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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of computing and informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of computing and informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface (IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

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China Communications 2024年第9期 11-26页

作者： Gao Ang Ren Xiaoyu Deng Bin Sun Xinshun Zhang Jiankang School of Electronic Information Northwestern Polytechnical University Key Laboratory of Near Ground Detection and Perception Technology Department of computing and informatics Bournemouth University

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals ***,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication *** the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert *** this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG) approach for the joint active and passive beamforming(Joint A&P BF) optimization without the channel estimation,where the base station(BS) and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE) *** to the 'centralized training and distributed execution' feature of MADDPG,each agent can execute the active or passive beamforming inde pendently based on its partial observation without referring to *** results demonstrate that the proposed deep reinforcement learning(DRL) approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials and Continua 2024年第3期80卷 4997-5027页

作者： Dobrojevic, Milos Jovanovic, Luka Babic, Lepa Cajic, Miroslav Zivkovic, Tamara Zivkovic, Miodrag Muthusamy, Suresh Antonijevic, Milos Bacanin, Nebojsa Technical Faculty Singidunum University Belgrade 11000 Serbia informatics and computing Singidunum University Belgrade 11000 Serbia Business Economics Singidunum University Belgrade 11000 Serbia computing and informatics Sinergija University Bijeljina 76300 Bosnia and Herzegovina Department for Information Systems and Technologies University "Union Nikola Tesla" Cara Dusana Belgrade 11080 Serbia Department for Computer Science and informatics School of Electrical Engineering University of Belgrade Belgrade 11000 Serbia Department of Electrical and Electronics Engineering Kongu Engineering College (Autonomous) Perundurai Erode 638060 India Department of Mathematics Saveetha School of Engineering (Deemed to be University) SIMATS Thandalam Chennai 602105 India MEU Research Unit Middle East University Amman 11831 Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smart-phones, computers, or tablets. It can occur through various channels, such as social media, text messages, online forums, or gaming platforms. Cyberbullying involves using technology to intentionally harm, harass, or intimidate others and may take different forms, including exclusion, doxing, impersonation, harassment, and cyberstalking. Unfortunately, due to the rapid growth of malicious internet users, this social phenomenon is becoming more frequent, and there is a huge need to address this issue. Therefore, the main goal of the research proposed in this manuscript is to tackle this emerging challenge. A dataset of sexist harassment on Twitter, containing tweets about the harassment of people on a sexual basis, for natural language processing (NLP), is used for this purpose. Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning (ML) input: Term frequency inverse document frequency (TF-IDF) and Bidirectional encoder representations from transformers (BERT). The well-known eXtreme gradient boosting (XGBoost) ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not. Additionally, with the goal of reaching better performance, several XGBoost models were devised conducting hyperparameter tuning by metaheuristics. For this purpose, the recently emerging Coyote optimization algorithm (COA) was modified and adjusted to optimize the XGBoost model. Additionally, other cutting-edge metaheuristics approach for this challenge were also implemented, and rigid comparative analysis of the captured classification metrics (accuracy, Cohen kappa score, precision, recall, and F1-score) was performed. Finally, the best-generated model was interpreted by Shapley additive explanations (SHAP), and useful insights were gained about the behavioral patterns of people w

关键词： Smartphones

Data Temperature Informed Streaming for Optimising Large-Scale Multi-Tiered Storage

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Big Data Mining and Analytics 2024年第2期7卷 371-398页

作者： Dominic Davies-Tagg Ashiq Anjum Ali Zahir Lu Liu Muhammad Usman Yaseen Nick Antonopoulos Department of computing University of DerbyDerbyDE221GBUK Department of informatics University of LeicesterLeicesterLE17RHUK Department of Computer Science COMSATS University IslamabadIslamabad 45550Pakistan Edinburgh Napier University EdinburghEH114BNUK

Data temperature is a response to the ever-growing amount of *** data have to be stored,but they have been observed that only a small portion of the data are accessed more frequently at any one *** leads to the concept of hot and cold *** data can be migrated away from high-performance nodes to free up performance for higher priority *** studies classify hot and cold data primarily on the basis of data age and usage *** present this as a limitation in the current implementation of data *** is due to the fact that age automatically assumes that all new data have priority and that usage is purely *** propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their *** identify new metadata variables and user-defined variables to extend the current data temperature *** further establish rules and conditions for limiting unnecessary movement of the data,which helps to prevent wasted input output(I/O)*** also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data *** proposed system provides higher accuracy,increases performance,and gives greater user control for optimal positioning of data within multi-tiered storage solutions.

关键词： data temperature hot and cold data multi-tiered storage metadata variable multi-temperature system

IoT Task Offloading in Edge computing Using Non-Cooperative Game Theory for Healthcare Systems

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of computing and informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1973-1986页

作者： Shouyong Jiang Jinglei Guo Yong Wang Shengxiang Yang School of Automation Central South UniversityChangsha 410083China Department of computing Science University of AberdeenAberdeen AB243FXUK School of Computer Science Central China Normal UniversityWuhan 430079China School of Computer Science and informatics De Montfort UniversityLeicester LE19BHUK

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective ***,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation *** paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective *** bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower *** subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the *** bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective *** analysis and component analysis have been also carried out to further justify the proposed algorithm.

关键词： Bilevel decomposition evolutionary algorithm many-objective optimisation multi-objective optimisation