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A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

IEEE Transactions on Network and Service Management 2024年

作者： Tang, Jun Guo, Bing Shen, Yan Garg, Sahil Kaddoum, Georges Hossain, M. Shamim Sichuan University College of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China École de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Lebanese American University Artificial Intelligence and Cyber Systems Research Center Beirut03797751 Lebanon King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy. © 2024 IEEE.

关键词： Data assimilation

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Quantum Artificial Intelligence: Unlocking the Future of Technology

Quantum Artificial Intelligence: Unlocking the Future of Tec...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： G. Ignisha Rajathi L.R. Priya A. Vegi Fernando R. Vedhapriyavadhana S. Sasindharan J. Mohanalin Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India Department of Electronics and Communication Engineeing Farncis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India School of Computing Engineering and Physical Sciences University of the west of Scotland London UK Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Electrical and Electronics Engineering College of Engineering Perumon Kerala India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Quantum Artificial Intelligence (QAI) stands for the combination of the exercise of ‘quantum computing’ and ‘artificial intelligence’ that drives breakthrough transformation across various industries. In this paper, it is discussed on how QAI is bringing change across industries including pharmaceuticals, finance, weather prediction, supply chain, security, smart cities, agriculture, telecommunication, and education. With quantum mechanics conceptions, QAI enhance computation ability to address more data, and enhance the algorithms. Quantum technologies have potential for attachment to AI systems and make the previously unachievable innovations with the help of overgrowing the classical models. As a conclusion, this paper also discusses its result on the methodological difficulties and ethical issues of QAI, the need to build strategic partnerships and the need for well-regulated legislation. The results extend our understanding of QAI to apprehend the future of technology and its application for solving some of the greatest problems that the world faces today.

关键词： Drugs Ethics Technological innovation Quantum computing Smart cities Finance Weather forecasting Security Artificial intelligence Optimization

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A New Clustering Approach based on Trust and Rat Swarm Algorithm for WSN Applications

A New Clustering Approach based on Trust and Rat Swarm Algor...

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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： Walid Osamy Ahmed Salim Amel Al Ali Ahmed M. Khedr Computer Science Department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Math. Department Faculty of Science Zagazig University Zagazig Egypt Department of CS College of Sci. and Arts Al-Methnab Qassim University KSA Dept. of Information Systems Sharjah University Sharjah UAE Dept. of Computer Science Sharjah University Sharjah UAE

In this paper, a clustering approach called CATRSO is proposed. The selection of cluster heads (CH) is performed by considering the trust value of the nodes in order to select the most trustworthy nodes as CH and Rat Swarm Optimizer is employed for CH selection process. The trust value of the nodes and remaining energy are taken into account while designing the fitness function. In addition, a chain routing approach is employed between CHs for energy savings. The results demonstrate that the CATRSO technique is successful in selecting the most trustworthy nodes as CH and outperforms earlier efforts in the literature in terms of energy efficiency, average network lifetime, and trustworthiness of selected CHs.

关键词： Wireless communication Measurement Wireless sensor networks Simulation Clustering algorithms Rats Routing

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ssVERDICT: Self-Supervised VERDICT-MRI for Enhanced Prostate Tumour Characterisation

arXiv

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arXiv 2023年

作者： Sen, Snigdha Singh, Saurabh Pye, Hayley Moore, Caroline M. Whitaker, Hayley Punwani, Shonit Atkinson, David Panagiotaki, Eleftheria Slator, Paddy J. Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Centre for Medical Imaging University College London London United Kingdom Department of Targeted Intervention Division of Surgery and Interventional Science University College London London United Kingdom Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Cardiff United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Purpose: Demonstrating and assessing self-supervised machine learning fitting of the VERDICT (Vascular, Extracellular and Restricted DIffusion for Cytometry in Tumours) model for prostate. Methods: We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares (NLLS) and supervised deep learning. We do this quantitatively on simulated data, by comparing the Pearson’s correlation coefficient, mean-squared error (MSE), bias, and variance with respect to the simulated ground-truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods’ performance in discriminating benign from cancerous tissue via Wilcoxon’s signed-rank test. Results: In simulations, ssVERDICT outperforms the baseline methods (NLLS and supervised DL) in estimating all the parameters from the VERDICT prostate model in terms of Pearson’s correlation coefficient, bias, and MSE. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. Conclusion: ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting, and shows for the first time, fitting of a complex three-compartment biophysical model with machine learning without the requirement of explicit training labels. © 2023, CC BY.

关键词： Diffusion

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Active vision: A promising technology for achieving zero-defect manufacturing

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Procedia computer science 2024年 232卷 2821-2830页

作者： Konstantinos A. Tsintotas Ioannis Kansizoglou Fotios K. Konstantinidis Spyridon G. Mouroutsos Georgios Ch. Syrakoulis Foivos Psarommatis Yiannis Aloimonos Antonios Gasteratos Department of Production and Management Engineering Democritus University of Thrace 12 Vas. Sophias Xanthi GR-671 32 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Xanthi GR-671 00 Greece Department of Informatics SIRIUS University of Oslo Gaustadalleen 23B 0373 Oslo Noway Research Center on Production Engineering and Management (CIGIP) Universitat Polite`cnica de València Spain Department of Computer Science University of Maryland College Part MD-207 42 United States

Quality constitutes the cutting-edge of contemporary industry, much more sharpened with the emergence of Industry 4.0. Yet, the assurance of delivering high-quality products remains a challenging problem that requires cautious controls and timely actions through the entire phases of the production cycle. To that end, zero-defect manufacturing (ZDM) has given prominence to a comprehensive methodology that ensures quality control and defects eradication at every phase of the procedure, prioritizing the continuous supervision of each distinctive component to satisfy the corresponding quality assurance standards. Continuous supervision and detection of possible flaws are subjective to exploiting high-tech sensors for high-precision monitoring and assessment. Considering the above, adopting active vision technology can further enhance the promising capabilities of ZDM. The paper highlights the active vision's benefits to the ZDM strategy and introduces a practical framework for applying such technology through the different stages. Innovations and challenges that have to be taken into consideration are also discussed.

关键词： Active Vision Zero Defect Manufacturing Industry 4.0 Machine Vision Manufacturing

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Enhanced Rheumatoid Arthritis Treatment Optimization Through Deep Learning

Enhanced Rheumatoid Arthritis Treatment Optimization Through...

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Disruptive Technologies (ICDT), International Conference on

作者： Siva Venkatesh Arcot T. Buvaneswari K.R. Kavitha R. Manickavasagam Suguna M M Muthulekshmi Contact Center Business Unit Cisco Systems INC Little Elm Texas USA Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Biomedical Engineering Alpha College of Engineering Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Inflammation and joint destruction are hallmarks of Rheumatoid Arthritis (RA), a chronic autoimmune condition. To improve patient outcomes, it is essential to optimize treatment plans. According to this research, integrating deep learning (DL) methods into a Convolutional Neural Network (CNN) provides a new method to optimize RA treatments. The CNN model is used to decipher intricate medical data, such as imaging and patient records, find trends, and predict how patients react to treatment. To assess the efficacy of the proposed model in predicting ideal treatment methods, it was trained on a large dataset that included patient images and clinical data. The CNN model offers a potential tool for doctors to adapt RA management plans since results show it significantly improves prediction accuracy and treatment personalization. This method allows for data-driven decisions in RA management, improving patient care and enhancing treatment performance.

关键词： Deep learning Adaptation models Accuracy Soft sensors Predictive models Arthritis Data models Convolutional neural networks Optimization Biomedical imaging

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Challenging the Anomaly Detection Paradigm: CNN-CRF Approach

Challenging the Anomaly Detection Paradigm: CNN-CRF Approach

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

作者： P. Kanagaraju Swati Kedar Nadganndi Tukaram N. Sawant Saranya Ekambaram Palak Keshwani Yadavalli Suresh Kumar Department of Computat ional Intelligence SRM Institute of Science and Technology Chennai India Department of Instrumentation Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India FST The ICFAI University Raipur India Department ofCSE Aditya College of Engineering Surampalem India

Since intrusion detection has gained so much popularity, scientists continue to dedicate a lot of time and energy to studying it. Even after all these years of research, the intrusion detection community still has problems. Recognizing previously unseen attack patterns is hampered by a high rate of false positives, which is an ongoing pro blem. However, new research has shown that there are exits from this impasse. Intrusion detection relies heavily on the ability to spot anomalies, or unexpected behavior, because they indicate the presence of malicious activity. Data preparation, feature selection, and model training are all parts of the suggested approach. Machine learning algorithms can analyze the data for the development of a predictive model thanks to data preparation methods. For feature selection, the proposed approach relied on principal component analysis and iterative grading. After collecting the features, CNN -CRF is utilized to train the models. The proposed method is superior to the popular algorithms CNN and CRF. There is a 98.63% success rate when using this strategy.

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Gop Size Effect on VVC Encoder Performance with MRF Varation

Gop Size Effect on VVC Encoder Performance with MRF Varation

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Rana Jassem Taheni Damak Mohamed Ali Ben Ayed Nouri Masmoudi Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq New Technologies and Telecommunications Systems (NTS’Com) Sfax University Tunisia Laboratory of electronics and technology’s information (LETI) Sfax University Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The exponential growth of multimedia content and applications underscores the ongoing challenge of developing advanced video coding standards that not only enhance compression rates but also incorporate essential features while addressing increasing complexity in computational processing. Among these standards, the Versatile Video Coding (VVC) standard stands out for its superior efficiency compared to its predecessors. As one of the key parameters in video compression algorithms, the size of Group of Pictures (GOP) determines the frames interdependency in the encoded video. The GOP size significantly influences the performance of a Video Coding Standard (VVC) encoder, particularly in conjunction with Multiple Reference Frame (MRF) variation. Adjusting the GOP size can have a profound impact on compression efficiency and visual quality for video coding. In this study, we investigate the effect of varying MRF configurations and GOP sizes on the performance of VVC encoder. Furthermore, we advance the notion that achieving diverse MRF configurations is feasible through strategic adjustments to the number of reference frame within each reference list. In this paper, we present statistical results showing the impact of different Quantization Parameters (QPs) and GOP size on encoder performance for each configuration. Based on our results, varying GOP size with MRF configurations has a significant positive impact on encoder performance as measured by bitrate and execution time for several values of QP without affecting the quality of the reconstructed video and the resulted bitrate.

关键词： Video coding Radio frequency Visualization Quantization (signal) Bit rate Video compression Streaming media

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Novel Bendable Mnxca(0.90−X)Ni0.10fe2o4 Substrate Based Symmetrical Metamaterial Absorber for Rf Energy Harvesting Application to Achieve Sustainable Development Goals

SSRN

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SSRN 2024年

作者： Hasan, Mohammad Shakibul Islam, Mohammad Tariqul Ouda, Mohammad Alenezi, Abdulmajeed M. Soliman, Mohamed S. Samsuzzaman, Md. Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Electrical Engineering Technology College of Engineering & Technology University of Doha for Science and Technology Doha24449 Qatar Department of Electrical Engineering Faculty of Engineering Islamic University of Madinah Madinah41411 Saudi Arabia Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer and Communication Engineering Faculty of Computer Science and Engineering Patuakhali Science and Technology University Bangladesh

This article presents a bendable substrate MnxCa(0.90−x)Ni0.10Fe2O4 based metamaterial absorber for RF energy harvesting from Wifi frequency of 5 GHz to the achievement of the Sustainable Development Goals. The bendable metamaterial absorber (BMMA) energy harvester unit cell constructed with the physical dimension of 21 × 21 *** proposed unit cell consist of cross sign shaped resonator surrounded by circular ring resonator is located middle portion and boundary split outer ring resonator. Based on the simulation findings, the proposed BMMA energy harvester provides maximum energy harvesting efficiency of 99.42% at 5 GHz. Due to the symmetrical design, the energy harvester unit cell shows excellent angle insensitive up to 90 degrees for incident angle and polarization angle. The proposed BMMA exhibits single negative properties, and it shows excellent harvesting efficiency for various bending conditions. The lightweight, flexibility of the structure indicate that MnxCa(0.90−x)Ni0.10Fe2O4 based bendable metamaterial absorber energy harvester have significant potential for RF energy harvesting application from wifi frequency of 5 GHz and it achieve Sustainable Development Goals (SDGs), specially alignment with SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities),and SDG 13 (Climate Action). © 2024, The Authors. All rights reserved.

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ETDHDNet: An advanced densenet-based extended texture descriptor for efficient tuberculosis prediction in CXR images

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Intelligence-Based Medicine 2025年

作者： Asmaa Shati Amitava Datta Atif Mansoor Ghulam Mubashar Hassan Department of Computer Science and Software Engineering The University of Western Australia Australia Department of Informatics and Computer Systems King Khalid University Saudi Arabia

Chest X-ray imaging (CXR) for the prediction of tuberculosis (TB) is essential in medical diagnostics, as it plays a vital role in early detection and the development of effective treatment plans. Although Convolutional Neural Networks (CNNs) are effective in extracting features from images in a hierarchical manner, they have limitation of texture-related features due to their emphasis on capturing global patterns. To address this shortcoming, we propose an Extended Texture Descriptor Histogram DenseNet (ETDHDNet), a model designed for TB prediction from CXR images by integrating texture analysis with deep feature learning. ETDHDNet consists of three key components: the Extended Texture Descriptor Histogram (ETDH) module to capture multi-scale texture features across fine, medium, and coarse granularities; a hierarchical feature learning unit with densely connected layers for extracting high-level features; and a neural network for handling binary and multi-class TB prediction. Experimental results demonstrate that ETDHDNet surpasses existing methods in TB prediction from CXR images across three datasets. For binary classification, the model achieves an AUC of 0.998 on TBX11K, 0.997 on the Tuberculosis Chest X-ray Database, and 0.930 on the Shenzhen Dataset, as well as an AUC of 0.993 for multi-class TB prediction on TBX11K.

关键词： Texture analysis Deep learning Tuberculosis prediction Chest X-rays (CXR) DenseNet

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