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Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1262-1272页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA IEEE

The core task of tracking control is to make the controlled plant track a desired *** traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps *** this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control *** the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to *** discounted iterative scheme under the new cost function for the special case of linear systems is ***,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

关键词： Adaptive critic design adaptive dynamic programming(ADP) approximate dynamic programming discrete-time nonlinear systems reinforcement learning stability analysis tracking control value iteration(VI)

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On Big Data Forensic and Forensic Cloud Environment

On Big Data Forensic and Forensic Cloud Environment

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2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022

作者： Tabona, Oteng Ramokapane, Kopo M. Maupong, Thabiso M. Higher Colleges of Technology Faculty of Computer and Information Systems Abu Dhabi United Arab Emirates University of Bristol Department of Computer Science Bristol United Kingdom Botswana International University of Science and Technology Department of Computer Science and Information System Palapye Botswana

ISBN: (纸本)9781665487580

The number of electronic devices involved in cyber related crimes has increased significantly in the last few years. This is primarily due to advancements in technology and increased accessibility to devices. This poses significant challenges with respect to forensic investigations as investigators are faced with processing huge volumes of data from various sources within a limited time. Due to the urgency, frequency, and magnitude of some cases, investigators may miss deadlines, and crucial leads. In this paper, we consider these above stated challenges and propose a new definition for big data forensics, which recognises the time aspect and variety of sources when carrying our digital forensics. This definition will help digital forensic tool designers consider these critical aspects and design comprehensive tools to combat the limitations of the current state-of-the-art. We review an existing tool, the Forensic Cloud Environment (FCE), against the proposed definition. We show how its design leverages the power of Hadoop Distributed File system (HDFS), HBase, and MapReduce to provide a comprehensive solution for big data forensics. In conclusion, we provide an argument for FCE as a solution for resolving big data forensic challenges based on general Scientific Working Group on Digital Evidence (SWGDE) forensic principles. © 2022 IEEE.

关键词： Big data

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Review of Data Visualization Techniques in IoT Data 8

Review of Data Visualization Techniques in IoT Data

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8th International Conference on Information technology Trends, ITT 2022

作者： Il-Agure, Zakea Dempere, Juan Computer Information System Higher Colleges of Technology Ras Al Khaimah United Arab Emirates Faculty of Business Higher Colleges of Technology Ras Al Khaimah United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Data visualization is considered the most efficient solution in IoT. It visualizes the endless stream of information collected by the respective smart systems to exponentially increase its value due to the meaningful insights it provides. Notably, numerous tools and techniques visualize the meaningful information obtained. It is, however, prudent that the analysis of this information considers the possibilities of anomalies within the data. The proper mechanisms must be implemented to detect these anomalies to ensure that the visualized information is accurate and reliable. This research discussion delves into reviewing current literature on the different tools and techniques that could detect anomalies in IoT. The review would reveal that the machine learning and deep learning approaches are the most-used approaches for anomaly detection. Several images would emerge regarding the visualization tools, including Grafana, Tableau, Orange, Power Bi tool, Kibabana, and Plotly. Tableau was identified as the popular and leading software used by numerous entities for visualizing the data in meaningful insights leading to powerful decisions. Some of its salient features include offering Api's and sophisticated developer tools such that different companies could use to meet specific needs, it has a less learning curve and has the ability to handle with extensive amounts of data generated in the network. Notably, the fusion of Big Data and IoT will continue to develop and advance both the existing and new visualization tools. These developments will arise because the IoT networks will continue to obtain data from various sources as the number of smart devices increases tremendously. © 2022 IEEE.

关键词： Data visualization

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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International Electrical Engineering Congress (iEECON)

作者： Waris Rattanimit Thanyarat Nomponkrang Electrical Technology and Automation System Faculty of Engineering and Technology Siam Technology College Bangkok Thailand Department of Computer Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

In the digital era, learning programming has become an essential skill, and assessing students' understanding in this area plays a crucial role in enhancing educational quality. However, evaluating subjective programming exam responses, especially those involving conceptual explanations or procedural descriptions, remains a challenging task due to the diversity and complexity of answers. Manual grading can lead to delays and inconsistencies in scoring. This research aims to develop an automated system for evaluating Thai subjective programming exams using Natural Language Processing (NLP) techniques to assess the correctness of responses. The system is implemented in Python, utilizing the ThaiNLP library for word tokenization, alongside Machine Learning algorithms such as Naive Bayes, Decision Tree, and Support Vector Machine (SVM) for classification and prediction of answers. The experimental dataset includes responses from 120 students who answered subjective questions about Python commands. Expert testing indicated high user satisfaction with an average score of 4.05. The findings show that SVM achieved the highest accuracy at 85.33%. This system can enhance efficiency and consistency in exam evaluation, reducing grading time and contributing to standardized scoring.

关键词： Support vector machines Electrical engineering Machine learning algorithms Manuals Machine learning Tokenization Libraries Programming profession Python Testing

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Rattanimit, Waris Nomponkrang, Thanyarat Siam Technology College Electrical Technology and Automation System Faculty of Engineering and Technology Bangkok Thailand King Mongkut's University of Technology North Bangkok Department of Computer Education Bangkok Thailand

ISBN: (纸本)9798331543952

关键词： Support vector machines

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Examination of Unsolicited Content on Public Service Platforms Across Multiple Devices

Examination of Unsolicited Content on Public Service Platfor...

引用

Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Yi Wu Yingshuo Wang Yanan Cheng Wangjun Yao Hong Liu Zhaoxin Zhang Faculty of Computing Harbin Institute of Technology Weihai China National Computer System Engineering Research Institute of China Beijing China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

Public service websites have become an integral part of people's everyday existence. And the proliferation of mobile devices has further facilitated access to these websites. These websites not only have formal differences between mobile and desktop, but more importantly, may lead to the differentiation and wider dissemination of unsolicited content. Although the existing studies have recognized the universality of the unsolicited content carried by them, they have not carried out a detailed analysis of the transmission forms of these unsolicited content and the defective rate of public websites. To address this issue, this paper use python collected 150 video websites and 150 novel websites through a combination of multiple search engines based on keyword algorithms. By analyzing the algorithm, it conducted mobile and desktop comparisons of the acquisition of unsolicited content on these two specific types of public service websites. The results show that both types of public service websites carry unsolicited content. Domain analysis of these unsolicited websites and their associated unsolicited content revealed that 92.86% of the IP addresses are located overseas. Moreover, the proportion of unsolicited websites on mobile devices can reach 60.42%, significantly higher than the 22.7% on desktop devices. Additionally, there are differences in the forms of unsolicited content displayed on mobile and desktop devices, with a discrepancy rate of up to 38.24%. Unsolicited websites on desktop devices exhibit a more diverse range of unsolicited content. This study provides the first in-depth study of these distinctions, facilitating the proposal of technical and regulatory solutions.

关键词： Search engines Mobile handsets Regulation Internet IP networks Proposals Artificial intelligence

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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PalmPrint Recognition using Deep Convolutional Neural Networks 2

PalmPrint Recognition using Deep Convolutional Neural Networ...

引用

2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2022

作者： Younus, Salema Abdulali, Ezz Omar Amhmed Bozed, Kenz Boudjellal, Sif Eddine Benghazi5213 Libya Benghazi University Department of Computer System Design Benghazi Libya University Ferhat Abbes- Setif 1 Faculty of Technology Setif Algeria

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Palmprint recognition system is gaining popularity in the biometrics research field, and it is considered as one of the most essential behavioral aspects of biometrics. The proposed approach was evaluated using the Hong Kong Polytechnic University (PolyU) palmprint database and a non-classical technique for palmprint recognition using InceptionResnet-v2 pre-trained deep neural networks (DNNs) and by adding some layers such as fully connected with Rectified Linear Unit (ReLU), dropout, and fully connected with softmax. And received a score of 99.21% with loss validation 0.173 %. © 2022 IEEE.

关键词： Biometrics

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