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Detection of DoS Attacks Using Machine Learning Based Intrusion Detection System
Conventional intrusion detection systems are not always sufficient due to the increasing sophistication and frequency of Denial-of-Service (DoS) attacks. This work presents a novel solution to this problem by leveraging machine learning techniques to increase the precision and efficacy of real-time intrusion detection. The system keeps a careful eye on network traffic patterns, looking for any irregularities that would point to a denial-of-service attack. An Intrusion Detection System (IDS) that utilizes machine learning technologies - specifically, neural networks and support vector machines - allows for real-time adaptation to new attack patterns. A combination of rigorous simulations and real-world testing provides empirical support for the IDS's quick detection and mitigation of DoS threats. This initiative makes a major contribution to the development of cybersecurity defenses. 2024 IEEE. -
A Hybrid Approach for Predictive Maintenance Monitoring of Aircraft Engines
The realm of aircraft maintenance involves predictive maintenance, which utilizes historical data and machine parts' performance to anticipate the need for maintenance activities. The primary focus of this paper is to delve into the application of predictive maintenance of aircraft gas turbine engines. Our methodology involves assigning a randomly chosen deterioration value and monitoring the change in flow and efficiency over time. By carefully analyzing these factors, we can deduce whether the engines are at fault and whether their condition will deteriorate further. The ultimate objective is to identify potential engine malfunctions early to prevent future accidents. Recent years have witnessed the emergence of multiple machine learning and deep learning algorithms to predict the Remaining Useful Life (RUL) of engines. The precision and accuracy of these algorithms in assessing the performance of aircraft engines are pretty promising. We have incorporated a hybrid model on various time series cycles to enhance their efficacy further. Employing data collected from 21 sensors, we can predict the remaining useful life of the turbine engines with greater precision and accuracy. 2024 IEEE. -
Delay Minimization Technique to improve the efficiency of Parameter Optimized Hysteretic Current Controlled Parallel Hybrid ETPA in Mobile Communication
This paper proposes a delay minimization technique to improve the efficiency of a parameter-optimized hysteretic current-controlled parallel hybrid envelope tracking power amplifier (etpa). In a hysteretic current-controlled hybrid topology, a linear amplifier operates parallel with a hysteretic current-controlled switching converter. Block level simulation of etpa is performed using the simulink tool. The traditional parameter optimization technique is first implemented, and its limitation is analysed. The proposed delay minimization technique helps to overcome the limitation of the traditional approach and has been proven to be valid for any input frequency. The proposed technique offers an efficiency improvement of 14.9% compared to the traditional technique for an input frequency of 20mhz and provides an average efficiency improvement of 6.26% for an input frequency range of 2mhz to 60mhz. 2024 IEEE. -
Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks
Analyzes the existing method of detecting adversarial attacks on deep neural networks, proposed by researchers from Carnegie Mellon University and the Korean Institute of Advanced Technologies (KAIST) Ko, G. and Lim, G in 2021. Examines adversarial attacks, as well as the history of research on the topic. The paper considers the concepts of interpreted and not interpreted neural networks and features of methods of protection of the types of neural networks considered. The method for protecting against adversarial attacks is also considered to be applicable to both types of neural networks. An example of an attack simulation is given, which makes it possible to identify a sign showing that an attack has been committed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring the Influence of Ethnicity and Environmental Values on Eco-Entrepreneurship: A Structural Equation Modeling Approach
In today's world, sustainability is of immense importance due to population growth, pollution and resource depletion. Consequently, there is an urgent need to devise future-oriented strategies for sustaining life on Earth. The rise of green business and the Sustainable Development Goals (SDGs) reflect society's growing awareness and commitment to environmentally friendly living. Our research examines the link between eco-entrepreneurship and the SDGs among young adults who are the next generation of entrepreneurs. We aim to understand how these individuals plan to incorporate the SDGs into their future business. Conducted primarily through surveys of 17- to 26-year-olds, our research uses the Statistical Equation Model (SEM) to analyze the relationship between eco-entrepreneurship, the SDGs and today's youth. In addition, we examine how current educational practices influence young adults' attitudes toward sustainability. By delving into these aspects, our paper seeks to improve the understanding of how young adults, our future leaders, perceive and pursue green business and sustainable development goals, ultimately determining the importance of these concepts for our future. 2024 IEEE. -
Food Recommendation System using Custom NER and Sentimental Analysis
In today's fast-paced lifestyle, the need for efficient and personalized solutions is paramount, especially in the category of dining experiences. This research responds to this demand by proposing a better food recommendation system for Zomato reviews. It targets the audience who are not aware of the best cuisines and search for user reviews online. Utilizing custom Named Entity Recognition (NER) and sentiment analysis, the system seeks to understand and cater to individual food preferences extracted from user Reviews. Specifically, improving the analysis by extracting reviews for ten restaurants in the city of Kolkata. By providing a specific solution to address the current research gap in the area of restaurants recommendation systems, the system recommends top choices for neighboring restaurants and best food based on the sentimental analysis of the chosen menu items. 2024 IEEE. -
Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN)
Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. 2024 IEEE. -
EmploChain: A Blueprint for Blockchain-Driven Transformation in Employee Life Cycle Management
Integrating blockchain technology into human resource management presents both transformative opportunities and implementation challenges that need to be addressed. This paper proposes a blockchain-based EmploChain Framework, a decentralized ledger approach specifically designed to enable Employee Life Cycle Management by harnessing the potential of blockchain technology. The study looks at the potential benefits of the proposed framework, including increased security, transparency, and automation. The paper also looks at potential limitations like scalability concerns and implementation costs and explores the possible solutions to overcome them. The aim of this research is to provide a thorough understanding of the framework's implications, thereby facilitating informed decisions to implement EmploChain Framework for managing the Employee Life Cycle of an organization.. 2024 IEEE. -
Predicting Coal Prices: A Machine Learning Approach for Informed Decision-Making
This research addresses the critical need for accurate coal price prediction in the dynamic global market, crucial for informing strategic decisions and investment choices. With coal playing a vital role in the world energy mix, its price fluctuations impact industries and economies worldwide. The study employs advanced machine learning models, including Linear Regression, Random Forest, SVM, Adaboost, and ARIMA, to enhance prediction precision. Key features such as S&P 500, Crude Oil Price, CPI, Exchange Rates, and Total Electricity Consumption are identified through feature importance analysis. The Random Forest model emerges as the most effective, emphasizing the significance of key variables. Leveraging explainable AI techniques, the study provides transparent insights into model decision-making, offering valuable information for risk management and strategic decision-making in the volatile coal market 2024 IEEE. -
How AI and other Emerging Technologies are Disrupting Traditional HR Practices
With technology running and changing this whole generation and the way it works, this dynamic leads to changes in the conventional ways of Human resource management (HRM). The environment of HRM has shifted from traditional to modern with the use of various automation tools with the help of digital transformations that include Artificial intelligence (AI) in employee management, multiple software to track the applications, payroll, performance management systems. These have caused a drastic change in the basic traditional operations in human resource management. This paper is a study about how AI and various other emerging technologies have a significant effect on the workplace, the employees, and their mindset on the dynamic digital environmental transformation. 2024 IEEE. -
Precision Corn Price Prediction with Advanced ML Techniques
In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE. -
Uncovering User Attitudes and Satisfaction Levels with HRMS Applications: Insights from Sentiment Analysis
This study examines employee perspectives on various features and specifications of Human Resource Management System (HRMS) applications, as expressed in online discussion boards. An in-depth literature review was conducted to identify key factors, followed by topic modeling on unstructured text data. Sentiment analysis using the Li-Hu method and a tweet profile helped gauge employee satisfaction with HRMS applications. The findings suggest a moderate level of satisfaction among users, offering insights for companies to enhance user interfaces and software development. By addressing negative attitudes and fostering positive ones, businesses can cultivate better relationships with users. This research also aids in identifying top-performing HRMS applications in the market, highlighting the features and specifications that set them apart from competitors. Overall, the study serves as a valuable resource for organizations aiming to improve their HRMS offerings and user experiences. 2024 IEEE. -
Empowering Kirana Shops through Digital Ecosystem and Physical Infrastructure for Unprecedented Efficiency and Elevated Customer Experience
In today's evolving retail environment, it is important to ensure the sustenance of unorganised small retailers. Efforts should be made to make these retailers innovative and competitive. This study focuses on the need to upgrade the digital and physical infrastructure of Kiranas. Initially, researchers examined store physical layouts. Primary data analysis from Indian consumers via online surveys confirms the significance of store design. The layout directly influences impulse purchases. Unlike in modern retail stores where consumers often shop with family and friends, prompting unplanned purchases due to product visibility and tactile engagement, Kirana shops can capitalise on these behaviours. The study proposes an Artificial Intelligence (AI) model for Kirana shops, illustrating its potential value. AI-driven data analysis offers invaluable insights into operational dynamics, leveraging advanced algorithms to process vast datasets encompassing sales, inventory, and customer interactions. This approach enables uncovering intricate patterns, accurate demand forecasting, and optimising inventory levels, enhancing operational efficiency. Additionally, AI-driven sentiment analysis of customer feedback facilitates personalised marketing strategies, improving customer satisfaction. By enhancing infrastructure and embracing AI-based data analysis, Kirana shops can stay competitive, adapt to market changes, and ensure sustained growth in the evolving retail landscape. 2024 IEEE. -
Stock Performance Prediction of HRM Firms: A Machine Learning Approach Utilizing Info Edge and Quess Corp
HR firms help drive economic growth by facilitating the acquisition and retention of top talent, fostering innovation and optimizing operational efficiency. The stock prices of these firms serve as a nuanced representation of their standing in the market. However, predicting stock prices proves to be a complex task due to the dynamic nature of the market. This paper delves into finding the most effective approach for forecasting stock prices within the HR sector, employing a diverse range of machine learning techniques. The investigation encompasses utilizing statistical methods like Simple Moving Average, RSI, Stochastic Indicators, and VIX India data alongside 'Machine learning approaches such as Linear Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Gradient Boosting, and Neural Network.' To augment the analysis, a comprehensive study is conducted, integrating both top-performing and bottom-performing HRM firms (Info Edge Ltd and Quess Corporation) based on market capitalization. The outcomes derived from this study aim to lay the groundwork for future research endeavors in the realm of stock predictions specific to the HRM industry. 2024 IEEE. -
Decoding Customer Lifetime Value to Unlock Business Success with Predictive Machine Learning Approach
This study highlights how crucial customers are for a company's success who directly impacts revenue and overall business value. This study focuses on analysis of customer lifetime value, the research uses data from 5000 customers with 8 important features with the main goal of predicting customer lifetime value. Business leaders often face choices about where to invest in marketing, like loyalty programs, incentives and ads or nothing. The study suggests that customer lifetime value is a key metric for making smart decisions, which measures how much a customer spends over their time with a company. To predict this value, the research explored different machine learning models - linear regression, decision tree regressor, random forest, and AutoML regressor. Each model is checked for how well it predicts customer spending habits. The results show that AutoML regression stands out for its accuracy without overcomplicating things. This study offers insights for businesses looking to improve their customer-focused strategies and long-term success. 2024 IEEE. -
LegalMind System and the LLM-based Legal Judgment Query System
LegalMind-GPT represents a notable advancement in legal technology, specifically tailored for the finance sector. This research paper introduces LegalMind-GPT, a system that integrates Large Language Models (LLMs) to develop a Legal Judgment Query System for financial legal contexts. The study focuses on the application of LLMs, particularly LLAMA-2, Claude AI, and FLAN-T5-Base, for interpreting and analysing complex legal documents in finance. The aim is to evaluate the system's effectiveness in providing accurate legal judgments and insights. The comparative analysis of these LLMs shows that LegalMind-GPT, powered by these models, significantly improves the accuracy and efficiency of legal analysis in the finance domain. 2024 IEEE. -
Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing
Skin diseases are a major global health concern for which prompt and precise diagnosis is necessary for effective treatment. Convolutional neural networks (CNN), one of the deep learning techniques, have shown potential in automating the diagnostic procedure. The goal of this research is to enhance the effectiveness of skin disease categorization by fusing the capabilities of CNNs - particularly the VGG architecture - with the histogram equalization preprocessing method. In image processing, histogram equalization is a commonly used approach to enhance the contrast and general quality of medical photographs, which include photos of skin conditions. In order to improve the characteristics and details of dermatological pictures for this study, we employed histogram equalization as a preprocessing step. This allowed CNN to extract pertinent features more quickly. 2024 IEEE. -
A Study of the Influence of Investor Sentiment based on News and Event on the Cryptocurrency Market during Russia Ukraine War
As a new and emerging digital asset, Cryptocurrency has been traded for more than a decade, reaching a very high market capitalization and continuing to increase its volume of trading at a very rapid pace. Many countries have legalized or are considering legalizing cryptocurrency as a trading platform for this asset, and many companies worldwide accept it as a medium of exchange. As a result of this expansion, many researches in finance literature have focused on studying the efficiency of this cryptocurrency market. In line with this literature, this paper examines, using the abnormal returns and abnormal trading volumes methodologies, the dynamics of investors' reaction to the arrival of unexpected information like The Russia Ukraine War regarding the Cryptocurrency market in the context of the two hypotheses: the uncertain information and the efficient market hypotheses. 2024 IEEE. -
FIN2SUM: Advancing AI-Driven Financial Text Summarization with LLMs
In the modern financial sector, the rapid digitalization of financial reports necessitates efficient and reliable text summarization tools. This research introduces FIN2SUM, a novel framework designed for summarizing the managerial analysis and discussion sections of 10-K reports from top NASDAQ-listed companies. The study aims to evaluate Large Language Models (LLMs) in financial text summarization, highlighting LLAMA-2's adeptness in processing complex financial information, thus making FIN2SUM a vital tool for analysts and decision-makers. The methodology includes a thorough evaluation of three state-of-the-art LLMs - LLAMA-2, FLAN, and Claude 2 - using BERT and ROUGE scores. The research concludes that FIN2SUM, enhanced by LLAMA-2, significantly advances AI-driven financial text summarization. 2024 IEEE. -
Harnessing the Power of Simulation Games for Effective Teaching in Business Schools
This research delves into the effectiveness of simulation games, in business education specifically focusing on how they improve decision making skills, critical thinking, real world business applications, student engagement and problem-solving abilities. While simulation games are widely recognized as cutting edge tools that provide learning experiences beyond traditional methods there remains a gap in empirical research assessing their overall impact on educational outcomes. Using a combination of analysis and qualitative case studies this study seeks to address this gap by examining how simulation games influence factors in business education. The methodology involves using a one-way ANOVA to compare learning outcomes across business disciplines and conducting detailed case studies for context. The results reveal effects of integrating simulation games into curricula on the mentioned learning outcomes. These findings highlight the importance of incorporating simulation games into business education to enhance students learning experiences effectively. By offering insights on optimizing and tailoring the use of simulation games in education settings this study contributes to improving teaching practices in business schools and encourages research into the interaction, between educational technology and learning efficacy. 2024 IEEE.