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Malpractice Detection in Examination Hall using Deep Learning
Various institutions administer tests at designated examination locations, chosen third-party and approved centers, and have established standards for installing CCTV cameras and conducting frisking under the supervision of designated personnel. Some institutions are using online proctoring, which enables students to take exams from any location. In all of the aforementioned scenarios, human monitoring is conducted, and maintaining a high level of vigilance may be challenging due to administrative oversight or intentional allowance of malpractice for personal gain. The malpractice detection may be attributed to acts like as plagiarism, unauthorized sharing of papers, and non-verbal communication. The study is conducted by capturing the dataset in the classroom of Christ University. The proposed approach is based on the YOLO framework. The movies are processed in real time to identify hand rotation, paper extraction, and classify the motion. The accuracy for the Head_right class is significantly higher than that of the Head_left class. The system is implemented using the programming language Python and has the potential for future expansion to provide real-time monitoring. 2024 IEEE. -
Analyzing Dual-Stage Inverter Performance for Solar Grid Integration
This paper presents a comprehensive analysis of the performance of dual-stage inverters in the context of solar grid integration through simulation. Dual-stage inverters are increasingly recognized for their potential to enhance the efficiency and reliability of solar power systems by mitigating grid disturbances and optimizing energy extraction. Through detailed simulation studies, this research evaluates key performance metrics such as grid stability, power quality, and energy conversion efficiency. The simulation environment enables the exploration of various operational scenarios and system configurations to assess the versatility and robustness of dual-stage inverter solutions. Furthermore, the study investigates the impact of control strategies and parameter variations on the overall performance of dual-stage inverters, providing valuable insights for system optimization and design. 2024 IEEE. -
Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification
Classification is one of the primary tasks in data mining and machine learning which is used for categorizing data into classes. In this paper, brain MRI images are used for classification of tumors into three categories namely, Meningioma, Glioma, and Pituitary Tumor. These methodologies used are spatial based, depth based, feature map based and depth based CNN showcasing the power of deep learning in automating the tumor detection process. To evaluate the performance of several deep learning models, data is divided into training and testing data where a generalization method is used for comparison. The experimental results demonstrate promising accuracy, showing that a few techniques are valuable tools for radiologists and physicians, along with further analysis. The best accuracy obtained is 96% using MobileNet and ResNet50 in comparison to other CNN methodologies used in this paper. 2024 IEEE. -
A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models
Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE. -
Secure Decentralization: Examining the Role of Blockchain in Network Security
Blockchain generation has emerged as a novel answer for securing decentralized networks. This technology, which was first created for use in crypto currencies, has received enormous interest in recent years because of its capability for boosting protection in various industries and community protection. The essential precept at the back of block chain technology is the decentralization of statistics garage and control. In a decentralized network, no central authority may control the statistics. Rather, the facts are shipped amongst multiple nodes, making it immune to tampering and single factors of failure. One of the most important advantages of blockchain in community protection is its capacity to offer cozy and transparent communication amongst community customers. Through cryptographic techniques, block chain can affirm the identities of network participants and ensure the authenticity of records trade. This feature is extraordinarily valuable in preventing unauthorized access and facts manipulation. 2024 IEEE. -
Analysis of Nine Level Single-Phase Cascaded H-Bridge Inverters for EVs
This paper explores the design and operation of a Modular Nine-Level Inverter (MLI)-Electric Vehicle (EV) charging system, incorporating solar energy to power domestic loads and charge EVs. The system comprises a solar panel, DC-DC regulator, and MLI for efficient energy conversion. The MLI's modular design reduces complexity and enhances efficiency. Equivalent circuits illustrate voltage level generation, while PWM control regulates power device switching for precise output control. Performance metrics, including regulated DC supply voltage and staircase nine-level output voltage, demonstrate the system's capability for diverse applications. A nearly sinusoidal current waveform and harmonic analysis underscore the system's effectiveness in delivering stable power with reduced harmonic distortion. Comparisons between filtered and unfiltered output highlight the importance of filtering techniques in improving power quality. Overall, the MLI-EV charging system showcases advancements in renewable energy integration, offering a versatile solution for sustainable electricity generation and EV charging. 2024 IEEE. -
Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique
Cardiovascular diseases (CVDs) pose a significant global health challenge. Early and accurate diagnosis is crucial for effective treatment. This research focuses on developing a robust classification system for CVDs using machine learning techniques. This study proposes an enhanced Random Forest (RF) model optimized for big data environments and explore the potential of CNN-based classification. By leveraging medical imaging data and employing these advanced algorithms, we aim to improve the accuracy and efficiency of CVD diagnosis. 2024 IEEE. -
Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach
The number of possibilities to analyze educational data using data mining techniques is expanding, with the goal of improving learning outcomes. There is an explosion in data produced by online and virtual education, e-learning platforms, and institutional IT. Using these statistics, teachers could gain valuable insights into their students' learning habits. Academic performance of students and other useful information can be analyzed with the help of educational data mining. Model training consists of three primary steps: data preprocessing, feature selection, and training the model. To eliminate unwanted problems like noise and redundant attributes, data preparation is necessary. By prioritizing which features to calculate, the mRMR algorithm lowers calculation costs. Feature selection plays a crucial role in training A-CNN-BiLSTM models. The suggested approach routinely outperforms BiLSTM and CNN, two state-of-the-art algorithms. With a data accuracy percentage of 96.57%, it's clear that there was a significant improvement. 2024 IEEE. -
Quantum Computings Path toSupremacy: Progress in the NISQ Epoch
Quantum computing leverages the principles of quantum mechanics for information processing, with qubits serving as the fundamental units of quantum information. Qubits are quantum states where information processing can be engineered. Qubits possess the unique ability to encode, manipulate and extract information, enabling remarkable parallelism in computation. This enhanced computational speed, called quantum supremacy, promises to transcend established complexity boundaries. Significant strides have been made in demonstrating quantum supremacy through various experiments, most notably Googles 2019 experiment utilizing the Sycamore quantum processor to solve a problem that would stymie classical supercomputers for millennia. Other research groups, such as the Chinese team employing Jiuzhang and Zuchongzhi quantum processors, have achieved similar feats, showcasing the profound computational capabilities of quantum computers. It is essential to underscore that quantum supremacy does not signify quantum computers superiority across all tasks; current quantum computers remain constrained in their applicability, excelling primarily in specific problem domains. Nevertheless, recent advancements in quantum computing are noteworthy and ongoing development promises to expand their problem-solving capacities. This paper offers an introductory overview of quantum computing and an assessment of three prominent quantum supremacy experiments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model
The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. 2024 IEEE. -
Enhancing Digital Citizenship Through Secure Identification Technologies in the Global Unified Digital Passport
Passports play a vital role in enabling international movement and security, as well as confirming one's identity. However, the existing passport system has many problems and limitations, such as identity fraud, passport falsification, human smuggling, terrorism, and border control. Despite the fast growth and adoption of digital technologies in various fields, the passport system has not been able to adapt to the changing demands and expectations of the global community. Therefore, there is a pressing need to investigate and develop a digital passport and verification system that can address the shortcomings of the conventional passport system and provide a more safe, convenient, and effective way of managing and verifying the identity and travel history of individuals across the world. This paper presents the solution and requirement for the development of a digital passport system that can be applied globally and universally. The paper proposes a conceptual framework and a technical architecture for the digital passport system, based on the principles of blockchain, biometrics, and cryptography. The paper also discusses the possible benefits, challenges, and implications of the digital passport system for various stakeholders, such as travelers, governments, airlines, and immigration authorities. The paper aims to contribute to the research and innovation of digital identity and citizenship, as well as to the progress of the sustainable development goals (SDGs) related to peace, justice, and strong institutions. 2024 IEEE. -
Machine Learning Insights into Predicting Crude Oil Prices
This research paper delves into the complexities of crude oil, highlighting its extraction, composition, and transformation into valuable derivatives. Examining the pricing dynamics, it explores the intricate interplay of social and economic factors that shape crude oil's value, emphasizing its critical role in global energy and industrial sectors. A forecasting model is introduced, focusing on key factors - heating oil, SPX, GPNY, and EU DOL EX - utilizing five machine learning models. Historical data reveals the efficacy of conventional models, particularly Random Forest, in predicting crude oil prices, enhanced by feature engineering techniques. The paper concludes by suggesting avenues for further exploration, offering valuable insights for readers in this dynamic research domain. 2024 IEEE. -
A Study of Factors Affecting the Adoption of Digital Currencies
Digital currency has taken into the world slowly but steadily, rising in the leads of trades and commercialization, which can create a huge impact on the economic wellbeing. Digital currencies can be further classified into Cryptocurrencies, Virtual currencies and Central bank digital currencies. In this research we study thefactors of adopting digital currencies. Primary data has been collected using structured questionnaire. A total of 140 responses are used for the purpose of analysis. We have used correlation and heatmap foranalysing the impact of the identified factors such as Technological, Economical and Social. 2024 IEEE. -
Classifying AI-generated summaries And Human Summaries Based on Statistical Features
In an age where artificial intelligence knows no bounds, it's crucial to know if the textual content is reliable. But, the task of identifying AI-generated content within vast volumes of textual data is a big challenge. The existing studies in feature-based classification only explored prompt-based text responses. This paper explores methods to identify AI-generated summaries using feature-based machine-learning techniques. This study uses the BBC News Summary dataset. The summaries for the dataset are then generated using three of the top-performing summarisation models. Different statistical features like Zipf's Law Score, Flesch Reading Ease Score, and the Gunning Fog Index are used for extracting features for the classification model. The aim is to differentiate AI-generated summaries from human-written summaries. The main part of the study involves extracting the statistical features from the summarized texts, which are then classified using different classification models. Different models like Support Vector Machine (SVM), Random Forest, Decision Tree, and Logistic Regression models are used in the paper. Grid Search is also used to fine-tune SVM for the best results. The right model depends on what the need is. Whether it's accuracy, F1 score, or a mix of both, there are different options to lead you to the truth. The feature-based approach in this paper helps in more explainable classification and can compare how statistical text features are different for human-written summaries and generated summaries. 2024 IEEE. -
Innovation Characteristics, Personality traits and their impact on Fintech Adoption-P2P Lending
This paper investigates moderating influence of innovation attributes on the perceptions of Peer-to-Peer or P2P lending users and the influence of innovativeness traits on instrumental beliefs regarding the adoption of P2P lending. Two technology adoption theories were combined to develop the conceptual map denoting antecedent factors. Using 464 responses, structural equation modeling analysis was used to test the hypotheses. Performance expectancy, effort expectancy, social influence, and perceived compatibility were salient antecedents of P2P lending adoption. Perceived compatibility moderates the relationship between performance expectancy, facilitating conditions, and buying intentions. Innovativeness trait predicts performance expectancy and effort expectancy of P2P lending users. 2024 IEEE. -
A Quantitative Analysis of Trading Strategy Performance Over Ten Years
This study conducts a comparative analysis of two trading strategies over a ten-year period to assess their profitability and risk. Strategy 1 operates on a simple buy at close and sell at open principle, while Strategy 2 trades only when the closing price is above the 200-day moving average, introducing a conditional filter for market entry. Through the evaluation of performance metrics including total PNL, drawdown, standard deviation, and Sharpe ratio, the research highlights the differences in risk and return between the strategies. Results indicate Strategy 1 achieves higher profitability but at the cost of greater risk, as shown by larger drawdowns. Conversely, Strategy 2's conditional approach yields slightly lower returns but demonstrates a superior risk-adjusted performance. The findings emphasize the significance of risk management and the potential benefits of conditional filters in trading strategies, offering valuable insights for traders and investors in making informed strategy selections. 2024 IEEE. -
Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach
In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem. 2024 IEEE. -
Charting the Future of Fintech: Unveiling Finoracle through an In-depth Comparison of LLAMA 2, FLAN, and GPT-3.5
The research paper compares three Large Language Models (LLMs): LLAMA 2, FLAN, and GPT-3.5, in summarizing financial technology (fintech) news. Using 100 articles and the Rouge scoring system, it focuses on LLAMA 2's superior performance in creating concise and precise summaries. The study also introduces FinSage, a new framework utilizing LLAMA 2, promising to enhance fintech text analysis and decision-making. It concludes that LLAMA 2 sets a new standard for AI in financial data processing and analysis. 2024 IEEE. -
User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews
The digital revolution has led to significant changes in the global as well as Indian banking sector. The introduction of mobile banking apps has provided increased convenience to customers, who can now avail various banking services remotely. Thus, it is imperative to study the customers' sentiments regarding these applications and find scope for improvement, so that customers can seamlessly operate their bank accounts without having to visit bank branches. Thus, the primary purpose of this research is to study the perceptions of customers towards mobile applications of six major banks in India. A sample of 3000 reviews left by users of these apps was scraped from Google Play Store and sentiment analysis was conducted using RoBERTa-base model from the Transformers library. This was followed by topic modeling using Latent Dirichlet Allocation to find the aspects that are most important to the users. Results revealed that user experience is majorly driven by customer support service, features and functionality of apps, and app performance. Our findings shall help banks identify key areas of improvement so that they can work on enhancing overall customer experience. Despite the growing popularity of mobile banking, this study is the first of its kind in Indian context. 2024 IEEE. -
Understanding the use of Regression Analysis in Business Analytics to understand the perceptions of Students about Quality in Higher Education
For a very long time, researchers in a variety of fields have utilized regression analysis as a crucial tool for data analysis and result interpretation. Regression analysis has also been widely employed in the business world to determine what factors influence consumers' decisions to purchase any of the company's products. Comprehending the interplay of these variables will enable the business to conduct a more thorough consumer analysis and boost sales. This essay is an attempt to comprehend students' perceptions on the qualities they consider important while applying to universities. Regression analysis is another approach used in this article to determine how the quality criteria affect the respondents' overall happiness. 2024 IEEE.