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IEEE Open Journal of the Communications Society 2025年 6卷 4295-4310页

作者： Mohammed, Yasir I. Hassan, Rosilah Hasan, Mohammad Kamrul Islam, Shayla Abbas, Huda Saleh Khan, Muhammad Asghar Khan, Muhammad Attique Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Department of Computer Science Computer Science and Engineering College Taibah University Madinah Saudi Arabia Department of EE College of Engineering Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protocols, proving especially effective in dynamic applications such as military surveillance, disaster management, and environmental monitoring. Nevertheless, traditional routing algorithms, initially developed for terrestrial networks, often fail to meet the unique challenges of FANETs, notably their high mobility and frequently changing network topologies. A framework was proposed to address these challenges;this paper formulates a multi-objective optimization problem aimed at optimizing UAV trajectories, enhancing energy efficiency, and maximizing communication range to improve overall data forwarding performance. A Reinforcement Learning (RL)-based agent is created that constantly enhances its decision-making capacity by utilizing real-time feedback and dynamically chooses best forwarding tactics. This work also combines developments in large-scale data collecting from Wireless Sensor Networks (WSNs), using mobile sinks supported by FANETs in conjunction with multi-objective optimization approaches to improve data collecting efficiency greatly. Experimental tests show that the suggested RL-based techniques outperform conventional routing protocols by properly lowering delays and raising the Packet Delivery Ratio (PDR). Moreover, simulation findings show the better scalability and adaptability of RL-enabled UAV networks, stressing its possible use in dynamic real-world situations such as disaster relief operations and environmental monitoring tasks. © 2020 IEEE.

关键词： Drones

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An Early Detection of Tomato Plant Disease with Deep Reinforcement Learning and CNN 5th

An Early Detection of Tomato Plant Disease with Deep Reinfor...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Singh, Jagendra Saraswat, Mala Ramesh, Gopisetty Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Murugesan, Ashok School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of EIE CVR College of Engineering Telangana Ibrahimpatnam India Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science & Engineering Srinivasa Ramanujan Institute of Technology Autonomous Anantapur India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Kuthambakkam India

ISBN: (纸本)9789819795147

Machine learning models have been tested in the current research for detecting and classifying tomato diseases at their early stages. This is one of the core tasks of modern agriculture, being extremely beneficial in maintaining the health and, accordingly, the production of tomatoes. In order to achieve this, a dataset of 3450 images was totaled, containing both the diseased and healthy leaves of tomato. The dataset was split with a 70% portion for training and the rest 30% for testing. Therefore, the current research seeks to train and evaluate the performance of several machine learning models, including Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and VGG-16 architecture;the results showed that the CNN model had the highest measure over the precision, recall, F1 score, and accuracy that averaged over 96.8%, 96.5%, 96.6%, and 96.77%, respectively. The KNN model showed particularly high performance over the metrics of interest with precision, recall, F1 score, and accuracy of 94.3%, 94.0%, 94.1%, and 94.32%, respectively. The RNN model depicted some excellent indicators that pointed to the ability to catch the sequential dependencies with a precision, recall, F1 score, and accuracy of 88.2%, 83.5%, 85.7%, and 85.60%, respectively, which was still lower than CNN and KNN. Finally, the VGG-16 architecture showed quite competitive results over the precision, recall, F1 score, and accuracy—91.7%, 93.2%, 92.4%, and 92.20%, respectively. Therefore, it is evident that the current research provided clear evidence that the employment of ML was effective as pertaining to the accurate detection and classification of the tomato diseases with the CNN model displaying robustness and being highly potential. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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High Spatio-Temporal Imaging Reconstruction for Hybrid Spike-RGB Cameras

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 586-598页

作者： Xia, Lujie Xiong, Ruiqin Zhao, Jing Wang, Lizhi Zhu, Shuyuan Fan, Xiaopeng Huang, Tiejun Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Beijing Normal University School of Artificial Intelligence Beijing100875 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Department of Computer Science and Technology Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China

The acquisition of high-resolution image sequence for dynamic scenes of fast motion remains challenging due to motion blur caused by fast object movement. As a novel neuromorphic sensor, spike camera records the changing light intensity via spike stream of ultra-high temporal resolution, excelling in motion recording but limited in spatial resolution. This paper proposes a method for high spatio-temporal resolution (HSTR) imaging with a hybrid Spike-RGB camera, utilizing the information from spike stream to enhance the temporal resolution and the information from RGB images to enhance the spatial resolution of texture details. For this purpose, we present HSTR-Net, a dedicated network to process the spike and RGB data, which incorporates three key innovations: 1) A temporal control encoder enabling flexible temporal reconstruction through spike stream processing with embedded time parameters, eliminating the requirement to train multiple inference models;2) Motion-aware feature projection that aligns RGB frame details to target timestamps using spike-derived motion offsets;3) An adaptive transformer-based fusion strategy establishing cross-modal spatial correlations through mutual attention mechanisms. Extensive experiments demonstrate state-of-the-art performance on synthetic benchmark datasets with 5.23 dB PSNR and 6.94% SSIM improvement. It also shows visually impressive performance on real-world captured spike dataset. © 2015 IEEE.

关键词： High speed cameras

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ACSarF:a DRL-based adaptive consortium blockchain sharding framework for supply chain finance

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digital Communications and Networks 2025年第1期11卷 26-34页

作者： Shijing Hu Junxiong Lin Xin Du Wenbin Huang Zhihui Lu Qiang Duan Jie Wu School of Computer Science Fudan UniversityShanghai 200438China Shanghai Blockchain Engineering Research Center Fudan UniversityShanghai 200438China Information Sciences and Technology Department Pennsylvania State UniversityAbingtonPA 19001USA

Blockchain technologies have been used to facilitate Web 3.0 and FinTech ***,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain ***,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based *** address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning *** proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF ***,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF *** evaluate the proposed framework,we conducted extensive experiments in a typical SCF *** obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.

关键词： Web 3.0 Consortium blockchain Blockchain sharding Supply chain finance FinTech regulation

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Boosting the angiogenesis potential of self-assembled mesenchymal stem cell spheroids by size mediated physiological hypoxia for vascularized pulp regeneration

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Acta Biomaterialia 2025年 198卷 102-114页

作者： Yuan, Xiaojing Wang, Shuyi Yuan, Zuoying Wan, Zhuo Zhang, Linxue Song, Rui Ge, Lihong Zhao, Yuming Department of Pediatric Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Department of Mechanics and Engineering Science and Beijing Innovation Center for Engineering Science and Advanced Technology College of Engineering Peking University Beijing100871 China Department of Biomedical Engineering College of Future Technology Peking University Beijing100871 China Center of Basic Medical Research Institute of Medical Innovation and Research Peking University Third Hospital Beijing100191 China

Hypoxia is a pivotal factor in enhancing the vascularization potential of both two-dimensional (2D) cultured cells and three-dimensional (3D) cellular spheroids. Nevertheless, spheroids that closely mimic the in vivo microenvironment often experience excessive hypoxia, leading to the necrotic core and the release of toxic byproducts, ultimately impeding the regenerative process. To balance cell vitality and pro-angiogenic properties of cellular spheroids, this study investigates size-dependent hypoxia in stem cell spheroids utilizing an oxygen transfer finite element model. Subsequently, we develop 3D cultured stem cells from human exfoliated deciduous teeth (SHED) spheroids with regulated size-dependent hypoxia. Comprehensive assessments indicate that SHED spheroids, inoculated at a density of 50,000 cells, display moderate physiological hypoxia, which optimizes their pro-angiogenic potential, fusion capacity, and reattachment ability. Compared with SHED sheets, SHED spheroids enhance vascularized pulp regeneration more effectively with a tightly connected odontoblastic-like layer. Moreover, high-throughput transcriptome sequencing and RT-qPCR analysis further confirm the spheroids' ability to promote angiogenesis and odontogenic differentiation. This study not only introduces a practical and effective approach for regulating size-dependent hypoxia in cellular spheroids, and simultaneously enhancing cell vitality and angiogenic potential, but also paves the way for the clinical application of SHED spheroids in regenerative dental pulp therapies. Statement of significance: The core of three-dimensionally cultured cellular spheroids often experiences hypoxia, and maintaining a balance between the activity and functionality of long-term cultured spheroids in the inevitably hypoxic microenvironment remains a significant challenge. This study introduces a method to optimize the hypoxic conditions of SHED spheroids by employing a reaction-diffusion model, which modulates

关键词： Stem cells

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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Massive GaN micro-LED array based underwater wireless optical communication

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Optics Express 2025年第9期33卷 19527-19534页

作者： Lv, Zhijian Liu, Xinyi Wu, Shuang Zhang, Wenwei Yue, C. Patrick Liu, Zhaojun Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Integrated Circuit Design Center Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

High-speed underwater wireless optical communication technology is a much-needed communication technology in current underwater applications, and micro-LED is a promising light source option. Considering the limitation of transmission distance due to the low optical power of a single micro-LED pixel, an underwater wireless optical communication system based on a novel massive GaN micro-LED array includes more than 5000 pixels fabricated via flip-chip technology is proposed, which can achieve 165-Mbps and 90-Mbps at 37.7 m and 73.5 m, respectively. Therefore, the feasibility of realizing a high data rate and long transmission distance in an underwater wireless optical communication system based on a novel massive micro-LED array is confirmed. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Fiber to the x

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