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A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products:An Application for Medical-Tech Classification 认领 引用 被引量：6
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作者 Inzamam Mashood Nasir Muhammad Attique Khan +3 位作者 Majed Alhaisoni Tanzila Saba Amjad Rehman Tassawar Iqbal 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第9期1017-1033,共17页
Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these met... Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these methods utilized the custom datasets,containing a few hundred images and fewer classes,to evaluate the performances of their models without comparing it,with some standard datasets.This article takes advantage of utilizing a standard publicly dataset taken from a competition,and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN.In addition,to classify the superheroes efficiently,a custom 17-layer deep convolutional neural network is also proposed.The computed results achieved overall classification accuracy of 97.9%which is significantly superior to the accuracy of competition’s winner. 展开更多
关键词 Superheroes deep convolutional neural network data augmentation transfer learning machine learning
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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI 认领 引用 被引量：3
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作者 Armughan Ali Hooria Shahbaz Robertas Damaševicius 《Computers, Materials & Continua》 SCIE EI 2025年第4期1367-1398,共32页
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t... Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model. 展开更多
关键词 Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection
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A Multi-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare 认领 引用
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作者 Veena Dillshad Muhammad Attique Khan +5 位作者 Muhammad Nazir Jawad Ahmad Dina Abdulaziz AlHammadi Taha Houda Hee-Chan Cho Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2025年第6期5299-5321,共23页
Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting... Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance. 展开更多
关键词 Smart health skin cancer internet of things deep learning residual blocks fusion optimization
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ECSA-Net:A Lightweight Attention-Based Deep Learning Model for Eye Disease Detection 认领 引用
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作者 Sara Tehsin Muhammad John Abbas +4 位作者 Inzamam Mashood Nasir Fadwa Alrowais Reham Abualhamayel Abdulsamad Ebrahim Yahya Radwa Marzouk 《Computers, Materials & Continua》 SCIE EI 2026年第5期1290-1323,共34页
Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with co... Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings. 展开更多
关键词 Channel-spatial attention explainable AI eye disease classification fairness in diagnostics lightweight deep learning transparency in healthcare
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Deep Learning-Based Classification of Fruit Diseases:An Application for Precision Agriculture 认领 引用 被引量：10
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作者 Inzamam Mashood Nasir Asima Bibi +5 位作者 Jamal Hussain Shah Muhammad Attique Khan Muhammad Sharif Khalid Iqbal Yunyoung Nam Seifedine Kadry 《Computers, Materials & Continua》 SCIE EI 2021年第2期1949-1962,共14页
Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artific... Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT). 展开更多
关键词 Agriculture deep learning feature selection feature fusion fruit classification
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Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain 认领 引用 被引量：5
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作者 Nazeer Muhammad Rubab +3 位作者 Nargis Bibi Oh-Young Song Muhammad Attique Khan Sajid Ali Khan 《Computers, Materials & Continua》 SCIE EI 2021年第2期2199-2216,共18页
Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agricult... Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques. 展开更多
关键词 Plants diseases wavelet transform fast algorithm deep learning feature extraction classification
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Classification of Citrus Plant Diseases Using Deep Transfer Learning 认领 引用 被引量：5
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作者 Muhammad Zia Ur Rehman Fawad Ahmed +4 位作者 Muhammad Attique Khan Usman Tariq Sajjad Shaukat Jamal Jawad Ahmad Iqtadar Hussain 《Computers, Materials & Continua》 SCIE EI 2022年第1期1401-1417,共17页
In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and producti... In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques. 展开更多
关键词 Citrus plant disease classification deep learning feature fusion deep transfer learning
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Classification of Positive COVID-19 CT Scans Using Deep Learning 认领 引用 被引量：3
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作者 Muhammad Attique Khan Nazar Hussain +5 位作者 Abdul Majid Majed Alhaisoni Syed Ahmad Chan Bukhari Seifedine Kadry Yunyoung Nam Yu-Dong Zhang 《Computers, Materials & Continua》 SCIE EI 2021年第3期2923-2938,共16页
In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new tes... In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis.In response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed rapidly.One potential diagnostic tool is a reverse-transcription polymerase chain reaction(RT-PCR).RT-PCR,typically a time-consuming process,was less sensitive to COVID-19 recognition in the disease’s early stages.Here we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)scans.In the proposed method,contrast enhancement is used to improve the quality of the original images.A pretrained DenseNet-201 DL model is then trained using transfer learning.Two fully connected layers and an average pool are used for feature extraction.The extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning features.Fusing the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the technique.In the proposed method,a new parallel high index technique is used to fuse two optimal vectors;the outcome is then passed on to an extreme learning machine for final classification.Experiments were conducted on a collected database of patients using a 70:30 training:Testing ratio.Our results indicated an average classification accuracy of 94.76%with the proposed approach.A comparison of the outcomes to several other DL models demonstrated the effectiveness of our DL method for classifying COVID-19 based on CT scans. 展开更多
关键词 Coronavirus contrast enhancement deep learning features optimization fusion classification
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Classication of COVID-19 CT Scans via Extreme Learning Machin 认领 引用 被引量：2
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作者 Muhammad Attique Khan Abdul Majid +5 位作者 Tallha Akram Nazar Hussain Yunyoung Nam Seifedine Kadry Shui-Hua Wang Majed Alhaisoni 《Computers, Materials & Continua》 SCIE EI 2021年第7期1003-1019,共17页
Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia... Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia and normal CT scans.These images were retrieved from the Radiopaedia COVID-19 website.The images were divided into training and test sets in a ratio of 70:30.Then,multiple features were extracted from the training data.We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity.We next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA tness.Based on the ELM losses,the most discriminatory features were selected and saved as an ELM Model.Test images were sent to the model,and the best-selected features compared to those of the trained model to allow nal predictions.Validation employed the collected chest CT scans.The best predictive accuracy of the ELM classier was 93.9%;the scheme was effective. 展开更多
关键词 Coronavirus classical features feature fusion feature optimization prediction
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Recognition and Tracking of Objects in a Clustered Remote Scene Environment 认领 引用 被引量：2
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作者 Haris Masood Amad Zafar +5 位作者 Muhammad Umair Ali Muhammad Attique Khan Salman Ahmed Usman Tariq Byeong-Gwon Kang Yunyoung Nam 《Computers, Materials & Continua》 SCIE EI 2022年第1期1699-1719,共21页
Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision.Computer vision is one of the most active research fields that lies at the intersection of dee... Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision.Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision.This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shapemoving objects while accommodating the shift and scale invariances that the object may encounter.The first part uses the Maximum Average Correlation Height(MACH)filter for object recognition and determines the bounding box coordinates.In case the correlation based MACH filter fails,the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e.,affine scale invariant feature transform(ASIFT).ASIFT is used to accommodate object shift and scale object variations.ASIFT extracts certain features from the object of interest,providing invariance in up to six affine parameters,namely translation(two parameters),zoom,rotation and two camera axis orientations.However,in this paper,only the shift and scale invariances are used.The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient(APG)technique to periodically update the coordinates of the object encapsulated in the bounding box.At the end,a comparison of the proposed algorithm with other stateof-the-art tracking algorithms has been presented,which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors. 展开更多
关键词 Object racking MACH filter ASIFT particle filter recognition
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Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning 认领 引用 被引量：2
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作者 Khalid Mahmood Aamir Muhammad Ramzan +5 位作者 Saima Skinadar Hikmat Ullah Khan Usman Tariq Hyunsoo Lee Yunyoung Nam Muhammad Attique Khan 《Computers, Materials & Continua》 SCIE EI 2022年第4期17-33,共17页
This paper focuses on detecting diseased signals and arrhythmias classification into two classes:ventricular tachycardia and premature ventricular contraction.The sole purpose of the signal detection is used to determ... This paper focuses on detecting diseased signals and arrhythmias classification into two classes:ventricular tachycardia and premature ventricular contraction.The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person.The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency(IF).Once a signal taken from a patient is detected,then the classifier takes that signal as input and classifies the target disease by predicting the class label.While applying the classifier,templates are designed separately for ventricular tachycardia and premature ventricular contraction.Similarities of a given signal with both the templates are computed in the spectral domain.The empirical analysis reveals precisions for the detector and the applied classifier are 100%and 77.27%,respectively.Moreover,instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08–0.6 Hz.This indicates a serious loss of high-frequency contents in the spectrum,implying that the heart’s overall activity is slowed down.This study may help medical practitioners in detecting the heart disease type based on signal analysis. 展开更多
关键词 Heart disease signals preprocessing detection machine learning
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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization 认领 引用 被引量：2
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作者 Abida Sharif Imran Sharif +6 位作者 Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2023年第6期5379-5393,共15页
The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles... The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles. 展开更多
关键词 Internet of vehicles internet of things fuzzy logic optimization path planning
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An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification 认领 引用 被引量：2
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作者 Ahsan Aziz Muhammad Attique +5 位作者 Usman Tariq Yunyoung Nam Muhammad Nazir Chang-Won Jeong Reham R.Mostafa Rasha H.Sakr 《Computers, Materials & Continua》 SCIE EI 2021年第11期2653-2670,共18页
Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of... Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique. 展开更多
关键词 Brain tumor data normalization transfer learning features optimization features fusion
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Segmentation and Classification of Stomach Abnormalities Using Deep Learning 认领 引用 被引量：2
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作者 Javeria Naz Muhammad Attique Khan +3 位作者 Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry 《Computers, Materials & Continua》 SCIE EI 2021年第10期607-625,共19页
An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification... An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep learning.The first bleeding region is segmented using a hybrid approach.The threshold is applied to each channel extracted from the original RGB image.Later,all channels are merged through mutual information and pixel-based techniques.As a result,the image is segmented.Texture and deep learning features are extracted in the proposed classification task.The transfer learning(TL)approach is used for the extraction of deep features.The Local Binary Pattern(LBP)method is used for texture features.Later,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors.The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier.The experimental process is evaluated on the basis of two datasets:Private and KVASIR.The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set.It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison. 展开更多
关键词 Gastrointestinal tract contrast stretching segmentation deep learning features selection
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A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification 认领 引用 被引量：1
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作者 Abdul Haseeb Muhammad Attique Khan +5 位作者 Majed Alhaisoni Ghadah Aldehim Leila Jamel Usman Tariq Taerang Kim Jae-Hyuk Cha 《Computers, Materials & Continua》 SCIE EI 2023年第12期3895-3920,共26页
Diagnosing gastrointestinal cancer by classical means is a hazardous procedure.Years have witnessed several computerized solutions for stomach disease detection and classification.However,the existing techniques faced... Diagnosing gastrointestinal cancer by classical means is a hazardous procedure.Years have witnessed several computerized solutions for stomach disease detection and classification.However,the existing techniques faced challenges,such as irrelevant feature extraction,high similarity among different disease symptoms,and the least-important features from a single source.This paper designed a new deep learning-based architecture based on the fusion of two models,Residual blocks and Auto Encoder.First,the Hyper-Kvasir dataset was employed to evaluate the proposed work.The research selected a pre-trained convolutional neural network(CNN)model and improved it with several residual blocks.This process aims to improve the learning capability of deep models and lessen the number of parameters.Besides,this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase.The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm.These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier.The experiment worked on the HyperKvasir dataset,which consists of 23 stomach-infected classes.At last,the proposed method obtained an improved accuracy of 93.90%on this dataset.Comparison is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved. 展开更多
关键词 Gastrointestinal cancer contrast enhancement deep learning information fusion feature selection machine learning
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COVID19 Classification Using CT Images via Ensembles of Deep Learning Models 认领 引用 被引量：1
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作者 Abdul Majid Muhammad Attique Khan +4 位作者 Yunyoung Nam Usman Tariq Sudipta Roy Reham R.Mostafa Rasha H.Sakr 《Computers, Materials & Continua》 SCIE EI 2021年第10期319-337,共19页
The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcript... The recent COVID-19 pandemic caused by the novel coronavirus,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has had a significant impact on human life and the economy around the world.A reverse transcription polymerase chain reaction(RT-PCR)test is used to screen for this disease,but its low sensitivity means that it is not sufficient for early detection and treatment.As RT-PCR is a time-consuming procedure,there is interest in the introduction of automated techniques for diagnosis.Deep learning has a key role to play in the field of medical imaging.The most important issue in this area is the choice of key features.Here,we propose a set of deep learning features based on a system for automated classification of computed tomography(CT)images to identify COVID-19.Initially,this method was used to prepare a database of three classes:Pneumonia,COVID19,and Healthy.The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach.In the next step,two advanced deep learning models(ResNet50 and DarkNet53)were fine-tuned and trained through transfer learning.The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach.For each deep model,the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach.Later,the selected features were merged using the new minimum parallel distance non-redundant(PMDNR)approach.The final fused vector was finally classified using the extreme machine classifier.The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%.Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework. 展开更多
关键词 COVID19 preprocessing deep learning information fusion firefly algorithm extreme learning machine
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SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles 认领 引用
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作者 Abida Sharif Muhammad Imran Sharif +5 位作者 Muhammad Attique Khan Nisar Ali Abdullah Alqahtani Majed Alhaisoni Ye Jin Kim Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2023年第5期2383-2396,共14页
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 architectu... 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 architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness. 展开更多
关键词 Internet of vehicles(IOV) content dissemination multi mediator data traffic management
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Classification of Retroviruses Based on Genomic Data Using RVGC 认领 引用
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作者 Khalid Mahmood Aamir Muhammad Bilal +3 位作者 Muhammad Ramzan Muhammad Attique Khan Yunyoung Nam Seifedine Kadry 《Computers, Materials & Continua》 SCIE EI 2021年第12期3829-3844,共16页
Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms.AIDS,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus infection... Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms.AIDS,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus infections.Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods.The National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the public.This research aims to classify the different types of rotavirus genome sequences available at the NCBI.First,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage.Based on some significant results,the number of features used for classification is reduced to five.The classification shall be carried out in two phases.The first phase of classification shall select only two features.Unclassified data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three features.Three data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the models.The performance of the classifier is analyzed using the test data set.Also,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare results.The results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset. 展开更多
关键词 Retroviruses machine learning bioinformatics classification
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WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks 认领 引用
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作者 Sohaib Manzoor Hira Manzoor +5 位作者 Saddaf Rubab Muhammad Attique Khan Majed Alhaisoni Abdullah Alqahtani Ye Jin Kim Byoungchol Chang 《Computers, Materials & Continua》 SCIE EI 2023年第5期2347-2363,共17页
Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal str... Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too weak.In this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi networks.An association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance.To implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator.The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach. 展开更多
关键词 Association multi-criterion SDN Wi-Fi
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Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review 认领 引用 被引量：7
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作者 Nazish Naheed Muhammad Shaheen +2 位作者 Sajid Ali Khan Mohammed Alawairdhi Muhammad Attique Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期315-344,共30页
In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential grow... In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided. 展开更多
关键词 Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions
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