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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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 Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques
IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers. 2024 IEEE. -
Deep Insights into 3D Face Reconstruction from Blurred 2D Inputs: A Comprehensive Framework
This framework outlines a multi-stage methodology for 3D face reconstruction driven by advancements in deep learning. The process involves image preprocessing with deblurring techniques and subsequent feature extraction using CNNs alongside traditional methods. Deep learning adapts to diverse image challenges, ensuring accuracy in 3D reconstructions. In medical imaging, the proficiency of 3D CNNs and GANs shines in extracting structures from MRI and CT scans. Post-processing steps encompass mesh smoothing and texture mapping for enhanced visual quality. Evaluation metrics (MAE, RMSE, IoU) guarantee the precision of depth estimations. Applications of deep learning span across CNNs, 3DMM, GANs, and networks for landmark detection and dense correspondence. Challenges include optimizing eye reconstruction, expanding applications, and addressing concerns related to data quality, privacy, and hardware requirements. 2024 IEEE. -
Blockchain-Enabled Smart Contracts in Agriculture: Enhancing Trust and Efficiency
This study explores the important role of blockchain technology in the transformation of agriculture and presents a new way to integrate chatbots and smart contracts to solve the problem of persistence. Leverage the decentralized structure and security of the blockchain to increase traceability, transparency and fairness in agricultural product prices. A user-friendly chatbot built in Python using Tkinter that acts as a bridge between farmers and the Ethereum-based blockchain pricing algorithm. Smart contracts used in Solidity dynamically adjust crop prices based on the weather in real time, making it possible for prices to react and adjust. Simulations and tests in Ganache validate the proposed method, confirming its economic value and effectiveness in many agricultural cultures. This study delves into analytics, including latency and production time, to demonstrate the benefits of the blockchain model in creating transparent, farmer-centric and region-specific crop prices. The importance of this research is to support continuous change in agricultural technology, paving the way for the introduction of appropriate and fair prices. According to the amendment, the integration of advanced machine learning, further integration and collaboration with agricultural stakeholders should be developed in the future. This work sets a good path for agriculture, promoting transparency, fairness and quick access to the best crop prices, thus ensuring security and agricultural technology. 2024 IEEE. -
Predicting Song Popularity Using Data Analysis
In today's music landscape, predicting a song's success is crucial for musicians, record labels, and streaming platforms. This paper introduces a methodology for estimating popularity using Spotify data, termed the 'Proxy Popularity Score.' Three models - Random Forest, LightGBM Regressor, and XGBoost Regressor - are utilized for prediction. Performance metrics including mean absolute error, mean squared error, root mean squared error, and R-squared error are employed to evaluate model accuracy. Correlation values of 99.85%, 99.87%, and 99.84% are achieved for XGBoost, LightGBM, and Random Forest respectively. The study concludes with a ranking of songs based on predicted popularity scores. 2024 IEEE.