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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ali, Noor Ali, Khadija Ali, Fatimah Ali, Aaminah Ali, Nisar Ali, Raja Hashim A-levels Lahore Grammer School 55 Main Gulberg Lahore54660 Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad43701 Pakistan Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GIK institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an AI-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study. © 2024 IEEE.

关键词： Students

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Balancing Human-robot Collaborative Disassembly Line by Using Dingo Optimization Algorithm 4

Balancing Human-robot Collaborative Disassembly Line by Usin...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Xiwang Zhou, Liangbo Zhou, Mengchu Wang, Weitian Wang, Jiacun Qi, Liang Liaoning Petrochemical University Information And Control Engineering Fushun China Liaoning Petrochemical University Information And Control Fushun China New Jersey Institute Of Technology Electrical And Computer Engineering NewarkNJ United States Montclair State University School Of Computing Montclair United States Monmouth University Computer Science And Software Eng. West Long BranchNJ United States Shandong University Of Sci. And Tech. Computer Intelligent Sci. And Tech. Qingdao China

ISBN: (纸本)9798350315790

Disassembling and recycling scrapped products play important roles in effectively reducing environmental pollution and improving resource sustainability. A multi-product human-robot collaborative disassembly-line-balancing problem (MHDP) arises from doing so and represents an important yet to be solved in the remanufacturing field. This work explores how to use optimization algorithms to schedule tasks between humans and robots to maximize remanufacturing profit in human-robot collaboration contexts. Specifically, we investigate MHDP and develop a mixed integer programming model considering various system constraints. We propose a dingo optimization algorithm that simulates wild dogs' social hierarchy and chasing processes to solve MHDP. We compare the proposed algorithm with an exact solution finder, i.e., IBM CPLEX and a well-known intelligent optimization method, i.e., Genetic Algorithm to show its competitive performance in terms of solution accuracy and efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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Integrating FACTS technologies into renewable energy systems: potential and challenges

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International Journal of Ambient Energy 2024年第1期45卷

作者： Nazir, Muhammad Shahzad Ullah, Hayat Larik, Nauman Ali Chu, Zhang Tian, Peng Sohail, Hafiz M. Raute, Reiko Faculty of Automation Huaiyin Institute of Technology Huai’an China Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Concordia University Montreal Canada School of Electrical Power South China University of Technology Guangzhou China School of Economics and Management South China Normal University Guangzhou China Department of Electrical Engineering Faculty of Engineering University of Malta Msida Malta

The proliferation of renewable energy systems into smart grids is becoming increasingly vital as the globe continues to shift toward sustainable energy sources. However, renewable energy’s intermittent nature can cause power quality complications such as voltage swings and frequency irregularities. Interestingly, FACTS technologies can provide improved control facilities for power flow and voltage regulation. The global FACTS market worthed $1.18 billion in 2020 and is predicted to be $1.91 billion by 2026, expanding at 7.5%. This study entertains FACTS technologies and their integration into renewable energy systems. This study deliberates the function of several FACTS devices, including Static Var Compensators, Static Synchronous Compensators, and Unified Power Flow Controllers, in enhancing power quality. The study also expanded to the problems and opportunities that come with integrating FACTS technologies into renewable energy systems. This study offers the current state of FACTS technologies and their prospective applications for improving power quality integrated renewable energy systems issues. The review will be an interesting useful source for industry and academia researchers in power systems and renewable energy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： FACTS technologies power quality green energy utilisation clean energy smart grid renewable energy sources

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Recognizing Human-object Interactions for a Home Assistive Robot 8

Recognizing Human-object Interactions for a Home Assistive R...

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8th International Conference on Automation, Control and Robotics engineering, CACRE 2023

作者： Wang, Liangliang Huo, Guanglei Chen, Xinwei Huang, Chengxi Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot - Xiaozhi II verify its robustness. © 2023 IEEE.

关键词： Human robot interaction

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection 8

Heterogeneous Graph Convolutional Network for Visual Reinfor...

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8th International Conference on Automation, Control and Robotics engineering, CACRE 2023

作者： Wang, Liangliang Huang, Chengxi Chen, Xinwei Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method. © 2023 IEEE.

关键词： Reinforcement learning

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

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computer Systems Science & engineering 2022年第6期41卷 1043-1056页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Xingming Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszowVoivodeship100031Poland

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the *** order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this *** proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square ***,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

关键词： 3-D medical watermarking robust watermarking PCA BF-PSO

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Novel Algorithm For Adjacent Vertex-Distinguishing Edge Coloring Of Large-Scale Random Graphs

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Journal of engineering Science and technology Review 2021年第3期14卷 69-75页

作者： Huanping, Zhao Peijin, Zhu Jingwen, Li Huojie, Shi School of Computer and Software Nanyang Institute of Technology Nanyang 473004 China School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China Department of Biological Systems Engineering Washington State University PullmanMI98101 United States

Graph coloring is one of the areas in graph theory with high research importance. Adjacent vertex-distinguishing edge coloring is a type of multi-conditional coloring in graph coloring, but existing associated studies lack analysis on constraint conditions. In this study, a novel algorithm was designed to increase the adjacent vertex-distinguishing edge coloring efficiency of large-scale random graphs. Sub-graphs were produced in this work by using a nondestructive segmentation algorithm to reduce the scale of random graphs, and random pre-coloring was performed on the edges of each sub-graph. Iteration was performed step by step in accordance with regulations by searching the conflict set of inaccurate edge coloring until the color met the requirements of the ultimate objective function. Next, the sub-graphs that were colored successfully were combined to realize adjacent vertex-distinguishing edge coloring of large-scale random graphs. Afterward, the accuracy of the adjacent vertex-distinguishing edge chromatic number was proven through theoretical analysis and experimental comparison. Several experiments were also performed on random graphs with less than 4000 vertexes and an edge density smaller than 0.1. Results show that when the number of vertexes is greater than 2000 and the edge density exceeds 0.07, the run time generally rings from 0.9 s to 1.5 s, whereas the run time for other random graphs is between 0.6 and 1.2 s. The algorithm can solve the adjacent vertex-distinguishing edge chromatic number of random graphs effectively, and the time complexity of the algorithm do not exceed O(n3). The proposed algorithm provides evidence for solving the shortest path of large-scale random graphs. © 2021 school of Science, IHU. All rights reserved

关键词： Multiobjective optimization

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Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

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computers, Materials & Continua 2022年第5期71卷 3035-3050页

作者： Xiaorui Zhang Xun Sun Xingming Sun Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The leakage of medical audio data in telemedicine seriously violates the privacy of *** order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio *** scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains *** order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark *** to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well *** the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible *** experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.

关键词： Telemedicine privacy protection audio watermarking robust reversible watermarking two-stage embedding

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BedIDS: An Effective Network Anomaly Detection Method by Fusing Behavior Evolution characteristics

BedIDS: An Effective Network Anomaly Detection Method by Fus...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhen Liu Changzhen Hu Chun Shan Junkai Yi Computer School Beijing Information Science & Technology University Beijing China Beijing Key Laboratory of Software Security Engineering Technology School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Automation Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Leveraging artificial intelligence models to enhance the performance of intrusion detection systems has become an important component in the field. However, as the scale of networks continues to expand, the structure of networks becomes more complex, and the amount of data in the networks grows larger. Existing methods are facing numerous challenges, including difficulties in constructing training datasets for models, challenges in transferring and reusing models, and high costs associated with model training. This paper introduces a novel approach named BedIDS. This method involves constructing the evolutionary process of network behavior and calculating the evolutionary characteristics of network behavior. Using only the most fundamental five network traffic features, including IP addresses, BedIDS achieves rapid and accurate detection performance on a device equipped with a 3060ti graphics card. We conducted tests using the CICIDS2017 and UNSW-NB15 datasets to evaluate its performance. Experimental results demonstrate that BedIDS maintains high detection accuracy and improves detection speed while requiring a relatively low AI computing force.

关键词： Training Performance evaluation Radio frequency Privacy Costs Accuracy Intrusion detection Telecommunication traffic Feature extraction Security

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