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Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

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Intelligent Automation & Soft Computing 2023年第5期36卷 2111-2128页

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research CenterFaculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingﬁrm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new *** techniques for identifying and detecting worker performance in industrial applications are based on computer vision *** widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is *** the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction *** suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker *** proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work *** addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed *** an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalﬁndings revealed that the suggested model could accurately recognize the actions of construction ***,we examined the impact of window size and sensor position on the identiﬁcation efﬁciency of the proposed method.

关键词： Complex human activity recognition wearable inertial sensors deep learning construction workers automatic recognition

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The Impact of the digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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Comprehensive Comparative Analysis of Breast Cancer Forecasting Using Machine Learning Algorithms and Feature Selection Methods 23th

Comprehensive Comparative Analysis of Breast Cancer Forecast...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Singh, Jagendra Dharani, M. Shelke, Nitin Arvind Sajid, Mohammad Alsahlanee, Abbas Thajeel Rhaif Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Electronics and Communication Engineering K. S. R. Institute for Engineering and Technology Tiruchengode India Department of Computer Science Aligarh Muslim University Aligarh India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Ghaziabad India

ISBN: (纸本)9783031648120

This research leveraged machine learning models, including Deep Neural Network (DNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM), to predict breast cancer from CT and MRI scans. A dataset comprising 2345 instances of malignant and benign cases was meticulously curated, with 80% allocated for training and 20% for testing. The experimental results revealed the DNN as the top-performing model, exhibiting remarkable accuracy (95.2%), precision (94.8%), recall (95.6%), and F1 score (95.2%). The ANN also demonstrated strong performance, achieving an accuracy of 93.6% with balanced precision and recall scores. In contrast, the SVM, while respectable, fell slightly behind the machine learning models in terms of overall accuracy and performance. Detailed confusion matrices further elucidated the models’ capabilities and limitations, providing valuable insights into their diagnostic prowess. These findings hold great promise for breast cancer diagnosis, offering a non-invasive and highly accurate means of early detection. Such a tool has the potential to enhance patient care, reduce the strain on healthcare systems, and alleviate patient anxiety. The success of this research highlights the transformative impact of advanced machine learning in medical imaging and diagnosis, signaling a path toward more efficient and effective healthcare solutions. Further research and clinical validation are essential to translate these promising results into practical applications that can positively impact patients and healthcare providers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Toolkit for Satellite Visualization using Auxiliary Data 15

Toolkit for Satellite Visualization using Auxiliary Data

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Veena, O.S. Suhas, G.K. Department of Computer Science & Engineering Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur572106 India

ISBN: (纸本)9798331300579

This paper provides a complete model for satellite tracking that uses Two Line Elements (TLE) data to accurately predict and track satellite locations over *** three-phase graphical user interface of the model allows for real-time viewing of satellite positions and critical tracking data: input, prediction, and *** work offers a comprehensive method for tracking and visualizing satellites in low Earth orbit (LEO), geostationary orbit, and remote sensing orbit utilizing TLE *** latitude and longitude calculations allow for ongoing trajectory mapping, which makes it easier to track satellite movement *** and clarity are further enhanced by visualizing satellite *** their courses, satellite names are added to make identification *** integrated method has significant benefits for applications including space exploration, telecommunication, and environmental ***, it makes constellation dynamics and satellite interactions more understandable, developing satellite-based technologies for a variety of useful uses. © Grenze Scientific Society, 2024.

关键词： Graphical user interfaces

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Automated AI Research on Cyber Attack Prediction and Security Design 6

Automated AI Research on Cyber Attack Prediction and Securit...

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

作者： Madala, Ravikiran Vijayakumar, N. Nandini, N. Verma, Shanti Chandvekar, Samidha Devendra Singh, Devesh Pratap Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States Karpagam College of Engineering Department of Computer Science and Technology Coimbatore India Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India L J University Department of Computer Application Gujarat Ahmedabad India Department of Information Technology Maharashtra New Panvel India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

A fast expanding topic of study on automated AI is focused on the prediction and prevention of cyber-attacks using machine learning algorithms. In this study, we examined the research on applying machine learning algorithms to the problems of strategic cyber defense and attack forecasting. We also provided a technique for assessing and choosing the best machine learning models for anticipating cyber-attacks. Our findings show that machine learning methods, especially random forest and neural network models, are very accurate in predicting cyber-attacks. Additionally, we discovered a number of crucial characteristics, such as source IP, packet size, and malicious traffic that are strongly associated with the likelihood of cyber-attacks. Our results imply that automated AI research on cyber-attack prediction and security planning has tremendous promise for enhancing cyber-security and averting cyber-attacks. © 2023 IEEE.

关键词： automated AI research cyber-attack prediction feature correlation machine learning neural network random forest security design

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Design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

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computers, Materials & Continua 2023年第7期76卷 1309-1320页

作者： Puneet Kumar Mishra Amrita Rai Nitin Sharma Kanika Sharma Nitin Mittal Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Department of Electronics and Communication Engineering G L Bajaj Institute of Technology and ManegmentGretear NoidaIndia Department of Electronics and Communication Engineering Chandigarh UnievrsityMohaliPunjabIndia Department of Electronics and Communication Engineering NITTTRChandigarhIndia University Center of Research&Development Chandigarh UniversityMohali121102India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah University11952Al-MajmaahSaudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2D atomic layers,are the main focus of this research *** impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm *** modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

关键词： Graphene tunnel field effect transistor(TFET) band to band tunnelling subthreshold swing

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Abstractive Text Summarization in Arabic-like Script using Multi-Encoder Architecture and Semantic Extraction Techniques

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IEEE Access 2025年 13卷 104977-104991页

作者： Fatima, Wajiha Rizvi, Syed Saqib Raza Ghazal, Taher M. Kharma, Qasem M. Ahmad, Munir Abbas, Sagheer Furqan, Muhammad Adnan, Khan Muhammad Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Networks and Cybersecurity Amman Jordan Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Software Engineering Department Amman Jordan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Korea University University College Seoul 02841 South Korea Prince Mohammad Bin Fahd University Computer Science Department Alkhobar Saudi Arabia Capital University of Science & Technology Department of Software Engineering Islamabad Pakistan Gachon University Faculty of Artificial Intelligence and Software Department of Software Seongnam-si 13120 South Korea

In the field of Natural Language Processing (NLP), the task of text summarization plays a vital role in understanding textual content and producing concise summaries. Text summarization approaches can be categorized as either extractive or abstractive, with the latter largely unexplored in the Arabic script languages. Previous research on abstractive summarization for the Urdu language has progressed in creating logical and succinct summaries but has failed to preserve the underlying semantics, often resulting in inconsistent and ambiguous outputs. This study focuses on designing and implementing an abstractive text summarization model for the Urdu language by achieving the semantic meaning of the original text. The proposed model improves readability and comprehension by generating concise and meaningful overviews of original content while introducing new words and expressions. The study implements a multi-layer transformer encoder as part of the mBART (Multilingual BART) model. The encoder consists of multiple stacked transformer layers. It utilizes self-attention mechanisms to process and understand the input sequences effectively, achieving a BERTScore of 90%, a BLEU score of 43%, and ROUGE scores of 80.3% for ROUGE-1, 74.3% for ROUGE-2, and 80.3% for ROUGE-L. These results demonstrate the efficacy of the model in producing coherent and high-quality abstractive summaries for the Urdu language. © 2013 IEEE.

关键词： Abstractive Text Summarization (ATS) Natural Language Processing (NLP) Semantic Meaning Text Generation Text Summarization (TS)

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Quantum-Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

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IEEE Internet of Things Journal 2025年第12期12卷 21225-21239页

作者： Jeong, Seon-Geun Duc, Pham Dang Anh Do, Quang Vinh Noh, Dae-Il Tung, Nguyen Xuan Chien, Trinh Van Pham, Quoc-Viet Hasegawa, Mikio Sekiya, Hiroo Hwang, Won-Joo Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Hanoi University of Science and Technology School of Information and Communication Technology Hanoi100000 Viet Nam Ton Duc Thang University Faculty of Electrical and Electronics Engineering Wireless Communications Research Group Ho Chi Minh City700000 Viet Nam Trinity College Dublin School of Computer Science and Statistics DublinD02PN40 Ireland Tokyo University of Science Department of Electrical Engineering Tokyo162-8601 Japan Chiba University Graduate School of Engineering Chiba263-8522 Japan Pusan National University School of Computer Science and Engineering Center for Artificial Intelligence Research Busan46241 Korea Republic of

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, subchannel assignment, and power allocation in a multiautonomous aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two subproblems: 1) a QA-based clustering and 2) subchannel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two subproblems. For the first subproblem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second subproblem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time. © 2014 IEEE.

关键词： Combinatorial optimization

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Real time dynamic regulation method of main road signal lights based on data clustering in the environment of internet of vehicles

Advances in Transportation Studies

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Advances in Transportation Studies 2022年第Special issue期4卷 91-102页

作者： Tong, X.C. Zhou, L. Department of Science and Technology Xi'an Siyuan University Xi'an710038 China Digital Architecture Engineering Research Center of Shaanxi Universities Xi'an Siyuan University Xi'an710038 China

Conventional trunk road signal light regulation has poor real-time performance and low regulation accuracy. This paper designs a real-time dynamic adjustment method of main road signal lights based on data clustering in the Internet of Vehicles environment. First of all, the cycle and traffic conditions of signal lights are analyzed to determine the change rule of signal lights. Then, calculate the capacity and determine the signal lamp control parameters. Finally, the similarity of signal lamp real-time control parameters is determined through data clustering, and the shortest signal cycle and the best cycle value are obtained. The real-time control model is built with OD matrix to achieve real-time dynamic control of signal lamp. The experimental results show that the real-time adjustment time of the method is within lis, the adjustment accuracy reaches 90%. The new method can effectively improve the adjustment efficiency of trunk road signal light. © 2022, Aracne Editrice. All rights reserved.

关键词： Vehicles

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