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Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.Nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Ishfaq Yaseen Abu sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

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2D seismic interpretation of Sawan gas field integrated with petrophysical analysis:A case study from Lower Indus Basin,Pakistan

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Energy Geoscience 2023年第2期4卷 35-48页

作者： Abd Ur Rehman Khan Muhammad Amar Gul Rizwan sarwar Awan Ashar Khan Khawaja Hasnain Iltaf Sibt.E Hassan Butt Oil and Gas Development Company IslamabadPakistan CAS Key Laboratory of Crust-Mantle Materials and Environments University of Science and Technology of ChinaHefei230026China School of Resources and Environmental Engineering Hefei University of TechnologyHefei230009China Water and Power Development Authority Pakistan Department of Earth&Environmental Sciences The University of Texas at ArlingtonUnited States Geological Survey of Pakistan LahorePakistan

The Lower Indus Basin is the leading hydrocarbon-bearing sedimentary basin in *** study has been conducted on the Sawan gas field located in the Lower Indus Basin,adjacent to a few other wellknown gas fields of Pakistan like Kadanwari,Qadirpur,and Miano gas *** research aims to present the spatial distribution and reservoir potential of the productive zones of the Lower Goru *** present study utilized various two-dimensional(2D)seismic lines and well-log data(Sawan-01 and Sawan-02)to investigate the structural and stratigraphic features of the *** stratigraphic layers are mildly deepening in the southeast *** 2D seismic interpretation of the research area identifies the existence of extensional remanents,i.e.,normal *** extensional structures are associated with horst and graben geometry that acts as a trapping mechanism for *** logs are used to identify the reservoir's diverse lithology and petrophysical *** results indicate fair to good effective porosities,low shale volume,and high hydrocarbon saturation(>55%),signifying good reservoir potential in C interval of the Lower Goru Formation.

关键词： Sawan Seismic Well-logs Goru formation Cretaceous Petrophysics

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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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Computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

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DELLA蛋白生物信息分析及在油菜中的功能研究

DELLA蛋白生物信息分析及在油菜中的功能研究

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作者： Rehman sarwar 江苏大学

学位级别：硕士

上世纪60年代,甘蓝型油菜得到广泛的推广,成为种植最多的油料作物之一。其高品质油可用于食品和制药行业,榨油后的豆粕可用作高蛋白的动物饲料。然而,甘蓝型油菜的产量极易受各种非生物和生物胁迫的影响,如高盐度、干旱、高/低温和病原... 详细信息

上世纪60年代,甘蓝型油菜得到广泛的推广,成为种植最多的油料作物之一。其高品质油可用于食品和制药行业,榨油后的豆粕可用作高蛋白的动物饲料。然而,甘蓝型油菜的产量极易受各种非生物和生物胁迫的影响,如高盐度、干旱、高/低温和病原体侵染。这些胁迫导致世界许多地区油菜的产量下降,并限制了油菜种植面积进一步增加。DELLA蛋白是植物生长的一个重要阻遏物,但有助于植物在持续胁迫条件下生存。虽然DELLA蛋白缺乏DNA结合域,但可以与不同激素家族的转录因子或转录调节因子相互作用。目前,在拟南芥和谷类植物中,DELLA蛋白研究已取得了较大进展。但是,DELLA蛋白在油菜中研究较少,主要由于一下原因:1)涉及多个基因、激素和代谢途径,2)对外部因素的敏感性,3)以及甘蓝型油菜的A和C基因组的同源性。本研究对10个甘蓝型油菜Bna DELLA基因成员的系统发育关系、共基因关系、蛋白质基序、基因结构和顺式元件进行了系统描述和分析。此外,通过荧光定量PCR(q RTPCR)分析了八种不同组织(根、成熟角果、叶、花、花芽、茎、茎尖、种子)中Bna DELLA基因的表达情况。RNA-Seq数据也用来预测不同胁迫条件下的Bna DELLA基因表达模式,例如冷、热、干旱、脱落酸(ABA)、盐度和核盘菌感染。我们通过GO分析以及Bna DELLA基因家族靶mi RNAs来研究Bna DELLA基因功能。这些分析结果丰富了我们对甘蓝型油菜Bna DELLA基因的功能研究,也有助于我们研究具有更耐逆境的甘蓝型油菜。本研究主要阐述Bna DELLA基因家族中的两个成员Bna RGL1和Bna RGL2的功能。我们对Bna RGL1 and Bna RGL2的进化关系和基因组结构分析表明,它们与拟南芥中At DELLA RGL1和RGL2基因同源性很高,表明它们具有相同的功能和结构。此外,通过BLAST和MCScan X方法,我们发现在甘蓝型油菜加倍过程中,片段和串联重复事件是Bna RGL1和Bna RGL2产生差异的主要原因。Bna RGL1和Bna RGL2的基因组结构在家族成员中高度保守,但基序和顺式元件分布不均匀,表明它们的功能有重叠或分化。q RTPCR分析表明Bna RGL1和Bna RGL2分别在花器官和种子中的表达,表明了在胁迫下Bna DELLA基因调节植物生理的起着不同作用。为了研究Bna RGL1和Bna RGL2对胁迫的反应,我们研究了油菜在干旱和盐处理下不同时期这两个基因的表达水平。Bna RGL1和Bna RGL2转录水平在处理2小时和8小时后显著增加,但在16小时处理后降低,这表明了Bna DELLA基因存在通过mi RNAs的转录后调节。此外,我们检测赤霉素处理下种子中Bna DELLA基因的表达水平,这表明Bna RGL1和Bna RGL2在调节细胞伸长和种子休眠中起着负面作用。为了确定和鉴定甘蓝型油菜中Bna RGL1和Bna RGL2的功能,我们克隆了甘蓝型油菜品种‘K407’中这两个基因的全长编码序列并进行基因注释。同时,我们构建了两个基因的过表达、RNAi和CRISPR敲除载体并转化油菜。Bna RGL1和Bna RGL2过表达转基因植株表现出严重的矮化现象,但更耐受干旱胁迫。此外,在过表达转基因植株中,分枝数变多,这表明了Bna RGL2在分枝分化中的发挥作用。Bna RGL1基因的N末端DELLA结构域通过CRISPR编辑技术导致一个碱基的缺失,该突变体的表型与过表达转基因植株类似,表明对赤霉素降解的不敏感反应。而Bna RGL2基因的C末端GRAS结构域中的一个碱基的缺失则表现对赤霉素的敏感反应,表明了DELLA结构域在Bna DELLAs中的作用。此外,Bna RGL2基因RNAi转基因植株表现为早花现象。这些结果表明了Bna RGL1和Bna RGL2蛋白的功能在油菜中有分化。此外,我们还通过RLM-RACE验证了Bna RGL1的mi RNA切割位点,表明bnami RNA290不依赖赤霉素而靶向Bna RGL1。随后,亚细胞定位表明Bna RGL1和Bna RGL2定位在烟草叶片和拟南芥原生质体的细胞核中,这与先前的研究一致,并进一步表明了Bna RGL1和Bna RGL2基因的转录调节作用。在这项研究中,我们通过基因工程来产生具有发芽率高和抗倒伏能力强,并能耐受恶劣环境条件的半矮秆油菜。我们的研究结果可以为油菜分子设计育种的应用提供了依据,并为油菜生产的遗传改良奠定了理论基础。

关键词：油菜赤霉素基因组研究 DELLA

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Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

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Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed SAlmasoud Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Mesfer Al Duhayyim Asma Abbas Hassan Elnour Manar Ahmed Hamza Abdelwahed Motwakel Abu sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop *** of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification *** this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and *** aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient *** addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected *** study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the ***,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for *** order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was *** results were assessed under different *** obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization

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Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

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Computers, Materials & Continua 2022年第6期71卷 5979-5994页

作者： Anwer Mustafa Hilal Fahd NAl-Wesabi Mohamed Tahar Ben Othman Khaled Mohamad Almustafa Nadhem Nemri Mesfer Al Duhayyim Manar Ahmed Hamza Abu sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer Science College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Information Systems College of Computer and Information SystemsPrince Sultan UniversitySaudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision *** the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare *** this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare *** initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried *** addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature ***,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

关键词： Cognitive internet of things machine learning parameter tuning alzheimer’s disease healthcare decision making

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Genetic variability predicting breeding potential of upland cotton(Gossypium hirsutum L.)for high temperature tolerance

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Journal of Cotton Research 2023年第2期6卷 81-97页

作者： FAROOQ Amjad SHAKEEL Amir SAEED Asif FAROOQ Jehanzeb RIZWAN Muhammad CHATTHA Waqas Shafqat sarwar Ghulam RAMZAN Yasir Cotton Research Station Ayub Agricultural Research InstituteFaisalabad 38000Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38040Pakistan

Background High temperature stress at peak flowering stage of cotton is a major hindrance for crop *** study aimed to increase genetic divergence regarding heat tolerance in newly developed cultivars and *** cotton genotypes and 40 F1(hybrids)were tested under field conditions following the treatments,viz.,high temperature stress and control at peak flowering stage in August and October under April and June sowing,*** The mean squares revealed significant differences among genotypes,treatments,genotype×treatment for relative cell injury,chlorophyll contents,canopy temperature,boll retention and seed cotton yield per *** genetic diversity among 50 genotypes was analyzed through cluster analysis and heat susceptibility index(HSI).The heat tolerant genotypes including FH-Noor,NIAB-545,FH-466,FH-Lalazar,FH-458,NIAB-878,IR-NIBGE-8,Weal-AGShahkar,and heat sensitive,i.e.,CIM-602,Silky-3,FH-326,SLH-12 and FH-442 were hybridized in line×tester fashion to produce F1 *** breeding materials’populations(40 F1)revealed higher specific combining ability variances along with dominance variances,decided the non-additive type gene action for all the *** best general combining ability effects for most of the traits were displayed by the lines,i.e.,FH-Lalazar,NIAB-878 along with testers FH-326 and *** combining ability effects and better-parent heterosis were showed by the crosses,viz.,FH-Lalazar×Silky-3,FH-Lalazar×FH-326,NIAB-878×Silky-3,and NIAB-878×FH-326 for seed cotton yield and yield contributing traits under high temperature *** Heterosis breeding should be carried out in the presence of non-additive type gene action for all the studied *** best combiner parents with better-parent heterosis may be used in crossing program to develop high yielding cultivars,and hybrids for high temperature stress tolerance.

关键词： High temperature Upland cotton Peak flowering Heterosis Gene action Combining ability

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CEO个人特征对中国并购的影响

CEO个人特征对中国并购的影响

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作者： Muhammad sarwar 厦门大学

学位级别：硕士

本文考察了首席执行官的个人特征对他们在中国转型经济中的并购决策模式的影响。通过利用23542公司年的面板数据发现,接近退休年龄的CEO已经完成了大部分成功收购。但是事件研究分析显示,老年CEO被视为价值破坏的来源,而年轻CEO则获得52... 详细信息

本文考察了首席执行官的个人特征对他们在中国转型经济中的并购决策模式的影响。通过利用23542公司年的面板数据发现,接近退休年龄的CEO已经完成了大部分成功收购。但是事件研究分析显示,老年CEO被视为价值破坏的来源,而年轻CEO则获得52%的公告回报,且结果具有统计显着性。这一发现与私人合并成本假说是一致的。首席执行官的教育水平和任职期限已经对中国的并购产生了负面影响,而受到更好管理的企业受到年轻首席执行官的更多收购。与首席执行官有政治联系的公司遇到的CEO个人特征对中国和国有企业(国有企业)收购的影响相对较小。首席执行官的个人特征对并购的决策过程没有影响。总体而言,本研究为各利益相关方在并购前夕更好地调整其利益提供了借鉴意义,同时也为中国转型经济下CEO们的个人特征的复杂现象提供了理解。

关键词：新兴市场个人特征合并&收购退休企业管理

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基于多晶硅太阳能电池和阵列式单晶硅太阳能电池探测器的无线光通信

基于多晶硅太阳能电池和阵列式单晶硅太阳能电池探测器的无线光通...

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作者： sarwar Rohail 浙江大学

学位级别：硕士

无线光通信(OWC)是一种主要应用于空中和水下的通信技术,目前前景广阔且发展迅速。在过去的二十年间,通过射频(RF)通信的用户数量迅速增加带来通信数据流激增,这导致现有带宽越来越不能满足日益增长的流量需求。由OWC衍生的可见光通信(V... 详细信息

无线光通信(OWC)是一种主要应用于空中和水下的通信技术,目前前景广阔且发展迅速。在过去的二十年间,通过射频(RF)通信的用户数量迅速增加带来通信数据流激增,这导致现有带宽越来越不能满足日益增长的流量需求。由OWC衍生的可见光通信(VLC)和水下无线光通信(UWOC)以及无线通信(UWOC),都可以克服射频通信的局限性,承载庞大的用户通信数据流。利用这些技术,我们可以将现有的照明基础设施重新加以利用来实现通信,因此相比传统方式可以花费更低的成本、更少的时间和精力。VLC和UWOC使用发光二极管(LED)和激光二极管(LD)传输数据,利用光电二极管(PIN)或雪崩光电二极管(APD)作为光电探测器。但是在实际应用过程中存在以下困难。1它们需要链路对准凸透镜来聚光。2在这些光电探测器工作的接收侧,必须具有偏置电路和放大器来满足外部大功率要求。这一部分内容非常具有挑战性。在本论文中,我们使用现有低成本太阳能电池作为可见光通信(VLC)和水下无线光通信(UWOC)的光电探测器,提出了一种新颖经济的自供电太阳能电池技术。在一个80厘米的水下信道中,我们利用单多晶太阳能电池,基于UWOC设计的电路成功实现了峰值电压440mV音频信号的发送和接收。然后,我们利用单多晶太阳能电池作为光学检测器,用16-QAM OFDM进行了可见光通信的实验研究。成功实现了 2米信道空气信道中的通信,数据速率为15.03 Mbit/s,误码率为1.6883 x 10-3。最后,使用单晶太阳能电池阵列作为光学探测器,我们分别在7米水下通道上使用16-QAM和32-QAM OFDM实现了 40.0 Mbit/s和44.0 Mbit/s的数据速率。作为光学探测器的低成本单晶太阳能电池用于设计实验中的阵列,其具有20mm×20mm的大接收面积和20°的接收角。在7米水箱中生成水波以模拟真实的海洋场景,以测量作为光学探测器的阵列太阳能电池的性能。使用16-QAM OFDM的数据速率为36.0 Mbit/s,最大比率组合(MRC)和等增益组合(EGC)的测量误码率分别为6.40 x 10-4和6.91 x 10-4。在我们的实验演示中,利用三种不同尺寸和两种类型的太阳能电池材料,例如聚-25mm×30mm,单-20mm×20mm和单-10mm× 10mm,来研究空气和水下通道上的通信链路。

关键词：无线光通信信号传输数据速率光学探测器

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Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

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Computers, Materials & Continua 2022年第5期71卷 3853-3867页

作者： Anwer Mustafa Hilal Imène ISSAOUI Marwa Obayya Fahd N.Al-Wesabi Nadhem NEMRI Manar Ahmed Hamza Mesfer Al Duhayyim Abu sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Natural and Applied Sciences Community CollegeQassim UniversityBuraydahSaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a31220Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how prediction is done in AI *** mental disorder,i.e.,Autism Spectral Disorder(ASD)needs to be identified and classified at early stage itself in order to reduce health *** this background,the current paper presents XAI-based ASD diagnosis(XAI-ASD)model to detect and classify ASD *** proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization(BFO)-based Feature Selection(FS)*** addition,Whale Optimization Algorithm(WOA)with Deep Belief Network(DBN)model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of *** order to ensure a better ASD diagnostic outcome,a series of simulation process was conducted on ASD dataset.

关键词： Explainable artificial intelligence autism spectral disorder feature selection data classification machine learning metaheuristics

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