International Journal of Engineering and Management Research
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Sentiment Analysis of Social Media Data for Product and Brand Evaluation: A Data Mining Approach Unveiling Consumer Preferences, Trends, and Insights
Get PDFSentimental Analysis is an ongoing research field in Text Mining Arena to determine the situation of the market on particular entities such as Products, Services...Etc. This paper is a journal on sentiment analysis in social media that explores the methods, social media platforms used, and their application. It can be called a computational treatment of reviews, subjectivity, and sentiment. Social media contain a large amount of raw data that has been uploaded by users in the form of text, videos, photos, and audio. The data can be converted into valuable information by using sentiment analysis. We aim to collect details like Age, Gender, Education, Marital status, Salary, etc. So there requires data mining techniques like clustering. The Apriori Algorithm is the main algorithm used in our project. The Apriori algorithm is the general algorithm that can be used by developers according to their needs and implemented in their projects
A Study on Enhancing Government Efficiency and Public Trust: The Transformative Role of Artificial Intelligence and Large Language Models
Get PDFThis paper examines the transformative potential of Artificial Intelligence (AI), specifically Large Language Models (LLMs), in enhancing government efficiency and public sector service delivery. By integrating AI into various governmental functions such as automated administrative tasks, public safety, resource management, citizen services, policy development, and fraud detection, governments worldwide can significantly streamline operations, improve decision-making, and enhance citizen engagement. Detailed potential case studies from the United States’ IRS and local government agencies like SSA illustrate the successful implementation of AI, demonstrating its substantial benefits in operational efficiency and public satisfaction. The study concludes with strategic recommendations for further AI adoption, emphasizing the importance of robust governance, continuous technological investment, workforce training, and maintaining public trust. This research underscores AI\u27s critical role in modernizing government functions and fostering a more responsive and inclusive public service landscape
Smart RFID and IoT-Based Patient Monitoring Systems in Modern Healthcare
No full textRadio Frequency Identification (RFID) is used to identify the characteristics of an object wirelessly using radio waves. This project combines the power of RFID technology with real-time health monitoring in a hospital setting. Utilizing RFID tags for patient identification and asset tracking, the system seamlessly integrates with temperature sensor, blood pressure sensor and SpO2 sensor. The collected data is then transmitted to an Internet of Things (IoT) platform for continuous monitoring. The system not only ensures accurate patient identification and efficient asset management, but also provides real-time oxygen saturation, blood pressure rate and temperature data. This information is displayed on the LCD screen, offering healthcare professionals immediate insights into patient health. This innovative solution enhances patient care, promotes proactive medical interventions, and exemplifies the IOT in advancing healthcare
A Driver Health Monitoring-based Accident Prevention System for Commercial Vehicles in India
No full textIn the growing nation, road transportation is crucial. Accident rates will rise in tandem with an increase in road traffic. There are several categories in which accidents might arise. Among these reasons, one significant cause—the accident resulting from a heart attack—is chosen here. The majority of the time, it is seen that drivers who experience heart attacks abruptly lose control of their vehicles, which leads to accidents. The suggested system aims to address this problem by identifying heart attack symptoms early and automatically slowing down the vehicle. It also applies a gentle hand brake to bring the car to a stop, and it uses GSM services to send a GPS-coordinated message to the relevant department, such as the health department. The goal of the suggested approach is to raise the survival rate
Design and Assessment of Automatic Arc Welding Machine Based on Programming Logic Controller
Get PDFIn the industry, automated technologies have gained significant traction since they appreciably contribute to productivity and enable better, faster production with less worker management. Equipment used in welding processes in different locations might cause problems for the welder, particularly when large regions are needed. An Arc welding machine is commonly used in the Automobile industry, railroads, industrial piping, and bridge power plants. An arc welding machine requires skilled workers to be properly safe. The present research provides the design of an autonomous robotic arc welding system based on the benefits of employing programmable logic controller (PLC). In examine the benefits of PLC over traditional microcontroller solutions that are currently authorized for use in industrial settings, studies have been conducted on the behavior of a PLC responsible for carrying out the developed machine control functions. This study models automatic arc welding equipment that uses a PLC for safety purposes. The electrode is mounted on a welding tray that moves vertically up and down, joining sheets. The recently developed instrument is simple to use, accurate, precise, quick, cost-effective, and guarantees worker safety while in use
National Education Policy 2020: Paving the Path for Holistic Management Education in India
Get PDFThe National Education Policy (NEP) of 2020 signifies a paradigm shift in the landscape of education in India. The NEP 2020 envisages a transformative journey towards a holistic and multidisciplinary educational framework. This research delves into the sub-theme of "Focus on holistic, multidisciplinary learning instead of rote learning" within the context of management education. The research explores the rationale behind this shift, analyzing how the NEP 2020 aligns with the need for holistic education in the field of management. It examines various provisions of the policy to understand the policy\u27s inclination to break away from the conventional rote memorization model. It investigates how holistic learning approach fosters a more comprehensive understanding of management concepts, encouraging management students to connect theoretical knowledge with real-world applications. Furthermore, the research explores the integration of multidisciplinary learning and incorporation of Indian Knowledge System (IKS) within the management curriculum. The NEP 2020 advocates for a flexible and broad-based education system, allowing students to explore diverse subjects and develop a well-rounded skill set. This approach not only enhances the adaptability of future managers but also nurtures a holistic perspective essential for effective leadership in today\u27s dynamic business environment. Through an in-depth analysis of the NEP 2020 provisions and their implications for management education, this research sheds light on the challenges and opportunities presented by the shift from rote learning to holistic, multidisciplinary learning. The findings contribute valuable insights into the ongoing educational reforms in India for educators, policymakers and stakeholders in the management education sector
Enhancing Security Measures in Edge Computing for Financial Services
Get PDFEdge computing presents a promising frontier for financial services, offering real-time data processing and reduced latency. However, the decentralized nature of edge networks introduces significant security challenges. This research explores current security vulnerabilities specific to edge computing in financial services and proposes a professional approach to enhance security measures. By integrating robust authentication, encryption protocols, and proactive monitoring strategies, financial institutions can mitigate risks and safeguard sensitive data in edge environments effectively. Yet, the dispersed nature of edge networks presents notable security challenges that demand careful consideration.
Edge computing has fundamentally transformed data processing by decentralizing computation closer to the point of data generation, thereby reducing latency and enhancing efficiency across various sectors, including financial services (Shi et al., 2016). However, the widespread adoption of edge devices and decentralized data processing introduce significant security challenges, particularly for financial institutions that manage vast amounts of sensitive data (Yigit et al., 2018). These institutions are prime targets for cyber threats amidst the distributed computing landscape (Aazam et al., 2016).
This research aims to tackle these challenges by proposing comprehensive security measures tailored specifically for edge computing environments in the financial sector. Edge computing represents a paradigm shift in how computational tasks are executed, optimizing real-time decision-making and operational efficiency in diverse industries (Shi et al., 2018). Yet, the expanded attack surface of edge networks necessitates robust security frameworks to mitigate risks effectively (Mao et al., 2017).
By adopting these measures, financial organizations can uphold the confidentiality, integrity, and availability of sensitive data processed at the edge. This proactive approach not only addresses current security concerns but also establishes a foundation for trust and resilience in the evolving digital landscape of financial services. As edge computing continues to shape the industry, prioritizing robust security frameworks becomes increasingly imperative to safeguarding sensitive financial information and maintaining regulatory compliance
Consumers Perspectives on Adoption and Promotion of Millet-Based Products
Get PDFThis study explores consumer attitudes towards millet-based foods and identifies strategies to enhance their market penetration. Findings reveal widespread awareness of millets and a positive perception of their health benefits. However, challenges such as limited availability and price sensitivity hinder widespread adoption. The research aims to analyse factors influencing consumer purchasing behaviour towards millet-based products, examining health outcomes and consumer satisfaction associated with these foods. Furthermore, it seeks to understand consumer preferences in the context of purchasing millet-based foods and snacks, thereby identifying emerging market opportunities in this sector. Recommendations include expanding educational efforts, improving distribution channels, emphasizing health benefits in marketing, offering competitive pricing, diversifying product offerings, and innovating new millet-based products. Addressing these recommendations can unlock the market potential of millet-based foods, promoting healthier diets and economic growth in the food industry. A total sample of 273 is taken from Consumers. The data have been collected using Structured Questionnaire. For the analysis purpose various tools are used such as Pie chart, Bar chart, Ranking, SPSS (Statistical Package for Social Sciences) technique such as Chi Square and Correlation are used
Analyzing Financial News Sentiment with NLP to Forecast Market Trends
Get PDFIn the era of information explosion, public sentiment, significantly influenced by various information sources including major portals and social media, plays a pivotal role in shaping financial markets. Investor and consumer emotions are highly susceptible to news, rumors, and comments, as exemplified by the "GameStop vs. Wall Street" event where social media influence led to a surge in GameStop\u27s stock value, peaking at $30 billion—over 100 times higher than its value in August. Key social media figures, such as Elon Musk and Donald Trump, can also drastically affect markets with a single tweet, as seen with the Bitcoin and Dogecoin booms following Musk\u27s endorsements.
Market sentiment can escalate in cycles of optimism, amplifying positive news and diminishing negative news, or vice versa in periods of pessimism, leading to market despair. This research employs a BERT model for sentiment analysis of financial emotions and integrates Ensemble Empirical Mode Decomposition (EEMD) with machine learning for financial market analysis, including the commodities and stock markets.
The study utilizes the latest data, including monthly (from January 2005 to September 2020) and weekly (from January 7, 2005, to October 2, 2020) gold price data points. The dataset is strategically divided into an 8:2 ratio for training and testing, with 151 monthly data points for training, 38 for testing, and 657 weekly data points for training, alongside 165 for testing. The Mean Absolute Percentage Error (MAPE) is used to gauge the forecasting model\u27s accuracy, a standard metric for assessing predictive model performance.
By integrating EEMD with correlation analysis, this paper aims to elucidate the volatility of gold price closing values and identify the primary drivers of market fluctuations. The robust methodological framework presented enhances the precision of gold price predictions, offering valuable insights for investors and market analysts
Reach, Resonance and Relevance of Social Media Influencers on Generation Z and Alpha Generation
No full textSocial media influencers (SMIS) are playing an increasingly dominant role in marketing, especially towards Gen Z and Gen Alpha. This study explores how these generations perceive SMIS and how it influences their consumer behavior, purchasing decisions, and lifestyle choices. Gen Z and Gen Alpha are particularly susceptible to influencer marketing due to their digital nativity and reliance on social media for information and entertainment. The study defines three key characteristics of SMIS: reach, resonance, and relevance. Reach refers to the number of people a SMI can Reach with their content. Resonance refers to the level of connection and engagement an influencer has with their audience. Relevance refers to how closely aligned a SMI content is with a brand and its target audience. The study highlights the implications of these findings for marketers, influencers, and scholars. Marketers can leverage influencer Marketing to reach their target audience and influence consumer Behavior by choosing SMIS with high reach, relevance, and resonance. Influencers can learn how to build deeper connections with their audience and create content that resonates with Gen Z and Gen Alpha. Scholars can use this research to understand how generational dynamics interact with technology and inform future studies on consumer behavior and digital influence. Overall, this study provides valuable insights into the growing importance of SMIS in the digital age and their impact on consumer behavior, particularly among Gen Z and Gen Alpha