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An Image Semantic Communication system Based on GAN and Relative Position-Encoding

IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Laboratory of Cryptography Application Technology Innovation and Testing Verification Ministry of Industry and Information Technology Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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A Proposed Mala-Net CNN Algorithm for Malaria Early Detection Based on Thick Blood Smear 2

A Proposed Mala-Net CNN Algorithm for Malaria Early Detectio...

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2nd International Symposium on information Technology and Digital Innovation, ISITDI 2024

作者： Indra, Zul Jusman, Yessi Elfizar Mahdiyah, Evfi Salambue, Roni Winarso, Doni Universitas Riau Departement of Computer Science Pekanbaru Indonesia Universitas Muhammadiyah Yogyakarta Departement of Electrical Engineering Bantul Indonesia Universitas Muhammadiyah Riau Departement of Information System Pekanbaru Indonesia

ISBN: (纸本)9798350389241

Malaria has spread worldwide since the early 20th century and causes nearly half a million deaths each year. Malaria is actually not a dangerous disease and can be easily cured if the treatment can be carried out effectively. Therefore, efforts to identify the presence of the plasmodium parasite early will greatly help reduce the death rate caused by this disease. However, the fact is that this disease often does not get serious attention. Generally, this disease is only treated in the critical phase because it is considered just a common flu. This research is intended to produce a computer-aided disease diagnosis (CAD) system enriched with CNN algorithms to assist in early malaria diagnosis. This CAD system is expected to provide an effective and reliable malaria diagnosis, and avoid the limitations of manual diagnosis. In order to develop the CAD applications, this study proposed a novel CNN architecture called as Mala-Net by utilizing red thick blood dataset. In conclusion, this study successfully surpassed previous studies used as benchmarks with an accuracy value above 97%. © 2024 IEEE.

关键词： Diseases

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Secure-by-Construction Synthesis for Control systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Insights into the Future: XAI Integration in O-RAN and Space Communication systems

Insights into the Future: XAI Integration in O-RAN and Space...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Tahir, H. Ahmed Nabi, Fahad Tariq, Muhammad Ziad Khan, Ahmad Fasih Mahmud, Azwan Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Kiel Department of Electrical Engineering & Information Technology Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (纸本)9798331530747

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as 'black boxes,' making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Radio access networks

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A Comprehensive Review on Cybersecurity Issues and Their Mitigation Measures in FinTech

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2024年第3期5卷 45-91页

作者： Ali, Guma Mijwil, Maad M. Buruga, Bosco Apparatus Abotaleb, Mostafa Muni University Arua00256 Uganda Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Library and Information Services Muni University Arua00256 Uganda Department of System Programming South Ural State University Chelyabinsk Russia

The fourth industrial revolution has seen the evolution and wide adoption of game-changing and disruptive innovation "financial technologies (FinTech)" around the globe. However, the security of FinTech systems and networks remains critical. This paper comprehensively reviews the evolving landscape of cybersecurity issues within the FinTech sector and explores effective mitigation measures. Four independent researchers reviewed relevant literature from IEEE Xplore Digital Library, ScienceDirect, Taylor & Francis, Emerald Insight, Springer, SAGE, Wiley Online Library, Hindawi, MDPI, ACM Digital Library, IGI Global, and Google Scholar. The research paper begins by examining the history and evolution of FinTech, drivers for the growth of FinTech, segments of the FinTech industry, FinTech ecosystem, FinTech business model, and FinTech application. Subsequently, it then delves into an analysis of the most pressing cybersecurity issues confronting FinTech firms, such as privacy concerns, data breaches, malware attacks, hacking, insider threats, identity theft, social engineering attacks, distributed denial-of-service attacks, and others. In response to these cybersecurity issues, the paper evaluates various mitigation strategies and best practices adopted by FinTech firms and regulatory bodies globally. These measures include technological solutions such as authentication and access control mechanisms, cryptography, big data analytics, intrusion detection/prevention systems, regular data backup, artificial intelligence and machine learning, cloud computing technologies, blockchain technologies, and fraud detection and prevention systems. The paper also emphasizes the importance of FinTech regulatory sandboxes, regulatory compliance, basic security training, continuous monitoring of threats, zero-trust policy, robust cybersecurity culture, regular testing, and stringent security policies to strengthen the FinTech ecosystem's cyber resilience. Based on empirical research, i

关键词： Denial-of-service attack

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Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第5期36卷 1915-1930页

作者： Rab Nawaz Bashir Imran Sarwar Bajwa Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel ABahaddad Khalid Ali Almarhabi Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science and IT Islamia University BahawalpurPakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Soil salinity is a serious land degradation issue in *** is a major threat to agriculture *** irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation *** the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)*** relationship between environmental conditions and ET rate is hard to be deﬁned by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)***-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity *** proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)*** validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm *** applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

关键词： Leaching fraction saline soil evapotranspiration machine learning calibrated evapotranspiration artiﬁcial intelligence blaney criddle method

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Distributed Hierarchical AI Architecture for Meta-Described Local Dynamic Maps in Autonomous Driving 16

Distributed Hierarchical AI Architecture for Meta-Described ...

引用

16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Miyata, Hiroshi Arimoto, Kazutami Saitoh, Masatoshi Hayami, Atsushi Mizuno, Hisayoshi Shimazu, Yukihiko Yokogawa, Tomoyuki Technoaccel Networks Corp. Tokyo Office Tokyo Japan Okayama Prefectural University Computer Science and System Engineering Sousya Okayama Japan Toyota Motor Corporation Information and Communication Planning Division Tokyo Japan Okayama Prefectural University Computer Science and System Engneering Sousya Okayama Japan

ISBN: (纸本)9798350377903

In autonomous driving, sharing information among vehicles to enhance safety is an important issue. However, it is not yet clear how each terminal car processes information and how to share it with other cars. Therefore, this paper proposes a distributed hierarchical AI architecture in which each edge distributed terminal of a car or intermediate cloud is equipped with a processing AI that generates metadata and builds them hierarchically. The hierarchical AI consists of multiple AI information processing layers and generates metadata links by concatenating the output of each layer. The hierarchical and spatially distributed nature of information processing allows for a compact digital twin that enables efficient spatiotemporal processing by receiving only the data necessary for that terminal and facilitating the processing necessary for that terminal. As a concrete example, we created software for vehicle front camera image processing for local dynamic maps for autonomous driving, and we evaluated the intensity and processing load of the proposed architecture. In the future, we will continue to study the software and hardware necessary to increase the hierarchy and speed of AI processing. © 2024 IEEE.

关键词： Spatio-temporal data

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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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