International Journal of Engineering and Management Research
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Consumer Rights Awareness and its Effect on Consumerism in Meerut
Get PDFConsumerism has been defined as a social movement seeking to augment the rights and powers of consumers in relation to sellers (Kotler, 2000). It has spread to developing countries including India but majority of Indian consumers have been observed to be relatively passive in utilizing their rights and the established consumer protection mechanisms. Consumer exploitation has therefore continued unabated in the market place. The present study is aimed at investigating the level of consumer rights awareness and the effect of consumer rights awareness on consumerism in the Meerut region through a survey of household consumers in the area. It covers the aspect of general awareness among consumers in the area under study regarding consumer protection and specific awareness of provision of consumer protection act 1986. A pre-designed self-administered questionnaire was used for data collection. The research observed that a lot of consumers are being exploited even with the awareness about their rights under legal framework of consumer protection act 1986. Despite so many legal provisions made by Government of India for the well being of consumers, but the complete protection is still awaited. The study recommends for a policy on consumer education and activation in the region. It also recommends that manufacturers should establish a division for consumer affairs to listen to consumer complaints addresses their issues
Integrative Analysis of FinTech Innovations in Industry: Enhancements and Challenges
Get PDFThis paper presents a detailed examination of the transformative role of Financial Technology (FinTech) innovations across various industry sectors, highlighting both the enhancements and the inherent challenges these technologies introduce. By integrating a range of data sources, including case studies and empirical research, the study evaluates the efficacy of FinTech applications in optimizing operational efficiency, enhancing data-driven decision-making, and fostering sustainable growth in industrial operations. While the adoption of FinTech offers significant advantages, such as improved transaction speed and accuracy, reduced costs, and enhanced security, it also poses several challenges. These include technological integration issues, regulatory compliance burdens, and the need for substantial upskilling of the workforce. The paper also explores the dynamic interplay between technological advances and regulatory frameworks that shape the adoption and impact of FinTech solutions. Through this integrative analysis, the study aims to provide stakeholders with a nuanced understanding of the benefits and obstacles associated with FinTech innovations in the industry, offering a roadmap for navigating the complexities of this rapidly evolving landscape
A Study on Supply Chain Challenges for the Indian FMCG Sector
No full textThe term "supply chain management" emerged in the 1980s, initially introduced by consultants and later explored by the business community. In simple terms, it establishes an integrated two-way communication system among organizations involved in the supply chain to efficiently manage high-quality inventory. Supply chain management encompasses a network of facilities and distribution options, managing the procurement, transformation, and distribution of materials in both the service and manufacturing sectors. The complexity of supply chains varies across industries and firms, involving multiple end products, shared components, and various transportation modes. SCM facilitates successful supply chain management execution through technology exchange and strategic alliances, collaborating with professional consulting firms and core technology vendors
Mathematical and Machine Learning Based Methods for UAV Simulation: A Systematic Literature Review
No full textUnmanned aerial vehicles (UAVs), frequently called drones, have become highly useful assets in various industries such as surveillance, transportation, and agriculture. Understanding and evaluating drone behavior is difficult because of the intricate interplay of factors such as velocity, altitude, remote controller input and flight path. Therefore, proficient, and knowledgeable workers are required for efficient drone operations. It is essential to have strong training programs for drone pilots to satisfy these expectations. The inclusion of drone simulators is a vital component of these training programs. Drone dynamics simulations are of great importance in several industries since they allow researchers to design and evaluate drones in intricate situations that would otherwise be dangerous or impractical. This paper examines different drone dynamic models based on principles of Newtonian fluid dynamics. The focus of this study is specifically on fundamental elements such as force, gravity, propeller characteristics, and air density. Moreover, this research examines the utilization of machine learning approaches for simulating drone dynamics. This study analyzes different simulation models and conducts a comparison to find areas of research that have not been addressed. After identifying these gaps, the research examines potential ways to address the drawbacks of current simulation models in the future. This research aims to offer valuable insights to future academics who are interested in constructing customized drone simulators. This work works as a core resource, directing the building of customized drone simulators for different uses
Forecasting Stock Prices through Time Series, Econometric, Machine Learning, and Deep Learning Models
No full textOver an comprehensive ending, scientist have loyal solid efforts to plan a strong and exact predicting foundation for guessing stock prices. Academic discourse emphasizes that intricately devised and refined predicting models occupy the competency to carefully and dependably expect future stock principles. This case introduces a various array of models, including methods to a degree period succession reasoning, econometrics, and miscellaneous knowledge-based approaches tailor-made for stock price guess. Analyzing dossier connecting from January 2004 to December 2019 for famous enterprises to a degree Sun Pharma Group, ICICI Bank, and Infosys Technologies, the models suffered rigorous preparation and estimate to judge their influence across various labors. This research engages a unique mixture of methodologies, containing an individual occasion order models , an econometric approach (ARIMA model), and a pair of machine intelligence model like MARS and Random Forest. Additionally, the study integrates two deep knowledge- located models, particularly the plain RNN and LSTM. This diverse array of models aims to supply a inclusive study of stock price activities vague areas. The study results emphasize the preeminence of Multivariate Adaptive Regression Splines (MARS) as the most able machine intelligence model, Short-term memory (LSTM) has emerged as a deep learning model. Importantly, MARS usually illustrates superior influence in the specific domain of transactions guessing across the Information Technology (resorting to Infosys dossier), Banking (illustration upon ICICI dossier), and Health (depending SUN PHARMA dossier) sectors
Machine Learning Approaches for Enhancing Customer Retention and Sales Forecasting in the Biopharmaceutical Industry: A Case Study
No full textThis study explores the evolving role of artificial intelligence (AI) in accelerating drug discovery and development in the biopharmaceutical industry. We research the integration of AI technologies, including machine learning algorithms, deep learning, and natural language processing, with traditional experimental techniques. Research focuses on four main areas: target identification and validation, identification and optimization, reproducible medicine, and precision medicine. Our findings show that an AI-driven approach has improved the efficiency and accuracy of the various stages of drug discovery, reducing the time and costs associated with bringing new treatments to action. Business. We analyze the synergistic effects of combining AI predictions with biological knowledge models, highlighting the potential for modeling and optimization. This study also examines the critical role of data quality and the importance of data models in training AI models. Additionally, we address issues of AI model interpretation and regulatory decision-making around AI-driven drug discovery. Ethical implications are discussed, including data privacy and equality for AI-driven healthcare innovations. Our research shows the potential of AI in changing the drug discovery process while highlighting the need for improved roles and technology in the biopharmaceutical sector
SeaMNF vs. LDA: Unveiling the Power of Short Text Mining in Financial Markets
No full textThe objective of this study is to construct a time series forecasting framework that incorporates textual features. By leveraging text mining techniques, we extract thematic and sentiment information from a vast array of news headlines related to the future. These text-derived features are then utilized as exogenous variables for prediction purposes. This paper addresses two critical questions: why headlines over full articles and why futures news over gold news. News headlines are considered summaries of the full articles, encapsulating most of the essential information. Additionally, our approach aligns with the work of Li et al. [1,2,3,4,5] which opted for news headlines to extract topics and sentiment information. The choice of futures news over gold news is justified by the scarcity of crude oil news and the established complex correlations between futures prices such as gold, natural gas, and crude oil. Research by Sujit & Kumar (2011) suggests that gold price fluctuations can impact the WTI index, and the dependence of different countries on crude oil can influence their currency exchange rates, thereby affecting the purchasing power of gold. Villar & Joutz (2006) indicate that a 20% temporary shock to WTI has a 5% contemporaneous impact on natural gas prices.[6,7,8,9]
We construct a daily topic strength index by following the SeaMNF approach, which allows us to calculate the probability of each headline belonging to each topic. The optimal number of topics is selected based on Pointwise Mutual Information (PMI) scores. Given the vast number of news articles published daily by media outlets, we compute the average weight of news as the topic strength for the day. The topic strength index for day t is defined as the sum of the weights of the first topic across all news articles published on that day.[10,11,12,13,14,15
Optimizing Healthcare Efficiency: The Role of Artificial Intelligence in Medical Records Management
Get PDFThe integration of artificial intelligence (AI) into medical records management is transforming healthcare delivery by optimizing patient care, research, and administrative processes. This research paper explores the multifaceted impact of AI on medical records management, elucidating its benefits, challenges, and future directions. Through a comprehensive analysis, the paper highlights AI\u27s role in automating data extraction and classification, streamlining clinical workflows, and enhancing decision support systems. AI-driven predictive analytics enable proactive intervention and personalized healthcare delivery, further optimizing resource allocation and patient outcomes. However, the adoption of AI in medical records management is accompanied by challenges, including algorithmic biases, data privacy concerns, and interoperability issues. Addressing these challenges necessitates collaborative efforts among stakeholders to ensure ethical, secure, and equitable AI adoption in healthcare settings. Looking ahead, future research may explore advancements in AI technology, such as semantic interoperability and federated learning, to unlock new opportunities for improving healthcare delivery. Through interdisciplinary collaboration and innovative solutions, AI holds the potential to revolutionize medical records management, paving the way for a more efficient, effective, and patient-centered healthcare system
A Survey of Object Classification and Detection Techniques in Assistance Systems for the Visually Impaired
No full textThe number of visually impaired individuals in the world is estimated to be 1 billion, as per WHO reports. Through a thorough examination of existing assistive technology and research, this paper provides a survey of object classification and detection techniques that are used in assistive technology by visually impaired individuals. We discuss the methodology’s drawbacks, s and functionalities of these techniques, and observe how sufficient they are in meeting the needs and requirements of the targeted users, and how they can be improved. As a result of this study, we identify areas with room for improvement in object detection in the assistive technology domain
Plant Health Monitoring System Using Machine Learning
No full textAgriculture has transformed into more than just a means to feed growing populations; it\u27s a crucial sector in India, engaging over 70% of the workforce and ensuring sustenance for a vast number of people. Plants play a pivotal role in ecosystems, supporting human life and wildlife by providing food. Preserving plant health is imperative, particularly in detecting diseases, as it directly impacts the quality and quantity of agricultural yields.This paper focuses on the technologies that are being used in plant health monitoring system which is being adopted nowadays in agriculture to make farming easy, for example image processing approaches for plant disease detection. Manually monitoring plant diseases is a difficult task. A manual plant disease monitoring system needs additional processing time and plant disease knowledge. As a result, a method for identifying plant diseases that is quick, automated, and accurate is required. As a result, image processing techniques are utilised to detect, process, and identify plant diseases since they are quick, automated, and accurate. Visualization is a traditional way of identifying diseases in plants, however it is not as effective in detecting diseases linked with plants. As a result, we can give a superior option, one that is both fast and precise, by employing image processing techniques that are more trustworthy than certain older methods