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AI-Assist Tool to Enhance the Reliability and Accuracy of Automated Speech and Writing Support for Students with Disabilities in Learning Languages

AI-Assist Tool to Enhance the Reliability and Accuracy of Au...

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IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

作者： D. Amruta Surana K. Devika Prema S Nilesh H. Dhannaseth N. Gopinath P. Tamilarasan Department of Computer Engineering Sinhgad Institute of Technology lonavala Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Nagpur Maharashtra India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Innovative educational technology have made it possible to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-based approaches have been among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the establishment of systems for both diagnosis and intervention calls for the adoption of A.I. techniques. Today, people from all socioeconomic backgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasible to communicate with children with special needs (SEN) in new ways thanks to innovative educational technology. In the past ten years, artificial intelligence (A.I.)-based strategies have been among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. techniques is necessary for the creation of systems for both diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and be successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in both text and audio versions after that. This makes it simple to assist any student with a disability in having good communication in a foreign language.

关键词： Cloud computing Navigation Educational technology Writing Speech enhancement Assistive technologies Reliability Artificial intelligence Socioeconomics Faces

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： N. Suganya P. Gouthami R. Sathiya S. Sivaranjani M. Murugesan Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources; they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user's data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness.

关键词： Surveys Cloud computing Data privacy Privacy Reviews Scalability Software Encryption Servers Identity management systems

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The impact of vaccination on the infection rate and the severity of Covid-19

arXiv

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arXiv 2022年

作者： Rayhan, Md. Abdullah-Al-Wadud, M. Ahmed, M. Helal Uddin Department Of Computer Science And Engineering Brac University Dhaka Bangladesh Department Of Software Engineering College Of Computer And Information Sciences King Saud University Riyadh Saudi Arabia Department Of Management Information Systems University Of Dhaka Dhaka1000 Bangladesh

This study aims to statistically assess the effectiveness of vaccination against SARS-CoV-2. It is indispensable to investigate the relationship between Covid-19 deadliness and vaccination in order to study the impact of vaccine in real-world. We studied rates of infection and death due to Covid-19 in different countries with respect to their levels of vaccination. People who received the required dose of vaccination were considered as fully vaccinated in this study. Based on the percentage of fully vaccinated population, countries were categorized into several groups. Though a high-level study on the vaccine effectiveness may not provide much insight for individual level differences, a global analysis is imperative to infer the influence of vaccination as a controlling measure of the pandemic. © 2022, CC BY-NC-ND.

关键词： Vaccines

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Towards Spectrum-Efficient 6G: Revolutionizing Beamforming with Vision and Deep Learning

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IEEE Network 2025年

作者： Mirza, Ahmad Faisal Hassan, Tayyib Ul Ullah, Syed Asad Basheer, Shakila Chauhdary, Sajjad Hussain Dev, Kapal Hassan, Syed Ali Islamabad44000 Pakistan Quetta87300 Pakistan Princess Nourah bint Abdulrahman University College of Computer and Information Science Department of Information Systems P.O. BOX 84428 Riyadh11671 Saudi Arabia Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of institute of intelligent systems Auckland Park 2006 South Africa Chitkara University Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Punjab Rajpura140401 India

As the world transitions to 6G networks, wireless communication is set for transformative advancements, enabling applications such as autonomous transportation, immersive augmented reality, and smart cities. These networks demand ultra-reliable, low-latency, and high-bandwidth communication, coupled with unprecedented spectrum efficiency and adaptability. This magazine explores how vision-assisted beamforming (VB) leverages visual data from base station (BS) cameras combined with advanced machine learning (ML) techniques to enhance signal quality, connectivity, and adaptability in dynamic environments. By enabling intelligent, real-time beam alignment, this approach ensures stronger, more reliable connections while mitigating inefficiencies in exhaustive beam training. Adapting seamlessly to rapidly changing scenarios, VB provides scalable and efficient communication systems, paving the way for smarter networks. Using real-world datasets, deep learning (DL) models demonstrate how visual context significantly enhances beamforming accuracy and efficiency. A case study on vehicleto- everything (V2X) communication showcases the paradigm’s ability to address critical 6G challenges by achieving precise beamforming without the need for channel state information (CSI). Unlike traditional methods reliant on sequential frames, this approach achieves accurate predictions using a single frame, reducing complexity and latency while delivering robust, high quality connectivity in real time. © 1986-2012 IEEE.

关键词： Beamforming

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Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods

arXiv

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arXiv 2023年

作者： Banupriya, V. Anusuya, S. Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Tamilnadu Karur India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Tamil Nadu Chennai India

Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. This may endanger the subjects' vision if they have diabetes. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness. Automated detection of the DR can be an extremely challenging task. Convolutional Neural Networks (CNN) are also highly effective at classifying images when applied in the present situation, particularly compared to the handmade and functionality methods employed. In order to guarantee high results, the researchers also suggested a cutting-edge CNN model that might determine the characteristics of the fundus images. The features of the CNN output were employed in various classifiers of machine learning for the proposed system. This model was later evaluated using different forms of deep learning methods and Visual Geometry Group (VGG) networks). It was done by employing the images from a generic KAGGLE dataset. Here, the River Formation Dynamics (RFD) algorithm proposed along with the FUNDNET to detect retinal fundus images has been employed. The investigation's findings demonstrated that the approach performed better than alternative approaches. © 2023, CC BY.

关键词： Convolutional neural networks

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Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4738-4752页

作者： Zhang, Le Bronik, Kevin Piechnik, Stefan K. Lima, Joao A. C. Neubauer, Stefan Petersen, Steffen E. Frangi, Alejandro F. University of Birmingham School of Engineering College of Engineering and Physical Sciences BirminghamB15 2TT United Kingdom University of Oxford Department of Engineering Science OxfordOX1 3PJ United Kingdom Division of Cardiovascular Medicine OxfordOX3 9DU United Kingdom Johns Hopkins University School of Medicine Division of Cardiology BaltimoreMD21287 United States Queen Mary University of London Centre for Cardiovascular Medicine The William Harvey Research Institute LondonE1 1BB United Kingdom Barts Health Nhs Trust Barts Heart Center LondonE1 1BB United Kingdom The University of Manchester Division of Informatics Imaging and Data Science School of Health Sciences The Department of Computer Science School of Engineering ManchesterM13 9PL United Kingdom

Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, and ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model's predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizable image acquisition cohorts, demonstrating superior performance compared to existing methods. © 2020 IEEE.

关键词： Image quality

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Deep Neural Networks Based Approach for Battery Life Prediction

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computers, Materials & Continua 2021年第11期69卷 2599-2615页

作者： Sweta Bhattacharya Praveen Kumar Reddy Maddikunta Iyapparaja Meenakshisundaram Thippa Reddy Gadekallu Sparsh Sharma Mohammed Alkahtani Mustufa Haider Abidi School of Information Technology and Engineering Vellore Institute of TechnologyVelloreIndia Department of Computer Science and Engineering Baba Ghulam Shah Badshah UniversityRajouriIndia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadhSaudi Arabia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadhSaudi Arabia

The Internet of Things(IoT)and related applications have witnessed enormous growth since its *** diversity of connecting devices and relevant applications have enabled the use of IoT devices in every *** the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely *** this work,an optimized deep neural networks based model is used to predict the battery life of the IoT *** present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed *** dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical *** processed data is normalized using the Standard Scaler *** Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the *** optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.

关键词： Battery life prediction moth flame optimization one-hot encoding standard scaler

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Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

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Journal of Neuroscience Research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

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Real-Time Oversight of Parking Space Management in IoT Edge Computing for Industry 4.0: A Case Study

Real-Time Oversight of Parking Space Management in IoT Edge ...

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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Alaa Fadhil Ala'anzy Mohammed Alaa Ala'anzy Member Umar Ali Bukar Tamara Zhukabayeva Dilaram Baumuratova Nurdaulet Karabayev Department of Business Administration Al-Hikma University College Baghdad Iraq Department of Computer Science SDU University Almaty Kazakhstan Centre for Intelligent Cloud Computing (CICC) Multimedia University Melaka Malaysia Department of Information Systems L.N. Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

The rapid increase in vehicle numbers, particularly in densely populated areas, leads to significant issues such as traffic congestion, time and fuel wastage during the search for parking spots, and elevated emissions of toxic gases from idling vehicles, contributing to increased pollution. Smart parking systems, leveraging advanced Internet of Things (IoT) technologies commonly seen in Industrial Internet of Things (IIoT) applications, offer a solution to these problems by reducing traffic congestion, lowering environmental pollution, and conserving fuel. Although cloud-based parking systems have been proposed, they often suffer from high latency and bandwidth requirements due to high network usage. Fog Computing presents a more effective alternative. This paper proposes a fog computing-based smart parking system designed to address these issues for SDU University. By providing real-time information about available parking spots with minimal latency, the system optimises space usage and guides drivers efficiently, particularly during peak hours, thereby reducing the need for parking area expansion. A network usage reducing approach was developed and tested using iFogSim2 toolkit, with network usage metric for a comparative analysis between fog and cloud environments. A real case study from SDU University Park was employed to validate the system. The results demonstrate that the fog-based system outperforms the cloud-based approach in terms of efficiency and responsiveness.

关键词： Cloud computing Pollution Environmentally friendly manufacturing techniques Automated parking Real-time systems Fuels Traffic congestion Industrial Internet of Things Edge computing Vehicles

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The role of intrusion detection systems and machine in protecting industrial control systems ICS environments

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AIP Conference Proceedings 2024年第1期3207卷

作者： Amenh A. Jasim Khattab M. Ali Alheeti College of Computer and Information Technology Computer Science Department University of Anbar Ramadi Iraq Computer Networking Systems Department College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

This paper provides an overview of current cybersecurity solutions for industrial control systems (ICS) that rely on machine learning. Especially in the field of anomaly detection in the Distribution Network Protocol (DNP3). Attacks on power grid systems are a common target for cybercriminals. The energy equivalent of an entire country can be saved through these systems. Therefore, it is necessary to protect these facilities from any security breaches, as they are vital for people. For this, the paper outlines a proposal for an intrusion detection system. It introduces a classification system that uses different machine learning methods to find the most effective model for detecting flaws related to this network protocol. standard IDS datasets for (DNP3) was used to create each model (7326 observations) containing nine network attack scenarios. In terms of performance, the Random Forest algorithm outperformed the competition, SVM and KNN. Thus, it is possible to obtain a predictive accuracy of about 99%. A categorization system is thus the result of the employed method.

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