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The relationship between energy consumption, economic growth and carbon dioxide emissions in Pakistan

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Financial Innovation 2020年第1期6卷 56-68页

作者： muhammad Kamran khan muhammad Imran khan muhammad Rehan School of Economics Northeast Normal UniversityChangchunJilinChina

Developing countries are facing the problem of environmental *** degradation is caused by the use of non-renewable energy consumptions for economic growth but the consequences of environmental degradation cannot be *** primary purpose of this study is to investigate the nexus between energy consumption,economic growth and CO_(2) emission in Pakistan by using annual time series data from 1965 to *** estimated results of ARDL indicate that energy consumption and economic growth increase the CO_(2) emissions in Pakistan both in short run and long *** on the estimated results it is recommended that policy maker in Pakistan should adopt and promote such renewable energy sources that will help to meet the increased demand for energy by replacing old traditional energy sources such as coal,gas,and *** energy sources are reusable that can reduce the CO_(2) emissions and also ensure sustainable economic development of Pakistan.

关键词： Energy consumption Economic growth CO_(2)emissions ARDL

De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization

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Chinese Journal of Electronics 2023年第1期32卷 166-176页

作者： Rizwan Sadiq muhammad Bilal Qureshi muhammad Mohsin khan Dept.of Electrical and Electronic Engineering Koc University Dept.of Electrical and Computer Engineering COMSATS University Dept.of Radiology and Biomedical Imaging Yale University Sino-Pak Center for Artificial Intelligence (SPCAI) Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology (PAF-IAST)

Synthetic aperture radar(SAR) imaging is an efficient strategy which exploits the properties of microwaves to capture images. A major concern in SAR imaging is the reconstruction of image from back scattered signals in the presence of noise. The reflected signal consist of more noise than the target signal and it is a challenging problem to reduce the noise in the collected signal for better reconstruction of an image. Current studies mostly focus on filtering techniques for noise removal. This can result in an undesirable point spread function causing extreme smearing effect in the desired image. In order to handle this problem, a computational technique, particle swarm optimization(PSO) is used for de-noising purpose and later the target performance is further improved by an amalgamation of Wiener ***, to improve the de-noising performance we have exploited the singular value decomposition based morphological filtering. To justify the proposed improvements we have simulated the proposed techniques and results are compared with the conventional existing models. The proposed method revealed considerable decrease in mean square error compared to Wiener filter and PSO techniques. Quantitative analysis of image restoration quality are also presented in comparison with Wiener filter and PSO based on the improvement in signal to noise ratio and peak signal to noise ratio.

关键词： Radar remote sensing Wiener filters Filtering Statistical analysis Radar polarimetry Particle swarm optimization Synthetic aperture radar

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

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Computers, Materials & Continua 2021年第2期66卷 1595-1612页

作者： Kashif Iqbal Sagheer Abbas muhammad Adnan khan Atifa Ather muhammad Saleem khan Areej Fatima Gulzar Ahmad School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Sciences GC UniversityLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Comsats University IslamabadLahore CampusLahore54000Pakistan

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of ***-based target detection and object classification have been improved due to the development of deep learning *** fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or *** target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion *** this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking *** build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking *** augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through *** results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported ***,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space *** results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.

关键词： Smart parking-lot detection deep convolutional neural network data augmentation region-of-interest object detection

Spatio-temporal evolution characteristics and pattern formation of a gas–liquid interfacial AC current argon discharge plasma with a deionized water electrode

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Plasma Science and Technology 2021年第2期23卷 17-29页

作者： Hafiz Imran Ahmad QAZI muhammad Ajmal khan Jianjun HUANG SZU-CASIPP Joint Laboratory for Applied Plasma College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060People's Republic of China Department of Engineering Physics Tsinghua UniversityBeijing 100084People's Republic of China

A discharge ignited by an AC power source in contact with deionized water as one of the electrodes is *** after initiation,the discharge exhibits a unique phenomenon:the gas-phase discharge is extended into the ***,a cone-like structure is observed at the liquid *** monitoring of current–voltage characteristics and liquid properties versus time suggests that the discharge shapes are functions of the liquid *** spatio-temporal profiles indicate the potential effects of water,ambient air impurities,and metastable argon on the discharge *** becomes more obvious near the liquid surface due to increasing production of various transient reactive species such as centerdot OH and NO ***,it is revealed that thermalization of the rotational population distributions of the rotational states(N′⩽6,J′⩽13/2)in the Q1 branch of the OH(A2Σ+,υ′=0→X2Π3/2,υ′′=0)band ro-vibrational system is influenced by the humid environment near the liquid *** addition,the transient behaviors of instantaneous concentrations of long-lived reactive species(LRS)such as H2O2,NO−2,and NO−3 are observed with lengthening the discharge *** production of multiple transient and LRS proposes AC excited gas–liquid argon discharge as a potential applicant in industrial wastewater cleaning,clinical medicine,and agriculture.

关键词： gas–liquid interface discharge emission patterns rotational temperature plasma chemistry

A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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Computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah muhammad Attique khan muhammad Sharif Usman Tariq Aimal khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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Computers, Materials & Continua 2022年第10期73卷 493-511页

作者： muhammad Zubair Rehman muhammad Aamir Nazri Mohd.Nawi Abdullah khan Saima Anwar Lashari Siyab khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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

Numerical Study of Hydrogen Peroxide Thermal Decomposition in a Shock Tube

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Journal of Thermal Science 2017年第3期26卷 235-244页

作者： muhammad Rizwan Bhatti Nadeem Ahmed Sheikh Shehryar Manzoor muhammad Mahabat khan Muzaffar Ali Department of Mechanical Engineering Capital University of Science and Technology Islamabad Pakistan Mechanical Engineering Department University of Engineering and Technology Taxila Pakistan

Hydrogen peroxide(H_2O_2) has its significance during the combustion of heavy hydrocarbons in the internal combustion(IC) engines. Owing to its importance the measurements of H_2O_2 dissociation rate have been reported mostly using the shock tube apparatus. These types of experimental measurements are although quite reliable but require high cost. On the other hand, numerical simulations provide low cost and reliable solutions especially using computation fluid dynamics(CFD) software. In the current study an experimental shock tube flow is modeled using open access platform OpenFOAM to investigate the thermal decomposition of H_2O_2. Using two different convective schemes, limited Linear and upwind, the propagation of shock wave and resultant dissociation reaction are simulated. The results of the simulations are compared with the experimental data. It is observed that the rate constant measured using the simulation data deviates from the experimental results in the low temperature range and approaches the experimental values as the temperature is raised.

关键词： Reaction kinetics Rate constant Numerical simulation Shock wave Reflected shock wave Contact discontinuity Expansion fan Internal flow Compressible flow Gas dynamics

An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks

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Computers, Materials & Continua 2021年第7期68卷 447-470页

作者： muhammad Adil Jehad Ali muhammad Sajjad khan Junsu Kim Ryan Alturki Mohammad Zakarya Mukhtaj khan Rahim khan Su Min Kim Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Engineering Department of AI Convergence NetworkAjou UniversitySuwon16499South Korea Department of Electronics Engineering Korea Polytechnic UniversitySlheungSouth Korea Department of Computer Science Abdul Wali Khan University Mardan23200Pakistan

Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication *** literature various mechanisms addressed the security issue of Industrial IoT networks,but proper maintenance of the performance reliability is among the common *** this paper,we proposed an intelligent mutual authentication scheme leveraging authentication aware node(AAN)and base station(BS)to identify routing attacks in Industrial IoT *** AAN and BS uses the communication parameter such as a route request(RREQ),node-ID,received signal strength(RSS),and round-trip time(RTT)information to identify malicious devices and routes in the deployed *** feasibility of the proposed model is validated in the simulation environment,where OMNeT++was used as a simulation *** compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection,communication cost,latency,computational cost,and *** results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7%.

关键词： Security industrial Internet of Things routing attacks routing protocols base station authentication aware nodes