The retail industry is undergoing a paradigm shift driven by advancements in technology and evolving consumer expectations. In this context, the Internet of Things (IoT) has emerged as a transformative force, offering innovative solutions to revolutionize traditional retail operations and enhance the shopping experience for consumers. This project presents the design, development, and implementation of an IoT-based Smart Shopping Trolley—a cutting-edge solution that leverages RFID technology, integrated communication modules, and microcontroller-based control mechanisms to redefine the retail experience. The IoT-based Smart Shopping Trolley is designed to address the inefficiencies and limitations of traditional retail operations, offering retailers and consumers alike a seamless, intuitive, and personalized shopping experience. By integrating RFID technology, the Smart Shopping Trolley enables real-time inventory tracking, automated checkout processes, and personalized promotions, leading to improved operational efficiency, enhanced customer satisfaction, and data-driven decision-making for retailers.
To investigate the factors contributing to fluctuations in market capitalization, identifying key drivers that influence changes in company valuations
Published Date - 01 May
Market capitalization is the total market value of all existing shares of a company and is believed to be one of the key elements affecting the status of a company in the financial market and investor perception (Larcker & Tayan, 2020). Capitalization of the market may react to many factors, for example, financial performance, the nature of investor sentiment, the state of the economy, and external events (Moradi et al. 2021). Being aware of these networks will save a lot of headaches for investors who value companies for their strategic decisions. The study commences by assessing the key determinants of share price swings and investigating the effects of earnings announcements, industry developments, and macroeconomic indicators on equity capitalization and company valuation.
An Intrusion Detection Framework With Classification Using Support Vector Machine
Published Date - 01 May
Intrusion detection is a very important component of security technologies which include adaptive security appliances, intrusion and detection systems in other words intrusion prevention systems, and firewalls. There rises an issue with the performance of different intrusion detection algorithms that are deployed. The effectiveness of intrusion detection relies on accuracy, which must be raised in order to reduce false alarms and boost detection rates. Recent work has employed techniques such as multilayer perceptron and' support vector machine (SVM) t' to address performance difficulties. These methods have drawbacks and' be ineffective when applied to' massive data sets, such systems and' network data. Large volumes of traffic data are analyzed by the intrusion detection system; thus, an effective classification method is required to solve the issue. This study examines this matter. Popular machine learning methods are used, including' random forest, extreme learning machine (ELM), an' SVM. These methods' reputation stems from their ability to' classify different data. Utilized by the NSL-knowledge discovery and data mining data set, which are regarded as a standard for ' attacker’ intrusion detection systems. The outcomes show that ELM works better'n alternative strategies!!!!
A REVIEW ON APPLICATIONS OF MACHINE LEARNING TECHNIQUES IN DIABETES HANDLING
Published Date - 01 May
Diabetes mellitus is a chronic metabolic disorder affecting millions of people worldwide. Early diagnosis and effective management of diabetes are crucial to prevent complications and improve patient outcomes. In recent years, the integration of machine learning (ML) techniques has shown promising results in aiding diabetes diagnosis and management. Various Machine learning techniques can be used like Support Vector Machine (SVM), K Nearest Neighbour (KNN), Random Forest, Decision Tree in detection and treatment of diabetes mellitus. This paper provides an overview of different machine learning applications used in diabetes handling. In addition, it also discusses how to use ML in this discipline. It is concluded that ML techniques is a good approach for controlling diabetes mellitus at an early stage.
Balancing Data Privacy and Processing in Higher Education: An Evaluation within Hyderabad's Jurisdiction
Published Date - 01 May
This research delves into the intricate balance between data privacy and processing in higher education institutions in Hyderabad, India, focusing on the notion of privacy and the significance of spatial and psychological seclusion to safeguard individuals' private affairs. It is anchored in the landmark 2017 Supreme Court verdict on the Right to Privacy, spurred by Justice K.S. Puttaswamy's challenge to the Aadhaar scheme in 2012. Commencing with outlining objectives and hypotheses, the paper conducts a comprehensive literature review and discusses the research methodology. Findings from surveys involving stakeholders aged 18 to 54, representing central, state, and private universities, reveal noteworthy issues regarding data security measures. Analysis juxtaposes hypotheses, findings, and prior research, addressing emerging technologies, legal frameworks, and diverse stakeholder perspectives. Conclusions advocate for enhanced technological infrastructure, strengthened legislation, increased training initiatives, and mechanisms for reporting concerns. Additionally, the paper underscores the necessity for long-term studies across various cultural contexts, engaging interdisciplinary experts to advance data privacy practices in higher education.