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

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Computers, Materials & Continua 2025年第1期82卷 1461-1461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 UniversityManhattanKS 66506USA Department of Computer Science University of Management and TechnologyLahore54000Pakistan Independent Researcher BradfordBD80HSUK Industrial Engineering Department Collage of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,muhammad Imran Sharif,ahmed Naeem,muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham *** Computers,Materials&Continua,2023,Vol.77,No.2,pp.2031–***:10.32604/cmc.2023.043687,URL:https://***/cmc/v77n2/54831,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,ST42DE,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.

关键词： Deep Code CIELAB

A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

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Computers, Materials & Continua 2023年第11期77卷 1599-1615页

作者： Nadeem Malik Saud Altaf muhammad usman Tariq Ashir ahmed muhammad Babar University Institute of Information Technology PMAS Arid Agriculture UniversityRawalpindi46000Pakistan Department of Marketing Operationsand Information SystemAbu Dhabi UniversityAbu Dhabi6844United Arab Emirates College of Computer and Information Sciences Prince Sultan UniversityRiyadh11442Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh11442Saudi Arabia

The severity of traffic accidents is a serious global concern,particularly in developing *** the main causes and contributing circumstances may reduce the severity of traffic *** exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter,blogs and *** such approaches are popular,there exists an issue of data management and low prediction *** article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory(XLNet-Bi-LSTM)to predict traffic collisions based on data collected from social ***,a Tweet dataset has been formed by using an exhaustive keyword-based searching *** the next phase,two different types of features named as individual tokens and pair tokens have been obtained by using POS tagging and association rule *** output of this phase has been forwarded to a three-layer deep learning model for final *** experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM *** has been shown that the proposed model achieved 94.2%prediction accuracy.

关键词： Accident XLNet Bi-LSTM association rule mining Twitter

3-D seismic interpretation of stratigraphic and structural features in the Upper Jurassic to Lower Cretaceous sequence of the Gullfaks Field,Norwegian North Sea:A case study of reservoir development

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Energy Geoscience 2021年第4期2卷 287-297页

作者： muhammad usman Numair ahmed Siddiqui Eduardo Garzanti muhammad Jamil Qazi Sohail Imran Luqman ahmed Laboratory for Provenance Studies Department of Earth and Environmental SciencesUniversity of Milano-BicoccaPiazza Della Scienza 420126MilanoItaly Geoscience Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Shale Gas Research Group Institute of Hydrocarbon RecoveryUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Institute of Geology University of the PunjabLahore56300Pakistan

The 3-D seismic dataset is a key tool to analyze and understand the mechanism of structural and stratigraphic hydrocarbon(HC)trapping in the *** used subsurface seismic characterization methods for fractures are based on the theory of effective anisotropy *** aim of this work is to improve the structural images with dense sampling of 3-D survey to evaluate structural and stratigraphic models for reservoir development to predict reservoir *** present study of the Gullfaks Field,located in the Norwegian North Sea Gullfaks sector,identifies the shallowest structural *** steepness of westward structural dip decreases eastward during the Upper Jurassic to Lower Cretaceous *** sands consist of the Middle Jurassic deltaic deposits and Lower Jurassic fluvial channel and delta plain *** supply steadily prevails on sea-level rise and the succession displays a regressive trend indicated by a good continuous stacking *** key factor for the development of reservoirs in the Gullfaks Field is fault transmissibility with spatially distributed *** majority of mapped faults with sand-to-sand contacts are non-sealing,which provide restriction for the HC flow between the fault *** traps for HC accumulation occur between the post-rift and syn-rift strata,*** set by extensional system,unconformity trap at the top of syndeposition,and structural trap due to normal *** reservoir quality in the studied area is generally excellent with average 35%porosity and permeability in the Darcy *** findings are useful to better understand the development of siliciclastic reservoirs in similar geological settings worldwide.

关键词： 3-D seismic model Mesozoic stratigraphic boundary Structural trap Reservoir quality Gullfaks Field

A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition

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Computers, Materials & Continua 2022年第11期73卷 2555-2576页

作者： muhammad Naeem Akbar Farhan Riaz ahmed Bilal Awan muhammad Attique Khan usman Tariq Saad Rehman National University of Sciences and Technology(NUST) Islamabad46000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj 11942Saudi Arabia

Human Action Recognition(HAR)is a current research topic in the field of computer vision that is based on an important application known as video *** in computer vision have introduced various intelligent methods based on deep learning and machine learning,but they still face many challenges such as similarity in various actions and redundant *** proposed a framework for accurate human action recognition(HAR)based on deep learning and an improved features optimization algorithm in this *** deep learning feature extraction to feature classification,the proposed framework includes several critical *** training fine-tuned deep learning models–MobileNet-V2 and Darknet53–the original video frames are *** feature extraction,pre-trained deep models are used,which are fused using the canonical correlation *** that,an improved particle swarm optimization(IPSO)-based algorithm is used to select the best *** that,the selected features were used to classify actions using various *** experimental process was performed on six publicly available datasets such as KTH,UT-Interaction,UCF Sports,Hollywood,IXMAS,and UCF YouTube,which attained an accuracy of 98.3%,98.9%,99.8%,99.6%,98.6%,and 100%,*** comparison with existing techniques,it is observed that the proposed framework achieved improved accuracy.

关键词： Action recognition deep learning features fusion features selection recognition

SI Engine Fueled with Gasoline, CNG and CNG-HHO Blend: Comparative Evaluation of Performance, Emission and Lubrication Oil Deterioration

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Journal of Thermal Science 2021年第4期30卷 1199-1211页

作者： usman muhammad HAYAT Nasir BHUTTA muhammad Mahmood Aslam Department of Mechanical Engineering University of Engineering and TechnologyLahore 54890Pakistan

Hydroxy gas (HHO) is one of the potential alternative fuels for spark ignition (SI) engine,notably due to simultaneous increase in engine performance and reduction in exhaust ***,impact of HHO gas on lubrication oil for longer periods of engine operation has not yet been *** study focuses on investigation of the effect of gasoline,CNG and CNG-HHO blend on lubrication oil deterioration along with engine performance and emissions in SI *** unit produces HHO gas at 4.72 L/min by using 6 g/L of KOH in the aqueous *** was supplied to the test engine at a pressure of 0.11 MPa using an electronically controlled solenoid *** tests were carried out at different speeds at 80%open throttle condition and various performance parameters such as brake power (BP),brake specific fuel consumption(BSFC),brake thermal efficiency (BTE),exhaust gas temperature and exhaust emissions (HC,CO_(2),CO and NO_(x))were *** addition,various lubrication oil samples were extracted over 120 h of engine running while topping for drain out volume and samples were analyzed as per ASTM ***-HHO blend exhibited better performance i.e.,15.4%increase in average BP in comparison to CNG,however,15.1%decrease was observed when compared to ***-HHO outperformed gasoline and CNG in the case of HC,CO_(2),CO and brake specific fuel consumption (31.1%decrease in comparison to gasoline).On the other hand,CNG-HHO produced higher average NO_(x) (12.9%) when compared to CNG ***,lubrication oil condition(kinematic viscosity,water contents,flash point and total base number (TBN)),wear debris (Iron (Fe),Aluminum(Al),Copper (Cu),Chromium (Cr)) and additives depletion (Zinc (Zn),Calcium (Ca)) presented a significant degradation in the case of CNG-HHO blend as compared to gasoline and *** oil analyses illustrated 19.6%,12.8%and 14.2%decrease in average viscosity,flash point and TBN for CNG-HHO blend ***,

关键词： spark ignition engine compressed natural gas hydrogen lubrication oil deterioration emission

Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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Computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar ahmed Tahir Alyas muhammad Waseem Iqbal muhammad usman Ashraf ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

Electromechanical Fields and Their Influence on the Internal Quantum Efficiency of GaN-Based Light-Emitting Diodes

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Acta Mechanica Solida Sinica 2018年第3期31卷 383-390页

作者： muhammad usman Kiran Saba Adnan Jahangir muhammad Kamran Nazeer muhammad Department of Engineering Sciences Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Swabi 23460 Pakistan Department of Mathematics COMSATS Institute of Information Technology Wah Cantt 47040 Pakistan Department of Electrical Engineering COMSATS Institute of Information Technology Wah Cantt 47040 Pakistan

The effect ofelectromechanical fields, i.e., polarization fields, on the efficiency droop of GaN-based light-emitting diodes is presented using both experimental and numerical analyses. The role of incorporating such polarization charge density in device performance is numerically investigated and further compared with the experimental results of internal quantum efficiency of three different devices in consideration.

关键词： Optoelectronic devices Photonic bandgap materials Visible and ultraviolet sources Light-emitting devices

Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase

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Journal of Pharmaceutical Analysis 2020年第4期10卷 320-328页

作者： muhammad usman Mirza Matheus Froeyen Department of Pharmaceutical and Pharmacological Sciences Rega Institute for Medical ResearchMedicinal ChemistryUniversity of LeuvenB-3000LeuvenBelgium

Recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019(COVID-19)and instigated a widespread fear,threatening global health *** date,no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are under way to test possible *** this urgent situation,computational drug discovery methods provide an alternative to tiresome high-throughput screening,particularly in the hit-to-lead-optimization *** of small molecules that specifically target viral replication apparatus has indicated the highest potential towards antiviral drug *** this work,we present potential compounds that specifically target SARS-CoV-2 vital proteins,including the main protease,Nsp12 RNA polymerase and Nsp13 *** integrative virtual screening and molecular dynamics simulations approach has facilitated the identification of potential binding modes and favourable molecular interaction profile of corresponding ***,the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition *** the current study lacks experimental validation,the structural information obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.

关键词： SARS-CoV-2 COVID-19 outbreak CoV-Mpro CoV-Nsp12 polymerase CoV-Nsp13 helicase

Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter:A Panacea to Green Computing

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Journal of Bionic Engineering 2019年第2期16卷 354-366页

作者： Mohammed Joda usman Abdul Samad Ismail Hassan Chizari Gaddafi Abdul-Salaam Ali muhammad usman Abdulsalam Yau Gital Omprakash Kaiwartya ahmed Aliyu Department of Maths Bauchi State University GadauBauchi 81007Nigeria Department of Computer Science Universiti Teknology MalaysiaSkudai Johor 81310Malaysia School of Computing and Technology Park CampusUniversity of Gloucestershire Cheltenham GL50 4SHUK Department of Computer Science Kwame Nkrumah University of Science and TechnologyKumasi 00233Ghana Department of Maths and Computer Federal College of Education Technical GombeGombe 072158Nigeria Department of Maths Abubakar Tafawa Balewa University BauchiBauchi 81027Nigeria Department of Computer and Information Technology Northumbria UniversityNewcastle NE1SSTUK

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to ***,datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous *** cloud datacenter,Virtual Machines(VMs)need to be allocated on various Physical Machines(PMs)in order to minimize resource wastage and increase energy *** allocation problem is *** finding an exact solution is complicated especially for large-scale *** this con text,this paper proposes an Energy-oriented Flower Pollination Algorithm(E-FPA)for VM allocation in cloud datacenter environments.A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a *** allocation uses a strategy called Dynamic Switching Probability(DSP).The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local *** considers a processor,storage,and memory constraints of a PM while prioritizing energy-oriented allocation for a set of *** performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware(GAPA)by 21.8%,Order of Exchange Migration(OEM)ant colony system by 21.5%,and First Fit Decreasing(FFD)by 24.9%.Therefore,E-FPA significantly improves datacenter performance and thus,enhances environmental sustainability.

关键词： virtualization green computing cloud datacenter energy optimization