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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Sychandran, C.S. Shreelekshmi, R. APJ Abdul Kalam Technological University Department of Computer Science and Engineering College of Engineering Trivandrum Kerala Thiruvananthapuram695016 India APJ Abdul Kalam Technological University Department of Computer Applications College of Engineering Trivandrum Kerala Thiruvananthapuram695016 India

ISBN: (纸本)9798350334128

With the advancement in digital image processing technology, there is a huge proliferation of computer-generated images. They are widely used in interactive games, film industry and cartoons. computer-generated images are photo-realistic because they are created using complex algorithms and high-end graphics processing power to accurately mimic real-world objects and environments' lighting, textures, and colors. Since computer-generated images are more realistic, even human eyes can barely distinguish them. So verifying image authenticity and differentiating computer-generated images from photographic images are inevitable. This paper investigates the performance of different deep learning models for detecting computer-generated images. We experiment on three datasets: DSTok, Columbia PRCG and Rahmouni. Experimental results show that the hybrid Xception ensemble outperforms the state-of-the-art methods and achieves the highest accuracy, precision, recall, F1 score and specificity on three benchmark datasets. © 2023 IEEE.

关键词： Deep learning

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Audio to Sign Language conversion using Natural Language Processing 3

Audio to Sign Language conversion using Natural Language Pro...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Nayana, J. Bhat, Suparna Nair, Rekha R Babu, Tina School of Engineering Department of Computer Applications Dayananda Sagar University Bengaluru India School of Engineering Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India

ISBN: (纸本)9781665474979

People who are deaf never get to enjoy things that hearing folks do, whether it's conversation, playing the game, going to seminars, or participating in virtual meetings, for example. The major challenge they have while interacting among regular people is connection, because every regular person has no concept what sign language is or how it works.. Our project's objective is to help those who have hearing loss develop a system for communication. The spoken message is converted into signs. It converts the spoken word into signs. This system receives voice as inputs, converts the soundtrack into a text, and displays the key symbols used in Indian language using well before graphics or GIFs. The use of this technology makes communication between those who have normal hearing and those who are difficult to hear easier This takes into account how your arms, body, and appearance are combined. Around the universe, there really are 135 different varieties of hand signals. There are numerous different sign languages, including American Sign Language (ASL), Indian Sign Language (ISL), British Sign Language (BSL), Australian Sign Language (Auslan), and many more In this initiative, Indian Sign Language is being used. The deaf community may take advantage of all the activities that hearing people engage in, from social engagement to accessing information, thanks to this technology. NLP is used to pre-process the text (Natural Language Processing). With the help of this technology, hearing-impaired people may participate in a wide range of activities, from socialising to accessing information NLP is used to post the text (Natural Language Processing). This programme converts audio into text, displays Indian sign language, and then accepts speech as input. Accuracy rate for the planned project work is 99%, and Validation accuracy is 96%. © 2022 IEEE.

关键词： component formatting insert style styling

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Enhancing Industrial applications with LSTM-Based Predictive Analytics 15

Enhancing Industrial Applications with LSTM-Based Predictive...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ramachandran, Lalitha Girinath, S. Padmavathi, N. Subramaniam, G. Chitra, M. Siva Sathya, V. R.M.K. Engineering college Department of Science and Humanities Chennai India Department of Computer Applications Tirupati India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India M. Kumarasamy College of Engineering Department of EEE Tamilnadu Karur India Kongu Engineering College Department of EEE Tamilnadu Perundurai India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Predictive analytics in industrial applications has the potential to significantly boost productivity and enhance operational efficiency. The research intends to solve is the difficulty of traditional models to capture the detailed temporal correlations and complex non-linear patterns that are common in industrial data. Long Short-Term Memory (LSTM)-based predictive analytics are the primary focus of this research, with the primary objective being the enhancement of industrial processes. Predicting complex and ever-changing patterns within operational data is a challenge in the modern industrial environment. The allocation of resources and decision-making processes are not as ideal as they may be. The LSTM architecture enables to use of the temporal correlations of the data in order to improve ability to predict the future. LSTM-based predictive analytics are significantly more accurate than conventional models when it comes to predicting the events that will occur in industrial processes. The findings shows LSTM-based predictive analytics have the potential to revolutionize industrial decision-making, which in turn leads to improved resource management and operational efficiency. © 2024 IEEE.

关键词： Industrial applications LSTM Operational efficiency Predictive analytics Temporal dependencies

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Characterization of Cyclic Diagnosability of Regular Diagnosable Networks

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IEEE Transactions on Reliability 2024年第1期73卷 270-278页

作者： Zhang, Hong Zhou, Shuming Cheng, Eddie Hsieh, Sun-Yuan Fujian Normal University School of Mathematics and Statistics Key Laboratory of Analytical Mathematics and Applications Ministry of Education Fuzhou350117 China Oakland University Department of Mathematics and Statistics RochesterMI48309 United States National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

The reliability of interconnection network ordinarily is measured by two significant indexes, namely, connectivity and diagnosability. The qualitative and quantitative reliability analysis relies on the choice of an appropriate mathematical modeling and assumptions consistent with the actual situation. The cyclic connectivity is a well-established index to evaluate the reliability of interconnection network. For a networkG, we use kc(G) to denote cyclic connectivity of G, which is the minimum size of the node cut setDsuch thatG - Dis disconnected and at least two of its components have cycles. Based on the cyclic connectivity, cyclic diagnosability (ct(G)) is proposed to measure the self-diagnostic capability of the networks. Up to this day, the cyclic connectivity of some special networks has been determined successfully, but the cyclic diagnosability of a great deal of networks is still up in the air. In this work, we investigate the measurable relationship between cyclic connectivity and 2-good connectivity under certain restrictions. Furthermore, we characterize the cyclic diagnosability of a class of networks in terms of character commonality of the networks. To be more specific, we show that ct(G) = kc(G) + (l - k) under the PMC model (PMC-M) and the MM∗ model (MM∗-M), where l is the regular degree of network and l ≥ 3, 1 ≤ k © 1963-2012 IEEE.

关键词： Program processors

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Intelligent traffic controller

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International Journal of Information Technology (Singapore) 2022年第4期14卷 2141-2153页

作者： Kumar, Sachin Baliyan, Anupam Tiwari, Anurag Tripathi, Aniket Kumar Jaiswal, Balram Department of Computer Science & Engineering Ajay Kumar Garg Engineering College Ghaziabad India Bharati Vidyapeeth’s Institute of Computer Applications and Management New Delhi India

This paper explores the application of dynamic traffic control timings using predefined input parameters. The method is a dynamic traffic algorithm that takes the rate of inflow, rate of outflow and queue length as input parameters to estimate the green-time that must be allocated to each road. The basic idea is to efficiently distribute the green-time based on traffic congestion in contrast to traditional methods of fixed time for traffic lights irrespective of their traffic status. The paper also compares the differences between these two methods based on some efficiency parameters using a simulator. © 2019, Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Average waiting time Congestion Control IOT Queue length

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DLIoMT: Deep Learning Approaches for IoMT-Overview, challenges and the future

DLIoMT: Deep Learning Approaches for IoMT-Overview, challeng...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Harshni, V. Singh, Satyendr Indhumathi, R. Kumar, Neelam Sanjeev Chowdhury, Subrata Patra, Sudhansu Shekhar Srm Institute of Science and Technology Department of Bme Ramapuram campus Chennai India Bml Munjal University Computer Science & Engg Department Gurugram India Vadapalani Campus Chennai India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Deemed to be University School of Compter Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine learning, conventional neural networks, recognition, and fuzzy logic are a few of the subsets of AI with unique capabilities and functions that can enhance modern medical science. Such innovations simplify human interference in clinical diagnostic procedures, decision making, and medical imaging. To improve human health, connected biomedical network devices and software applications are interconnected to form the Internet of Medical Things (IoMT), the next generation of bioanalytical tools. This review paper provides in-depth analysis of the IoMT encasing its definition, technologies, applications, challenges, and future prospects. © 2025 IEEE.

关键词： Electronic health record

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Adaptive Hybrid Fraud Detection System with Personalized Transaction Profiling 5

Adaptive Hybrid Fraud Detection System with Personalized Tra...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Musthafa, A. Syed Sree, Pokkuluri Kiran Fathima, H. Jayalakshmi.r, Angu Saranya, P. Murugesh, L. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India Department of Computer science and Engineering Vishnupur Andhra Pradesh Bhimavaram534202 India KSR College of Arts and Science Department of Computer Applications Tamil Nadu Tiruchengode637 215 India M. Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639113 India

ISBN: (纸本)9798331530013

This research study presents an Adaptive Fraud Detection System that uses personalized user behavior profiles and multiple layers of detection methods to spot fraud in real time. By clustering similar transaction patterns and applying machine learning techniques like ensemble models and autoencoders, the system can continuously improve its ability to detect unusual activity. Frequency of getting false alarm is reduced. Improved functional clarity in the system. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning Techniques for Fault Detection in Industrial Machinery 5

Deep Learning Techniques for Fault Detection in Industrial M...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Thiyagarajan, T. Tamilselvi, M. Tilak Babu, S.B.G. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India Nehru Institute of Information Technology and Management Department of Computer Applications Tamilnadu Coimbatore India Roever Engineering College Computer Science And Engineering Tamilnadu Perambalur India Aditya Engineering College Department of ECE India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The use of deep learning techniques for the purpose of improving fault detection in industrial machinery. It is of the utmost importance to have defect detection mechanisms that are both reliable and effective, since the complexity of industrial processes continues to increase. In this paper, the implementation of deep learning algorithms is investigated. These algorithms make use of neural networks to understand complex patterns and anomalies that are present in data coming from machinery. There are many different models that are being researched to see whether or not they are effective in detecting defects at early stages, limiting downtime, and eliminating costly interruptions. These models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). For the purpose of this , the performance of various methodologies is evaluated over a wide range of industrial situations, taking into consideration issues such as the variability of sensor data and noise. The findings demonstrate the promise of deep learning as a significant tool for enhancing defect detection skills, thereby paving the way for industrial equipment systems that are more reliable and resilient. © 2024 IEEE.

关键词： Condition monitoring

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ANN-Based Scalable Video Encoding Method for Crime Surveillance-Intelligence of Things applications

ANN-Based Scalable Video Encoding Method for Crime Surveilla...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Agarwal, Rupanshi Pathak, Paresh Tipu, Rupesh Kumar Singh, Digvijay Kalnawat, Aarti Dhabliya, Dharmesh Invertis University Department of Computer Science and Engineering Bareilly India School of Computer Science and Applications Iimt University Meerut India K. R. Mangalam University Department of Civil Engineering Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Pune India Viit Department of It Pune India

ISBN: (纸本)9798350301595

The proliferation of high-quality, networked CCTV cameras in public areas has raised the volume of constantly generated video data significantly in recent years. The need for scalable, distributed video processing infrastructure is growing as a result. In order to make unstructured videos supplied to the platform searchable and comprehensible by both humans and machines, an intelligent video analytics platform runs them through a series of trans-disciplinary algorithms. The use of video analytics extends well beyond just surveillance to include things like video asset management. Many different commercial and academic approaches exist here. Most current systems for face and object recognition, however, have a time-honored client-server architecture that leaves out support for increasingly intricate use cases. Furthermore, distributed computing is seldom used to manage such frameworks at a scale. Furthermore, no current works provide any kind of assistance with low-level networked video processing APIs. Not only did they ignore the expanding needs of customers, academics, and developers, but they also neglected to address a comprehensive service-oriented environment. In this work, we offer an Artificial Neural Network based Scalable Video Coding (ANN-SVC) framework for intelligent video surveillance as a means of resolving these problems. The suggested system can handle both live video feeds and video analytics in batches. Similarly, the batch processing data for each realtime stream is available. This work therefore has a connection to the idea of symmetry. We also provide Spark's first distributed video processing library. To guarantee scalability, efficacy, and fault-tolerance, SIAT takes use of cutting-edge distributed computing technology. Finally, in an effort to back up our assertions, we implement and test our suggested framework. The simulation shows better results in throughput, gooodput and PSNR. © 2023 IEEE.

关键词： Scalable video coding

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Social-Aware Group Display Configuration in VR Conference 38

Social-Aware Group Display Configuration in VR Conference

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hsu, Bay-Yuan Shen, Chih-Ya Yuan, Hao Shan Lee, Wang-Chien Yang, De-Nian Department of Industrial Engineering and Engineering Management National Tsing Hua University China Department of Computer Science National Tsing Hua University China Institute of Information Systems and Applications National Tsing Hua University China Department of Computer Science and Engineering Pennsylvania State University United States Institute of Information Science Academia Sinica Taiwan Research Center of Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)1577358872

Virtual Reality (VR) has emerged due to advancements in hardware and computer graphics. During the pandemic, conferences and exhibitions leveraging VR have gained attention. However, large-scale VR conferences, face a significant problem not yet studied in the literature - displaying too many irrelevant users on the screen which may negatively impact the user experience. To address this issue, we formulate a new research problem, Social-Aware VR Conference Group Display Configuration (SVGD). Accordingly, we design the Social Utility-Aware VR Conference Group Formation (SVC) algorithm, which is a 2-approximation algorithm to SVGD. SVC iteratively selects either the P-Configuration or S-Configuration based on their effective ratios. This ensures that in each iteration, SVC identifies and chooses the solution with the highest current effectiveness. Experiments on real metaverse datasets show that the proposed SVC outperforms 11 baselines by 75% in terms of solution quality. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Virtual reality

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