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AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

Computers, Materials & Continua 2021年第10期69卷 1039-1055页

作者： Fahad Alotaibi muhammad attique Yaser Daanial Khan Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Management and TechnologyLahore54000Pakistan Department of Information Technology University of GujratGujrat50700Pakistan

Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial ***,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain ***,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development *** anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and *** exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep *** proposed model performs best in conjunction with deep *** testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set *** proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and ***,the proposed model was also extensively probed in comparison with other existing *** performance of the proposed model also out-performs other existing *** outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based ***,it has the potential to assiduously support medical and bioinformatics research.

关键词： Prediction feature extraction machine learning bootstrap aggregation deep learning bioinformatics computational intelligence antiinflammatory peptides

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VISPNN:VGG-Inspired Stochastic Pooling Neural Network

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Computers, Materials & Continua 2022年第2期70卷 3081-3097页

作者： Shui-Hua Wang muhammad attique Khan Yu-Dong Zhang School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHUnited Kingdom Department of Computer Science HITEC University TaxilaTaxila47080Pakistan School of Informatics University of LeicesterLeicesterLE17RHUnited Kingdom

Aim Alcoholism is a disease that a patient becomes dependent or addicted to *** paper aims to design a novel artificial intelligence model that can recognize alcoholism more *** We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation ***-I is based on VISPNN by replacing stochastic pooling with ordinary max ***-II removes the 20-way data *** The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,*** The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.

关键词： Deep learning alcoholism multiple-way data augmentation VGG convolutional neural network stochastic pooling

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Gastrointestinal Tract Infections Classification Using Deep Learning

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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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Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 315-344页

作者： Nazish Naheed muhammad Shaheen Sajid Ali Khan Mohammed Alawairdhi muhammad attique Khan Faculty of Engineering&Information Technology Foundation University IslamabadIslamabadPakistan College of Computing and Informatics Saudi Electronic UniversityRayadhSaudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan

In the area of pattern recognition and machine learning,features play a key role in *** famous applications of features are medical imaging,image classification,and name a few *** the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of *** data repositories contain details information essential to support medical diagnostic decisions and also improve patient care *** the other hand,this growth also made it difficult to comprehend and utilize data for various *** results of imaging data can become biased because of extraneous features present in larger *** selection gives a chance to decrease the number of components in such large *** selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization *** correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast *** paper presents a review of feature selection techniques and attributes selection measures for medical *** 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 *** review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced ***,this review provides the importance of feature selection for correct classification of medical *** the end,critical analysis and future directions are provided.

关键词： Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions

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SNSVM:SqueezeNet-Guided SVM for Breast Cancer Diagnosis

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Computers, Materials & Continua 2023年第8期76卷 2201-2216页

作者： Jiaji Wang muhammad attique Khan Shuihua Wang Yudong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Breast cancer is a major public health concern that affects women *** is a leading cause of cancer-related deaths among women,and early detection is crucial for successful ***,breast cancer can often go undetected until it has reached advanced stages,making it more difficult to ***,there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early *** proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography *** extracted features are then utilized to train a support vector machine(SVM)for mammography image *** SqueezeNet-guided SVMmodel,known as SNSVM,achieved promising results,with an accuracy of 94.10% and a sensitivity of 94.30%.A 10-fold cross-validation was performed to ensure the robustness of the results,and the mean and standard deviation of various performance indicators were calculated across multiple *** model also outperforms state-of-the-art models in all performance indicators,indicating its superior *** demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography *** superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer *** may have significant implications for reducing breast cancer mortality rates.

关键词： SqueezeNet support vector machine breast cancer diagnosis

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Classification of Retroviruses Based on Genomic Data Using RVGC

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Computers, Materials & Continua 2021年第12期69卷 3829-3844页

作者： Khalid Mahmood Aamir muhammad Bilal muhammad Ramzan muhammad attique Khan Yunyoung Nam Seifedine Kadry Department of CS&IT University of SargodhaSargodha40100Pakistan Department of CS&IT University of MianwaliMianwali42200Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses by genome sequence is a biological problem that benefits from computational *** National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the *** research aims to classify the different types of rotavirus genome sequences available at the ***,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing *** on some significant results,the number of features used for classification is reduced to *** classification shall be carried out in two *** first phase of classification shall select only two *** data in the first phase is transferred to the next phase,where the final decision is taken with the remaining 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 *** performance of the classifier is analyzed using the test data ***,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare *** results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.

关键词： Retroviruses machine learning bioinformatics classification

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Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

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Computers, Materials & Continua 2021年第10期69卷 471-486页

作者： Sidra Naseem Kashif Javed muhammad Jawad Khan Saddaf Rubab muhammad attique Khan Yunyoung Nam Robotics and Machine Intelligence Engineering School of Mechanical and Manufacturing EngineeringNational University of Sciences and Technology(NUST)Islamabad44000Pakistan National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Electroencephalography is a common clinical procedure to record brain signals generated by human *** are useful in Brain controlled interfaces and other intelligent Neuroscience applications,but manual analysis of these brainwaves is complicated and time-consuming even for the experts of *** EEG analysis and classification techniques have been proposed to address this problem however,the conventional classification methods require identification and learning of specific EEG characteristics *** learning models can learn features from data without having in depth knowledge of data and prior feature *** of the great implementations of deep learning is Convolutional Neural Network(CNN)which has outperformed traditional neural networks in pattern recognition and image *** Wavelet Transform(CWT)is an efficient signal analysis technique that presents the magnitude of EEG signals as timerelated Frequency *** deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency *** improve classification accuracy,we propose an integrated CWT and CNN technique which classifies five types of EEG signals *** compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g.,GoogleNet,VGG16,***,the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross *** average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are,76.12%and 56.02%*** model produces results on a publicly available dataset:Epilepsy dataset by UCI(Machine Learning Repository).

关键词： Deep learning electroencephalography epilepsy continuous wavelet transform

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Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)

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Computers, Materials & Continua 2021年第1期66卷 589-602页

作者： Sadaf Qazi muhammad Usman Azhar Mahmood Aaqif Afzaal Abbasi muhammad attique Yunyoung Nam Predicitive Analytics Laboratory Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Software Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and *** Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free *** study proposes a defaulter prediction model for accurate identification of *** proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization *** machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)*** Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being *** this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive ***,predictive analytics can reinforce immunization programs by expediting targeted action to reduce ***,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.

关键词： Smart healthcare routine immunization predictive analytics defaulters vaccination machine learning targeted messaging

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WACPN:A Neural Network for Pneumonia Diagnosis

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Computer Systems Science & Engineering 2023年第4期45卷 21-34页

作者： Shui-Hua Wang muhammad attique Khan Ziquan Zhu Yu-Dong Zhang School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan

Community-acquired pneumonia(CAP)is considered a sort of pneumonia developed outside hospitals and *** diagnose community-acquired pneumonia(CAP)more efficiently,we proposed a novel neural network *** introduce the 2-dimensional wavelet entropy(2d-WE)layer and an adaptive chaotic particle swarm optimization(ACP)algorithm to train the feed-forward neural *** ACP uses adaptive inertia weight factor(AIWF)and Rossler attractor(RA)to improve the performance of standard particle swarm *** final combined model is named WE-layer ACP-based network(WACPN),which attains a sensitivity of 91.87±1.37%,a specificity of 90.70±1.19%,a precision of 91.01±1.12%,an accuracy of 91.29±1.09%,F1 score of 91.43±1.09%,an MCC of 82.59±2.19%,and an FMI of 91.44±1.09%.The AUC of this WACPN model is *** find that the maximum deposition level chosen as four can obtain the best *** demonstrate the effectiveness of both AIWF and ***,this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art *** model will be distributed to the cloud computing environment.

关键词： Wavelet entropy community-acquired pneumonia neural network adaptive inertia weight factor rossler attractor particle swarm optimization

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