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Gastrointestinal Tract Infections Classification Using Deep Learning

Computers, Materials & Continua 2021年第12期69卷 3239-3257页

作者： muhammad Ramzan Mudassar Raza muhammad sharif muhammad Attique Khan Yunyoung Nam Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan Department of Computer Science HITEC University TaxilaTaxila47080Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image ***,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI *** procedure is subjective and results in significant inter-/intraobserver variations in disease ***,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed ***,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract *** this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI ***,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused ***-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance *** study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches.

关键词： Convolutional neural network feature fusion gastrointestinal tract handcrafted features features selection

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Computer Decision Support System for Skin Cancer Localization and Classification

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Computers, Materials & Continua 2021年第7期68卷 1041-1064页

作者： muhammad Attique Khan Tallha Akram muhammad sharif Seifedine Kadry Yunyoung Nam Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan Department of Computer and Electrical Engineering COMSATS University IslamabadWah Campus47040Pakistan Department of Mathematics Beirut Arab UniversityBeirutLebanon Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep *** main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 ***,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image *** the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary ***,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected *** concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion *** the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)*** avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)*** selected features are finally passed to the softmax classifier for the final *** datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.

关键词： Skin cancer convolutional neural network lesion localization transfer learning features fusion features optimization

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Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

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Computers, Materials & Continua 2022年第1期70卷 619-635页

作者： Javaria Amin muhammad Almas Anjum muhammad sharif Seifedine Kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

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Computers, Materials & Continua 2023年第5期75卷 2383-2396页

作者： Abida sharif muhammad Imran sharif muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based ***,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network *** IoVbased content distribution is an emerging concept in which all the vehicles are connected via the *** to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new *** this,a novel strategy mediator framework is presented in this paper for managing the network resources ***-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the *** to the limited memory of edge devices,the delectable bloom filters are used for caching and ***,the proposed scheme is compared with the existing variants for validating its effectiveness.

关键词： Internet of vehicles(IOV) content dissemination multi mediator data traffic management

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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Computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin muhammad sharif muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

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Computers, Materials & Continua 2023年第7期76卷 861-879页

作者： Sara Khalid Jamal Hussain Shah muhammad sharif muhammad Rafiq Gyu Sang Choi Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Game and Mobile Engineering Keimyung UniversityDaegu42601Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and ***,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic *** these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar *** innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these *** technique aimed to devise an efficient,robust and accurate *** attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the ***,a new set of features is proposed,motivated by existing *** and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of ***,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)*** results are satisfactory when compared to the preceding work.

关键词： Traffic sign detection intelligent systems complexity vehicles color moments texture features

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Progress in electrocatalytic nitrate reduction for green energy:Catalyst engineering,mechanisms,and techno-economic feasibility

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Journal of Energy Chemistry 2024年第8期95卷 380-406,I0009页

作者： Hafiz muhammad Adeel sharif Hafiz muhammad Farooq Khan Sadeeq Ullah Yuwei Wang muhammad Ahmad Bo Yang Changping Li muhammad Bilal Asif Research Center for Eco-Environmental Engineering Dongguan University of TechnologyDongguan 523808GuangdongChina College of Chemistry and Engineering Shenzhen UniversityShenzhen 518060GuangdongChina FMH College of Medicine and Dentistry LahorePakistan School of Environment and Civil Engineering Research Center for Eco-Environment EngineeringDongguan University of TechnologyDongguan 523106GuangdongChina Advanced Membranes and Porous Materials Center(AMPMC) Physical Sciences and Engineering(PSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia

Ammonia(NH_(3))is an irreplaceable chemical that has been widely demanded to keep the sustainable development of modern ***,its industrial production consumes a huge amount of energy and releases extraordinary greenhouse gases(GHGs),leading to various environmental *** the green production of ammonia is a great challenge,which has been extensively pursued in the last *** this review,the most promising strategy,electrochemical nitrate reduction reaction(e-NO_(3)RR),is comprehensively investigated to give a complete understanding of its development and mechanism and provide guidance for future ***,owing to the complex reactions and limited selectivity,a comprehensive understanding of the mechanisms is crucial to further development and ***,NO_(3)^(-)RR is a promising strategy for simultaneous water treatment and NH_(3)production.A detailed overview of the recent progress in NO_(3)^(-)RR for NH_(3)production with nontransition and transition metal based electrocatalysts is *** addition,critical advanced techniques,future challenges,and prospects are discussed to guide future research on transition metal-based catalysts for commercial NH_(3)synthesis by NO_(3)^(-)reduction.

关键词： Nitrate reduction Electrolysis Pollution to solution Transition metal catalysts Ammonia synthesis Green energy

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Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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Computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： Javaria Amin muhammad Almas Anjum muhammad sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

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Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

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Computers, Materials & Continua 2021年第8期68卷 2451-2467页

作者： Javaria Amin muhammad sharif muhammad Almas Anjum Yunyoung Nam Seifedine Kadry David Taniar Department of Computer Science University of Wah47040Pakistan Department of Computer Science Comsats University IslamabadWah Campus47040Pakistan College of Electrical and Mechanical Engineering National University of Sciences&Technology(NUST)Islamabad44000Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab University115020Lebanon Faculty of Information Technology Monash UniversityClaytonVictoria3800Australia

Coronavirus 19(COVID-19)can cause severe pneumonia that may be *** diagnosis is *** tomography(CT)usefully detects symptoms of COVID-19 *** this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and *** the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung *** the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification ***3 is an effective decoder that helps to refine segmentation of lesion *** model was trained using fine-tuned hyperparameters selected after extensive ***,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented *** computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for *** performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital *** results were better than those of existing methods.

关键词： Deeplabv3 fusion genetic algorithm gabor watershed

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Mango Leaf Disease Identification Using Fully Resolution Convolutional Network

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Computers, Materials & Continua 2021年第12期69卷 3581-3601页

作者： Rabia Saleem Jamal Hussain Shah muhammad sharif Ghulam Jillani Ansari COMSATS University Islamabad Wah Campus47040Pakistan University of Education Lahore54000Pakistan

Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high *** leaf disease segmentation and identification are still a challenge due to variations in *** segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant *** solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango *** proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing *** evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,*** evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and ***,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other *** have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’***,an automated approach helps pathologists and mango growers detect and identify those diseases.

关键词： Mango plant leaf disease detection CNN FrCNnet segmentation

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