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An accurate a posteriori error estimator for the Steklov eigenvalue problem and its applications

science China Mathematics 2021年第3期64卷 623-638页

作者： Fei Xu Qiumei Huang Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China

In this paper, a type of accurate a posteriori error estimator is proposed for the Steklov eigenvalue problem based on the complementary approach, which provides an asymptotic exact estimate for the approximate eigenpair. Besides, we design a type of cascadic adaptive finite element method for the Steklov eigenvalue problem based on the proposed a posteriori error estimator. In this new cascadic adaptive scheme, instead of solving the Steklov eigenvalue problem in each adaptive space directly, we only need to do some smoothing steps for linearized boundary value problems on a series of adaptive spaces and solve some Steklov eigenvalue problems on a low dimensional space. Furthermore, the proposed a posteriori error estimator provides the way to refine meshes and control the number of smoothing steps for the cascadic adaptive method. Some numerical examples are presented to validate the efficiency of the algorithm in this paper.

关键词： Steklov eigenvalue problem a posteriori error estimator cascadic multigrid method adaptive finite element method complementary method

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Malware Classification using LSTM-CF Framework for Disk Forensic Analysis 2

Malware Classification using LSTM-CF Framework for Disk Fore...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Thirupathi, Chaithanyaka Yeddeli Sundaram, Jawahar Yeo, Kheng Cher Khan, Shujahat Ali Pritika Sellappan, Devaraju Energy and Resources Institute Charles Darwin University Faculty of Science and Technology Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India Charles Darwin University Faculty of Science and Technology Darwin Australia School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9798350383652

In the current era, digital forensic investigators need specialized tools to extract digital footprints from hard disks. The number of crimes is rising dramatically. This has resulted in an increased number of unresolved cybercrime cases including malicious software, hacking and cyberfraud. Disk forensics or disk investigation is a massive task. It takes weeks to collect the traces of the 1TB hard drive and analyze them. It is a challenging task to detect reliable evidence because of the worldwide use and advancement of digital communication technologies. By using automated tools, only predictable areas of the disk are often investigated, and as a result, latent evidence in the hidden area might be missed. In this paper, we present Chaithu's Framework (CF) that gives us a clear picture of the steps to follow and implement a machine learning algorithm which is able to predict whether the request is related to malware or not by analyzing the large world Microsoft Malware Prediction dataset collected from Kaggle. It consists of a total of 7.8 million data samples with 84 features collected in real time from Windows 7,8,9 and 10 systems. Using predictive machine learning algorithms, frauds can be detected automatically and autonomous actions can be taken to prevent them. © 2024 IEEE.

关键词： Hard disk storage

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing 43

A novel deep domain-adversarial method based on DRN-DATL for...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Zhuorui Wang, Xiaodong Ma, Bao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581581

In recent years, a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research orientation. However, there is still a gap between the existing diagnostic model and the practical application, i.e., 1) due to the variability of working conditions, the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance. 2) It is time-consuming and expensive to label a mass of monitoring data in practical situation. To address this problem, a domain-adversarial transfer learning method based on deep residual network (DRN-DATL) for rolling bearing fault diagnosis is proposed. Firstly, DRN is designed to extract features from raw vibration signal, which can avoid gradient problems. Then, the APReLU activation function is adopted to improved diagnostic accuracy under different working conditions. Meanwhile, domain-adversarial training is utilized to minimize distribution differences between source domain and target domain. Finally, seven comparison methods are selected for systematic analysis, and the results show that the proposed method can effectively improve the overall performance of the diagnosis model, which has achieved higher classification accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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A Binary and Multi Classification Model on Tax Evasion:A Comparative Study 1

A Binary and Multi Classification Model on Tax Evasion:A Com...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Shujaaddeen, Abeer Abdullah Ba-Alwi, Fadl Mutaher Zahary, Ammar T. Alhegami, Ahmed Sultan Alsabry, Ayman Al-Badani, Abdulkader M. Faculty of Computer and Information Technology Sana’a University Sana’a Yemen International University of Technology Twintech Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana’a University Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Faculty of Science and Engineering Department of Computers Aljazeera University Ibb Yemen

ISBN: (纸本)9798331533557

In this paper, a model was built to compare the performance of the following machine learning (ML) models: DT, RF, SVM, and MLP, using two types of classification: binary classification and multi classification. The researchers concluded that the MLP classifier was the most efficient using multi classifications, as the classifier gave an accuracy of 99.77%, a recall of 93.25%, a precision of 92.02%, and an F-score of 92.63%. Using the dataset provided by the Tax Authority of Yemen, which is related to the commercial and industrial profits tax explained in detail in other papers for the same authors, which consists of 1083 record, after the preprocessing of data. © 2024 IEEE.

关键词： Contrastive Learning

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Optimizing Zone Compliance for Distance Relay in Transmission Lines with Installed FACTS Devices 6

Optimizing Zone Compliance for Distance Relay in Transmissio...

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6th IEEE Conference on Energy Conversion, CENCON 2023

作者： Tavalaei, Jalal Afrouzi, Hadi Nabipour Sanjari, Mohamad J. Habibuddin, M.H. Barmala, Ehsan Javanmardzadeh, Abdolreza Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Kuching Malaysia Griffith University School of Engineering and Built Environment QLD Australia Universiti Teknologi Malaysia Faculty of Electrical Engineering Skudai Malaysia Institute for Higher Education Acecr Khuzestan Faculty of Engineering Ahvaz Iran

ISBN: (纸本)9798350325096

This article addresses the dysfunction of protection systems in the presence of FACTS devices, with a focus on resolving the issues of overreach and underreach commonly encountered by conventional distance relays. The article proposes the implementation of an adaptive distance relay that exploits a novel technique to tackle these challenges. The relay utilizes a modified boundary characteristic in the form of a quadrilateral zone. The boundaries of the rectified zone are determined using a 5th-degree polynomial equation. To strike a balance between improving adaptive boundary accuracy and minimizing zone expansion, the coefficients of the selected boundaries and their intersections are obtained through a consolidated application of a multi-objective genetic algorithm, strengthened by utilizing the Pareto front technique. Through extensive studies conducted on the enhanced quadrilateral zone of the distance relay, the effectiveness of this approach in establishing a robust protection system is demonstrated. By employing this adaptive distance relay approach, the occurrence of dysfunction in distance relays operation in the presence of FACTS devices can be significantly reduced compared to conventional relays. © 2023 IEEE.

关键词： Electric lines

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Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information Systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

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Distance Matrix and Markov Chain Based Sensor Localization in WSN

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Computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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Automated Diagnosis and Detection of Blood Cancer Using Deep Learning-Based Approaches: A Recent Study and Challenges 6

Automated Diagnosis and Detection of Blood Cancer Using Deep...

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Singh, Jaswinder Bir Luxmi, Vijay Guru Kashi University Faculty of Computing Talwandi Sabo India Guru Kashi University Department of Computer Science and Engineering Talwandi Sabo India

ISBN: (纸本)9798350304480

Blood cancer, also referred to as haematological malignancy, is a collection of cancers that affect the blood, bone marrow, and lymphatic systems. Early and accurate blood cancer detection is essential for effective treatment and enhanced patient outcomes. Deep learning algorithms have emerged as potent instruments for medical image analysis and disease detection in recent years. This paper intends to provide a comprehensive overview of the application of deep learning techniques to the detection of blood cancer. The paper begins by describing the various forms of blood cancer and the difficulties involved in their detection. The merits and weaknesses of various deep learning architectures and frameworks used in blood cancer research are described. The review then focuses on the datasets and imaging modalities that are commonly used for blood cancer detection. It discusses the pre-processing techniques used to improve image quality and reduce noise, as well as the data augmentation techniques used to increase the robustness of deep learning models in the proposed system architecture. The paper concludes with a discussion of prospective future directions and developments in the field. It highlights the significance of developing robust and interpretable deep learning models, incorporating multi-modal data for enhanced accuracy, and integrating clinical and genomic data to facilitate personalised treatment strategies. © 2023 IEEE.

关键词： Blood Cancer CNN Deep Learning Healthcare

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible Images

Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrare...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Song, Xu Xiao, Yongbiao Li, Hui Wu, Xiao-Jun Sun, Jun Palade, Vasile Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Coventry University Faculty of Engineering Environment and Computing Conventry United Kingdom

ISBN: (纸本)9798350306545

The fusion of visible light and infrared images has garnered significant attention in the field of imaging due to its pivotal role in various applications, including surveillance, remote sensing, and medical imaging. Therefore, this paper introduces a novel fusion framework using Res2Net architecture, capturing features across diverse receptive fields and scales for effective extraction of global and local features. Our methodology is structured into three fundamental components: the first part involves the Res2Net-based encoder, followed by the second part, which encompasses the fusion layer, and finally, the third part, which comprises the decoder. The encoder based on Res2Net is utilized for extracting multi-scale features from the input image. Simultaneously, with a single image as input, we introduce a pioneering training strategy tailored for a Res2Net-based encoder. We further enhance the fusion process with a novel strategy based on the attention model, ensuring precise reconstruction by the decoder for the fused image. Experimental results unequivocally showcase our method's unparalleled fusion performance, surpassing existing techniques, as evidenced by rigorous subjective and objective evaluations. © 2023 IEEE.

关键词： Image fusion

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