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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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.