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Correction:A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion

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Computers, Materials & Continua 2025年第1期82卷 1459-1459页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj hafiz tayyab rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Independent Researcher BradfordBD80HSUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,hafiz tayyab rauf,Mohammed ***-Meligy,Mohamed Sharaf,Abd Elatty *** Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–***:10.32604/cmc.2022.018621,URL:https://***/cmc/v70n1/44361,there was an error regarding the affiliation for the author hafiz tayyab *** of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.

关键词： Fusion Skin Feature

Identification of Thoracic Diseases by Exploiting Deep Neural Networks

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Computers, Materials & Continua 2021年第3期66卷 3139-3149页

作者： Saleh Albahli hafiz tayyab rauf Muhammad Arif Md Tabrez Nafis Abdulelah Algosaibi Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science Univesity of Gurjat52250Pakistan School of Computer Science Guanzghou UniversityGuangzhou510006China Department of Computer Science&Engineering Jamia HamdardNew DelhiIndia Department of Computer Science CCSITKing Faisal UniversitySaudi Arabia

With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five *** this gap is not filled with effective chest disease detection automation,the healthcare industry may face unfavorable *** are only several studies that targeted X-ray images of cardiothoracic *** of the studies only targeted a single disease,which is *** some related studies have provided an identification framework for all classes,the results are not encouraging due to a lack of data and imbalanced data *** research provides a significant contribution to Generative Adversarial Network(GAN)based synthetic data and four different types of deep learning-based models that provided comparable *** models include a ResNet-152 model with image augmentation with an accuracy of 67%,a ResNet-152 model without image augmentation with an accuracy of 62%,transfer learning with Inception-V3 with an accuracy of 68%,and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%.

关键词： GAN CNN chest diseases inception-V3 ResNet152

Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

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Computers, Materials & Continua 2021年第3期66卷 2317-2341页

作者： Saleh Albahli Anadil Alsaqabi Fatimah Aldhubayi hafiz tayyab rauf Muhammad Arif Mazin Abed Mohammed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of GujratGujratPakistan School of Computer Science Guanzghou UniversityGuangzhou510006China College of Computer Science and Information Technology University of AnbarRamadiIraq

Crimes are expected to rise with an increase in population and the rising gap between society’s income *** contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a *** and other preventive resources are limited and have to be *** conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are *** it possible for such algorithms to provide indicators of specific areas that may become criminal *** predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s *** paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output *** results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA *** naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.

关键词： Prediction machine learning crime prevention naïve bayes crime prediction classification algorithms

DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor hafiz tayyab rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

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Computers, Materials & Continua 2023年第11期77卷 2031-2047页

作者： Mudassir Khalil Muhammad Imran Sharif Ahmed Naeem Muhammad Umar Chaudhry hafiz tayyab rauf Adham E.Ragab Department of Computer Engineering Bahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science Kansas State UniversityManhattanKS66506USA Department of Computer Science University of Management and TechnologyLahore54000Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentST42DEUK Industrial Engineering Department Collage of EngineeringKing Saud UniversityPO Box 800Riyadh11421Saudi Arabia

Early detection of brain tumors is critical for effective treatment *** tumors in their nascent stages can significantly enhance the chances of patient *** there are various types of brain tumors,each with unique characteristics and treatment protocols,tumors are often minuscule during their initial stages,making manual diagnosis challenging,time-consuming,and potentially *** techniques predominantly used in hospitals involve manual detection via MRI scans,which can be costly,error-prone,and *** automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest *** research applies several data augmentation techniques to enhance the dataset for diagnosis,including rotations of 90 and 180 degrees and inverting along vertical and horizontal *** CIELAB color space is employed for tumor image selection and ROI *** deep learning models,such as DarkNet-53 and AlexNet,are applied to extract features from the fully connected layers,following the feature selection using entropy-coded Particle Swarm Optimization(PSO).The selected features are further processed through multiple SVM kernels for *** study furthers medical imaging with its automated approach to brain tumor detection,significantly minimizing the time and cost of a manual *** method heightens the possibilities of an earlier tumor identification,creating an avenue for more successful treatment planning and better overall patient outcomes.

关键词： Brain tumor deep learning feature extraction feature selection feature fusion transfer learning

COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

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Computers, Materials & Continua 2021年第5期67卷 1613-1627页

作者： Saleh Albahli Ahmad Algsham Shamsulhaq Aeraj Muath Alsaeed Muath Alrashed hafiz tayyab rauf Muhammad Arif Mazin Abed Mohammed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science Univesity of GurjatPakistan School of Computer Science Guanzghou UniversityGuangzhou510006China College of Computer Science and Information technology University of Anbar11RamadiAnbarIraq

Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their *** COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media *** phenomenon caused a state of panic among *** studies were conducted to stop the spread of fake news to help people cope with the *** this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)*** has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative *** results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment *** study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.

关键词： COVID-19 sentiment analysis natural language processing twitter social data mining sentiment polarity

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

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Computer Systems Science & Engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry hafiz tayyab rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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Computers, Materials & Continua 2022年第1期70卷 1617-1630页

作者： Khadija Manzoor Fiaz Majeed Ansar Siddique Talha Meraj hafiz tayyab rauf Mohammed A.El-Meligy Mohamed Sharaf Abd Elatty E.Abd Elgawad Department of Information Technology University of GujratGujrat50700Pakistan Department of Software Engineering University of GujratGujrat50700Pakistan Department of Computer Science COMSATS University Islamabad-Wah CampusWah Cantt47040Pakistan Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke-on-TrentUK Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Skin diseases effectively influence all parts of *** and accurate detection of skin cancer is necessary to avoid significant *** manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color ***,an automated system to identify these skin diseases is *** studies on skin disease classification using different techniques have been ***,previous techniques failed to identify multi-class skin disease images due to their similar *** the proposed study,a computer-aided framework for automatic skin disease detection is *** the proposed research,we collected and normalized the datasets from two databases(ISIC archive,Mendeley)based on six Basal Cell Carcinoma(BCC),Actinic Keratosis(AK),Seborrheic Keratosis(SK),Nevus(N),Squamous Cell Carcinoma(SCC),and Melanoma(M)common skin ***,segmentation is performed using deep Convolutional Neural Networks(CNN).Furthermore,three types of features are extracted from segmented skin lesions:ABCD rule,GLCM,and in-depth *** transfer learning is used for deep feature extraction,while a support vector machine(SVM)is used for *** results show that SVM outperformed other studies in terms of accuracy,as AK disease achieved 100%accuracy,BCC 92.7%,M 95.1%,N 97.8%,SK 93.1%,SCC 91.4%with a global accuracy of 95.4%.

关键词： AlexNet CNN GLCM SVMs skin disease

Alternative Strategy for Development of Dielectric Calcium Copper Titanate‑Based Electrolytes for Low‑Temperature Solid Oxide Fuel Cells

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Nano-Micro Letters 2025年第1期17卷 310-332页

作者： Sajid rauf Muhammad Bilal Hanif Zuhra tayyab Matej Veis MAKYousaf Shah Naveed Mushtaq Dmitry Medvedev Yibin Tian Chen Xia Martin Motola Bin Zhu College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060People’s Republic of China Department of Inorganic Chemistry Faculty of Natural SciencesComenius University BratislavaIlkovicova684215 BratislavaSlovakia Energy Storage Joint Research Center School of Energy and EnvironmentSoutheast UniversityNanjing 210096People’s Republic of China Hydrogen Energy Laboratory Ural Federal University620002 EkaterinburgRussia Laboratory of Electrochemical Devices Based on Solid Oxide Proton Electrolytes Institute of High Temperature Electrochemistry620066 EkaterinburgRussia School of Microelectronics Hubei UniversityWuhan 430062People’s Republic of China

The development of low-temperature solid oxide fuel cells(LT-SOFCs)is of significant importance for realizing the widespread application of *** has stimulated a substantial materials research effort in developing high oxide-ion conductivity in the electrolyte layer of *** this context,for the first time,a dielectric material,CaCu_(3)Ti_(4)O_(12)(CCTO)is designed for LT-SOFCs electrolyte application in this *** individual CCTO and its heterostructure materials with a p-type Ni_(0.8)Co_(0.15)Al_(0.05)LiO_(2−δ)(NCAL)semiconductor are evaluated as alternative electrolytes in LT-SOFC at 450–550℃.The single cell with the individual CCTO electrolyte exhibits a power output of approximately 263 mW cm^(-2) and an open-circuit voltage(OCV)of 0.95 V at 550℃,while the cell with the CCTO–NCAL heterostructure electrolyte capably delivers an improved power output of approximately 605 mW cm^(-2) along with a higher OCV over 1.0 V,which indicates the introduction of high hole-conducting NCAL into the CCTO could enhance the cell performance rather than inducing any potential short-circuiting *** is found that these promising outcomes are due to the interplay of the dielectric material,its structure,and overall properties that led to improve electrochemical mechanism in CCTO–***,density functional theory calculations provide the detailed information about the electronic and structural properties of the CCTO and NCAL and their heterostructure CCTO–*** study thus provides a new approach for developing new advanced electrolytes for LT-SOFCs.

关键词： LT-SOFCs Dielectric CaCu_(3)Ti_(4)O_(12) Semiconductor Ni_(0.8)Co_(0.15)Al_(0.05)LiO_(2−δ) Ionic conductivity Heterostructure electrolyte