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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Shukla, Garima Awasthi, Vanshaj Goswami, Prafull Singh, Sofia Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Indian Institute of Information and Technology Department of Computer Science and Engineering Sonipat India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies © 2024 IEEE.

关键词： Inductive power transmission

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A Novel Blockchain Framework for Enhancing Online Transactions

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International Virtual Conference on Industry 4.0, IVCI4.0 2021

作者： Shareef, Sk. Khaja Sridevi, R. Raju, V. Rama Rao, K. S. Sadasiva Computer Science and Engineering JNTUH Hyderabad India Computer Science and Engineering JNTUH College of Engineering Hyderabad Hyderabad India Computer Science and Engineering CMR College of Engineering & Technology Hyderabad Hyderabad India Department of MCA Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9789811999888

In the modern era, blockchain technology has essentially functioned in diverse facilities ranging from online applications like payment transactions, data transactions, healthcare information sharing, supply chain tracking, etc. The research is developed for online applications to exploit the blockchain concept in recording large data transactions. During the transaction, privacy protection is an important concern for securing the data in the cloud database. For this reason, numerous cryptographic algorithms were developed to end this issue. Therefore, in this research, a novel Homomorphic Serpent Blockchain (HSB) algorithm is developed to secure online transactions while avoiding security threats. Consequently, the performance of the proposed algorithm is compared with conventional techniques and attained the finest outcomes with high accuracy and less time duration. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Autotrack: a framework for query-based vehicle tracking and retrieval from CCTV footages using machine learning at the edge

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International Journal of Information technology (Singapore) 2023年第7期15卷 3827-3837页

作者： Sreedhar, Sreela Philip, Abin Oommen Sreeja, M.U. Department of Computer Science and Engineering Toc H Institute of Science and Technology Ernakulam India Department of Electronics Cochin University of Science and Technology Kochi India Department of Computer Science and Engineering-Cybersecurity Indian Institute of Information Technology Kottayam (IIIT Kottayam) Kottayam India

Automobile theft and hit and run incidents have become ever rampant than before urging the need for automated tracking systems. However, prompt detection of the vehicle involved in incident and recovery of the stolen vehicle is crucial as these are immediately forged to reduce the chances of getting caught. Hence, the proposed model puts forth a query-based framework to intelligently detect and track vehicles from CCTV surveillance footages using queries involving custom input features such as model, make, colour and license plate number. The model uses a combination of techniques for detecting the vehicle make and model, tracking vehicle, and for identifying the license plate number based on You Only Look Once (YOLO), Kalman filters, and Tesseract respectively. Further, the model performs vehicle detection and tracking from multiple surveillance videos which can ensure that the suspicious vehicle which is reported of either theft, license plate forgery or any unusual activity like hit and run cases is located immediately. The detection model is trained and deployed on edge device integrated to CCTVs and hence overcoming the drawbacks associated with a centralized system. The performance of the proposed vehicle detection model compared to baseline models on the basis of popular classification metrics precision, recall and f1-score proves the superiority of the proposed model compared to the existing ones. Additional experimental analysis of the entire framework further establishes the relevance of the combination of selected input features as well as emphasizes the significance of query-based models over its generic counterparts. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Edge computing Kalman filter Object detection Tracking Vehicle detection YOLO

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A Smart Car's Headlight Intensity Control System to Avoid Troxler's Effects at Night

A Smart Car's Headlight Intensity Control System to Avoid Tr...

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Saha, Shrabana Mondal, Md Ashifuddin Rehena, Zeenat Aliah University Department of Computer Science and Engineering Kolkata India Narula Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350311204

Traffic accidents are one of the major issues which have several reasons and Troxler's effect is one of them. Troxler's effect is an optical illusion of a human being due to a high amount of light falling on the eye. So, the purpose of this research work is to propose and implement such a system that can automatically control a car's headlight depending on the outside light intensity and upcoming vehicle distance. This proposed system used NodeMCU as a microcontroller to control the whole system where an LDR sensor is used to sense the outside light visibility and an ultrasonic sensor is used to detect the distance of the upcoming object. After recording the oncoming vehicle distance at a minimum distance at night it will set the headlight automatically by a low intensity which eliminates the problem of Troxler's effect and make each other visible. This model will help to prevent accidents by dynamically measuring the distance and adjusting the headlight. The system was practically tested through simulation and the results show that the proposed system works effectively to avoid road accidents. © 2023 IEEE.

关键词： Visibility

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Securing Neural Network-Based Personalized Medicine for Advanced Liver Cancer Detection in Healthcare

Securing Neural Network-Based Personalized Medicine for Adva...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kasturi, Salvadi Singh, Jagendra Agarwal, Mohit Kumar, Ashwini Mishra, Komal Sharma, Yogita Cvr College of Engineering Department of Computer Science Engineering Vastunagar India School of Computer Science Engineering & Technology Bennett University Greater Noida India Compucom Institute of Technology & Management Department of Computer Science Engineering Jaipur India Chandigarh University Department of University Institute of Computing Chandigarh India School of Management and Commerce Manav Rachna University Faridabad India

ISBN: (纸本)9798350382693

This research is aimed at discussing the critical meeting point of healthcare and artificial intelligence, including the need to protect the required treatment solution. Investigation into the utility of various deep learning techniques such as VGG 16, VGG 19, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN) for the prediction of liver cancer stages and their respective treatments has been completed. The generation of 2340 of medical images from multiple sources, including the internet and records of hospitals, as a dataset was performed in a secure cloud. The security measures have allowed only authorized personnel to access the data and ensured the privacy and secrecy of data. The created dataset was then separated into two parts: training, which consisted of training the model on 70% of images, and testing, which implied that 30% of images were used to test the generated model. For each of the run models, performance evaluation measurements such as accuracy, precision, recall, and F1 score were calculated. The results have shown the highest efficiency of VGG 19 model, which resulted in 98.44% of accuracy. For the generated model, a fictitious ROC curve and specificity rate were also provided to showcase the discrimination abilities of each of the submitted models. The results show that the utility of neural network-based personalized medicine is beneficial for the further progression of the early prediction stage of liver cancer and its treatment. The result showed that the proposed models has successfully contributed to the field of healthcare informatics and further opportunities for artificial intelligence application to increase patient outcomes in the form of personalized medicine that may be performed. © 2024 IEEE.

关键词： Convolutional neural networks

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Subject-Verb Agreement Error Handling using NLP: A Literature Review 15

Subject-Verb Agreement Error Handling using NLP: A Literatur...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Gautam, Prakhar Thakur, Jitendra Singh Mishra, Ashish Jabalpur Engineering College Department of Computer Science and Engineering Jabalpur India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India

ISBN: (纸本)9798350324433

Subject-Verb Agreement errors in English language are a common problem in Natural Language Processing. These errors occur when the Subject and Verb in a sentence do not agree in number or person, leading to a grammatically incorrect sentence. Over the past few decades, researchers have developed various approaches to automatically detect and correct these errors in text. In this paper, a comprehensive review has been provided of the existing literature on Subject-Verb Agreement error detection and correction in Natural Language Processing. This review study focuses on key techniques that are used in Rule-based, Machine learning, and Deep learning approaches to detect and correct the errors. The performance of the approaches is compared using various evaluation metrics, such as Accuracy, Precision, Recall, and F1 score on standard benchmark datasets. Additionally, the limitations and challenges of each approach are discussed, and potential directions for future research are suggested. This review will be helpful for researchers and practitioners in the field of Natural Language Processing, particularly those interested in Grammar error detection and correction in the English language. © 2023 IEEE.

关键词： Error detection

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Exploration of soliton structures and modulation instability analysis for the highly dispersive perturbed NLSE with sextic-power law refractive index

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Abdullah, Eman H. M. Ahmed, Hamdy M. Zaghrout, Afaf A. S. Bahnasy, Amal Ibrahim Ahmed Rabie, Wafaa B. Hashemi, M.S. Bayram, M. Department of Physics and Engineering Mathematics Higher Institute of Engineering El Shorouk Academy Cairo Egypt Department of Mathematics Faculty of Science Al Azhar University Cairo Egypt Department of Physics and Engineering Mathematics Higher Institute of Engineering and Technology Tanta Egypt Department of Mathematics Basic Science Faculty University of Bonab Bonab Iran Computer Engineering Biruni University Istanbul Turkey

This study introduces some novel soliton solutions and other analytic wave solutions for the highly dispersive perturbed nonlinear Schrödinger equation with generalized nonlocal laws and sextic-power law refractive index. This equation models the behavior of complex wave propagation. The improved modified extended tanh function governs the transmission of various solitons with different effects. New optical solitons are constructed as bright solitons, dark solitons, combo bright-dark solitons and singular solitons. Furthermore, exponential solutions, triangular solutions and rational wave solutions are shown. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. The study also includes modulation instability analysis to examine the stability of the derived wave solutions, supplemented by graphical simulations that depict their distinctive features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Solitons

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Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm

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International Journal of Cloud Computing 2023年第2-4期12卷 134-147页

作者： Suganya, R. Joseph, Niju P. Rajadevi, R. Ramamoorthy, S. Department of Information Technology Sri Krishna College of Technology Tamil Nadu India Bengaluru India Department of Information Technology Kongu Engineering College Perundurai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kanchipuram Tamilnadu Kattankulathur India

In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Clustering algorithms

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Stock Price Forecasting via Recurrent Neural Networks and Key Technical Indicators 3

Stock Price Forecasting via Recurrent Neural Networks and Ke...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Janathi, G. Somasundaram, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering TamilNadu Chennai India Tamil Nadu Chennai India Sri Muthukumaran Institute of Technology Department of Computer Science and Engineering TamilNadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavor is centered on enhancing the predictability of stock prices through the application of Recurrent Neural Networks (RNN) in conjunction with technical indicators such as Exponential Moving Averages (EMA-5 and EMA-10). The adjusted closing prices were judiciously employed throughout the modeling framework to address the challenges of autocorrelation, a prevalent phenomenon in time series data. The model underwent training utilizing a comprehensive dataset encompassing one year of historical stock information, featuring preprocessing methodologies to calculate the requisite indicators and facilitate data preparation for the RNN. The model's architecture incorporates optimized layers subjected to training over 50 iterations. Critical evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), were leveraged to gauge effectiveness, culminating in an impressive prediction accuracy of 0.9981 R2. This methodological approach demonstrated substantial efficiency in forecasting the subsequent day's stock closing price, thereby furnishing significant insights for traders engaged in short-term decision-making. Furthermore, the study encompasses visual representations such as actual versus predicted price graphs and error analysis plots to underscore the model's precision and dependability. © 2025 IEEE.

关键词： Recurrent neural networks

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