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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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 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. -
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. -
Sentiment Analysis of Lenders Motivation to Use a Peer-To-Peer (P2P) Lending Platform: LenDenClub.Com
Peer-To-Peer lending platforms are becoming a good investment avenue for lenders to invest their money in borrowers, who need money for a different purpose. As lending and borrowing of money is facilitated by the P2P lending platform, it becomes necessary for the platform to understand the users and accordingly fine tune the 'User Interface' (UI) and 'User Experience' (UX) of the platform. For lending and borrowing to take place through a platform it is necessary to have an 'n' number of lenders who are ready to lend money to an 'x' number of borrowers. This study is specifically done to understand lenders' motivation to use P2P lending platforms. This is a unique research work as sentiment analysis of lenders' motivation to use these platforms has not been explored earlier. The sentiment analysis technique was used to examine lenders' sentiments towards the use of P2P lending platforms. The research results show that, ~ 70 percent of lenders showed motivation to use P2P lending platforms as an investment avenue in the future. As the P2P lending platforms are relatively new more research can be carried out in future. 2024 IEEE. -
A Textual Analysis of Panchatantra, Enhanced by Technology from the Psychological Perspective
This research paper offers a textual analysis of the portrayal of animals in the Panchatantra tales, leveraging technology, Natural Language Processing (NLP) for enhanced insights. The study focuses on the interplay of anthropomorphism and stereotypes within these narratives, delving into the diverse stereotypes associated with specific animals in the stories. This analysis enhances our understanding of animal portrayal in children's literature. Natural Language Processing (NLP) techniques like textual classification and thematic analysis have been employed to examine the underlying archetypes embedded within the tales to comprehend stereotypes. Through a close examination of literary examples employing AntConc, a corpus analysis software, this paper provides readers with a nuanced understanding of how anthropomorphism and stereotypes influence human perceptions of animals and contribute to our understanding of the natural world. 2024 IEEE. -
High Gain Miniature Antenna Arrays for 2.4 GHz Applications
In this paper, miniature corporate feed Four Element Array (FEA), Eight Element Array (EEA) and Sixteen Element Array (SEA) are presented. The proposed antenna arrays are created on Rogers Duroid 5880 substrate with permittivity 2.2 and thickness of 0.782 mm. Initially, a single element antenna was created, then it was used in a corporate feed network designed for the 4-element array. As an extension, the 4-element array was used as a template and created an 8-element array and 16-element array to achieve high gain and directivity at 2.4 GHz. The proposed FEA, EEA, and SEA exhibit reflection coefficients of -25.55 dB, -37.14 dB, and -30.61 dB respectively. The peak gains obtained are 11.5 dB, 13.67 dB, and 16.76 dB respectively for FEA, EEA, and SEA. Also, the directivity has improved corresponding to the increase in the number of elements. Therefore, it can be a suitable candidate for applicationswhere extended range and coverage with better signal quality and higher data transfer rates is a priority. 2024 IEEE. -
Analyzing Job Satisfaction, Job Performance, and Attrition in International Business Machines Corporation through Python
Since workers significantly impact the firm's operation, businesses invest heavily in them. They must deliver better and more excellent performance to compete with the increasing competition. Employee performance is becoming more and more important for business success and staying ahead of the competition, so companies are putting more money into things like training, growth centers, and careers. The target audience was the employees working in International Business Machines Corporation. The data was analyzed through the process of Exploratory Data Analysis using Python. There is a 0.002297 link between Job Satisfaction and Performance Rating, and a 0.002572 correlation between Work Life Balance and Performance Rating. The relationship between work-life balance and job involvement is -0.01462, indicating a negative impact on work-life balance for people who are heavily interested in their occupations. The study would help Human Resources Managers formulate their policies and understand the employees better in the current environment. Here, Job Satisfaction and Performance Rating served as mediators, and the findings show that their influence on Attrition is minimal at this firm. 2024 IEEE. -
Detecting Infectious Disease Based on Social Media Data Using BERT Model
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE. -
Sugarcane Leaf Disease Classification Using Convolutional Neural Network
Indian sugarcane is of good quality, and the country exports significant quantities of sugar and sugarcane-based products to various countries worldwide. However, the quality of the sugarcane can vary depending on the specific variety and growing conditions, and exporters need to ensure that the product meets the quality standards of the importing country. To maintain the quality of sugarcane during transportation, it is crucial to ensure that it is harvested at the right time and handled carefully during loading and unloading. However, the farmers cultivating the sugarcane face a significant issue dealing with bacterial infection in the plants. In order to stop the disease from spreading further, we use Convolutional Neural Networks in our article to extract information from sugarcane leaves and construct an algorithm that accurately classifies bacterial leaves. Using an image dataset that includes pictures of both healthy and ill plants, identifying the image's key characteristics, and using the image to help the classifier provide a reliable result are all included in the total process. Our study will save farmers time and effort by identifying the decaying plants by looking for bacterial patches on the leaves. 2024 IEEE. -
Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN
Stock price prediction is a crucial area of financial market research, having significant implications for investors, traders, and analysts. However, given the dynamic and intricate nature of financial markets - which are impacted by a wide range of variables such as economic statistics, geopolitical developments, and market sentiment - accurately projecting stock prices is intrinsically difficult. Conventional techniques frequently fail to fully capture these dynamics, producing predictions that are not ideal. Recurrent Neural Networks (RNNs), one of the most recent developments in machine learning, provide potential methods to overcome these obstacles. Despite their potential, the effectiveness of different RNN architectures in stock price prediction remains an area of active research. This study compares four Recurrent Neural Network (RNN) architectures - Simple RNN, Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BiRNN) - for forecasting the Nifty 50 index values on the Indian National Stock Exchange (NSE) from the year 2000 to 2021. Using a comprehensive dataset spanning two decades, we assess each model's performance using the metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The data shows that the BiRNN model regularly outperforms the other models in all criteria i.e., MAE, MAPE, and MSE, indicating higher predictive accuracy. This study adds to the existing research by offering useful insights into the usefulness of RNN models, especially that of the BiRNN model for predicting stock prices, specifically in the setting of the Nifty 50 index. Our findings emphasize BiRNN's potential as a stock price prediction model and open new options for future research in this area. 2024 IEEE.