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modeling&Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects

Journal on Artificial Intelligence 2022年第4期4卷 245-259页

作者： Nadeem Jabbar Chaudhry m.bilal khan m.Javaid Iqbal Siddiqui muhammad Yasir Department of Computer Science Superior UniversityLahorePakistan Department of Computer Science University of Central PunjabLahorePakistan Department of mechanical System Engineering Tongmyong UniversityBusanKorea

Recently,outstanding identification rates in image classification tasks were achieved by convolutional neural networks(CNNs).to use such skills,selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB *** must be detected early to take timely corrective action due to production *** image classification up till now,a model-based method has been utilized,which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free *** problem of detecting steel surface defects has grown in importance as a result of the vast range of steel applications in end-product sectors such as automobiles,households,construction,*** processes for detections are time-consuming,labor-intensive,and *** strategies have been used to automate manual processes,but CNN models have proven to be the most effective rather than image processing andmachine learning *** using differentCNNmodels with fine-tuning,easily compare their performance and select the best-performing model for the same kinds of ***,it is important that using different CNN models either from fine tuning can be computationally expensive and ***,our study helps the upcoming researchers to choose the CNN without considering the issues of model complexity,performance,and computational *** this article,the performance of various CNN models like Visual Geometry Group,VGG16,VGG19,ResNet152,ResNet152V2,Xception,InceptionV3,InceptionResNetV2,NASNetLarge,mobileNetV2,and DenseNet201 with transfer learning techniques are *** models were chosen based on their popularity and impact in the field of computer vision research,as well as their performance on benchmark *** to the outcomes,DenseNet201 outperformed the other CNN models and had the greatest detection rate on the NEU dataset,falling in at 98.37 percent.

关键词： Convolutional neural network transfer learning defect detection

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Darcy-Forchheimer Hybrid Nano Fluid Flow with mixed Convection Past an Inclined Cylinder

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Computers, materials & Continua 2021年第2期66卷 2025-2039页

作者： m.bilal Imran khan Taza Gul Asifa Tassaddiq Wajdi Alghamdi Safyan mukhtar Poom Kumam Department of mathematics City University of Science and Information TechnologyPeshawar25000Pakistan Department of Basic Sciences and Humanities College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80261Saudi Arabia Basic Sciences Department Deanship of Preparatory YearKing Faisal UniversityAl Ahsa31982Saudi Arabia Center of Excellence in Theoretical and Computational Science(TaCS-CoE) SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand

This article aims to investigate the Darcy Forchhemier mixed convection flow of the hybrid nanofluid through an inclined extending *** different nanoparticles such as carbon nanotubes(CNTs)and iron oxide Fe3O4 have been added to the base fluid in order to prepare a hybrid *** partial differential equations for momentum,energy and convective diffusion have been changed into dimensionless ordinary differential equations after using Von Karman *** analysis method(HAm),a powerful analytical approach has been used to find the solution to the given *** effects of the physical constraints on velocity,concentration and temperature profile have been drawn as well for discussion *** numerical outcomes have been carried out for the drag force,heat transfer rate and diffusion rate *** Biot number of heat and mass transfer affects the fluid temperature whereas the Forchhemier parameter and the inclination angle decrease the velocity of the fluid *** results show that hybrid nanofluid is the best source of enhancing heat transfer and can be used for cooling purposes as well.

关键词： mixed convection similarity transformation HAm hybrid nanofluid CNTs Darcy Forchhemier inclined cylinder

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Organic additives in all-inorganic perovskite solar cells and modules:from moisture endurance to enhanced efficiency and operational stability

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Journal of Energy Chemistry 2022年第4期31卷 361-390页

作者： Yameen Ahmed Bilawal khan mbilal Faheem Keqing Huang Yuanji Gao Junliang Yang State Key Laboratory of Powder metallurgy School of Physics and ElectronicsCentral South UniversityChangsha 410083HunanChina Department of materials Science and Engineering City University of HongkongHongkong SAR 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054SichuanChina

Power conversion efficiency(PCE) of perovskite solar cells(PSC) has been skyrocketed to certified 25.5% owing to their improved and tunable optoelectronic properties. Although, various strategies have been adopted to date regarding PCE and stability enhancement within PSC technology, certain instability factors(moisture, heat, light) are hindering their commercial placement. Recently, all-inorganic PSCs got hype in the photovoltaic research community after they attained PCE > 20% and due to their significant endurance against heat and light mishmashes, but there only left moisture sensitivity as the only roadblock for their industrial integration. Here, we review the recent progress of additive inclusion into allinorganic(CsPbX_(3)) PSCs to stabilize their intrinsic structure and to withstand the performance limiting factors. We start with the detailed description of chemical instability of different perovskite compositions, phase segregation, and how organic molecules and dyes help to repair the structural defects to improve the overall PCE and stability of PSCs. moisture endurance as a result of chemical passivation through organic additives, low-dimensional inorganic PSCs to enhance device stability and scalable fabrication of CsPbX_(3) PSCs are also reviewed. The challenges of module degradation and design implications with proposed strategies and outlook are interpreted in the ending phrases of this review.

关键词： Perovskite solar cells Organic additives Chemical instability Phase segregation modules

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First principle studies of structural, elastic, electronic and optical properties of Zn-chalcogenides under pressure

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Journal of Semiconductors 2014年第7期35卷 1-9页

作者： muhammad bilal m.Shafiq Iftikhar Ahmad Imad khan Department of Physics University of MalakandChakdaraPakistan

Structural, elastic, electronic and optical properties ofzinc-chalcogenides （viz. ZnX, X = S, Se and Te） are studied in zinc-blende structure under hydrostatic pressure using the full-potential linearized augmented plane wave method. Generalized gradient approximation is used for exchange correlation potentials. Pressure-dependent lattice constants and bulk moduli are obtained using the optimization method. Young＇s modulus, Poisson＇s ratio, internal strain parameter and anisotropy are also calculated. The higher values of Young＇s modulus in comparison to the bulk modulus show that these materials are hard to break. Poisson＇s ratio is computed for the first time for these materials to the best of our knowledge and its values show higher ionic contribution in these materials. modified Becke and Johnson （mBJ） method is used to study band gaps, density of states, dielectric function and refractive index. Electronic study shows direct band gaps convert to indirect band gaps with increasing pressure in the case of ZnS and ZnTe. We compared our results with other theoretical and experimental results. Our results are far better than other theoretical results because mBJ is the best technique to treat Ⅱ-Ⅵ semiconductors.

关键词： semiconductors elastic properties electronic band structures

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Computational solution of an acid-mediated tumor-growth radial model under logistic growth regimes for normal and cancer cells

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International Journal of Biomathematics 2023年第3期16卷 45-62页

作者： Farhan khan mudassar Abbas Jorge E.macias-Diaz muhammad bilal khan Safar m.Alghamdi Department of mathematics and Computer Sciences University of Palermo 90133 PalermoItaly Department of mathematics and Applications “R.Caccioppoli”University of Naples Federico iiVia Cintia 80126NaplesItaly Department of mathematics School of Digital Technologies Tallinn University10120 TallinnEstonia Departamento de Matematicas y Fisica Universidad Autonoma de Aguascalientes AguascalientesAgs.20131Merico Department of mathematics COmSATS University Islamabad Islambad44000 Pakistan Department of mathematics and Statistics College of Science Taif University Taif 21944Saudi Arabia

Tumor invasion follows a complex mechanism which involves cell migration and *** study the processes in which primary and secondary metastases invade and damage the normal cells,mathematical models are often extremely *** this paper,we present a mathematical model of acid-mediated tumor growth consisting of radially symmetric reaction-diffusion *** assumption on the radial symmetry of the solutions is imposed here in view that tumors present spherical symmetry at the microscopic ***,we consider various empirical mechanisms which describe the propagation of tumors by considering cancer cells,normal cells,and the concentration of H+*** other assumptions,we suppose that these components follow logistictype growth ***,this is an important difference with respect to various other mathematical models for tumor growth available in the ***,we also add competition terms of normal and tumor cells *** carry out a balancing study of the equations of the model,and a numerical model is proposed to produce *** practical remarks derived from our assumptions are provided in the discussion of our simulations.

关键词： Cancer growth model radially symmetric system acid-mediated tumor growth computer simulations

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Pollution of water resources due to industrialization in arid zone of Rajasthan, India

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Journal of Environmental Sciences 2001年第2期13卷 218-223页

作者： khan m A Central Arid Zone Research Institute 印度 342003

In arid zone of Rajasthan, India, nearly 21,900 hm2 area along Bandi River has been affected by industrial effluent discharge into the river bed. Analysis of groundwater samples collected from these sites revealed that area at the vicinity of the river is most affected. The effluent water in the river is the main source of contamination of ground water. Concentration of sodium and chloride has been higher with proportional increase in TDS and EC value in the area within zone A (river water), zone B (0-100 m), zone C (101-250 m), zone D (251-500 m) and zone E (501-1000 m). There is decreasing trend in ion concentration from zone A to zone E and F, reflecting reduced effect of effluents. The pollution of water resources has resulted in the degradation of other natural resources such as land, soil and vegetation. Nearly 4463 hm2 (20.38%) area has been very severely affected. EC value at places exceeds 20 dS/m. Soil and land have become hard, compact and saline. Cropping intensity has declined. In the area (3633 hm2) affected severely, the EC value of water varies from 10-20 dS/m. Irrigation with such water has created problem of salinity, sodicity in soils. Herbal biomass declined considerably. Double cropping has almost stopped. Almost 8494 hm2 area is affected moderately. EC of ground water varies from 5 to 10 dS/m. Fragile surface crust, declining trend of phosphorous and potassium in the soil, poor density of natural vegetation are significant manifestations. Apart from this, 5305 hm2 area is slightly affected.

关键词： India Pollution of water resources due to industrialization in arid zone of Rajasthan

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Heart Disease Risk Prediction Expending of Classification Algorithms

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Computers, materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha mary bilal khan Abdullah A.Asiri Fazal muhammad Salman khan Samar Alqhtani Khlood m.mehdar Hanan Talal Halwani muhammad Irfan Khalaf A.Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied medical Science Najran University Kingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early *** failure is on the rise as a result of today’s *** healthcare business generates a vast volume of patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful *** methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning *** a comparative analysis,mean Absolute Error(mAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector machine(SVm).Overall,the SVm classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andmAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from *** SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for mAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

关键词： Heart disease heart disease datasets evaluation measures classification techniques

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Advancements in biocatalysis:From computational to metabolic engineering

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Chinese Journal of Catalysis 2018年第12期39卷 1861-1868页

作者： Aqib Zafar khan muhammad bilal Tahir Rasheed Hafiz m.N.Iqbal State Key Laboratory of microbial metabolism School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China School of Life Science and Food Engineering Huaiyin Institute of TechnologyHuaian 223003JiangsuChina School of Chemistry&Chemical Engineering State Key Laboratory of Metal Matrix CompositesShanghai Jiao Tong UniversityShanghai 200240China Tecnologico de monterrey School of Engineering and SciencesCampus MonterreyAve.Eugenio Garza Sada 2501MonterreyN.L.CP 64849Mexico

Through several waves of technological research and un‐matched innovation strategies,bio‐catalysis has been widely used at the industrial *** of the value of enzymes,methods for producing value‐added compounds and industrially‐relevant fine chemicals through biological methods have been developed.A broad spectrum of numerous biochemical pathways is catalyzed by enzymes,including enzymes that have not been ***,low catalytic efficacy,low stability,inhibition by non‐cognate substrates,and intolerance to the harsh reaction conditions required for some chemical processes are considered as major limitations in applied bio‐***,the development of green catalysts with multi‐catalytic features along with higher efficacy and induced stability are important for bio‐*** of computational science with metabolic engineering,synthetic biology,and machine learning routes offers novel alternatives for engineering novel ***,we describe the role of synthetic biology and metabolic engineering in *** learning algorithms for catalysis and the choice of an algorithm for predicting protein‐ligand interactions are *** importance of molecular docking in predicting binding and catalytic functions is ***,we describe future challenges and perspectives.

关键词： Biocatalysis Enzyme metabolic engineering Synthetic biology

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Liver Ailment Prediction Using Random Forest model

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Computers, materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal muhammad bilal khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar m Alqhtani muhammad Irfan Khlood m mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(mL)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of multi-Layer Perceptron(mLP),Average One Dependency Estimator(A1DE),Support Vector machine(SVm),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root mean Squared Error(RmSE),accuracy,recall,precision,specificity,matthew’s Correlation Coefficient(mCC),F-measure,and *** has an RRSE performance of 87.6766 and an RmSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Quantum Stackelberg Duopoly in a Noninertial Frame

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Chinese Physics Letters 2011年第7期28卷 5-8页

作者： Salman khan m.Khalid khan Department of Physics Quaid-i-Azam UniversityIslamabad 45320Pakistan

We study the influence of the Unruh effect on quantum Stackelberg *** is shown that the acceleration of a noninertial frame strongly affects the payoffs of the *** validation of the subgame perfect Nash equilibrium is limited to a particular range of acceleration of the noninertial *** benefit of the initial state entanglement in the quantum form of the duopoly in the inertial frame is adversely affected by the *** duopoly can become a follower advantage only in a small region of the acceleration.

关键词： acceleration Stackelberg Quantum

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