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Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

computer Modeling in engineering & Sciences 2022年第4期131卷 331-348页

作者： Dina M.Ibrahim Nada M.Elshennawy Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computers and Control Engineering Department Faculty of EngineeringTanta UniversityTanta37133Egypt

Dates are an important part of human *** are high in essential nutrients and provide a number of health *** fruits are also known to protect against a number of diseases,including cancer and heart *** fruits have several sizes,colors,tastes,and *** are a lot of challenges facing the date *** of the most significant challenges is the classification and sorting of *** there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting *** this paper,an augmented date fruits dataset was developed using Deep Convolutional Generative Adversarial Networks(DCGAN)and CycleGAN approach to augment our collected date fruit *** augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced *** are three types of dates in our proposed dataset:Sukkari,Ajwa,and *** dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the *** train these two models,we start with the original ***,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated *** resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.

关键词： Dates fruits data augmentation DCGAN CycleGAN,deep learning convolution neural networks

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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On the Blockchain Selection in Agri-Food Tracking Systems: Student Paper 5

On the Blockchain Selection in Agri-Food Tracking Systems: S...

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5th Conference on Blockchain Research and Applications for Innovative Networks and Services, BRAINS 2023

作者： Scanu, Fabio Control and Management Engineering Antonio Ruberti Sapienza University of Rome DIAG-Department of Computer Italy

ISBN: (纸本)9798350317824

In recent years, blockchain technology has spread widely in the agri-food sector. In particular, several blockchain-based tracking systems have been proposed. Most of these projects use mature technologies, such as Ethereum and Hyper-ledger. However, other blockchains were proposed recently (some particularly suited to IoT, commonly used in agri-food tracking systems). In the literature, several methodologies are proposed to guide the choice of the type of blockchain. These methodologies are generic, and a gap remains between the criteria used to guide the selection of the type of blockchain and how concrete use cases influence them. Building upon existing methodologies, this paper presents a specific approach for agri-food tracking. Once the parameters of the use case that impact the blockchain's performance have been identified, the methodology allows the association of these parameters with the criteria for identifying the types of blockchain to use (e.g., public permissionless) and the most suitable consensus algorithm. The output tuple \ [types], [algorithms], [functions]), therefore, allows the choice of the blockchain that best suits the usage scenario. © 2023 IEEE.

关键词： Blockchain

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power UniversityChina Department of Mathematics&Computer Science Fayetteville State UniversityFayettevilleNCUSA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA Department of Computer Science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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PVWA: Privacy-preserving and Verifiable Weighted Aggregation for Federated Learning

PVWA: Privacy-preserving and Verifiable Weighted Aggregation...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Zihan Wang, Xiaodong Yuan, Shuai Guan, Zhitao Du, Xiaojiang Guizani, Mohsen North China Electric Power University School of Control and Computer Engineering China Canada Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Machine Learning Department United Arab Emirates

ISBN: (纸本)9798350351255

Federated learning provides clients with a means of collaboratively training a global model without sharing their local data, managed by a central server. However, this server cannot always be trusted, as it may act dishonestly and compromise the privacy of clients' local data. Consequently, mechanisms for privacy preservation and aggregation verification become crucial components of a secure federated learning system. In addition, support for weighted aggregation is also essential to address the challenges posed by non-IID training data. In this article, we present the Privacy-Preserving and Verifiable Weighted Aggregation (PVWA) scheme. Our approach introduces a new privacy-preserving solution by leveraging masking and homomorphic encryption techniques to protect local and global models, respectively. The masking protocol facilitates secure weighted aggregation, whereas a verification mechanism based upon homomorphic hashing and bilinear aggregated signatures ensures the correctness of aggregated results. Experimental evaluations of the performance, compared against alternative methods on two datasets, demonstrate its effectiveness and efficiency. © 2024 IEEE.

关键词： Federated learning

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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Sliding Mode control in Power Converters and Drives: A Review

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 392-406页

作者： Ligang Wu Jianxing Liu Sergio Vazquez Sudip K.Mazumder IEEE Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Electronic Engineering Department Universidad de Sevilla41092 SevillaSpain Electrical and Computer Engineering Department University of Illinois ChicagoChicagoIL 60607 USA

Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched *** aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic *** fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and *** recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.

关键词： Motor drive power electronics systems sliding mode control

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baptti at GeoLingIt: Beyond Boundaries, Enhancing Geolocation Prediction and Dialect Classification on Social Media in Italy 8

baptti at GeoLingIt: Beyond Boundaries, Enhancing Geolocatio...

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8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023

作者： Koudounas, Alkis Giobergia, Flavio Benedetto, Irene Monaco, Simone Cagliero, Luca Apiletti, Daniele Baralis, Elena Department of Control and Computer Engineering Politecnico di Torino Turin Italy MAIZE Turin Italy

The proliferation of social media platforms has presented researchers with valuable avenues to examine language usage within diverse sociolinguistic frameworks. Italy, renowned for its rich linguistic diversity, provides a distinctive context for exploring diatopic variation, encompassing regional languages, dialects, and variations of Standard Italian. This paper presents our contributions to the GeoLingIt shared task, focusing on predicting the locations of social media posts in Italy based on linguistic content. For Task A, we propose a novel approach, combining data augmentation and contrastive learning, that outperforms the baseline in region prediction. For Task B, we introduce a joint multi-task learning approach leveraging the synergies with Task A and incorporate a post-processing rectification module for improved geolocation accuracy, surpassing the baseline and achieving first place in the competition. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Linguistics

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EverydayFantasy - A Mobile Computing Application in Mental Healthcare 18

EverydayFantasy - A Mobile Computing Application in Mental H...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Triohin, Victor Leba, Monica Ionica, Andreea Cristina University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Management and Industrial Engineering Petrosani Romania

ISBN: (纸本)9789893347928

The paper proposes a game, EverydayFantasy, based on the principles of serious games, game mechanics and gamification and intended for young people with cognitive behavior issues. The game is a mobile computing application in mental healthcare, in the architecture of which is integrated the connection to a database through a server and the use of dedicated APIs such as the one for locating on the map. The game creates a fantasy environment superimposed on the real environment in which the player is motivated to move, socialize and solve different challenges, all to improve his estimated state. © 2023 ITMA.

关键词： Serious games

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