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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

Computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque mohammed ali khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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PI-RADS V1和PI-RADS V2对前列腺癌诊断准确性比较研究的系统回顾

PI-RADS V1和PI-RADS V2对前列腺癌诊断准确性比较研究的系统回顾

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作者： mohammed Ameer ali khan Afrose 苏州大学

学位级别：硕士

目的:-通过对文献的系统回顾复习,探讨PI-RADS v1和PI-RADS v2对前列腺癌诊断的准确性。材料及方法:-系统回顾复习文献的来源:MEDLINE、PUBMED、谷歌学术。文献搜索截止日期为2018年3月30日。研究包括:以前列腺系统穿刺活检或前列腺癌... 详细信息

目的:-通过对文献的系统回顾复习,探讨PI-RADS v1和PI-RADS v2对前列腺癌诊断的准确性。材料及方法:-系统回顾复习文献的来源:MEDLINE、PUBMED、谷歌学术。文献搜索截止日期为2018年3月30日。研究包括:以前列腺系统穿刺活检或前列腺癌根治术后标本病理诊断结果为标准,比较PI-RADS v1和PI-RADS v2对前列腺临床显著癌的诊断准确性。结论:-与PI-RADS V1相比,PI-RADS V2因在确定总体评分时标准较为明确,在前列腺癌检出方面更有优势。

关键词： -PI-RADS V1 PI-RADS V2 前列腺癌 mp-MRI DWI DCE-MRI MRS

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On Power Allocation for Transmitter and Receiver Selection in MIMO Fading Channels

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通讯和计算机（中英文版） 2011年第6期8卷 483-489页

作者： Shaik Qadeer Niranjan Kumar Harinath Reddy mohammed Zafar ali khan Dept. of Electrical Engg M.J. College of Engg. Osmania University Hyderabad AP-500034 India IIIT Hyderabad AP-500008 India Dept. of Electrical Engg. HT Hyderabad AP -502205 India

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Post-COVID effect on heart after recovery based on hybrid EfficientNet-DBN with multilevel classification using ECG images

EngMedicine

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EngMedicine 2024年第2期1卷 4-15页

作者： mohammed Abdul Basith ali khan Edara Sreenivasa Reddy Department of Computer Science and Engineering Acharya Nagarjuna UniversityGuntur 522510Andhra PradeshIndia

The highly contagious and dangerous virus known as COVID-19 is caused by severe acute respiratory syndrome(SARS),which is disseminated *** disease is a major cause of death *** year after the COVID-19 pandemic,the risk of heart issues is *** heart issues involve irregular heartbeats,heart failure,which is the inability of the heart to pump correctly,and coronary disease,which builds up in the arteries and causes restriction in blood flow,heart attacks,***,classifying this disease at an early stage is ***,post-COVID effect on the heart after recovery with multilevel classification was performed using the hybrid EfficientNet+Deep Belief Network(EfficientNet+DBN)designed in this *** input image obtained from the dataset was delivered for binary image conversion and then allowed for feature ***,first-level disease classification was performed using the hybrid EfficientNet+DBN to detect whether the disease was in normal or abnormal *** it is categorized as abnormal,a second-level classification is performed using EfficientNet-DBN,which classifies myocardial infarction and COVID-19 *** Pearson's correlation coefficient was used for the post-COVID correlation *** experimental outcome showed that EfficientNet-DBN attained a maximum accuracy of 89.80%,sensitivity of 94.70%,and specificity of 91.80%.

关键词： Heart disease Deep learning(DL) Coronavirus Deep belief network(DBN) Restricted Boltzmann machine(RBM)

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Decoding molecular mechanisms:brain aging and Alzheimer's disease

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Neural Regeneration Research 2025年第8期20卷 2279-2299页

作者： Mahnoor Hayat Rafay ali Syed Hammad Qaiser Mohammad Uzair Khalid Al-Regaiey Roaa Khallaf Lubna Abdullah mohammed Albassam Imdad Kaleem Xueyi Wang Ran Wang Mehwish SBhatti Shahid Bashir Atta-ur-Rahman School of Applied Biosciences(ASAB) National University of Sciences and Technology(NUST)IslamabadPakistan Department of Biotechnology Quaid-i-Azam UniversityIslamabadPakistan Department of Biological Sciences Faculty of Basic&Applied SciencesInternational Islamic University Islamabad(IIUI)IslamabadPakistan Department of Bioengineering King Fahd University of Petroleum and MineralsDhahranSaudi Arabia Department of Physiology College of MedicineKing Saud UniversityRiyadhSaudi Arabia Department of Neurology Neuroscience CenterKing Fahad Specialist HospitalDammamSaudi Arabia Department of Mental Health Neuroscience CenterKing Fahad Specialist HospitalDammamSaudi Arabia Department of Biosciences Commission on Science and Technology for Sustainable Development in the South(COMSATS University)IslamabadPakistan Department of Psychiatry The First Hospital of Hebei Medical UniversityShijiazhuangHebei ProvinceChina Mental Health Institute of Hebei Medical University ShijiazhuangHebei ProvinceChina Department of Neurobiology and Behavior University of CaliforniaIrvineCAUSA Neuroscience Center King Fahad Specialist Hospital DammamDammamSaudi Arabia

The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for *** aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's *** these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging *** these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect *** review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.

关键词： Alzheimer’s disease brain aging cognitive health dementia molecular mechanisms neuronal activity neuroplasticity neurotransmission

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Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

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Computer Systems Science & Engineering 2023年第2期44卷 1679-1689页

作者： Abdullah Alshehri Nayeem khan ali Alowayr mohammed Yahya Alghamdi Information Technology Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Computer Science Department Faculty of Science and Arts at BuljurashiAl Baha UniversityAl Baha65799Saudi Arabia

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior *** framework models the user behavior as sequences of events representing the user activities at such a *** represented sequences are thenﬁtted into a recurrent neural network model to extract features that draw distinctive behavior for individual ***,the model can recognize frequencies of regular behavior to proﬁle the user manner in the *** subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular *** importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the *** detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including ***,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reﬂect a normal network workﬂ*** contrast,the irregular patterns can trigger an alert for a potential *** framework has been fully described where the evaluation metrics have also been *** experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM *** paper has been concluded with pro-viding the potential directions for future improvements.

关键词： Cybersecurity deep learning machine learning user behavior analytics

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Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 315-344页

作者： Nazish Naheed Muhammad Shaheen Sajid ali khan mohammed Alawairdhi Muhammad Attique khan Faculty of Engineering&Information Technology Foundation University IslamabadIslamabadPakistan College of Computing and Informatics Saudi Electronic UniversityRayadhSaudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan

In the area of pattern recognition and machine learning,features play a key role in *** famous applications of features are medical imaging,image classification,and name a few *** the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of *** data repositories contain details information essential to support medical diagnostic decisions and also improve patient care *** the other hand,this growth also made it difficult to comprehend and utilize data for various *** results of imaging data can become biased because of extraneous features present in larger *** selection gives a chance to decrease the number of components in such large *** selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization *** correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast *** paper presents a review of feature selection techniques and attributes selection measures for medical *** review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining *** review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced ***,this review provides the importance of feature selection for correct classification of medical *** the end,critical analysis and future directions are provided.

关键词： Medical imaging imaging data feature selection data mining attribute selection medical challenges future directions

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Deflection behavior of a prestressed concrete beam reinforced with carbon fibers at elevated temperatures

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Frontiers of Structural and Civil Engineering 2019年第1期13卷 81-91页

作者： mohammed FARUQI mohammed Sheroz khan Department of Civil and Architectural Engineering Texas A&M University--KingsvilleTexasUSA

Fiber reinforced polymer(FRP) have unique advantages like high strength to weight ratio,excellent corrosion resistance,improving deformability and cost *** advantages have gained wide acceptance in civil engineering *** tendons for prestressing applications are emerging as one of the most promising technologies in the civil engineering ***,the behavior of such members under the influence of elevated temperatures is still *** knowledge and application of this could lead to a cost effective and practical considerations in fire safety ***,this study examines the deflection behavior of the carbon fiber reinforced polymer(CFRP) prestressed concrete beam at elevated *** this article,an analytical model is developed which integrates the temperature dependent changes of effective modulus of FRP in predicting the deflection behavior of CFRP prestressed concrete beams within the range of practical *** model is compared with a finite element mode (FEM) of a simply supported concrete beam prestressed with CFRP subjected to practical elevated *** addition,comparison is also made with an indirect reference to the real behavior of the *** results of the model correlated reasonably with the finite element model and the real ***,a practical application is provided.

关键词： FRP CFRP concrete elevated temperatures deflections prestress

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Flow Injection Chemiluminescence Method for Nalbuphine Hydrochloride in Pharmaceutical Formulations Using Tris(2,2′-bipyridyl)ruthenium(II)Chloride-diperiodatocuprate(III)Reaction

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Chemical Research in Chinese Universities 2021年第3期37卷 629-638页

作者： AHMED khan MUHAMMAD Asghar mohammed Yaqoob MASOOD Ahmed Siddiqui SAMAR ali Department of Chemistry University of BalochistanSariab RoadQuetta87300Pakistan

A sensitive and selective method employing chemiluminescence(CL)coupled with flow injection(FI)is reported for nalbuphine hydrochloride(NAL)assay in pharmaceutical *** enhancement effect of NAL on the CL reaction between tris(2,2′-bipyridyl)ruthenium(II)chloride-diperiodatocuprate(III){Ru[(bpy)_(3)]^(2+)-Cu(III)complex}in acidic medium is used as analytical *** optimal conditions of the CL reaction were sulfuric acid 1.0×10^(−3) mol/L,Ru[(bpy)s]2+7.5×10^(−5) mol/L,Cu(III)/Ag(III)complexes 4.0×10^(−4)/5.0×10^(−4) mol/L,sample loop volume of 120µL and flow rate of 2.5 mL/*** sensitivities of the method in terms of detection(S/N=3)and quantification(S/N=10)limits are 5×10^(−4) and 0.001 ppm(1 ppm=1 mg/L),*** linear response of the instrument in the form of CL intensity with respect to NAL concentration is over the range 0.001-15.0 ppm(R^(2)=0.9999)with relative standard deviation from 0.8%to 3.2%and injection throughput of 120 injection/*** applications of the method include the quantitative analysis of NAL in pharmaceutical injection *** and the average results of the proposed method are not significantly different from the results of a reported method by applying F-and paired student *** most likely CL reaction mechanism is written in accordance with spectrophotometric and CL studies.

关键词： Ru[(bpy)_(3)]^(2+) Cu(III)/Ag(III)complex Chemiluminescence Flow injection analysis Nalbuphine hydrochloride Pharmaceutical

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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Computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir khan Asad Ullah Fasee Ullah Razaullah khan Inayat khan mohammed ElAffendi Gauhar ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the Computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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