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Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Beyond the Stats: How Investment Decisions Are Influenced by Non-Accounting Data
Making investment decisions is a complex process that is influenced by data from non-accounting and accounting sources. In order to better understand the importance of financial reports in comparison to non-accounting data [1], this article examines this complexity. The study is guided by three main objectives: determining the relative importance of financial reports against non-accounting sources; determining the effect of non-accounting information on investment decisions [2]; and investigating the role of demographic factors on this effect. The study finds that, when making investment decisions, shareholders more frequently turn to non-accounting sources through thorough analysis and statistical testing. Notably, credit rating agencies, stock indices, and brokers all have a big say in how decisions are made, highlighting their significance. This work improves our knowledge of how accounting and non-accounting data interact to influence investment decision-making. It emphasizes how crucial it is to take into account a variety of information sources in order to make wise financial decisions [3]. When navigating the ever-changing market landscape of today, investors, financial analysts, and politicians can benefit greatly from these ideas. 2024 IEEE. -
SmartHealth: Personalized Diet and Exercise Plans Using Similarity Modeling
Due to the growing prevalence of chronic diseases stemming from unhealthy lifestyles, a personalized approach to patient care is crucial. This paper delves into a system that utilizes cosine similarity and Pearson correlation to generate tailored diet and exercise plans, effectively managing chronic diseases. The system focuses on common chronic conditions like diabetes, hypertension, and thyroid disorders. Through sophisticated similarity modeling for diet and exercise, the proposed system provides integrated and personalized lifestyle recommendations, outperforming non-personalized or basic rule-based systems. 2024 IEEE. -
IoT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology
Extracting coal from Underground mine is a hazardous and tough job that needs continuous monitoring of environmental conditions to protect workers health and safety. Though some research works have explored wireless monitoring devices for underground mining, such as ZigBee and Wi-Fi technologies, they come with inherent restraints for instance restricted coverage, susceptibility to interference, reliability issues, security concerns, and high-power consumption. An Enhanced Safety Monitoring System for coal extraction from Underground Mines, employing LoRa communication technology for the effectual transmission of collected data to overcome existing challenges is discussed in this paper. The proposed system consists of two subsystems, one for monitoring the status of miners and another for comprehensive monitoring. LoRaWAN (Long Range Wide Area Network) is a multipoint protocol and this media access control (MAC) enables low-power devices to establish communication with Internet of Things (IoT) applications over extended wireless connections for long-range networks. LoRaWAN operates on lower radio frequencies, thereby providing a longer range of communication. This technology is known for its efficiency in optimizing LPWAN, offering extended range, extended battery life, robustness, and cost-effectiveness, making it highly suitable for industrial mining applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Quantum Convolutional Neural Network for Medical Image Classification: A Hybrid Model
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs' application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs' utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs' robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques. 2024 IEEE. -
Deep Learning Based Age Estimation Model
To improve accuracy and resilience in demographic categorization, this research presents a novel use of Convolutional Neural Networks (CNNs) for age prediction. Deep learning is utilized to achieve this goal. Precise estimation of age has become essential in a variety of areas, including human-computer interaction, marketing, and healthcare. The ability of CNNs to handle the intricacies of facial features for accurate demographic forecasts is examined in this study. The research covers every step of the age prediction process, including dataset collection, prepossessing, model architecture, and assessment measures. The CNN is trained to automatically extract hierarchical characteristics from facial photos, which enables the model to recognize complex patterns related to age. The architecture's flexibility to different lighting conditions, facial expressions, and postures. In this research, we deal with deep learning-based perceived age estimation in still-face pictures. Our Convolution Neural Network models (CNNs) have been trained prior on Image Net for picture classification, as they use the VGG architecture. In addition, we analyze the effects of tailoring over Web photos having known age, considering a lack of apparent age-annotated annotated images. In addition, this work adds to the increasing library of studies on the use of deep learning methods for demographic data evaluation by showing the effectiveness of CNNs to predict age. The results show how, in practical situations, CNNs could significantly enhance the precision and dependability of age prediction systems. 2024 IEEE. -
Explainable Artificial Intelligence: Frameworks for Ensuring the Trustworthiness
The growing computer power and ubiquity of big data are allowing Artificial Intelligence (AI) to gain widespread adoption and applicability in a wide range of sectors. The absence of an explanation for the conclusions made by today's AI algorithms is a significant disadvantage in crucial decision-making systems. For example, existing black-box AI systems are vulnerable to bias and adversarial assaults, which can taint the learning and inference processes. Explainable AI (XAI) is a recent trend in AI algorithms that gives explanations for their AI conclusions. Many contemporary AI systems have been shown to be vulnerable to undetectable assaults, biased against underrepresented groups, and deficient in user privacy protection. These flaws damage the user experience and undermine people's faith in all AI systems. This study proposes a systematic way to tie the social science notions of trust to the technology employed in AI-based services and products. 2024 IEEE. -
Ethical and Societal Implications of Artificial Intelligence in Space Mining
The advent of Artificial Intelligence (AI) in space mining marks a pivotal shift in the exploration and utilization of extraterrestrial resources. This paper presents a thematic analysis of the ethical, societal, technological, economic, and environmental implications of integrating AI in space mining operations. Through topic modeling of relevant literature, five key themes were identified: AI integration and ethical considerations, economic efficiency and equity, technological innovations and advancements, international collaboration and governance, and environmental sustainability and planetary protection. These themes highlight the potential of AI to revolutionize space mining, enhancing efficiency and enabling the extraction of valuable resources beyond Earth. However, they also underscore the need for robust ethical frameworks, equitable economic models, international cooperation, and sustainable practices to address the multifaceted challenges posed by this frontier. The paper concludes with recommendations for future research and policy-making, emphasizing the importance of inclusive, collaborative approaches to ensure the responsible and beneficial advancement of space mining. 2024 IEEE. -
Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach
This research includes an innovative approach to refine natural gas price predictions by employing advanced machine learning techniques, including Random Forest, Linear Regression, and Support Vector Machine algorithms. Against the backdrop of natural gas's increasing influence in the energy sector, both environmentally and economically, the study adopts a robust methodology using a comprehensive dataset from Kaggle. Through rigorous data preprocessing, feature engineering, and model training, the chosen algorithms are optimized to capture complex patterns within the data, demonstrating the potential to significantly enhance forecast precision. The application of these techniques aims to extract meaningful insights, providing stakeholders in the natural gas market with more accurate and reliable predictions, there by contributing to a deeper understanding of market dynamics and informed decision- making. 2024 IEEE. -
Optimizing Food Production with a Sustainable Lens: Exploring Blockchain Technology in Raw Plant Materials and Organic Techniques in Achieving Sustainable Development Goals
Amidst a rising population and mounting environ- mental concerns, India seeks a transformative approach to ensure food security and sustainable agriculture by 2030, as outlined in Sustainable Development Goal 2 (SDG 2). This research explores the immense potential of organic farming methods and raw plant materials to unlock this vision. Plants have a wealth of unrealized potential that extends beyond their conventional functions. The study looks at how different plant parts, like branches, leaves, stems, and even "waste"materials, can be used in a variety of ways to increase self-sufficiency, lessen environmental impact, and access renewable resources. Case studies from across the globe highlight this potential, highlighting the many advantages for the environment and communities. Additionally, the study investigates the innovative use of blockchain technology to promote a more transparent and resilient agricultural environment in India. Imagine blockchain-powered climate-smart practices, safe and transparent transactions, and precision agriculture led by sensor data. Water-efficient irrigation, environmentally friendly pest control, and strong traceability systems are all part of this vision, which aims to strengthen the Indian agricultural sector's resilience. The study suggests a framework of customized policy recommendations centered on non-losable farming methods in recognition of the need for wider implementation. This framework, created especially for the Indian context, supports the promotion of agrotourism, improved education and extension services, accessible financial risk management tools, and the smart redistribution of subsidies. The research highlights the transformative potential of this approach by highlighting the many benefits of these practices, including the environmental (less water use, increased biodiversity, improved soil health, and carbon sequestration), social (better community resilience, food security, farmer income, preservation of cultural heritage, equitable trade), and economic (premium market access, lower input costs, and higher yields) gains. In the end, this research offers a strong plan of action for India to greatly advance SDG 2 and create a more sustainable future for all of its people. A food system that feeds people and the environment can be developed by carefully using organic farming methods and unprocessed plant resources in conjunction with successful legislative initiatives. 2024 IEEE. -
Enhancing Workplace Efficiency with The Implementation of the Internet of Things to Advance Human Resource Management Practices
Improving human resource management via the use of the Internet of Things (IoT) is the focus of this research. The primary objective is to enhance productivity in the workplace. The researchers utilized a mix of qualitative (describing) and quantitative (numbers-based) techniques to collect and analyses data. This research shows that key HR KPIs are positively affected by using IoT in HRM. Businesses utilizing IoT for real-time monitoring have better operations and more engaged employees. The study found that state-of-the-art technology, extensive training, and effective change management were needed to overcome people's security concerns and unwillingness to change. The Internet of Things may transform HRM and corporate operations, according to study. According to study, companies should invest in people-focused technologies and services. It emphasizes creating a workplace that embraces new technology while prioritizing security and privacy. In conclusion, the study's results may help organizations navigate HRM and the IoT's changing terrain. It suggests linking HR and technology to improve workplace flexibility and efficiency. 2024 IEEE. -
Critical Estimation of CO2Emission Towards Designing a Framework Using BlockChain Technology
The automobile industry is a significant global contributor of carbon footprint this industry has impacted climate change, the research explores the existing methods of carbon footprint tracking and creates a framework by applying blockchain technology by connecting all the countries into one system as blockchain carries the capability to do due to its transparency, security and immutability the proposes of decentralised framework for real time tracking quarterly and implementing the necessary policies to mitigate the raising emission. The methodology encompasses of data analysis of using time series analysis globally and focusing certain parts of the world to show the emissions and creating a design that can help us in tracking the carbon footprint making all over the countries around to participate in suggesting to create a pathway for the future generations a better world as advance technologies come into the world for better ways to save the environment. 2024 IEEE. -
Artificial Intelligence (AI) in CRM (Customer Relationship Management): A Sentiment Analysis Approach
The use of customer relationship management (CRM) in marketing is examined in this essay. It looks at how CRM makes it possible to use reviews, integrate AI, conduct marketing in real time, and conduct more regular marketing operations. CRM tactics are illustrated through case studies of businesses like Uber, T-Mobile, Amazon, Apple, and Apple. CRM offers centralized data, better marketing and sales, and better customer support. There is also a discussion of the ethical, private, security, adoption, and scalability challenges of AI in CRM. In general, CRM makes data-driven decisions and customer insights easier to achieve to increase growth, loyalty, and engagement. 2024 IEEE. -
IOT-Enabled Supply Chain Management for Increased Efficiency
Deep learning methods have demonstrated potential Supply chain is a set or group of people as well as companies responsible for producing goods and getting it to their consumers. The producers of the raw materials are the first links in the chain, and the vehicle that delivers the finished goods to the client is the last. Lower costs and higher productivity are the benefits of an efficient supply network, which emphasizes the importance of management of supply chain. The internet of things, or IoT, is a network of mechanical and digital technology that can communicate with one another and send data without the need for human contact. Smart items were included into the conventional supply chain system to increase intelligence, automation potential, and intelligent decision-making. The existing supply chain system is offering previously unforeseen chances to increase efficiency and reduce cost. The aim and motive of our research is to analyze the methods of supply chain management where the main elements of IoT in management of supply chain will be highlighted. 2024 IEEE. -
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.