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Factor Analysis for Portfolio Returns: Investigating How Macroeconomic Factors Impact the Performance of the equity Portfolio
This paper investigates the complex relationship between macroeconomic factors and equity portfolio performance using regression analysis. In today's volatile financial environment, it emphasizes the importance of understanding how variables such as interest rates, inflation, money supply and GDP influence investment outcomes. Exact statistical techniques and historical data from a specific time period are used to uncover hidden factors affecting portfolio returns, with a particular focus on interest rates, inflation, money supply, and GDP. The goal of the research is to provide a comprehensive understanding of how these macroeconomic factors influence the equity investments. 2024 IEEE. -
Detection of Lung Cancer with a Deep Learning Hybrid Classifier
This article presents a deep learning framework combining a convolutional neural network (CNN) and a support vector machine (SVM) for lung cancer diagnosis. The model uses data divided into six groups: 250 images in the training set and 150 images in the test set. The work includes preliminary data and development using the Keras image data generator, VGG-16 architecture, high-level rules, and SVM classifier training with labels and vectors. The model achieves 90% accuracy with 85% selection impact and 75% cross-validation flexibility using VGG-16 and SVM hybrid classifier. This study finally revealed the classification of the model by multi-class ROC curve analysis and confusion matrix. 2024 IEEE. -
A Novel CNN Approach for Condition Monitoring of Hydraulic Systems
In the dynamic landscape of Industry 4.0, the ascendancy of predictive analytics methods is a pivotal paradigm shift. The persistent challenge of machine failures poses a substantial hurdle to the seamless functioning of factories, compelling the need for strategic solutions. Traditional reactive maintenance checks, though effective, fall short in the face of contemporary demands. Forward-thinking leaders recognize the significance of integrating data-driven techniques to not only minimize disruptions but also enhance overall operational productivity while mitigating redundant costs. The innovative model proposed herein harnesses the robust capabilities of Convolutional Neural Networks (CNN) for predictive analytics. Distinctively, it selectively incorporates the most influential variables linked to each of the four target conditions, optimizing the model's predictive precision. The methodology involves a meticulous process of variable extraction based on a predetermined threshold, seamlessly integrated with the CNN framework. This nuanced and refined approach epitomizes a forward-looking strategy, empowering the model to discern intricate failure patterns with a high degree of accuracy. 2024 IEEE. -
Emprical Study of Crypto Currency and its Adoption Among Indians
This paper investigates many factors that impact cryptocurrency awareness and acceptance in the Indian market. Data were obtained from 376 volunteers of various ages across India. The following paper presented a framework based on EFA (Exploratory Factor Analysis), CFA (Confirmatory Factor Analysis), and SEM (Structural Equation Model). Technology awareness, recommendations to others, attitude, social influence, and openness to technical education were all responsible for bitcoin adoption. Meanwhile, trust and perceived risk were not accountable for the adoption of crypto currency. No significant factors directly responsible for the adoption or abandonment of crypto currencies were mentioned in the papers that were read. The Indian market is still not thoroughly studied regarding crypto currency and the population using it. It would create a massive opportunity for crypto currency to operate in the Indian market once the factors responsible for crypto currency adoption are known 2024 IEEE. -
Customer Lifetime Value Prediction: An In-Depth Exploration with Regression, Regularization and Hyperparameter Tuning
In today's dynamic business environment, companies have been strategically shifting towards a customer-centric approach from their traditional product-centric focus. The main goal of this paper is to estimate customer lifetime value of 5,000 customers in the retail industry. This research follows a step-by-step approach to construct a multiple regression machine learning model. The model used in the study is based on the nine features to predict the customer life time value. First basic train-test split model is developed, which predicted 74% of variation in the customer lifetime value. This necessitates to improve the model performance, hence to address the multicollinearity problem lasso regularization is used. After lasso regularization , final model is trained with hyperparameter turning for further model performance improvement. The results show significant improvements in predicting customer lifetime value with the final model. This study suggests that the machine learning regression models can help to businesses to better understand how much value they can generate from individual customer.This deep understanding about customers helps retail businesses to align their customer engagement strategies to create a positive impact on the profitability and maximizing overall value offered to the customers. 2024 IEEE. -
Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP)
This study employs advanced machine learning algorithms to predict gold prices, using a comprehensive dataset from Bloomberg. The Gold Nexus Predictor (GNP), a key innovation, integrates historical data and economic indicators through advanced feature engineering. Methodologies include exploratory data analysis, model training with various algorithms like Linear regression, Random Forest, Ada Boost, SVM, and ARIMA, and evaluation using metrics like MSC, MAPE, and RMSE. The study's philosophical foundation emphasizes rationalism in economic forecasting and ethical model use. This research offers significant insights for investors and policymakers, enhancing understanding and decision-making in the gold market. 2024 IEEE. -
Enhancing Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health
Detection and effective remedies play a crucial role in revolutionizing banana crop health. The banana industry faces numerous challenges, including the prevalence of diseases and pests that can lead to significant yield losses. This paper explores the potential impact of detection techniques and remedies on improving banana crop management. Disease detection models based on machine learning, image processing and deep learning offer high accuracy in identifying diseases like Fusarium Wilt, Yellow Sigatoka, and Black Sigatoka. Implementing detection and targeted treatments can enhance crop productivity, reduce pesticide usage, and ensure sustainable banana production. 2024 IEEE. -
Facial Expression Recognition with Transfer Learning: A Deep Dive
In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. 2024 IEEE. -
Enhancing IoT Security Through Multilayer Unsupervised Learning and Hybrid Models
This research addresses the challenge of limited unsupervised learning in current IoT security research, which heavily relies on labelled datasets, hindering the detection of unknown threats. To overcome this constraint, the study proposes a sophisticated methodology integrating K-means clustering, autoencoders, and a hybrid model (combining both). The aim is to enhance detection capabilities without being reliant on prior labelled data. Emphasizing the need to go beyond traditional models, the research underscores the significance of incorporating a diverse range of smart home IoT devices to gain comprehensive insights. Tests conducted on the N-BaIoT dataset, which incorporates authentic traffic data from nine commercial IoT devices afflicted with Mirai and BASH-LITE infections, demonstrate the effectiveness of the suggested models. K-means clustering demonstrates excellence in precision, recall, and F1-scores, particularly in Doorbell and Thermostat categories. The Hybrid model consistently achieves high precision and recall metrics across various device categories by leveraging the strengths of both Kmeans and autoencoder techniques. Notably, the Autoencoder model stands out for its exceptional ability to achieve a perfect 100% detection rate for anomalies across all devices. This study highlights the robust performance of the proposed unsupervised learning models, emphasizing their strengths and potential areas for refinement in enhancing IoT network security. 2024 IEEE. -
Cardless Society: Assessing the Role of Cardless ATMs in Shaping the Future of Financial Transactions
The ubiquitous ATM faces a critical crossroads in a world where the digital pulse is becoming more and more ingrained. The sound of plastic clicking, which used to be a comforting symbol of financial independence, is becoming less audible in the background noise of near-field communication and the Erie silence of digital scans. This study goes beyond the physical card and explores the unexplored world of cardless ATM technology, where security, convenience meet and innovation completely reimagines the process of getting cash. The meticulous analysis and potential use of technology can completely twist the dynamic rhythm of this world. 2024 IEEE. -
Examining the Partnerships between AI and Business Technologies in the Contemporary Environment
In the last 20 years, businesses and individuals have undergone significant changes. Firstly, people's lives have changed due to the availability of intelligent artificial intelligence (AI) devices, and businesses have begun to use these devices to generate revenue. Secondly, as technology advances, businesses are adopting new technologies and growing more reliant on them in order to increase revenue and better understand their clientele. In the current era of business, companies are dealing with significant environmental changes, such as technology advancements, public regulations, competitive advantages, and structural changes in the competitive market. Their business strategies are converted as a result of the aforementioned ecosystem changes, and they go on to overcome these environmental changes. The primary goal of the work is to more accurately analyze different AI-enabled business models for data analytics. In the era of artificial intelligence, it also discusses secure commercial transactions and platform learning business strategies. Its goal is to investigate the different business models that are in use in the market today and to give readers a better knowledge of these models by shedding light on their characteristics. 2024 IEEE. -
Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India
As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE. -
Determining the Antecedents and Consequences of Brand Experience: A Study to develop a Conceptual Framework
In the marketing literature, one of the most talked- about subjects is brand experience (BE). Through an examination of the numerous studies conducted by BE researchers, this report attempted to determine the significance of BE in the body of recent literature. This paper culminates in the creation of a conceptual framework that prospective investigators might utilize to discern the diverse pathways inside BE. 2024 IEEE. -
Understanding the use of Regression Analysis in Business Analytics to understand the perceptions of Students about Quality in Higher Education
For a very long time, researchers in a variety of fields have utilized regression analysis as a crucial tool for data analysis and result interpretation. Regression analysis has also been widely employed in the business world to determine what factors influence consumers' decisions to purchase any of the company's products. Comprehending the interplay of these variables will enable the business to conduct a more thorough consumer analysis and boost sales. This essay is an attempt to comprehend students' perceptions on the qualities they consider important while applying to universities. Regression analysis is another approach used in this article to determine how the quality criteria affect the respondents' overall happiness. 2024 IEEE. -
User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews
The digital revolution has led to significant changes in the global as well as Indian banking sector. The introduction of mobile banking apps has provided increased convenience to customers, who can now avail various banking services remotely. Thus, it is imperative to study the customers' sentiments regarding these applications and find scope for improvement, so that customers can seamlessly operate their bank accounts without having to visit bank branches. Thus, the primary purpose of this research is to study the perceptions of customers towards mobile applications of six major banks in India. A sample of 3000 reviews left by users of these apps was scraped from Google Play Store and sentiment analysis was conducted using RoBERTa-base model from the Transformers library. This was followed by topic modeling using Latent Dirichlet Allocation to find the aspects that are most important to the users. Results revealed that user experience is majorly driven by customer support service, features and functionality of apps, and app performance. Our findings shall help banks identify key areas of improvement so that they can work on enhancing overall customer experience. Despite the growing popularity of mobile banking, this study is the first of its kind in Indian context. 2024 IEEE. -
Charting the Future of Fintech: Unveiling Finoracle through an In-depth Comparison of LLAMA 2, FLAN, and GPT-3.5
The research paper compares three Large Language Models (LLMs): LLAMA 2, FLAN, and GPT-3.5, in summarizing financial technology (fintech) news. Using 100 articles and the Rouge scoring system, it focuses on LLAMA 2's superior performance in creating concise and precise summaries. The study also introduces FinSage, a new framework utilizing LLAMA 2, promising to enhance fintech text analysis and decision-making. It concludes that LLAMA 2 sets a new standard for AI in financial data processing and analysis. 2024 IEEE. -
Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach
In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem. 2024 IEEE. -
A Quantitative Analysis of Trading Strategy Performance Over Ten Years
This study conducts a comparative analysis of two trading strategies over a ten-year period to assess their profitability and risk. Strategy 1 operates on a simple buy at close and sell at open principle, while Strategy 2 trades only when the closing price is above the 200-day moving average, introducing a conditional filter for market entry. Through the evaluation of performance metrics including total PNL, drawdown, standard deviation, and Sharpe ratio, the research highlights the differences in risk and return between the strategies. Results indicate Strategy 1 achieves higher profitability but at the cost of greater risk, as shown by larger drawdowns. Conversely, Strategy 2's conditional approach yields slightly lower returns but demonstrates a superior risk-adjusted performance. The findings emphasize the significance of risk management and the potential benefits of conditional filters in trading strategies, offering valuable insights for traders and investors in making informed strategy selections. 2024 IEEE. -
Innovation Characteristics, Personality traits and their impact on Fintech Adoption-P2P Lending
This paper investigates moderating influence of innovation attributes on the perceptions of Peer-to-Peer or P2P lending users and the influence of innovativeness traits on instrumental beliefs regarding the adoption of P2P lending. Two technology adoption theories were combined to develop the conceptual map denoting antecedent factors. Using 464 responses, structural equation modeling analysis was used to test the hypotheses. Performance expectancy, effort expectancy, social influence, and perceived compatibility were salient antecedents of P2P lending adoption. Perceived compatibility moderates the relationship between performance expectancy, facilitating conditions, and buying intentions. Innovativeness trait predicts performance expectancy and effort expectancy of P2P lending users. 2024 IEEE. -
Classifying AI-generated summaries And Human Summaries Based on Statistical Features
In an age where artificial intelligence knows no bounds, it's crucial to know if the textual content is reliable. But, the task of identifying AI-generated content within vast volumes of textual data is a big challenge. The existing studies in feature-based classification only explored prompt-based text responses. This paper explores methods to identify AI-generated summaries using feature-based machine-learning techniques. This study uses the BBC News Summary dataset. The summaries for the dataset are then generated using three of the top-performing summarisation models. Different statistical features like Zipf's Law Score, Flesch Reading Ease Score, and the Gunning Fog Index are used for extracting features for the classification model. The aim is to differentiate AI-generated summaries from human-written summaries. The main part of the study involves extracting the statistical features from the summarized texts, which are then classified using different classification models. Different models like Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression models are used in the paper. Grid Search is also used to fine-tune SVM for the best results. The right model depends on what the need is. Whether it's accuracy, F1 score, or a mix of both, there are different options to lead you to the truth. The feature-based approach in this paper helps in more explainable classification and can compare how statistical text features are different for human-written summaries and generated summaries. 2024 IEEE.