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Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations

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Chinese Physics Letters 2011年第2期28卷 20-23页

作者： junaid ali khan Muhammad Asif Zahoor Raja Ijaz Mansoor Qureshi Department of Electronic Engineering International Islamic UniversityIslamabadPakistan Department of Electrical Engineering Air UniversityIslamabadPakistan

We present an evolutionary computational approach for the solution of nonlinear ordinary differential equations(NLODEs).The mathematical modeling is performed by a feed-forward artificial neural network that defines an unsupervised *** training of these networks is achieved by a hybrid intelligent algorithm,a combination of global search with genetic algorithm and local search by pattern search *** applicability of this approach ranges from single order NLODEs,to systems of coupled differential *** illustrate the method by solving a variety of model problems and present comparisons with solutions obtained by exact methods and classical numerical *** solution is provided on a continuous finite time interval unlike the other numerical techniques with comparable *** the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.

关键词： technique methods ordinary

Novel Approach for a van der Pol Oscillator in the Continuous Time Domain

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Chinese Physics Letters 2011年第11期28卷 16-19页

作者： junaid ali khan Muhammad Asif Zahoor Raja Ijaz Mansoor Qureshi Department of Electronic Engineering International Islamic UniversityIslamabadPakistan

We investigate the continuous time domain numerical treatment of a van der Pol oscillator,applying the trial solution as an artificial feed-forward neural network model containing unknown adjustable *** optimization of the network is performed by simulated annealing in an unsupervised *** proposed scheme is tested successfully by its application in both non-stiff and stiff *** reliability and effectiveness is validated through comprehensive statistical *** obtained results are in good agreement with the classical RK45 method.

关键词： artificial neural stiff

Crosstalk of liver immune cells and cell death mechanisms in different murine models of liver injury and its clinical relevance

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Hepatobiliary & Pancreatic Diseases International 2017年第3期16卷 245-256页

作者： Hilal Ahmad khan Muhammad Zishan Ahmad junaid ali khan Muhammad Imran Arshad Institute of Microbiology University of Agriculture Faisalabad Pakistan Institute of Pharmacy Physiology and Pharmacology University of Agriculture Faisalabad Pakistan Department of Pathology The University of Faisalabad (TUF) Faisalabad Pakistan

BACKGROUND： Liver inflammation or hepatitis is a result of pluripotent interactions of cell death molecules, cytokines, chemokines and the resident immune cells collectively called as microenvironment. The interplay of these inflammatory mediators and switching of immune responses during hepatotoxic, viral, drug-induced and immune cell-mediated hepatitis decide the fate of liver pathology. The present review aimed to describe the mechanisms of liver injury, its relevance to human liver pathology and insights for the future therapeutic *** SOURCES： The data of mouse hepatic models and rele- vant human liver diseases presented in this review are system- atically collected from PubMed, ScienceDirect and the Web of Science databases published in English. RESULTS： The hepatotoxic liver injury in mice induced by the metabolites of CC14, acetaminophen or alcohol represent ne- crotic cell death with activation of cytochrome pathway, for- mation of reactive oxygen species （ROS） and mitochondrial damage. The Fas or TNF-a induced apoptotic liver injury was dependent on activation of caspases, release of cytochrome c and apoptosome formation. The ConA-hepatitis demonstrat- ed the involvement of TRAIL-dependent necrotic/necroptotic cell death with activation of RIPK1/3. The a-GalCer-induced liver injury was mediated by TNF-a. The LPS-induced hepatitis involved TNF-a, Fas/FasL, and perforin/granzyme cell death pathways. The MHV3 or Poly（I：C） induced liver injury was mediated by natural killer cells and TNF-a signaling. The necrotic ischemia-reperfusion liver injury was mediated by hypoxia, ROS, and pro-inflammatory cytokines; however, necroptotic cell death was found in partial hepatectomy. The crucial role of immune ceils and cell death mediators in viral hepatitis （HBV, HCV）, drug-induced liver injury, non-alcohol- ic fatty liver disease and alcoholic liver disease in human were discussed. CONCLUSIONS： The mouse animal models of hepatitis provide a parallel appro

关键词： liver immunobiology hepatitis therapy mode of cell death

Modeling and Classification of Photovoltaic Hotspot Fault with Shaded and Un-Shaded Conditions Using Mamdani-type Fuzzy Inference

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Modeling and Classification of Photovoltaic Hotspot Fault wi...

作者： junaid ali khan 河北工业大学

学位级别：硕士

Photovoltaic power generation as the main content of the energy revolution has attracted more and more attention and research all over the *** detection techniques play a decisive role in reliability as well as boost the output power and overall performance of a photovoltaic system(PVS).Faults that are shading and hotspots can be found in the PV module(PVM).Shading faults can occur due to continuous changes in the irradiance level of a module,soiling,dust or other shadows can cause such ***(HS)occurs when a cell in a module considerably heats up instead of generating power,and starts consuming power from adjacent cells or dissipates power which further results in reducing optimum power and leading to cell cracking and other degradation *** fault can be further categorized as shaded and unshaded *** HS is an HS fault with shading PV cells while unshaded HS is due to the cell's internal defects such as low *** this work,the photovoltaic system explores techniques to enhance the overall performance of the PVS by detection and classification of *** faults can occur in a module of a PVS which can degrade the system's reliability as well as reduce the optimal *** mitigate these PV faults,investigation of detection and classification techniques is of significance.A hotspot and shading faults are sometimes confusing and are of significance in identifying these *** this thesis,an algorithm has been proposed to identify these *** confusion causes such as environments,shading,and HS are *** the main objection is to classify the HS faults under shaded and un-shaded *** Impacts of HS on the overall performance of a PV system are also *** this thesis,HS fault is categorized as shaded and un-shaded HS,and hotspot in both shaded and un-shaded conditions has been analyzed,and then a technique called Mamdani-type Fuzzy logic system for detection and classification of Shading and HS bot

关键词： Photovoltaic System Hot spot faults Shading faults Faults Detection and Classification Fuzzy logic system

COVID-19 and comorbidities of hepatic diseases in a global perspective

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World Journal of Gastroenterology 2021年第13期27卷 1296-1310页

作者： Aqsa Ahmad Syeda Momna Ishtiaq junaid ali khan Rizwan Aslam Sultan ali Muhammad Imran Arshad Institute of Microbiology University of Agriculture FaisalabadFaisalabad 38040PunjabPakistan Institute of Physiology and Pharmacology University of Agriculture FaisalabadFaisalabad 38040PunjabPakistan

The worldwide outbreak of coronavirus disease 2019(COVID-19) has challenged the priorities of healthcare system in terms of different clinical management and infection transmission, particularly those related to hepatic-disease comorbidities. Epidemiological data evidenced that COVID-19 patients with altered liver function because of hepatitis infection and cholestasis have an adverse prognosis and experience worse health outcomes. COVID-19-associated liver injury is correlated with various liver diseases following a severe acute respiratory syndrome-coronavirus type 2(SARS-CoV-2) infection that can progress during the treatment of COVID-19 patients with or without pre-existing liver disease. SARS-CoV-2 can induce liver injury in a number of ways including direct cytopathic effect of the virus on cholangiocytes/hepatocytes, immune-mediated damage, hypoxia, and sepsis. Indeed, immediate cytopathogenic effects of SARSCoV-2 via its potential target, the angiotensin-converting enzyme-2 receptor, which is highly expressed in hepatocytes and cholangiocytes, renders the liver as an extra-respiratory organ with increased susceptibility to pathological outcomes. But, underlying COVID-19-linked liver disease pathogenesis with abnormal liver function tests(LFTs) is incompletely understood. Hence, we collated COVID-19-associated liver injuries with increased LFTs at the nexus of pre-existing liver diseases and COVID-19, and defining a plausible pathophysiological triad of COVID-19, hepatocellular damage, and liver disease. This review summarizes recent findings of the exacerbating role of COVID-19 in pre-existing liver disease and vice versa as well as international guidelines of clinical care, management, and treatment recommendations for COVID-19 patients with liver disease.

关键词： Liver disease COVID-19 Pathophysiology Epidemiology Prophylaxis

Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

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Computers, Materials & Continua 2022年第5期71卷 3667-3684页

作者： Usra Yasmeen Muhammad Attique khan Usman Tariq junaid ali khan Muhammad Asfand EYar ChAvais Hanif Senghour Mey Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of EE HITEC University TaxilaTaxilaPakistan Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in *** Pakistan,several kinds of fruits are produced and exported the other *** is an important fruit,and its production in Pakistan is higher than the other ***,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this *** manual diagnosis of these diseases required an expert person who is always a time-consuming and costly *** the agriculture sector,deep learning showing significant success in the last five *** research work proposes an automated framework using deep learning and best feature selection for citrus diseases *** the proposed framework,the augmentation technique is applied initially by creating more training data from existing *** were then modifying the two pre-trained models named Resnet18 and Inception *** modified models are trained using an augmented dataset through transfer *** are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few *** experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus *** these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,*** proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

关键词： Citrus diseases data augmentation deep learning features selection features fusion

Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classicatio

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Computers, Materials & Continua 2021年第7期68卷 1099-1116页

作者： Ayesha Bin T.Tahir Muhamamd Attique khan Majed Alhaisoni junaid ali khan Yunyoung Nam Shui-Hua Wang Kashif Javed Department of Computer Science HITEC UniversityTaxila47040Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUUK Department of Robotics SMME NUSTIslamabadPakista

Background:A brain tumor reects abnormal cell ***:Surgery,radiation therapy,and chemotherapy are used to treat brain tumors,but these procedures are painful and *** resonance imaging(MRI)is a non-invasive modality for diagnosing tumors,but scans must be interpretated by an expert ***:We used deep learning and improved particle swarm optimization(IPSO)to automate brain tumor *** scan contrast is enhanced by ant colony optimization(ACO);the scans are then used to further train a pretrained deep learning model,via transfer learning(TL),and to extract features from two dense *** fused the features of both layers into a single,more informative *** IPSO algorithm selected the optimal features,which were classied using a support vector ***:We analyzed high-and low-grade glioma images from the BRATS 2018 dataset;the identication accuracies were 99.9%and 99.3%,***:The accuracy of our method is signicantly higher than existing techniques;thus,it will help radiologists to make diagnoses,by providing a“second opinion.”

关键词： Brain tumor contrast enhancement deep learning feature selection classication

Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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Computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq junaid ali khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

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Computer Systems Science & Engineering 2023年第11期47卷 2397-2415页

作者： Abdul Haseeb Muhammad Attique khan Faheem Shehzad Majed Alhaisoni junaid ali khan Taerang Kim Jae-Hyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser ***,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is *** article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization *** pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature *** transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray *** the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature *** that,an improved whale optimization algorithm is developed for dimensionality *** selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification *** experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent *** on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.

关键词： Knee joints magnetic resonance imaging(MRI) deep learning fusion optimization neural network