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computer, Communication and Control (IC4), International Conference on

作者： R. Sivasankari T Veeramakali S M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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Robust Recovery of Low Rank Matrix by Nonconvex Rank Regularization 11th

Robust Recovery of Low Rank Matrix by Nonconvex Rank Regular...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zhang, Hengmin Luo, Wei Du, Wenli Qian, Jianjun Yang, Jian Zhang, Bob Department of Computer and Information Science University of Macau 999078 China School of Information Science and Engineering Key Laboratory of Advanced Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783030873578

As we know, nuclear norm based regularization methods have the real-world applications in pattern recognition and computer vision. However, there exists a biased estimator when nuclear norm relaxes the rank function. To solve this issue, we focus on studying nonconvex rank regularization problems for both robust matrix completion (RMC) and low rank representation (LRR), respectively. By extending both to a general low rank matrix minimization problem, we develop a nonconvex alternating direction method of multipliers (ADMM). Moreover, the convergence results, i.e., the variable sequence generated by the nonconvex ADMM is bounded and its subsequence converges to a stationary point. Meanwhile, its limiting point satisfies the Karush-Kuhn-Tucher (KKT) conditions provided under some milder assumptions. Numerical experiments can verify the convergence properties of the theoretical results and the performance shows its superiority on both image inpainting and subspace clustering. © 2021, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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A Two-Stage Packetized Energy Trading and Management Framework for Virtual Power Plants

A Two-Stage Packetized Energy Trading and Management Framewo...

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IEEE General Meeting Power& Energy Society

作者： Yuanliang Li Luyang Hou Jun Yan Yuhong Liu Mohsen Ghafouri Peng Zhang Concordia Institute for Information Systems Engineering Concordia University Montréal QC Canada School of Computer Science Beijing University of Posts and Telecommunications Beijing China Department of Computer Engineering Santa Clara University Santa Clara CA USA College of Information Engineering Shenzhen University Shenzhen China

Packetized energy management (PEM) enables virtual power plants (VPPs) to manage a population of controllable and distributed loads and generators in a request-reply manner by encapsulating the energy into fixed-duration/fixed-power energy packets. To participate in and benefit from the energy market in a transactive energy system, the VPP with PEM technique should customize its trading strategy by considering the feature of PEM. With this goal, this paper develops a dedicated two-stage packetized energy trading and management (PETM) framework for VPPs facilitated by using a co-simulation platform for a transactive energy system. Particularly, VPP bids for the energy quantity as a participant of the market with an objective of maximizing its designed utility in the first stage; and then in the second stage, the VPP launches PEM to manage a bunch of controllable devices of its customers by tracking a balancing signal produced in the first stage. We validate the proposed PETM framework using the developed co-simulation platform (PEMT-CoSim), and the results demonstrate the feasibility of the proposed framework.

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Dynamic Deep Clustering of High-Dimensional Directional Data via Hyperspherical Embeddings with Bayesian Nonparametric Mixtures 25

Dynamic Deep Clustering of High-Dimensional Directional Data...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Zhiwen Luo Wentao Fan Manar Amayri Nizar Bouguila The Concordia Institute for Information Systems Engineering Concordia University Montreal Quebec Canada Guangdong Provincial/Zhuhai Key Laboratory IRADS and Department of Computer Science Beijing Normal University-Hong Kong Baptist University (BNU-HKBU) United International College Zhuhai Guangdong China

ISBN: (纸本)9798400712456

Clustering high-dimensional directional data (i.e., L2 normalized vectors) presents significant challenges due to the intricate spherical representations of latent embeddings and the limitations of classical (non-deep) clustering techniques. Moreover, dynamically inferring the number of clusters remains a fundamental issue in existing deep clustering methods, especially those involving complex model-selection criteria. This paper addresses these challenges by introducing a novel deep nonparametric clustering framework that employs hyperspherical latent embeddings within a Variational Autoencoder architecture, enhanced by an infinite Von Mises-Fisher Mixture Model as a dynamic prior. This approach enables automatic adaptation of cluster numbers during training, eliminating the need for predefined clusters and traditional model selection processes. Our scalable architecture effectively integrates In-vMFMM with hyperspherical embeddings to tackle the complexities of directional data. Utilizing a joint training strategy, our method alternates between updating neural network parameters and adjusting mixture model priors via nonparametric variational Bayes. Empirical evaluations on benchmark datasets, including complex ImageNet-50, demonstrate that our approach significantly outperforms state-of-the-art deep nonparametric clustering methods. It also robustly estimates the number of clusters, showcasing its effectiveness and versatility in handling high-dimensional directional data.

关键词： bayesian nonparametric

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Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification

Equilibrium Optimizer with Deep Learning Model for Autism Sp...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： A. Praveena N. Senthamilarasi T. S. Karthik Abirami S.K Vijayakrishna Rapaka E Shyamali Das Department of Computer Science and Engineering Jansons Institute of Technology Karumathampatti Coimbatore Tamilnadu India Department of Information Technology Panimalar Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem Andhrapradesh India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamilnadu India Data Science Indian Institute of Technology Madras Chennai Tamilnadu India Department of Computer Science CMR Group of Institutions (SOSS) Bangalore Karnataka India

ISBN: (纸本)9781665460859

Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches.

关键词： Deep learning Autism Renewable energy sources Automation Mental health Physiology Classification algorithms

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Designing Consensus-Based Distributed Filtering over Directed Graphs

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Lv, Yuezu Duan, Zhisheng Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Systems Science School of Mathematics Southeast University Nanjing211189 China MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper proposes a novel consensus-on-only-measurement distributed filter over directed graphs under the collectively observability condition. First, the distributed filter structure is designed with an augmented leader-following measurement fusion strategy. Subsequently, two parameter design methods are presented, and the consensus gain parameter is devised utilizing local information exclusively rather than global information. Additionally, the lower bound of the fusion step is derived to guarantee a uniformly upper bound of the estimation error covariance. Moreover, the lower bounds of the convergence rates of the steady-state performance gap between the proposed algorithm and the centralized filter are provided with the fusion step approaching infinity. The analysis demonstrates that the convergence rate is, at a minimum, as rapid as exponential convergence under the spectral norm condition of the communication graph. The transient performance is also analyzed with the fusion step tending to infinity. The inherent trade-off between the communication cost and the filtering performance is revealed from the analysis of the steady-state performance and the transient performance. Finally, the theoretical results are substantiated through the validation of two simulation examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT

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China Communications 2021年第6期18卷 39-52页

作者： Wugedele Bao Celimuge Wu Siri Guleng Jiefang Zhang Kok-Lim Alvin Yau Yusheng Ji School of computer science and information engineering Hohhot Minzu CollegeHohhot 010051China Graduate School of Informatics and Engineering The University of Electro-Communications1-5-1ChofugaokaChofu-shiTokyo182-8585 Japan Institute of Intelligent Media Technology Communication University of ZhejiangZhejiang 310018China School of Science and Technology Sunway University5Jalan UniversitiBandar Sunway47500 Petaling JayaSelangorMalaysia Information Systems Architecture Research Division National Institute of Informatics2-1-2HitotsubashiChiyoda-kuTokyo 101-8430 Japan

In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular *** learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user ***,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still *** this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular *** proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with *** clients also work as forwarder nodes in information sharing among network *** client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking *** use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

关键词： vehicular IoT federated learning client selection networking scheme

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COVID-19 Infection Detection in X-Ray Images Using Enhanced Image Segmentation and Machine Learning Models

COVID-19 Infection Detection in X-Ray Images Using Enhanced ...

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Information Technology (ACIT)

作者： Khalid M.O. Nahar Mohammad Ebrahim Megdad Rabia Emhamed Al Mamlook Mohammad Aljaidi Besan K.M. Nahar Mohammad K. M. Nahar Samar Hussni Anbarkhan Hayel Khafajeh Waleed Amer Faculty of Computer Studies Arab Open University Riyadh Saudi Arabia Computer Science Department Yarmouk University Irbid Jordan Dept. of Mechanical and Industrial Engineering University of Zawia Zawia Libya Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Faculty of Medicine Yarmouk University Irbid Jordan Faculty of Applied Medical Science Jordan University of Science and Technology Irbid Jordan Information Systems Department Northern Border University Rafha Saudi Arabia Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cybersecurity Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The COVID-19 pandemic has shown a significant challenge to healthcare associations worldwide demanding the progress of reliable diagnostic techniques. Chest X-ray imaging is an easily available and cost-effective tool for evaluating lung defects combined with COVID-19. This study aims to introduce a novel COVID-19 infection detection and recognition method in X-ray images through image segmentation. By employing machine learning (ML) methods, particularly CNN-based U-Net architectures, the lung sections are accurately segmented, and infection sections are classified based on specific radiological features. The model was trained on a large dataset of annotated X-ray images assisting it to distinguish between healthy lungs, COVID-19-affected lungs, and other lung pathologies. The proposed method apprehended a segmentation accuracy of 96.6% with a Dice coefficient of 94.36% offering a potential solution for mechanical detection and 98.4% with KNN analysis of COVID-19 infection in chest X-rays in that way assisting radiologists in making faster and more accurate diagnoses.

关键词： COVID-19 Support vector machines Image segmentation Accuracy Image recognition Pandemics Lungs Nearest neighbor methods X-ray imaging Biomedical imaging

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Multi-layer perceptron's neural network with optimization algorithm for greenhouse gas forecasting systems

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Environmental Challenges 2023年 11卷

作者： Nanda, Ashok Kumar S, Neelakandan Gupta, Sachi Saleth, Angel Latha Mary S, Ramya. Kiran, Siripuri Department of CSE B V Raju Institute of Technology Telangana Narsapur Medak India Department of Computer Science and Engineering R.M.K Engineering College Chennai India Department of Information Technology IMS Engineering College Ghaziabad India Department of Computer Science and Business Systems Sri Eshwar College of Engineering India Department of Electronics and Communication R.V.College of Engineering India Department of CSE(Networks) Kakatiya Institute of Technology & Science Telangana Warangal 15 India

China, India, and the United States consume the most energy and emit the most CO2. According to ***, India's CO2 emission is 1.80 tnes per capita, which is harmful to living beings: hence, this study exhibits India's harmful CO2 emission effect and forecasts CO2 emission for the next ten years using univariate time-series data from 1980 to 2019. A multilayer perceptron is used in this study to analyse 2099 experimental data of binary systems made up of CO2 and ionic liquids and predict solubility. 33 different types of ionic liquids are represented in the dataset, which spans a wide variety of solubilities, pressures, and temperatures. In recent decades, greenhouse gas (GHG) emissions have caused air pollution and environmental problems in several countries. A precise prediction is essential for managing and planning for the decrease of greenhouse gas emissions. Furthermore, the Modified Coyote Optimization Algorithm was used to extract required properties. The empirical data revealed that predictions obtained from Multi-Layer Perceptron's Neural Network (MPNN) were more accurate than those derived from other models. The MPNN-MCOA identified a link between CO2 emissions, economic growth, and entrepreneurship. Government, personal liberty, education, and pollution all have a negative correlation. We conclude by emphasising the critical role of machine learning in achieving carbon neutrality, from global-scale energy management to the revolutionary potential of atomic-scale MPNN-MCOA simulations for application development. As a result, one of the most reliable approaches for estimating greenhouse gas (GHG) emissions from agricultural regions and companies was recommended: the MPNN-MCOA model. The Artificial neural network is trained with three input combinations with three combinations of thermodynamic variables such as temperature (T), pressure (P), critical temperature (Tc), critical pressure, the critical compressibility factor (Zc), and the acentric f

关键词： Co2 emission Forecasting Greenhouse gas (GHG) Machine-learning algorithms Modified coyote optimization algorithm (MCOA) Multi-layer perceptron's neural network (MPNN)

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Supervised Machine Learning Strategy for detection of covid19 patients

Supervised Machine Learning Strategy for detection of covid1...

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： I. S. Hephzi Punithavathi K. Deepa Cheruku Poorna Venkata Srinivasa Rao S. Raja Gopal P. Rajasekar Ashok Kumar Department of Artificial Intelligence Vidya Jyothi Institute of Technology Hyderabad Telagana India Department of Computer Science and Artificial Intelligence SR University Kavaraipettai Hasanparthy Telangana India Department of Information Technology Laki Reddy Balireddy College of Engineering Mylavaram Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Computer Science Banasthali Vidyapith Rajasthan India

The current severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) public health catastrophe, both human lives have been lost and the economy has disrupted severely the current scenario. In this paper, we develop a detection module using a series of steps that involves pre-processing, feature extraction and detection of covid-19 patients based on the images collected from the computerized tomography (CT) images. The images are initially pre-processed and then the features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then finally classified using back propagation neural network (BPNN). The simulation is conducted to test the efficacy of the model against various CT image datasets of numerous patients. The results of simulation shows that the proposed method achieves higher detection rate, and reduced mean average percentage error (MAPE) than other existing methodologies.

关键词： COVID-19 Training Support vector machines Computed tomography Computational modeling Predictive models Sensitivity and specificity

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