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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on computer Vision Techniques

computer Modeling in engineering & sciences 2024年第4期139卷 759-782页

作者： Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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Uvcgan-Dehaze: a dehazing method for unpaired images

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Soft Computing 2024年第20期28卷 12217-12226页

作者： Li, Canlin Zhang, Xiangfei Zhang, Wenjiao Su, Haowen Bi, Lihua School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China School of Software Engineering Zhengzhou University of Light Industry Zhengzhou China

To solve the problems of feature loss and color difference after image dehazing and poor dehazing effect in real hazy images, a method UVCGAN-Dehaze is proposed for unpaired image dehazing. In the proposed model, the generator learns the relationship between image features and low-frequency features at multiple scales by combining the U-Net with the transformer. We strengthen the connection between spatial and channel features by adding Convolutional Block Attention Module(CBAM) before the skip connection of each layer of U-Net of the generator and adaptively adjusting the weight allocation of the feature map, so that the image after dehazing can better retain the detailed information of the image. Simultaneously, in order to reduce the feature loss and color difference after image dehazing, we propose a loss function named Feature Laplacian Perception (FLP) loss, which jointly constrains the generator by fusing the perception loss and Laplacian loss, so that the generated image reduces the feature loss and color difference. The method not only generates dehazing images with high perceptual quality in terms of important regions and details, but also avoids content and color loss. Extensive experiments verify the effectiveness of the method on synthetic images and real hazy images, and the proposed method achieves better dehazing results when compared with the experimental results of previous typical methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Color

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Design of high output DC/DC boost circuit without boost transformer 8

Design of high output DC/DC boost circuit without boost tran...

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8th International Conference on Communication and Information Systems, ICCIS 2024

作者： Hong, Qin Zhou, Liang Jia, Zhongning Liu, Chun Liu, Jianbo Software Engineering Institute of Guangzhou Department of Computer Science Guangzhou China

ISBN: (纸本)9798331531171

The development of IOT smart devices has necessitated increasingly stringent requirements for DC power sources in terms of output voltage, size, and cost. For instance, Avalanche Photodiodes (APDs), utilized as photodetectors, require a significantly high reverse bias voltage during the detection of feeble optical signals to achieve optimal gain and high signal-to-noise ratio, thereby ensuring exceptional sensitivity. To address the diverse demands of various smart devices for DC power, a high-output DC/DC boost circuit without a step-up transformer has been innovatively designed. This circuit is comprised of four fundamental components: an oscillator circuit, a comparator circuit, a voltage multiplier circuit, and a constant-current and voltage-stabilizing circuit. The paper conducts a comprehensive theoretical analysis of the circuit's topology, parameter selection, and operational process, which is further validated through Multisim simulation. Additionally, a PCB circuit was designed, fabricated, and underwent rigorous practical testing. The test results conclusively demonstrate that the designed boost circuit proficiently provides APDs with the appropriate bias voltage, eliminating the need for boost transformer. It boasts high output voltage, a simplistic structure, small size, and low cost, enabling it to seamlessly share the same input power source with the APD detection circuit and be integrated into the same PCB layout. © 2024 IEEE.

关键词： avalanche photodiode boost circuit direct current stabilized power supply oscillator voltage multiplier circuit

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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science China(Technological sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Face Anti-Spoofing with Unknown Attacks:A Comprehensive Feature Extraction and Representation Perspective

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Journal of computer science & Technology 2024年第4期39卷 827-840页

作者： Li-Min Li Bin-Wu Wang Xu Wang Peng-Kun Wang Yu-Dong Zhang Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230000China School of Data Science University of Science and Technology of ChinaHefei 23000China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215000China

Face anti-spoofing aims at detecting whether the input is a real photo of a user(living)or a fake(spoofing)*** new types of attacks keep emerging,the detection of unknown attacks,known as Zero-Shot Face Anti-Spoofing(ZSFA),has become increasingly important in both academia and *** ZSFA methods mainly focus on extracting discriminative features between spoofing and living ***,the nature of the spoofing faces is to trick anti-spoofing systems by mimicking the livings,therefore the deceptive features between the known attacks and the livings,which have been ignored by existing ZSFA methods,are essential to comprehensively represent the ***,existing ZSFA models are incapable of learning the complete representations of living faces and thus fall short of effectively detecting newly emerged *** tackle this problem,we propose an innovative method that effectively captures both the deceptive and discriminative features distinguishing between genuine and spoofing *** method consists of two main components:a two-against-all training strategy and a semantic *** two-against-all training strategy is employed to separate deceptive and discriminative *** address the subsequent invalidation issue of categorical functions and the dominance disequilibrium issue among different dimensions of features after importing deceptive features,we introduce a modified semantic *** autoencoder is designed to map all extracted features to a semantic space,thereby achieving a balance in the dominance of each feature *** combine our method with the feature extraction model ResNet50,and experimental results show that the trained ResNet50 model simultaneously achieves a feasible detection of unknown attacks and comparably accurate detection of known *** results confirm the superiority and effectiveness of our proposed method in identifying the living with the interference of both known

关键词： face anti-spoofing spoof detection zero-shot learning convolutional neural network deep learning

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