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Molybdenum-induced tuning 3d-orbital electron filling degree of CoSe_(2) for alkaline hydrogen and oxygen evolution reactions

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Chinese Chemical Letters 2023年第2期34卷 274-278页

作者： Sumaira nazar hussain Yana Men Zhen Li Pingping Zhao Gongzhen Cheng Wei Luo College of Chemistry and Molecular Sciences Wuhan UniversityWuhan 430072China School of Printing and Packaging Wuhan UniversityWuhan 430072China Suzhou Institute of Wuhan University Suzhou 215123China

The development of high-performance non-precious metal-based robust bifunctional electrocatalyst for both hydrogen evolution reaction(HER) and oxygen evolution reactions(OER) in alkaline media is essential for the electrochemical overall water splitting technologies. Herein, we demonstrate that the HER/OER performance of Co Se_(2)can be significantly enhanced by tuning the 3d-orbital electron filling degree through Mo doping. Both density functional theory(DFT) calculations and experimental results imply that the doping of Mo with higher proportion of the unoccupied d-orbital(P_(un)) could not only serve as the active center for water adsorption to enhance the water molecule activation, but also modulate the electronic structures of Co metal center leading to the optimized adsorption strength of*H. As expected, the obtained Mo-Co Se_(2)exhibits a remarkable bifunctional performance with overpotential of only 85 m V for HER and 245 m V for OER to achieve the current density of 10 m A/cm^(2)in alkaline *** work will provide a valuable insight to design highly efficient bifunctional electrocatalyst towards HER and OER.

关键词： Alkaline HER OER Cobalt selenide Proportion of unoccupied d-orbital(Pun) 3d-orbital electron filling degree DFT

An Integrated Deep Learning Framework for Fruits Diseases Classification

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Computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq nazar hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five *** affect the quality and quantity of fruits,thereby disrupting the economy of a *** computerized techniques have been introduced for detecting and recognizing fruit ***,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the ***,we propose an integrated deep learning framework for classifying fruit *** consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and *** proposed method comprises several important ***,data increase is applied,and then two different types of features are *** the first feature type,texture and color features,i.e.,classical features,are *** the second type,deep learning characteristics are extracted using a pretrained *** pretrained model is reused through transfer ***,both types of features are merged using the maximum mean value of the serial ***,the resulting fused vector is optimized using a harmonic threshold-based genetic ***,the selected features are classified using multiple *** evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

Classification of Positive COVID-19 CT Scans Using Deep Learning

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Computers, Materials & Continua 2021年第3期66卷 2923-2938页

作者： Muhammad Attique Khan nazar hussain Abdul Majid Majed Alhaisoni Syed Ahmad Chan Bukhari Seifedine Kadry Yunyoung Nam Yu-Dong Zhang Department of Computer Science HITEC University TaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Division of Computer Science Mathematics and ScienceCollins College of Professional StudiesSt.’John’s UniversityNew YorkUSA Department of Mathematics and Computer Science Beirut Arab UniversityLebanon Department of Computer Science and Engineering Soonchunhyang UniversityAsanSouth Korea Department of Informatics University of LeicesterLeicesterUK

In medical imaging,computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate *** response to the coronavirus 2019(COVID-19)pandemic,new testing procedures,medical treatments,and vaccines are being developed *** 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 *** we introduce an optimized deep learning(DL)scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography(CT)*** 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 *** fully connected layers and an average pool are used for feature *** extracted deep features are then optimized with a Firefly algorithm to select the most optimal learning *** the selected features is important to improving the accuracy of the approach;however,it directly affects the computational cost of the *** 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 *** were conducted on a collected database of patients using a 70:30 training:Testing *** 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.

关键词： Coronavirus contrast enhancement deep learning features optimization fusion classification

Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

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Computers, Materials & Continua 2022年第2期70卷 3281-3294页

作者： nazar hussain Muhammad Attique Khan Usman Tariq Seifedine Kadry Muhammad Asfand E.Yar Almetwally M.Mostafa Abeer Ali Alnuaim Shafiq Ahmad Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Data Science Noroff University CollegeNorway Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543KSASaudi Arabia Department of natural and engineering sciences College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11421Saudi Arabia Faculty of Engineering Industrial Engineering DepartmentKing Saud UniversityRiyadh11421Saudi Arabia

Agriculture is an important research area in the field of visual recognition by *** diseases affect the quality and yields of ***-stage identification of crop disease decreases financial losses and positively impacts crop *** manual identification of crop diseases,which aremostly visible on leaves,is a very time-consuming and costly *** this work,we propose a new framework for the recognition of cucumber leaf *** proposed framework is based on deep learning and involves the fusion and selection of the best *** the feature extraction phase,VGG(Visual Geometry Group)and Inception V3 deep learning models are considered and *** fine-tuned models are trained using deep transfer *** are extracted in the later step and fused using a parallel maximum fusion *** the later step,best features are selected usingWhale Optimization *** best-selected features are classified using supervised learning algorithms for the final classification *** experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%.A comparison with recent techniques shows the significance of the proposed method.

关键词： Cucumber diseases database preparation deep learning parallel fusion features selection

Reinforcement Learning Algorithm for Solving Load Commitment Problem Considering a General Load Model

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Journal of Energy and Power Engineering 2013年第6期7卷 1150-1162页

作者： Thythodath Parambath Imthias Ahamed Sayed Danish Maqbool nazar hussain Malik Department of Electrical Engineering King Saud University Riyadh 11421 Saudi Arabia

Under the smart grid paradigm, in the near future all consumers will be exposed to variable pricing schemes introduced by utilities. Hence, there is a need to develop algorithms which could be used by the consumers to schedule their loads. In this paper, load scheduling problem is formulated as a LCP （load commitment problem）. The load model is general and can model atomic and non-atomic loads. Furthermore, it can also take into consideration the relative discomfort caused by delay in scheduling any load. For this purpose, a single parameter ＂uric＂ is introduced in the load model which captures the relative discomfort caused by delay in scheduling a particular load. Guidelines for choosing this parameter are given. All the other parameters of the proposed load model can be easily specified by the consumer. The paper shows that the general LCP can be viewed as multi-stage decision making problem or a MDP （Markov decision problem）. RL （reinforcement learning） based algorithm is developed to solve this problem. The efficacy of the algorithm is investigated when the price of electricity is available in advance as well as for the case when it is random. The scalability of the approach is also investigated.

关键词： Reinforcement learning Markov decision problem demand response load scheduling problem load model.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

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Computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq hussain Rahimullah Rabih Najibullah Haqjoo Mobeen nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

Classication of COVID-19 CT Scans via Extreme Learning Machin

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Computers, Materials & Continua 2021年第7期 1003-1019页

作者： Muhammad Attique Khan Abdul Majid Tallha Akram nazar hussain Yunyoung Nam Seifedine Kadry Shui-Hua Wang Majed Alhaisoni Department of Computer Science HITEC UniversityTaxila47040Pakistan Department of Electrical Engineering COMSATS University IslamabadWah CampusWah CanttPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon Department of Mathematics University of LeicesterLeicesterUK College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia

Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)*** scheme operates in four ***,we prepared a database containing COVID-19 pneumonia and normal CT *** images were retrieved from the Radiopaedia COVID-19 *** images were divided into training and test sets in a ratio of 70:***,multiple features were extracted from the training *** used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive *** next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA *** on the ELM losses,the most discriminatory features were selected and saved as an ELM *** images were sent to the model,and the best-selected features compared to those of the trained model to allow nal *** employed the collected chest CT *** best predictive accuracy of the ELM classier was 93.9%;the scheme was effective.

关键词： Coronavirus classical features feature fusion feature optimization prediction

Multiclass Stomach Diseases Classication Using Deep Learning Features Optimization

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Computers, Materials & Continua 2021年第6期 3381-3399页

作者： Muhammad Attique Khan Abdul Majid nazar hussain Majed Alhaisoni Yu-Dong Zhang Seifedine Kadry Yunyoung Nam Department of Computer Science HITEC UniversityTaxila47040Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Informatics University of LeicesterLeicesterUK Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

In the area of medical image processing,stomach cancer is one of the most important cancers which need to be diagnose at the early *** this paper,an optimized deep learning method is presented for multiple stomach disease *** proposed method work in few important steps—preprocessing using the fusion of ltering images along with Ant Colony Optimization(ACO),deep transfer learning-based features extraction,optimization of deep extracted features using nature-inspired algorithms,and nally fusion of optimal vectors and classication using Multi-Layered Perceptron Neural Network(MLNN).In the feature extraction step,pretrained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning *** on,the activation function is applied to Global Average Pool(GAP)for feature ***,the extracted features are optimized through two different nature-inspired algorithms—Particle Swarm Optimization(PSO)with dynamic tness function and Crow Search Algorithm(CSA).Hence,both methods’output is fused by a maximal value approach and classied the fused feature vector by *** datasets are used to evaluate the proposed method—CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%.The comparison with existing techniques,it is shown that the proposed method shows signicant performance.

关键词： Stomach infections deep features features optimization fusion classication

Properties of Certain Subclasses of Analytic Functions Involving q-Poisson Distribution

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1465-1477页

作者： Bilal Khan Zhi-Guo Liu nazar Khan Aftab hussain Nasir Khan Muhammad Tahir School of Mathematical Sciences and Shanghai Key Laboratory of PMMP East China Normal UniversityShanghai200241China Department of Mathematics Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Mathematics King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics FATA UniversityAkhorwal(Darra Adam Khel)FR Kohat26000Pakistan

By using the basic(or q)-Calculus many subclasses of analytic and univalent functions have been generalized and studied from different viewpoints and *** this paper,we aim to define certain new subclasses of an analytic *** then give necessary and sufficient conditions for each of the defined function *** also study necessary and sufficient conditions for a function whose coefficients are probabilities of q-Poisson *** validate our results,some known consequences are also given in the form of Remarks and Corollaries.

关键词： Analytic function q-difference operator sufficient condition q-Poisson distribution