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AI-Driven Home Climate Optimization: The Role of ChatGPT in Enhancing AC Efficiency
The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized home automation, yet traditional air-conditioning (AC) systems still struggle with energy inefficiency. Our research presents a novel solution, integrating AI, IoT, and user-centric design with ChatGPT, to optimize AC systems responsively to occupants' needs. Our methodology employs ChatGPT's capability to analyze historical data, discern patterns, and provide intelligent recommendations for AC operation. This transcends the functions of standard smart thermostats through AI-driven decision-making, optimizing every AC operational moment for both comfort and energy conservation. The system's foundation in data-driven decisions ensures alignment with external and internal conditions, enhancing energy efficiency and user comfort. 2024 IEEE. -
Yoga Posture Recognition Using Image Processing
Yoga is an ancient Indian practice that focuses on maintaining balance through various techniques like asanas and meditation. Traditional Indian yoga involves physical postures, regulated breathing, meditation, and relaxation techniques. The practice, rooted in physical, mental, and spiritual disciplines, offers numerous benefits. In this paper, we present an approach for classifying four prominent yoga poses: Goddess Pose (Utkata Konasana), Tree Pose(Vrksasana), Dead Body Pose (Savasana), and Downward Dog Pose (Adho Mukha Svanasana) using image processing techniques. The proposed methodology leverages sophisticated feature extraction techniques that analyse the posture's shape to help capture the details of the posture like the centroid, eccentricity, convex hull, etc. The subsequent classification process employs Support Vector Machines (SVM) enabling accurate categorization based on the extracted features. This blend of traditional wisdom and modern technology offers a promising tool for automating posture recognition, benefiting yoga practitioners and instructors, and can be extended to other real-life scenarios like odd posture detection. 2024 IEEE. -
Examining how ERP-Enabled Supply Chain Integration Works within the Quadrants of the Four-Wheeler Industry
Enterprise Resource Planning (ERP) systems have become indispensable tools for managing complex supply chains in the automotive industry. This paper provides an in-depth examination of ERP-based supply chain integration within the four-wheeler automobile sector, focusing on its impact, challenges, and best practices. Through a comprehensive review of existing literature and case studies, we analyze the key components of ERP systems, their integration with supply chain processes, and the benefits they offer to automotive manufacturers. Additionally, we explore the role of ERP in enhancing operational efficiency, inventory management, demand forecasting, and customer satisfaction. Furthermore, we discuss the challenges associated with ERP implementation and provide recommendations for successful integration and optimization of ERP systems in the four-wheeler automobile industry. 2024 IEEE. -
Advancing the Evaluation of Oral Fluency in English for Specific Classrooms: Harnessing Natural Language Processing Tools for Enhanced Assessment
A crucial component of language learning and teaching is evaluating students' speaking abilities. Natural language processing (NLP) techniques have been employed recently in language assessment to automate the evaluation process and produce more impartial and reliable findings. In this study, we offer a speaking evaluation tool based on Natural Language Processing (NLP) that assesses a learner's speaking ability in real-time using cutting-edge algorithms. The instrument is altered to assess the fundamental facet of speaking skills - Fluency. As a result of the tool's immediate feedback, learners may pinpoint their areas of weakness and focus on honing their language abilities. The usefulness of the instrument was assessed through an intervention with a sample size of 30 students of the post-graduate students of a college in Pune, India. Python libraries, including random and re, were utilized to implement the algorithm. Data preprocessing involved accurate transcription of videos using an online tool and manual checking for corrections. Despite acknowledging limitations, such as potential biases in manually inserted hesitation markers, the study serves as a pivotal step toward automated fluency assessment, presenting exciting prospects for NLP and language education advancements. 2024 IEEE. -
'Enhancing Electricity Price Forecasting': Integrating Macro-Economic Factors And Renewable Energy Dynamics in A Machine Learning Approach
In the ever-evolving electricity market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates renewable energy, macro-economic indicators, and external factors to enhance forecasting accuracy. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the electricity market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in electricity price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, AdaBoost, and ARIMA, the study aims to improve prediction accuracy. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into electricity market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the electricity sector. 2024 IEEE. -
Global Analysis of Quantum Technology Discourse
he study provides a thorough exploration of the global quantum technology landscape, offering valuable insights for researchers, policymakers, and industry stakeholders. It employs advanced analytical methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) for topic modeling. The research focuses on understanding discussion intensity, geographical distribution, co-mentioning patterns among countries, prevalent topics, and keyword-based trends. Utilizing diverse datasets, the study employs heatmaps, network analysis, and thematic analysis to categorize textual data. Evaluation metrics like Topic Coherence and Network Centrality Measures contribute to a robust methodology.Key findings include dominant discussions on quantum computing and investment strategies, with focused attention on governmental roles in R&D and specific quantum computer research. Notably, there is a niche focus on quantum algorithmic risks in Australia. Document characteristics vary, with some blending multiple themes and others centered around a single topic. LDA topic modeling and network analysis identify key countries, showcasing global hotspots and potential collaborations in quantum technology discussions. 2024 IEEE. -
Nexus Between Credit Conditions, Financial Literacy, and Loan Accessibility Among Indian MSMEs
We examine the interplay among commercial bank loan terms, financial literacy, and formal loan accessibility for micro, small and medium enterprises (MSMEs). Despite recent strides in integrating MSMEs into commercial bank portfolios via micro-lending initiatives, persistent challenges hinder their access to formal credit. Drawing from empirical data and existing literature, this study explores the nuanced impacts of loan terms and financial literacy on SMEs ability to secure formal loans. Addressing gaps in prior research, we concurrently analyse borrower characteristics and credit regulations influence on formal loan accessibility. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Impact of ESG Practices on the Firm's Performance: A Longitudinal Study on Emerging Markets
This study investigated the relationship between business performance in emerging markets (BICS countries) and ESG disclosure scores. Overall, it did not find any correlation between different performance indicators and ESG scores. It's interesting to see that higher overall ESG scores were linked to greater share prices and earnings per share (EPS). This implies that businesses with robust ESG policies may ultimately perform better than others. The study emphasises how ESG may help create value and support sustainable corporate success in emerging markets. It highlights how crucial ESG is to investors, companies, and legislators. 2024 IEEE. -
Real-Time Fire Detection Through the Analysis of Surveillance Videos
The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain. 2024 IEEE. -
Resource Curse - Impact of Renewable Natural Resources on Economic Growth in the U.S. using ARDL Approach
The analyses of the resource paradox in the United States of 29 years are conducted by the econometric model of ARDL. The dataset taken for the study is from the source of World Bank. After testing the stationarity and cointegration of 4 independent variable and one dependent variables of Gross Domestic product, this study will be giving the conclusion of long term and short-term relations of the variables to show the existence of Resource curse in the US within the 29 years of dataset. Causation test shows that there doesn't exist any particular causal relations between the variables and hence there need to be thorough study in this phenomenon. 2024 IEEE. -
Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques
This study investigates the synergistic application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies in optimizing traffic management systems. Through a mixed-methods research design, it evaluates the potential of these technologies to enhance urban traffic flow and reduce congestion. The research emphasizes the critical importance of data quality, ethical considerations, and the selection of appropriate technological solutions based on specific urban traffic scenarios. Findings highlight the significant role of integrated AI and geospatial analyses in improving traffic predictions and operational efficiency. Future work will focus on developing more sophisticated models that ensure privacy, equity, and adaptability to new transportation trends. 2024 IEEE. -
Advanced Approaches for Hate Speech Detection: A Machine and Deep Learning Investigation
The prevalence of online social media platforms has led to an alarming rise in the frequency of cyberbullying and hate speech. This study uses a variety of machine-learning approaches and deep- learning algorithms to identify hate speech. The goal is to create a thorough and successful method for locating and categorizing hate speech on online networks. Our suggested approach intends to deliver a comprehensive solution to address the urgent problem of cyberbullying and hate speech in the digital sphere by leveraging the strength of these cutting-edge techniques. We work to make social media users' online experiences safer and more welcoming by identifying and addressing such harmful online actions. Through rigorous experimentation, we evaluate the efficacy of these methodologies, ultimately revealing that the Bidirectional Gated Recurrent Unit (Bi-GRU) outperforms the other employed techniques. The Bi-GRU model demonstrates superior hate speech detection capabilities, substantiated by robust performance metrics. This research contributes to the field by providing empirical evidence that deep learning models, such as Bi-GRU, can significantly advance hate speech detection accuracy. The findings underscore the potential of leveraging advanced neural architectures in the pursuit of fostering a more inclusive and respectful digital space. 2024 IEEE. -
An Analysis of Grimms' Transmedia Storytelling in the Age of Technology
This research paper delves into an intersection of traditional literature and transmedia storytelling, with particular emphasis on Grimms' tales and its television series adaptation. Providing young audiences with engaging and dynamic experiences, transmedia storytelling involves delivering a single story across numerous platforms. Utilizing narrative analysis, this research seeks to uncover hidden themes, character growth, and story dynamics by breaking down the complex presentation and structure of stories in diverse media. Natural Language Processing (NLP) techniques like thematic analysis, sentiment analysis, keyword sentiment analysis have been employed to examine the differences between the presentation of these stories in varied formats as well as evaluating audience reception. It also assesses the degree to which transmedia adaptations support the resuscitation of beloved children's books in popular culture. By incorporating digital surrealism and aspects of technology, this paper enhances our understanding of how traditional stories captivate audiences across various media forms while maintaining their timeless quality. 2024 IEEE. -
An Outlook of Gender Differential Happiness in India
Studies on happiness and subjective wellbeing, in general, are aplenty, but applying a gender lens to it is comparatively rare, especially in the Indian context. The social construction of gender roles will influence happiness being a subjective matter. This paper explores this idea of gender differential happiness in light of India's peculiar social and cultural context. Using the World Value Survey (WVS) for India (Wave 6) in 2012 and Ordinary Least Square (OLS) regression analysis, the study finds that self-reported happiness is gender differential in India. Factors such as marital status, educational attainments, managerial roles and thrust on women empowerment were found to be vital for happiness for all. However, there are visible patriarchal gender stereotype notions with factors such as individual autonomy and homemaking. 2024 IEEE. -
Impact of Digital Media Marketing on Consumer Buying Decisions
Digital Marketing has become one of the most discussed topics in the field of management in the recent past. With the advent of social media, digital marketing has even garnered more attention. It has directly or indirectly influenced the buying behaviour of the customers also. This paper has tried to understand the impact of digital marketing in influencing the impulsive buying behaviour of the customers. 2024 IEEE. -
Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence
Sentiment based stock price direction detection has been an exciting study in the field of finance which is drawing a lot of attention from the investor community. Sentiments are used to detect the changes in the stock price movements for the subsequent periods. Investor community uses these sentiments derived from news, celebrity speech and events to plan trading and investment strategies. Several studies have been done in the past with sentiments, but use of Earnings Call Transcripts (ECT) has not been explored for market intelligence hitherto. Standard dictionary based lexicons like Vader, AFINN and NRC have not performed well in finance as they are domain agnostic. There is a need to develop a financial lexicon based on the ECT corpora, which may provide a better lift over the standard lexicons. This study has observed that Vader has performed poorly as opposed to the newly developed financial lexicon. Machine learning based generative lexicon engine using Bayesian approach, which is termed as FNB Lex was developed in this research study to overcome the limitations of standard domain agnostic lexicons. The lexicon development was performed on quarterly Earning Call Transcripts (ECT) of sixteen IT companies spanning over ten years. The study also investigates the detection of inverse effect in stock price movements based on the sentiments of the previous period. Machine Learning (ML) models like Naive Bayes, FNB Lex, SVM and biLSTM were developed and their discriminatory powers were assessed. NB Lex provided much better lift in detecting the inverse effect as opposed to other models. 2024 IEEE. -
ELCCFD: An Efficient and Enhanced Credit Card Fraud Detection using Enhanced Deep Learning Principle
Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud Detection (ELCCFD) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN's output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFD methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems. 2024 IEEE. -
Exploring the Frontier: Space Mining, Legal Implications, and the Role of Artificial Intelligence
This analysis delves into the multifaceted dimensions of space mining and artificial intelligence, exploring technological advancements, legal challenges, environmental concerns, and ethical implications. Through topic modeling and sentiment analysis of 160 articles, five core themes are identified: Technological and Exploration Advances, Resource Extraction and Environmental Concerns, Legal and AI Integration, Ethical and Paradigm Shifts, and challenges and Innovations in Space Mining. The discussion highlights the optimistic yet cautious outlook on space mining, emphasizing the need for continued innovation, comprehensive legal frameworks, ethical stewardship, and environmental protection as humanity ventures into this new frontier. 2024 IEEE. -
Digital Water Dynamics: Analyzing VA Tech Wabag's Influence on India's Water Technology Landscape
This research delves into the transformative role of VA Tech Wabag in India's water technology landscape, amid the burgeoning challenges of water scarcity, pollution, and infrastructure inadequacies. Leveraging a comprehensive review of literature and fundamental analysis, the study underscores the global shift towards digitalization and sustainability in water management, situating VA Tech Wabag's initiatives at the forefront of this paradigm shift. Through innovative digital water solutions and large-scale infrastructure projects, the company has markedly enhanced water quality and availability across diverse urban and rural settings, underpinning its financial resilience and growth trajectory despite regulatory and fiscal hurdles. The discussion extrapolates the implications of these technological advancements, highlighting the company's commitment to environmental stewardship, community engagement, and the imperative for continuous innovation within a dynamic industry landscape. Conclusively, the paper affirms VA Tech Wabag's pivotal contributions to water security and resilience, advocating for future research on the scalability of digital water technologies and their long-term impacts on resource management. This study, enriched with specific data points and analyses, aims to offer a well-substantiated overview of VA Tech Wabag's influence on shaping a sustainable and efficient water technology ecosystem in India. 2024 IEEE. -
Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach
This research introduces a hybrid model for copper price prediction, employs advanced machine learning models (linear regression, random forest, SVM, Adaboost, ARIMA), and utilizes the SHAP method for model interpretability. The study focuses on transportation-related variables over a 10-year period from Bloomberg Terminal, employing STL decomposition for time series forecasting. Key features impacting copper prices are identified, emphasizing the significance of demand, transportation, and supply. The Random Forest model highlights the critical role of demand. Addressing transportation supply constraints is crucial for enhancing model output in the dynamic copper market. 2024 IEEE.