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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer Detection

arXiv

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arXiv 2024年

作者： Galazis, Christoforos Wu, Huiyi Goryanin, Igor Department of Computing Imperial College London London United Kingdom National Heart & Lung Institute Imperial College London London United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom Okinawa Institute Science and Technology Okinawa Japan MMWR Ltd United Kingdom

Improving breast cancer detection and monitoring techniques is a critical objective in healthcare, driving the need for innovative imaging technologies and diagnostic approaches. This study introduces a novel multi-tiered self-contrastive model tailored for microwave radiometry (MWR) in breast cancer detection. Our approach incorporates three distinct models: Local-MWR (L-MWR), Regional-MWR (R-MWR), and Global-MWR (G-MWR), designed to analyze varying sub-regional comparisons within the breasts. These models are integrated through the Joint-MWR (J-MWR) network, which leverages self-contrastive results at each analytical level to improve diagnostic accuracy. Utilizing a dataset of 4,932 female patients, our research demonstrates the efficacy of our proposed models. Notably, the J-MWR model achieves a Matthew’s correlation coefficient of 0.74 ± 0.018, surpassing existing MWR neural networks and contrastive methods. These findings highlight the potential of self-contrastive learning techniques in improving the diagnostic accuracy and generalizability for MWR-based breast cancer detection. This advancement holds considerable promise for future investigations into enabling point-of-care testing. The source code is available at: https://***/cgalaz01/self_contrastive_ mwr. © 2024, CC BY.

关键词： Radiometry

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How to Perform Energy-Balanced Underwater Data Collection in AUV-Aided UASNs: A Social Welfare-Based Node Clustering Approach

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IEEE Transactions on Computational Social Systems 2024年

作者： Lin, Chuan Han, Guangjie Lu, Chang Hussain Shah, Syed Bilal Zhang, Yu Wang, Feng Northeastern University Software College Shenyang110819 China Hohai University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Nanjing210013 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Nanjing210013 China Dalian University of Technology School of Software Dalian116024 China Dar Al-Hekma University School of Engineering Computing and Informatics Jeddah21589 Saudi Arabia Suzhou Vocational University School of Engineering Suzhou215004 China

The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors. © 2014 IEEE.

关键词： Marine applications

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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Industrial Semantic Communications for Tool Wear Monitoring in Industrial IoT Networks

Industrial Semantic Communications for Tool Wear Monitoring ...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Mingqiang Zhang Yajuan Yang Baokun Song Zexin Chen Xiaocong Ma Dongyang Li Na Li Feng Cao Yanping Zhang Haixia Zhang School of Cyber Science and Engineering Qufu Normal University Qufu China College of Oceanography and Space Informatics China University of Petroleum (East China) Huangdao China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Control Science and Engineering Shandong University Jinan China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong University Jinan China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

In this paper, we propose an Industrial Semantic Communications (ISC) framework to ensure efficient and in-telligent communication in Industrial Internet-of-Things (IIoT) networks, and further apply the proposed framework to the task of tool wear monitoring. A semantic-aware tool wear monitoring network (TWM-Net) is designed to accurately identify the idling state from tool wear data, thus assisting the semantic data transmission process of the ISC framework. Then, a demo software system is implemented to verify the delay performance of the proposed ISC framework. Experimental results demonstrated that the proposed ISC framework can facilitate the semantic information exchange between edge industrial system and cloud platform together with the deep compressed sensing method, and significantly reduce the amount of data transmissions in IIoT networks. The source code and dataset is available through https://***/pnixsoft/TWM-Net.

关键词： Vibrations Source coding Software systems Multitasking Delays Data communication Monitoring Information exchange Industrial Internet of Things Testing

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Effect of COVID-19 on Photovoltaic Demand and Installation

Effect of COVID-19 on Photovoltaic Demand and Installation

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2023 International Conference on Green Energy, computing and Intelligent technology, GEn-CITy 2023

作者： Zulfiqar, Faiza Muhammad-Sukki, Firdaus Abu-Bakar, Siti Hawa Bani, Nurul Aini Mas'ud, Abdullahi Abubakar School of Computing Engineering & the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom Renewable Energy Research Laboratory Electrical Engineering Section British Malaysian Institute Universiti Kuala Lumpur Gombak53100 Malaysia Razak Faculty of Technology & Informatics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Department of Electrical Engineering Jubail Industrial College Al Jubail35718 Saudi Arabia

ISBN: (纸本)9781839539596

The research investigates the effects of the COVID-19 pandemic on the photovoltaic (PV) sector. This research examines the alterations in PV demand and installation amidst the COVID-19 pandemic and identifies the underlying factors that have influenced these changes. The article delves into the difficulties encountered by PV installers amidst the ongoing pandemic. This study delves deeper into the effects of government policies and investor circumstances on the PV industry amidst the pandemic. It examines the policies that have influenced the supply chain, demand, financing, and shift towards renewable energy. The results suggest that the outbreak of the pandemic has resulted in noteworthy alterations in the demand and installation of PV systems, which have had both favourable and unfavourable effects on the sector. The response of the industry to the pandemic has been significantly influenced by government policies and investor conditions. The investigation culminates by proposing potential avenues for further inquiry within this domain. © The Institution of Engineering & technology 2023.

关键词： COVID-19

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Automatic classification and isolation of cracks on masonry surfaces using deep transfer learning and semantic segmentation

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Journal of Building Pathology and Rehabilitation 2023年第1期8卷 1-14页

作者： Aliu, Abdulmalik Adozuka Ariff, Nor Rima Muhamad Ametefe, Divine Senanu John, Dah College of Built Environment Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Centre of Studies for Building Surveying College of Built Environment Universiti Teknologi Mara (UiTM) Selangor Shah Alam 40450 Malaysia Wireless Communication Technology Group College of Engineering Department of Electrical Engineering Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia School of Information Science College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) Selangor Puncak Perdana 40150 Malaysia

Masonry structures represent a significant portion of the built environment and are among the most commonly constructed buildings worldwide. However, manually identifying cracks in masonry walls is a dominant yet flawed approach, owing to its arduous, time-consuming, subjective, and ambiguous nature, particularly in inaccessible areas. Artificial intelligence, through computer vision and deep learning, has exhibited competence in several identification and classification tasks. Despite the success of existing models, their generalization efficacy remains largely unexplored. Motivated by this, we propose a crack classifier based on deep transfer learning and data augmentation, which we evaluate on various masonry images to determine its generalization performance. Our method achieved sensitivity, specificity, precision, and F1-score of 99.98%, 99.95%, 99.95%, and 99.96%, respectively. Additionally, we employed two types of semantic segmentation techniques, canny edge and heatmap, to isolate the identified cracks and compared their efficacy. Both techniques accurately isolated the crack region effectively. However, we observed some differences in their performance. The canny edge segmentation method was more effective at detecting sudden changes in edge structure, resulting in sharper and more precise edges around the crack region. Conversely, the heatmap segmentation method was better at highlighting the color contrast around the crack region, which helped to concentrate the color around the crack region and make it stand out more prominently. Overall, our findings demonstrate that automated masonry crack detection using deep transfer learning with data augmentation is effective for detecting cracks in varied masonry surfaces, with significant potential applications in building maintenance and safety. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Canny edge Crack detection Data augmentation Deep transfer learning Heatmap Masonry

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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A Generic Digital Twin Framework for Collaborative Supply Chain Development

A Generic Digital Twin Framework for Collaborative Supply Ch...

引用

computing and Big Data (ICCBD), International Conference on

作者： Xin Lu Wei Wang Weidong Li Yanguo Jing Xiaoxia Li Department of Computing and Informatics Bournemouth University Bournemouth UK School of Engineering Scienceion University of Skövde Skövde Sweden School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China Faculty of Business Computing and Digital Industries Leeds Trinity University Leeds UK College of Informatics Huazhong Agricultural University Wuhan China

Current Supply Chains (SCs) are complex and diverse along with fragile to SC disruptions. This leads urgently needs to develop an intelligent, transparent, collaborative and resilient SC system to cope with unexpected SC disruptions. Digital twin (DT) is one of the most promising solutions to develop smart SCs that has been extensively studied recent years. However, SCDT paradigm is still at an early stage. This paper presents a generic and modularized five layers DT framework to provide a flexible and collaborative solution, which can be compatible with different DT systems in various SCs. The feasibility of the proposed framework is validated through a practical implementation in a distributed eyewear industry.

关键词： Industries Integrated optics Supply chains Collaboration Big Data Current supplies Digital twins

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Generation of Dynamic Authorization Using Cryptography For Private-Keyless Data Sharing

SSRN

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SSRN 2023年

作者： Lu, Hai Zhu, Yan Yau, Stephen S. Zhu, Guizhen Lv, Kewei School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Data and Communication Science Technology Research Institute Beijing100191 China School of Cyber Security University of Chinese Academy of Sciences Beijing100093 China

Private-keyless access is emerging as an inevitable trend in the evolution of future information systems. It helps simplify key management, enhance security, and increase flexibility of access authorization. Hereby, we introduce a novel security model, Cryptographic Resource Enabled Infrastructure (CREI), to support private-keyless access and policy-based data custody. Firstly, in the data custody phase, data resource and its session key are uploaded in ciphertext form to CREI, and the access authorization complies with multiple policies designated by the provider. Secondly, in the dynamic authorization phase, traditional keys or keycards are replaced with real-time attribute credentials issued by multiple authorities. CREI makes dynamically cryptographic policy decision according to the credentials and the cryptographic policy (cryptopolicy). Technically, we design the Credentials-Driven Cryptosystem (CDC) over ideal lattice to build anti-quantum confidence for CREI. In CDC, preimage Gaussian sampler is adopted to sample short vector as real-time attribute credential instead of user's private key. Small Policy Matrix (SPM) is implemented to convert monotone policies into optimized cryptopolicies in order to reduce accumulated error. The security analysis confirms that the credentials, cryptopolicy and its decision result are existentially unforgeable, ensuring the semantic security of the whole CREI. Experimental results indicate CREI has lower storage and computational costs than existing schemes. Meanwhile, despite continuous improvements to the access policies as system evolves, CREI can simply select an appropriate policy among multiple candidates to make authorization decisions without having to re-encrypt data. © 2023, The Authors. All rights reserved.

关键词： Authorization

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