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Comparison of CNN-based deep learning architectures for rice diseases classification

Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

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New Methods for Lunar Impact Crater Detection Based on YOLO v7 with Deformable ConvNets

New Methods for Lunar Impact Crater Detection Based on YOLO ...

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2023 IEEE International Conference on Electrical, Automation and computer engineering, ICEACE 2023

作者： Chen, Siyi Liang, Jiarui Zhu, Jionghao Tian, Xiaolin School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong Shenzhen China

ISBN: (纸本)9798350309614

The Lunar impact crater is one of the most remarkable geographic features on the surface of the Lunar. The size and shape of the Lunar impact crater are important for future Lunar landings and resource development on the Lunar surface. Hence, detecting impact craters is an essential research orientation. In this paper, we introduced two novel convolutional neural networks that combine YOLO v7 and deformable convolution of Deformable Convolutional Networks, called YOLOv7-DCN and YOLOv7-DCNv2. With the global Lunar CCD data provided by NASA, the experiment has been conducted under the same settings. The result of the experiment showed that the Precision of YOLOv7-DCN is 77.36%, and the Precision of YOLOv7-DCNv2 is 77.19%. Both values exceed the Precision of YOLO v7, which is 75.86%. Improvements have been found in several other key metrics. Demonstrating that YOLOv7-DCN and YOLOv7-DCNv2 have better performance than YOLO V7 in detecting Lunar impact crater. © 2023 IEEE.

关键词： Convolutional Neural Networks Crater Detection Deep Learning Deformable ConvNets v2 YOLO v7

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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Resilient Distributed Optimization for Multi-Agent Cyberphysical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 3952-3967页

作者： Yemini, Michal Nedic, Angelia Goldsmith, Andrea J. Gil, Stephanie Bar-Ilan University Faculty of Engineering Israel Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Harvard University Computer Science Department School of Engineering and Applied Sciences CambridgeMA02139 United States

This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. © 1963-2012 IEEE.

关键词： Multi agent systems

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Zero-dynamics attack detection based on data association in feedback pathway

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Magnetic properties and magnetocaloric effects in Eu(Ti,Nb,Mn)O_(3) perovskites

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Journal of Rare Earths 2024年第8期42卷 1560-1567,I0006页

作者： Junfeng Wang Huicai Xie Quanyi Liu Zhihong Hao Zhaojun Mo Qi Fu Xinqiang Gao Jun Shen Ganjiang Innovation Academy Chinese Academy of SciencesGanzhou341119China Faculty of Materials Metallurgy and Chemistry Jiangxi University of Science and TechnologyGanzhou341000China School of Rare Earth and New Materials Engineering Gannan University of Science and TechnologyGanzhou341000China Department of Energy and Power Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing100081China

In perovskite EuTiO_(3),the magnetic characteristics and magnetocaloric effect(MCE) can be flexibly regulated by converting the magnetism from antiferromagnetic to *** the present work,a series of Eu(Ti,Nb,Mn)O_(3) compounds,abbreviated as ETNMO for convenience of description,was fabricated and their crystallography,magnetism together with cryogenic magnetocaloric effects were systematically *** crystallographic results demonstrate the cubic perovskite structure for all the compounds,with the space group of *** magnetic phase transitions are observed in these second-order phase transition(SOPT) *** joint substitution of elements Mn and Nb can considerably manipulate the magnetic phase transition process and magnetocaloric performance of the ETNMO *** the Mn content increases,gradually widened-ΔS_(M)-T curves are obtained,and two peaks with a broad shoulder are observed in the-ΔS_(M)-T curves for Δμ_(0)H≤0-1 *** a field change of 0-5 T,the values of maximum magnetic entropy change(-ΔS_(M)^(max)) and refrigeration capacity(RC) are evaluated to be 34.7 J/(kg·K) and 364.9 J/kg for EuTi_(0.8625)Nb_(0.0625)Mn_(0.075)O_(3), 27.8 J/(kg·K) and367.6 J/kg for EuTi_(0.8375)Nb_(0.0625)Mn_(0.1)O_(3),23.2 J/(kg·K) and 369.2 J/kg for EuTi_(0.8125)Nb_(0.0625)Mn_(0.125)O_(3),17.1 J/(kg·K) and 357.6 J/kg for EuTi_(0.7875)Nb_(0.0625)Mn_(0.15)O_(3),*** co nsiderable MCE parameters make the ETNMO compounds potential candidates for cryogenic magnetic refrigeration.

关键词： ETNMO perovskites Cryogenic refrigeration Magnetic phase transition Magnetocaloric effect Rare earths

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Fe_(3)O_(4)@SiO_(2)-Protein A-oHSV/CD63 Ab for Capturing Virus and Exosomes

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Journal of Analysis and Testing 2024年第3期8卷 335-350页

作者： Wenqing Zheng Pingyi Zheng Ran Zhao Xinyu Xu Xiao Zhang Xiaoqian Yuan Ying Xu Zichuan Liu Youxin Li Tianjin Key Laboratory for Modern Drug Delivery and High-Efficiency Collaborative Innovation Center of Chemical Science and EngineeringSchool of Pharmaceutical Science and TechnologyFaculty of MedicineTianjin UniversityTianjin300072China

Protein A modified magnetic spheres(Fe_(3)O_(4)@SiO_(2)-Protein A)with 22.7 emu/g saturation magnetization were prepared and *** spheres had a diameter of 10 nm and was stable up to 229.2℃,and can bond 19.694 mg/g of Protein *** Fe_(3)O_(4)@SiO_(2)-Protein A was firstly combined with oHSV-BJ-2-A antibody to capture oHSV from disease venom,and the amount of binding virus was 1.92×10^(7)to 3.44×10^(7)PFU/***_(3)O_(4)@SiO_(2)-Protein A was modified with CD63 antibody,which was further fixed using dissuccinimide octylate(DSS)as a crosslinking *** prepared Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab was useful in isolating and enriching exosomes from cell *** with commercial kits,Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab demonstrated a better purification effect,which was successfully monitored using capillary *** total content of exosomes protein extracted using Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 was 3.4 mg/g,which was obviously higher than that reported in some *** addition,Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab also showed its repeatability through bonding and elution of 5 cycles,which was effective in cost *** indicated Fe_(3)O_(4)@SiO_(2)-Protein A-CD63 Ab had the potential for large-scale purification of exosomes in practical applications.

关键词： Immunomagnetic spheres Antibody Exosome Herpes simplex virus

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circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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Frontiers of computer science 2023年第5期17卷 217-225页

作者： Yajing GUO Xiujuan LEI Lian LIU Yi PAN School of Computer Science Shaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

关键词： circRNAs RBPs CNN BiLSTM self-attention mechanism

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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