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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Computers, Materials & Continua 2023年第7期76卷 367-382页

作者： Mona Jamjoom ahmed elhadad Hussein Abulkasim Safia Abbas Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt Faculty of Science New Valley UniversityEl-Kharga72511Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt

Several pests feed on leaves,stems,bases,and the entire plant,causing plant *** a result,it is vital to identify and eliminate the disease before causing any damage to *** detecting plant disease and treating it is pretty challenging in this *** processing is employed to detect plant disease since it requires much effort and an extended processing *** main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf ***,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and *** gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant *** to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

关键词： SVM machine learning GLCM algorithm K-means clustering LBP

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An Efficient Indoor Localization Based on Deep Attention Learning Model

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Computer Systems Science & Engineering 2023年第8期46卷 2637-2650页

作者： Amr Abozeid ahmed I.Taloba Rasha M.Abd El-Aziz Alhanoof Faiz Alwaghid Mostafa Salem ahmed elhadad Department of Computer Science College of Science and ArtsJouf UniversityQurayyatSaudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityEgypt Computer Science Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

Indoor localization methods can help many sectors,such as healthcare centers,smart homes,museums,warehouses,and retail malls,improve their service *** a result,it is crucial to look for low-cost methods that can provide exact localization in indoor *** this context,imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an ***-based localization faces many issues,such as image scale and rotation ***,image-based localization’s accuracy and speed(latency)are two critical *** paper proposes an efficient 6-DoF deep-learning model for image-based *** model incorporates the channel attention module and the Scale PyramidModule(SPM).It not only enhances accuracy but also ensures the model’s real-time *** complex scenes,a channel attention module is employed to distinguish between the textures of the foregrounds and *** model adapted an SPM,a feature pyramid module for dealing with image scale and rotation variance ***,the proposed model employs two regressions(two fully connected layers),one for position and the other for orientation,which increases outcome *** on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error(MSE)for both position and *** the indoor 7-Scenes dataset,the MSE for the position is reduced to 0.19 m and 6.25°for the ***,on the outdoor Cambridge landmarks dataset,the MSE for the position is reduced to 0.63 m and 2.03°for the *** to the findings,the proposed approach is superior and more successful than the baseline methods.

关键词： Image-based localization computer vision deep learning attention module VGG-16

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