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基于模型的汽车电动助力转向系统故障诊断

汽车工程 2019年第7期41卷 839-850页

作者：陈奇汪金成 Qadeer ahmed 姚志刚陈无畏合肥工业大学机械工程学院合肥230009 美国俄亥俄州立大学汽车研究中心哥伦布43212 合肥工业大学车辆与交通工程学院合肥230009

为实现电动助力转向系统(EPS)的状态监测和关键故障诊断,本文中引入基于模型的结构分析法(sa)故障诊断理论,结合EPS数学模型和系统关键故障,建立系统故障数学模型;利用sa方法中的系统结构表征、DM分解和故障隔离矩阵分析其故障的可检测... 详细信息

为实现电动助力转向系统(EPS)的状态监测和关键故障诊断,本文中引入基于模型的结构分析法(sa)故障诊断理论,结合EPS数学模型和系统关键故障,建立系统故障数学模型;利用sa方法中的系统结构表征、DM分解和故障隔离矩阵分析其故障的可检测和可隔离性,获得EPS故障诊断系统最优传感器配置,并得到5组结构最小型超定方程集(MSO sets);设计5组MSO残差,并搭建MATLAB/Simulink故障诊断系统模型,检验5组残差的信号特征和故障状态,验证EPS故障诊断设计系统的有效性,为后续的试验验证奠定基础。

关键词：电动助力转向系统故障诊断故障模型结构分析故障检测与识别系统

Molecular Phylogeny of Walterinnesia aegyptia (Reptilia, Ela.idae) isolated from Ha.il Province, saudi Ara.ia

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Open Journal of Applied Sciences 2022年第10期12卷 1661-1672页

作者： ahmed Mohajja Alshamma.i ahmed Badry Bander hamad Aloufi Eman El-Abd Biology Department Faculty of Science University of Ha’il Ha’il Saudi Arabia Department of Zoology Faculty of Science Al-Azhar University Cairo Egypt Head of the Department of Ra.ia.ion Sciences Medical Research Institute Alexandria University Alexandria Egypt

Walterinnesia aegyptia.is one of the most venomous snakes belonging to the fa.ily Ela.idae found in the Middle East and Africa. in addition to its ecological importance, it is accused of millions of deaths due to snakebites. Because molecular identifica.ion of snakes is crucial for the antivenom drug industry, mitochondrial genes are used to identify, characterize, and infer genetic diversity among different venomous snake species. Data of Walterinnesia collected from samples across saudi Ara.ia were compared based on the mitochondrial 16S and 12S rRNA sequences with other Ela.idae related taxa to assess the phylogenetic rela.ionship. The phylogenetic analysis strongly supports the monophyly of the genus Walterinnesia based on two genes that represent different species of Ela.idae. in addition, a close rela.ionship between Walterinnesia aegyptia and W. morga.i was found. Our molecular data showed that W. morga.i from Riyadh, saudi Ara.ia, is nearly genetically identical (D = 0) with W. aegyptia from Ha.il and Riyadh, saudi Ara.ia, and Sinai, Egypt. Further study is required based on more material and detailed morphological and genetic analysis.

关键词： Ela.idae MtDNA Phylogeny saudi Ara.ia Walterinnesia aegyptia

少数民族开放教育教学模式的构建及其实现——以内蒙古电大蒙汉双语教育为例

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现代远距离教育 2012年第6期 27-32页

作者：哈萨史亚飞七十三内蒙古广播电视大学内蒙古呼和浩特010051 内蒙古师范大学内蒙古呼和浩特010020

现代远程开放教育的办学形式突破了时空限制,使求学者在一定的条件下随时随地选择适合自己的学习活动,很适合边疆地区的少数民族学员,给他们创造了接受高等教育的均等机会。本文基于现代远程开放教育的特点,结合我国边疆少数民族地区的... 详细信息

现代远程开放教育的办学形式突破了时空限制,使求学者在一定的条件下随时随地选择适合自己的学习活动,很适合边疆地区的少数民族学员,给他们创造了接受高等教育的均等机会。本文基于现代远程开放教育的特点,结合我国边疆少数民族地区的实际和蒙汉双语教育的特殊性,提出了少数民族现代远程开放教育教学模式构建的基本要素及其实现条件,阐述了相关条件建设是确保教学模式运行的基础与保障。

关键词：少数民族开放教育教学模式

Artificial intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

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Computers, Materials & Continua 2022年第4期71卷 143-157页

作者： Anwer Mustafa Hilal Badria Sulaiman Alfurhood Fahd N.Al-Wesa.i Manar ahmed hamza Mesfer Al Duhayyim Huda G.iskandar Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and iT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia

Smart city promotes the unifica.ion of conventional urban infrastructure and informa.ion technology (iT) to improve the qua.ity of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collabora.ion among the public as well as private sectors to install iTplatforms to collect and exa.ine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (Ai) based tools to handlehealthcare crisis situa.ions in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (sa) in social networking can provide valuableinforma.ion regarding public opinion towards government actions. With thismotiva.ion, this paper presents a new Ai based sa tool for healthcare crisismanagement (Aisa-HCM) in smart cities. The Aisa-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COViD-19. The proposed Aisa-HCM technique involves distinct opera.ions such as pre-processing, feature extraction, and classifica.ion. Besides,brain storm optimiza.ion (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to va.ious classes. The use of BSO and BAsalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance va.ida.ion of the Aisa-HCMtechnique takes place using Twitter data and the outcomes are exa.inedwith respect to va.ious measures. The experimental outcomes highlighted theenhanced performance of the Aisa-HCM technique over the recent state ofart sa approaches with the ma.imum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.

关键词： Smart city sentiment analysis artificial intelligence healthcare management metaheuristics deep learning parameter tuning

Artificial intelligence Enabled Apple Leaf Disease Classifica.ion for Precision Agriculture

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Computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesa.i Ama.i Abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.Elta.ir Manar ahmed hamza Abu sarwar Zama.i Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and iT Sana’a UniversityYemen Department of informa.ion Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and *** recent developments of computer vision and artificial intelligence(Ai)techniques find a way for effective detection of plants,diseases,weeds,pests,*** the other hand,the detection of plant diseases,particularly apple leaf diseases using Ai techniques can improve productivity and reduce crop ***,earlier and precise apple leaf disease detection can minimize the spread of the *** works make use of tra.itional image processing techniques which cannot assure high detection rate on apple leaf *** this motiva.ion,this paper introduces a novel Ai enabled apple leaf disease classifica.ion(AiE-ALDC)technique for precision *** proposed AiE-ALDC technique involves orienta.ion based data augmenta.ion and Gaussian filtering based noise removal *** addition,the AiE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature ***,water wave optimiza.ion(WWO)technique is employed as a hyperparameter optimizer of the CapsNet ***,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf *** design of AiE-ALDC technique incorpora.ing theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the *** range of experiments was performed to showcase the supremacy of the AiE-ALDC *** experimental results demonstrate the promising performance of the AiEALDC technique over the recent state of art methods.

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmenta.ion

An Optimized Deep Learning Model for Emotion Classifica.ion in Tweets

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Computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesa.i Yash Singh Patha.ia Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar ahmed hamza Mohammad Mahza.i Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and iT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The task of automa.ically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from na.ional leaders to the average *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** analysis in Twittermitigates the va.ious issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combina.ion of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare va.ious deep learning and machine learning models and categoriza.ion based on the techniques *** main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content informa.ion in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better *** step of our mentioned technique is to preprocess the data.in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关键词： Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classifica.ion

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Computers, Materials & Continua 2022年第6期71卷 5699-5715页

作者： Mesfer Al Duhayyim Taiseer Abdalla Elfa.il Eisa Fahd NAl-Wesa.i Abdelza.ir Abdelmaboud Manar ahmed hamza Abu sarwar Zama.i Mohammed Rizwanullah Radwa Marzouk Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of informa.ion Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and iT Sana’a UniversitySana’aYemen Department of informa.ion Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of informa.ion Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Mathema.ics Faculty of ScienceCairo UniversityGiza12613Egypt Corresponding Author:Manar Ahmed Hamza.Email:ma.hamza@psau.edu.sa

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal *** understandings obtained from the data can assist municipal authorities handle assets and services *** the same time,the massive increase in environmental pollution and degrada.ion leads to ecological imbalance is a hot research ***,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of *** of the commonly available wastes are paper,paper boxes,food,glass,*** order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and *** to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classifica.ion becomes possible by the identifica.ion and detection of *** this aspect,this paper designs an intelligence DRL based recycling waste object detection and classifica.ion(iDRL-RWODC)model for smart *** goal of the iDRLRWODC technique is to detect and classify waste objects using the DL and DRL *** iDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object *** addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a ***,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet *** order to ensure the enhanced waste classifica.ion performance of the iDRL-RWODC technique,a series of simula.ions take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques

关键词： Smart cities deep reinforcement learning computer vision image classifica.ion object detection waste management

Green-synthetized selenium nanoparticles using berberine as a promising anticancer agent

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Journal of integra.ive Medicine 2022年第1期20卷 65-72页

作者： Mohamed S.Othman Sofian T.Obeidat Amal H.Al-Baga.i Mohamed A.Fareid Alaa Fehaid ahmed E.Abdel Moneim Ba.ic Sciences Department Deanship of Preparatory YearUniversity ofHa'ilHail 2240Saudi Arabia Faculty of Biotechnology October University for Modem Science and ArtsGiza 12451Egypt Chemistry Department Faculty of ScienceUniversity of Ha%Hail 2240Saudi Arabia Department of Botany and Microbiology Faculty of ScienceAl-Azhar UniversityCairo 35527Egypt Forensic Medicine and Toxicology Department Faculty of Veterinary MedicineMansoura UniversityDakahlia 35516Egypt Zoology and Entomology Department Faculty of ScienceHelwan UniversityCairo 11795Egypt

Objective: The chemo-preventa.ive and therapeutic properties of selenium nanoparticles(Se NPs) have been documented over recent decades and suggest the potential uses of Se NPs in medicine. Biogenic Se NPs have higher biocompa.ibility and sta.ility than chemically synthesized nanoparticles, which enhances their medical applica.ions, especially in the field of cancer therapy. This study evaluated the potential of green-synthetized Se NPs by using berberine(Ber) as an antitumor agent and elucidated the mecha.ism by which these molecules combat Ehrlich solid tumors(ESTs).Methods: Se NPs containing Ber(Se NPs-Ber) were synthesized using Ber and Na2 Se O3 and characterized with Fourier transform infrared spectroscopy. Sixty male Swiss albino mice were then acclima.ized for one week, injected with Ehrlich ascites tumor cells, and divided into four groups: EST, EST + cispla.in(5 mg/kg), EST + Ber(20 mg/kg), and EST + Se NPs-Ber(0.5 mg/kg). At the end of a 16-day observa.ion period, 12 mice from each group were eutha.ized to analyze differences in the body weight, tumor size,gene expression, and oxida.ive stress markers in the four groups. Three mice from each group were kept a.ive to compare the survival ***: Treatment with Se NPs-Ber significantly improved the survival rate and decreased the body weight and tumor size, compared to the EST group. Se NPs-Ber reduced oxida.ive stress in tumor tissue,as indicated by a decrease in the lipid peroxida.ion and nitric oxide levels and an increase in the glutathione levels. Moreover, Se NPs-Ber activated an apoptotic cascade in the tumor cells by downregula.ing the B-cell lymphoma 2(Bcl-2) expression rate and upregula.ing the Bcl-2-associated X protein and caspase-3 expression rates. Se NPs-Ber also considerably improved the histopathological altera.ions in the developed tumor tissue, compared to the EST ***: Our study provides a new insight into the potential role of green-synthesized Se NPs by using Ber as

关键词： Selenium nanoparticles Berberine Ehrlich solid tumor Oxida.ive stress Apoptosis

Optimal Hybrid Feature Extraction with Deep Learning for COViD-19 Classifica.ions

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Computers, Materials & Continua 2022年第6期71卷 6257-6273页

作者： Majdy M.Elta.ir ibra.im Abuna.i Fahd NAl-Wesa.i Anwer Mustafa Hilal a.il Yousif Abdelwahed Motwakel Mesfer Al Duhayyim Manar ahmed hamza Department of informa.ion Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of informa.ion Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and iT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Novel corona.irus 2019(COViD-19)has affected the people’s health,their lifestyle and economical status across the *** applica.ion of advanced Artificial intelligence(Ai)methods in combina.ion with ra.iological ima.ing is useful in accurate detection of the *** also assists the physicians to take care of remote villages *** current research paper proposes a novel automatedCOViD-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray *** objective of the presented technique is for performing binary and multi-class classifica.ion of COViD-19 analysis from chest X-ray *** presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering(MF)-based pre-processed,feature extraction and finally,binary(COViD/Non-COViD)and multiclass(Normal,COViD,saRS)***,in OHFE-based feature extraction,Gray Level Co-occurrence Matrix(GLCM)and Histogram of Gra.ients(HOG)are integrated *** presented OHFE-ODNN model includes Squirrel Search Algorithm(Ssa)for finetuning the parameters of *** performance of the presented OHFEODNN technique is conducted using chest x-rays *** presented OHFE-ODNN method classified the binary classes effectively with a ma.imumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.

关键词： COViD-19 classifica.ion deep learning ra.iological images