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

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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A Nonlinear Model Predictive Control Strategy for Trajectory Tracking of Omnidirectional Robots 16th

A Nonlinear Model Predictive Control Strategy for Trajector...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Ribeiro, Jorge Diogo Sobreira, Héber Miguel Moreira, António Paulo Faculty of Engineering University of Porto Porto4200-465 Portugal INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (纸本)9783031817236

This paper presents a novel Nonlinear Model Predictive Controller (NMPC) architecture for trajectory tracking of omnidirectional robots. The key innovation lies in the method of handling constraints on maximum velocity and acceleration outside of the optimization process, significantly reducing computation time. The controller uses a simplified process model to predict the robot’s state evolution, enabling real-time cost function minimization through gradient descent methods. The cost function penalizes position and orientation errors as well as control effort variation. Experimental results compare the performance of the proposed controller with a generic Proportional-Derivative (PD) controller and a NMPC with integrated optimization constraints. The findings reveal that the proposed controller achieves higher precision than the PD controller and similar precision to the NMPC with integrated constraints, but with substantially lower computational effort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive control systems

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Density-Clustering Aggregation for Personalized Federated Learning With AI-Enabled Aerial and Edge Computing in UAVs

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IEEE Internet of Things Journal 2025年第9期12卷 11220-11232页

作者： Chien, Wei-Che Lin, Chih-Hsun Zhu, Tianli Dai, Cheng Garg, Sahil Mukherjee, Amrit National Dong Hwa University Department of Computer Science and Information Engineering Hualien City974301 Taiwan Sichuan University College of Computer Science Sichuan Chengdu610017 China École de Technologie Supérieure Electrical Engineering Department MontrealH3C 1K3 Canada Applied Science and Technology Dubai United Arab Emirates Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India University of South Bohemia Faculty of Science Department of Computer Science České Budějovice370 05 Czech Republic

This research introduces the density-clustering-based aggregation for personalized federated learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for uncrewed aerial vehicles (UAVs). The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in automatically determining cluster numbers using EPS neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning (ML) tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning (FL) forward in complex and dynamic settings. © 2014 IEEE.

关键词： Federated learning

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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Data-Driven Genetic Algorithm for the Optimization of Water Distribution Networks: A New Surrogate Model for Estimating Investment and Operational Costs in Pumping Stations 27th

Data-Driven Genetic Algorithm for the Optimization of Water ...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Gajardo-Sepúlveda, Nicolás Faúndez-Lizama, Thalía Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766039

The increase in water consumption and demand has highlighted the need to improve the efficiency of water distribution networks (WDNs). Pumping stations (PS) represent a significant challenge due to their high energy consumption and associated operational costs. This study presents a data-driven evolutionary optimization methodology focused on predicting cost and penalties in the design and operation of PS. The methodology integrates the functioning of genetic algorithms with machine learning techniques, developing a surrogate model capable of predicting associated costs and increasing the computational efficiency of optimization models. A case study is presented to validate the methodology, showing a significant reduction of 31.8% in evaluation times and a decrease in optimization time compared to traditional models. The results indicated that despite the reduction in computational effort, the increase in the final cost of the network was minimal, with only a 2.42% increase compared to the baseline model. These findings underscore the effectiveness of combining machine learning with genetic algorithms for the optimization of PS in WDNs, improving computational efficiency while maintaining high standards in solution quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Data-Driven Evolutionary Algorithms for Optimizing Pumping Stations in Water Distribution Networks: Classifier-Guided Search Space Reduction 27th

Data-Driven Evolutionary Algorithms for Optimizing Pumping S...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Faúndez-Lizama, Thalía Gajardo-Sepúlveda, Nicolás Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766060

In the context of increasing water scarcity and the need to enhance water distribution network efficiency, this study focuses on optimizing the design of pumping stations using data-driven evolutionary algorithms guided by classification models. These facilities are critical components in water networks due to their high energy consumption and role in maintaining necessary water pressure. The proposed approach integrates a machine learning classifier model within the evolutionary process to pre-label solutions as feasible or infeasible, thereby reducing search space and computational costs associated with hydraulic simulations. Classification models include support vector classifier, artificial neural networks, and random forest. These models are trained on data generated from initial evolutionary optimization, enabling the classifier to predict the viability of new solutions. Results show that all tested classification models reduced the number of objective function evaluations and consequently, required hydraulic simulations. In some cases, the optimal solution defined by the original method was improved, demonstrating dual benefits by reducing both cost and computational effort. This work highlights that data-driven evolutionary optimization can effectively tackle highly complex problems, offering potential future applications in other areas of civil engineering and water resource management. Implementing this methodology promises significant time and resource savings in large-scale optimization processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hydraulic models

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A Novel Approach on Advanced Fruit Disease Identification Using RCNN

A Novel Approach on Advanced Fruit Disease Identification Us...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, R. Boopathi, P. Rajan, M. Sundar Bangalore India Shri Nehru College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798331509828

The early diagnosis of diseases in fruits holds immense importance for agricultural industries, as it directly impacts production quality and quantity. This study introduces a novel approach utilizing Recursive Convolutional Neural Network (RCNN) for automated citrus plant disease detection. Through a comprehensive methodology involving data collection, image pre-processing, segmentation, feature extraction using Linear Principal Component Analysis (LPCA), and classification, the proposed RCNN algorithm showcases promising results. Experimental evaluation demonstrates the efficacy of the RCNN-based approach, with precision averaging at 0.929, recall at 0.946, accuracy at 0.945, F-measure at 0.943, and minimal time complexity. Compared to traditional manual inspection methods, RCNN offers a more accurate and efficient solution, significantly reducing the burden on farmers and experts while enhancing fruit disease detection capabilities. Overall, the proposed RCNN-based approach presents a significant advancement in fruit disease identification, offering precise and timely detection to mitigate agricultural losses. With its superior performance metrics and streamlined process, RCNN stands as a promising tool for revolutionizing fruit disease diagnosis and improving agricultural productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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StegMamba: Distortion-Free Immune-Cover for Multi-Image Steganography With State Space Model

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4576-4591页

作者： Luo, Ting Zhou, Yuhang He, Zhouyan Jiang, Gangyi Xu, Haiyong Qi, Shuren Zhang, Yushu Ningbo University College of Science and Technology Ningbo315212 China Ningbo University Faculty of Information Science and Engineering Ningbo315211 China The Chinese University of Hong Kong Department of Mathematics Hong Kong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Multi-image steganography ensures privacy protection while avoiding suspicion from third parties by embedding multiple secret images within a cover image. However, existing multi-image steganographic methods fail to model global spatial correlations to reduce image damage at the low computation cost. Moreover, they do not account for the anti-distortion capability of the cover image, which is crucial for achieving imperceptible and ensuring security. To overcome these limitations, we propose StegMamba, a distortion-free immune-cover for multi-image steganography architecture with a state space model. Specifically, we first explore the potential of the linear computational cost model Mamba for data hiding tasks through a steganography Mamba block (SMB), whose efficiency makes it suitable for real-time applications. Subsequently, considering that images with distortion resistance reduce embedding damage, the original cover image is reconstructed through immune-cover construction module (ICCM) and associated with the steganography task. Moreover, well-coupled features facilitate fusion, and thus a wavelet-based interaction module (WIM) is designed for effective communication between the immune-cover and the secret images. Compared with the state-of-the-art global attention-based methods, the proposed StegMamba obtains PSNR gains of 3.30 dB, 1.37 dB, and 1.92 dB for the stego image, and two secret recovery images, respectively, and the reduction of 2.87% in detection accuracy for anti-steganalysis. © 1991-2012 IEEE.

关键词： Cost reduction

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Batch Microfabrication of Boron-Doped Diamond-Based Microsensor for Seconds Level Measurement of Dual Parameters of Chemical Oxygen Demand and Conductivity in Tap Water

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IEEE Sensors Journal 2025年第9期25卷 14606-14616页

作者： Han, Yunzhe Li, Jianqian Lu, Shengkang Yang, Ping Zhang, Jin Yu, Zhengyin Yin, Jiawen Jin, Qinghui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences State Key Laboratory of Transducer Technology Shanghai200050 China

With increasing concern about water safety, particularly the impact of tap water quality on human health, there is an urgent need for a biologically safe, multiparameter microsensor for rapid, online monitoring of tap water quality. This work reports on a batch fabricable miniature sensor based on microelectromechanical system (MEMS) technology for rapid detection of chemical oxygen demand (COD) and conductivity in tap water. The sensor is a silicon-glass structure comprising a boron-doped diamond (BDD) working electrode with biosafety properties. Three electrodes are integrated into the silicon wafer for the detection of COD, two of which can also be constructed as a conductivity sensing unit. Additionally, a microchamber is integrated into the glass. The excellent linearity (0.992 for COD and 0.996 for conductivity) and ideal detection range of the sensor (5–150 mgL-1 for COD and 98– 890 μS cm-1 for conductivity) are witnessed after a systematic performance investigation, and a satisfactory response time (2 s) was obtained by using a microchamber as the detection unit. As the sensors were prepared in a batch using the MEMS, the response of multiple sensors was verified through experimentation and found to be in good agreement. Furthermore, the impact of conductivity on the detection of COD was examined. In this article, a miniature ( 12 x 12 mm), low-cost, biologically safe dual-parameter sensor for COD and conductivity has been designed and prepared using the MEMS. It provides a core sensor component for online monitoring of multiple parameters in tap water. © 2001-2012 IEEE.

关键词： Silicon wafers

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