCED BUSINESS INTELLIGENCE IN FORTUNE 1000 COMPANIES: STRATEGIES, CHALLENGES, AND OUTCOMES

This study investigates the transformative impact of big data analytics and business intelligence on the operations and strategic decision-making of Fortune 1000 companies, with a focus on Walmart. Walmart's integration of advanced data analytics tools has enabled significant optimization across various business areas, including inventory management, customer engagement, and supply chain operations. Leveraging big data, Walmart has gained deep insights into customer behavior, allowing for accurate demand forecasting and streamlined operations, which enhance operational efficiency and competitive advantage. The study highlights Walmart's use of predictive analytics to improve inventory management and supply chain efficiency, demonstrating how analyzing purchasing patterns and customer preferences reduces stockouts and excess inventory, thus boosting customer satisfaction and minimizing costs. Despite its advanced infrastructure, Walmart faces challenges in data integration and real-time analytics due to data silos created by its vast operations. Enhancing real-time analytics integration and data governance practices is crucial to ensure data quality, security, and compliance. Additionally, the study examines Walmart's strategic use of dynamic pricing algorithms to adjust prices in real-time based on market conditions, effectively maximizing sales and profitability, aligning with previous research on dynamic pricing benefits in retail. Furthermore, the broader economic implications of Walmart's data-driven strategies are discussed, noting that while Walmart's efficient operations and lower prices benefit consumers, they also pose challenges for small local businesses. This study provides a detailed analysis of Walmart's leverage of big data analytics and business intelligence to sustain its competitive advantage and drive business success, offering valuable insights for other Fortune 1000 companies on the importance of technology, organizational culture, and governance in achieving sustained business success. &nbsp

Python For Data Analytics: A Systematic Literature Review Of Tools, Techniques, And Applications

In the era of big data, the ability to collect, process, and analyze data efficiently has become a vital component for decision-making across various industries. Python, as a versatile programming language, has emerged as a powerful tool for data analytics due to its extensive libraries and user-friendly nature. This systematic literature review explores Python’s role in streamlining data analytics by examining its applications across various stages of the data analysis process, including data collection, cleaning, manipulation, and visualization. Key Python libraries such as NumPy, Pandas, and Matplotlib are discussed, highlighting their functionality in handling large datasets and enabling accurate and efficient analysis. Real-world examples demonstrate how Python can be applied in diverse sectors, from retail to healthcare, enhancing decision-making processes through data-driven insights. Furthermore, the limitations of Python, as well as alternative data analysis tools such as R and RapidMiner, are explored to provide a comprehensive view of Python’s place in modern data analytics. The review concludes that while Python offers significant advantages in data analysis, a combination of tools may often be necessary to meet the complex demands of today’s data-driven industries

DESIGN AND DEVELOPMENT OF A SMART FACTORY USING INDUSTRY 4.0 TECHNOLOGIES

This systematic literature review examines the operational and organizational impacts of Industry 4.0 technologies on smart factories, drawing on insights from 120 peer-reviewed articles published between 2010 and 2024. The study follows the PRISMA guidelines to ensure a transparent and rigorous review process, focusing on the key enablers of smart manufacturing, including cyber-physical systems (CPS), the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and machine learning (ML). The findings reveal that smart factories offer significant benefits, including enhanced flexibility and customization, predictive maintenance that reduces downtime by up to 50%, and improved supply chain integration through real-time data sharing. Big data analytics plays a crucial role in optimizing operations by allowing factories to perform continuous real-time adjustments, improving efficiency and reducing resource waste. The review also highlights the evolving role of the workforce, with a growing need for technical skills and increased human-machine collaboration in smart manufacturing environments. However, challenges such as interoperability, cybersecurity, and the economic feasibility of large-scale smart factory implementations remain underexplored in the literature. Emerging technologies like blockchain and 5G offer promising solutions, but further research is required to assess their full potential. Overall, this review provides a comprehensive understanding of the current state of smart factory technologies and outlines key areas for future research, particularly in addressing gaps related to standards, workforce adaptation, and security concerns