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Economic and Urban Dynamics: Investigating Socioeconomic Status and Urban Density as Moderators of Mobile Wallet Adoption in Smart Cities
This research paper examines the complex correlation between socioeconomic factors, urban density, and the acceptance of mobile wallet technology in smart cities. The study investigates how socioeconomic status and urban density influence the adoption of mobile wallets. Smart cities have experienced a significant increase in the adoption of mobile payment solutions such as Apple Pay, and Google Pay, noted for their technological innovation and ability to enhance living standards. These digital payment platforms provide ease, security, and efficiency, revolutionizing how individuals engage in financial transactions and navigate urban environments. The study examines the many aspects that impact this phenomenon, focusing on the significance of comprehending how socioeconomic status and urban density influence the acceptance of mobile wallets. The study utilizes a meticulous research technique, which involves evaluating the reliability and validity of constructs, analyzing Heterotrait-Monotrait (HTMT) ratios, conducting tests for discriminant validity, and doing variance inflation factor (VIF) analysis. These measures are taken to ensure the strength and reliability of the report's conclusions. The research's importance is further supported by model fit statistics and hypothesis testing conducted through bootstrapping. The results emphasize that the inclusion of mobile wallet functions, the legal framework, and the development of smart city infrastructure have a substantial influence on the acceptance of mobile wallets. However, the impact of urban density on mobile wallet adoption is more intricate and multifaceted. This study provides significant insights into the dynamic field of technology uptake in urban regions, with implications for politicians, entrepreneurs, and urban planners seeking to promote financial inclusion and technological integration in smart cities. 2024 IEEE. -
Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach
Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the Natural Language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition. 2024 IEEE. -
Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE. -
Enhancing Industrial Equipment Reliability: Advanced Predictive Maintenance Strategies Using Data Analytics and Machine Learning
In today's dynamic industrial landscape, optimizing machinery performance and minimizing downtime are paramount for sustained operational excellence. This paper presents advanced predictive maintenance strategies, with a focus on leveraging machine learning and data analytics to enhance the reliability and efficiency of industrial equipment. The study explores the key components of predictive maintenance, including data collection, condition monitoring, predictive models, failure prediction, optimized maintenance scheduling and the extension of equipment longevity. The paper discusses how predictive maintenance aligns with modern industrial paradigms. The study evaluated the performance of five popular forecasting models like Random Forest, Linear Regression, Exponential Smoothing, ARIMA, and LSTM, to estimate maintenance for industrial equipment. The effectiveness of each model was evaluated using a number of performance metrics. The percentage of the variation in the real data that the model can explain is shown by the R-squared number. The lowest MSE, RMSE, and greatest R-squared values indicate a model's accuracy. The study highlights practical implications across diverse industries, showcasing the transformative impact of predictive maintenance on minimizing unplanned downtime, reducing maintenance costs, and maximizing the lifespan of critical machinery. When it comes to predictive maintenance for industrial machinery, the LSTM model has been shown to be the most accurate and efficient model with the highest R-squared value, indicating a better fit and higher predictive ability. As technology continues to evolve, the paper discusses future directions, including the integration of artificial intelligence and advanced analytics, and emphasizes the importance of continuous improvement in refining predictive maintenance strategies for the evolving needs of industries worldwide. 2024 IEEE. -
Impact of AI Technology Disruption on Turnover Intention of Employees in Digital Marketing
The rapid integration of AI technology into the digital marketing sector has prompted a need to understand its effects on employee perspectives and behaviors. This study investigates how AI adoption influences job insecurity, turnover intention, and job mobility among digital marketing professionals. Addressing concerns about AI rendering roles obsolete is crucial for fostering a supportive work environment. Turnover intention, influenced by AI adoption and potential job dissatisfaction, offers insights into employees' commitment to the industry. Job mobility, influenced by growth prospects and alignment with AI-driven workplaces, sheds light on career aspirations. Our study involving 303 employees of digital marketing industry in India reveals that AI disruption significantly impacts turnover intention, with job insecurity mediating this effect. Additionally, mistreatment by superiors increases turnover intention. Overall, this research underscores the profound impact of AI technology on employees' attitudes, behaviors, and career decisions in digital marketing, providing valuable insights into their perceptions and engagement 2024 IEEE. -
MediCrypt: A Model with Symmetric Encryption for Blockchain Enabled Healthcare Data Protection
In the dynamic field of medicine, combining blockchain technology and data security becomes a vital strategy to solve the problem of protecting sensitive medical data. This study presents a new way to improve the security and privacy of medical data, using MediCrypt as an example of two- way encryption. Doctors initially used algorithms like AES or Blowfish to retrieve medical data. Smart contracts on the Ethereum-based blockchain introduce a layer of protection, combining SHA-256 with symmetric encryption technology. The multi-level transmission model includes encryption time, encryption time, elapsed time, and encryption size. Functionality in this model involves managing patient records (EHR), counterfeit drugs, drug reviews, clinical outcomes, and consent for all care areas. As shown in the methodology, the user ecosystem facilitates the exchange of information by defining the roles and responsibilities of doctors/pharmacists, administrators, and patients. The study shows the deployment of the MediCrypt model in three distinct stages. Distinct comparison of encryption time is done for different encryption algorithms. Also, parameters of MediCrypt model is compared with existing healthcare based blockchain models. 2024 IEEE. -
Flight Arrival Delay Prediction Using Deep Learning
This project is aimed to solve the problem of flight delay prediction. This problem does not only affect airlines but it can cause multiple problems in different sectors i.e., commercial (Cargo aviation), passenger aviation, etc. There are a number of reasons why flights can be delayed, with weather being the main one. Our goal in this study is to forecast flight delays resulting from a variety of reasons, such as inclement weather, delayed aircraft, and other issues. The dataset gives itemized data on flight appearances and postponements for U.S. air terminals, classified via transporters. The information incorporates metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. For the purpose of predicting flight delays, the outcomes of several machine learning algorithms are examined, including Ridge, Lasso, Random Forest, Decision Tree, and Linear regression. With the lowest RMSE score of 0.0024, the Random Forest regressor performed the best across all scenarios. A deep learning model using a dense neural network is built to check how accurate a deep learning model will be while predicting the delay and the result was an RMSE score of 0.1357. 2024 IEEE. -
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Due to advancements in infrastructural modulations, architectural design is one of the most peculiar and tedious processes. As the technology evolves to the next phase, using some latest techniques like generative adversarial networks, creating a hybrid architectural design from old and new models is possible with maximum accuracy. Training the model with appropriate samples makes it evident that the designing phase will be simple for even a layman by including proper parameters such as material description, structural engineering, etc. This research paper suggests a hybrid model for an architectural design using generative adversarial networks. For example, merging Romes architectural style with Italys will accurately and precisely recover the pixel-level structure of 3D forms without needing a 2D viewpoint or 3D annotations from a real 2D-generated image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Approximate Binary Stacking Counters for Error Tolerant Computing Multipliers
To increase the power and efficiency of VLSI circuits, a new, creative multiplying methodology is required. Multiplication is a crucial arithmetic operation for many of these applications. As a result, the newly proposed error-tolerant computing multiplier is a crucial component in the design of approximate multipliers that are both power and gate efficient. We have created approximative multipliers for several operand lengths using this suggested method and a 45-nm library. Depending on their probability, the approximation for the accumulation of changing partial products varies. In compared to approximate multipliers that were previously given, the proposed circuit produces better results. When column-wise generate elements are added to the modified partial product matrix using an OR gate, the output is usually accurate. The amount of energy used, and its silicon area have been considerably reduced in the suggested multiplier when compared to traditional multipliers by 41.92% and 18.47%, respectively. One of the platforms that these suggested multipliers are suitable for is the image processing application. 2024 IEEE. -
Level Shifted Phase Disposition PWM Control for Quadra Boost Multi Level Inverter
This article introduces a novel boost switched capacitor Inverter (NBSCI) that significantly advances existing designs. Many recently developed multilevel voltage source inverters stand out for their ability to reduce the number of DC sources while markedly improving voltage levels with fewer switching devices. Building on these advancements, our work proposes an innovative inverter arrangement that, utilizing 1 DC source, eight switches and 3 capacitors, achieves 9-level output voltage waveforms. The increased range of voltage levels facilitates the generation of high-quality sine wave output signals with minimal Total Harmonic Distortion (THD). Also, this work employs Level shifted - Phase Disposition (LS-PD) pulse width modulation techniques to generate gating signals, ensuring the production of superior output waveforms. The article also presents various simulation results conducted using MATLAB-SIMULINK, providing a comprehensive assessment of the proposed configuration's precise effectiveness under diverse modulation index. 2024 IEEE. -
Blockchain-Enabled Resume Verification: Architectural Innovations for Secure Credential Authentication in the Digital Era
In the contemporary digital landscape, the verification of resume credentials poses a significant challenge, with the integrity of such information being crucial for job seekers and employers alike. This paper presents an avant-garde architectural framework that utilizes blockchain technology to revolutionize the storage, verification, and sharing of resume information, thus ensuring an unparalleled level of security and reliability. Through the implementation of a decentralized ledger that is both immutable and tamper-evident, this innovative architecture facilitates the permanent recording of academic credentials, employment history, and professional accomplishments, thereby enabling immediate and verifiable access for potential employers and educational institutions 2024 IEEE. -
Characteristic Mode Analysis of Closed Metal Geometric Ring Shapes
In this study, the characteristic mode theory is used to better explain the physical behavior of a few simple closedshaped geometries. The bandwidth coverage, resonant behavior, and modal current distributions for several ringshaped geometries are shown and discussed. It has been demonstrated that the triangular, rectangular, and square ring geometries can result in multi-band performance, whereas the hexagonal, circular, square, and triangular rings are promising candidates for circularly polarized antenna designs. 2024 IEEE. -
Brain Tumor Prediction Using CNN Architecture and Augmentation Techniques: Analytical Results
The brain, a complex organ central to human functioning, is susceptible to the development of abnormal cell growth leading to a condition known as brain cancer. This devastating disease poses unique challenges due to the intricate nature of brain tissue, making accurate and timely diagnosis critical for effective treatment. This research explores automated brain tumor prediction through Convolutional Neural Networks (CNNs) and augmentation techniques. Utilizing a task reused learning approach with the help of VGG-16, Mobile-Net and Xception architecture, the proposed model achieves exceptional accuracy (99.54%, 99.72%) and robust metrics. This Research explores the Augmentation techniques to enhance the precision and accuracy of the model used. The study surveys related models, emphasizing advancements in automated brain tumor classification. Results demonstrate the efficacy of the model, showcasing its potential for real-world applications in medical image analysis. Future directions involve dataset expansion, alternative architectures, and incorporating explanation techniques. This research contributes to the evolving landscape of artificial intelligence in healthcare, offering a promising avenue for accurate and efficient brain tumor diagnosis. 2024 IEEE. -
An Outlook on Sustainable Business Practices through Virtual Reality Marketing
Technologies and businesses blend progressively and work towards creating a sustainable future through the company's marketing strategies. The purpose of the study is to find out the various sustainable outcomes of Virtual Reality Marketing (VRM). The exploratory research identified immersive experience, experiential economy, positive image creation, positive travel decisions, and repeat purchase as the constructs of VRM, and a total of 418 people were surveyed to analyze those constructs. The data were analyzed through statistical tests such as t-test, One-way ANOVA, and Chi-square with the help of SPSS software. The study shows a positive relationship between customers and virtual reality marketing. The results predict that businesses that have incorporated VRM tend to likely have a high-profit margin and more sustainable returns compared to their peer competitors. 2024 IEEE. -
Towards a Model: Examining the Positive Associations of Warmth, Competence, and Familiarity with Musicians' Attitudes Towards AI
This study investigates attitudes towards AI musicians through a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. Data analysis focuses on the interplay between Anthropomorphism Degree (AD), Listening Type (LT), Warmth (W), Competence (C), Attitude (A), and Familiarity (F). The sample comprises 211 valid responses from college students, exploring perceptions via a questionnaire. Results indicate significant positive associations between attitudes towards AI and Competence, Familiarity, and Warmth. However, predictive validity analysis suggests caution in relying solely on the PLS-SEM model. Importance-Performance Analysis (IPMA) highlights competence as the primary influencer of attitudes towards AI, emphasizing its critical role over Warmth and Familiarity. This study contributes to understanding the nuanced dimensions of human interactions with AI musicians. 2024 IEEE. -
Real-Time Cyber-Physical Risk Management Leveraging Advanced Security Technologies
Conducting an in-depth study on algorithms addressing the interaction problem in the fields of machine learning and IoT security involves a meticulous evaluation of performance measures to ensure global reliability. The study examines key metrics such as accuracy, precision, recall, and F1 scores across ten scenarios. The highly competitive algorithms showcase accuracy rates ranging from 95.5 to 98.2%, demonstrating their ability to perform accurately in various situations. Precision and recall measurements yield similar information about the model's capabilities. The achieved balance between accuracy and recovery, as determined by the F1 tests ranging from 95.2 to 98.0%, emphasizes the practical importance of data transfer in the proposed method. Numerical evaluation, in addition to an analysis of overall performance metrics, provides a comprehensive understanding of the algorithm's performance and identifies potential areas for improvement. This research leads to advancements in the theoretical vision of machine learning for IoT protection. It offers real-world insights into the practical use of robust models in dynamically changing situations. As the Internet of Things environment continues to evolve, the study's results serve as crucial guides, laying the foundation for developing strong and effective security systems in the realm of interaction between virtual and material reality. The Author(s) 2024. -
Priority-driven Unbalanced Transportation Problem (PUTP) to obtain better Initial Feasible Solution
In this paper, we tackle the Priority-driven Unbalanced Transportation Problem (PUTP), a scenario where total demand exceeds total supply. An innovative algorithm, the Penalty-driven Priority-driven Unbalanced Transportation Problem (PPUTP) is introduced to solve this challenge. PPUTP allocates supplies to high-priority demands by computing penalties and sequentially addressing the most penalized demands, thereby ensuring priority demands are met efficiently. A comparative analysis with Vogel's Approximation Method (VAM) across various problem sets ranging from 5x5 to 50x50 dimensions demonstrates the efficiency of our algorithms. PPUTP consistently shows lower percentage increments from the optimal solution, indicating its robustness in providing near-optimal solutions. This study highlights the importance of algorithm selection based on problem set dimensions and complexity in Priority-driven Unbalanced Transportation Problem, with PPUTP emerging as a versatile and robust solution across various scenarios. 2024 IEEE. -
Antecedents of Ethical Goods and Services Tax Culture among young adults - Special Reference to Maharashtra and Karnataka
Since the implementation of the Goods and Services Tax (GST) in 2017, it has become clear that this new Indian indirect tax system is here to stay. The Indian GST Council is continuously deliberating and making efforts to improve GST revenue collection at the state and central levels. The focus is now on the young adults in the country who will play a vital role in shaping the future of GST compliance. Their tax mentality and behaviour in contributing to GST revenue as daily consumers will determine the ethical tax culture in India. They need to understand how crucial their role is in discouraging evasive practices by sellers in the unorganised retail sector at the point of sale. The study utilized structural equation modelling to test the acceptability of the model. The process was supported by a structured questionnaire, with 324 respondents between the age group of 17-30 years. Understanding GST significantly influences acceptance of GST as a tax system, however, the acceptance of the GST tax system does not significantly lead to young adults discouraging the evasive behaviour of sellers in the unorganised retail sector at the point of sale. And, finally, the discouragement of evasive behaviour by young adults does influence the possibility of an ethical GST tax culture. The respondents majorly represented young adults between 17-20 years of age. The model has not measured the existence of covariance among the variables, nor has any mediating or moderating factors been identified, as GST tax culture in the Indian context is still unexplored and GST in itself is relatively new in the country. 2024 IEEE. -
ThermAI: Exploring Temperature Analysis Through Diverse Machine Learning Models
Meteorological forecasting is crucial in multiple industries, including agriculture, aviation, and daily routines. The objective of this inquiry is to improve temperature predictions by examining and comparing several machine learning methods, such as linear regression, decision trees, and random forests. This work aims to fill the gap in assessing machine learning models for temperature forecasting on a broader scale by utilising the comprehensive Indian meteorological dataset, which covers a wide range of geographical regions. The research utilises a thorough technique that includes gathering data, selecting relevant features, choosing appropriate models, and evaluating the results using R-squared and Mean Square Error metrics. The findings demonstrate that the Random Forest model surpasses both multiple linear regression and decision trees in terms of performance, displaying superior accuracy and reduced prediction errors. This study enhances proactive weather management and decision-making processes by offering valuable insights and tools to stakeholders in various industries. The work is organised into distinct sections that encompass a literature review, methodology, results, and conclusions, providing a comprehensive viewpoint on developments in temperature forecasting. 2024 IEEE. -
The Development of Structured Tele Based Medicine Concept Using Programmable System
In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for 'Video Adaptation framework, across security gateways, based on transcription,' Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform's versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer. 2024 IEEE.