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Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Journal of Dynamics, Monitoring and Diagnostics 2023年第4期2卷 243-251页

作者： Yufei Gui Ziqiang Lang Zepeng Liu hatim laalej Department of Automatic Control and Systems Engineering University of SheffieldSheffield S102TNUK Advanced Manufacturing Research Centre University of SheffieldSheffield S102TNUK

Tool condition monitoring(TCM)is a key technology for intelligent *** objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing ***,an innovative TCM approach based on sensor data modelling and model frequency analysis has been *** from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process ***,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health *** order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency *** feature dimension is reduced by principal component analysis *** the moving average strategy is exploited to generate monitoring variables and overcome the effects of *** milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end *** results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

关键词： intelligent manufacturing multivariate control chart Nonlinear Autoregressive with eXogenous Input modelling Nonlinear Output Frequency Response Functions tool condition monitoring

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Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer

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Computers, Materials & Continua 2022年第5期71卷 2303-2314页

作者： hatim G.Zaini Computer Engineering Department College of Computer and Information TechnologyTaif UniversityAl HuwayaTaif 26571Saudi Arabia

This paper gives and analyses data-driven prediction models for the energy usage of *** utilized include readings of temperature and humidity sensors from a wireless *** building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system *** a mapping function between the input variables and the continuous output variable is the work of *** paper discusses the forecasting framework FOPF(Feature Optimization Prediction Framework),which includes feature selection optimization:by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached.k-nearest neighbors(KNN)Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning *** comparison study showed the powerful,best accuracy and lowest error of KNN with RMSE=***,the suggested ensemble model’s performance is assessed using a one-way analysis of variance(ANOVA)test and the Wilcoxon Signed Rank Test.(Two-tailed P-value:0.0001).

关键词： Prediction optimization appliances energy feature selection

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Strong Band-Edge Emission from ZnS Quantum Dots Stabilized by Gum Arabic

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Chinese Physics Letters 2008年第12期25卷 4480-4481页

作者： hatim Mohamed El-Khair Dirar Department of Physics Faculty of Science and Technology Ai-Neelain University PO Pox 12702 Khartoum 11121 Sudan

ZnS quantum dots （QDs）, prepared by soft-condensation, exhibit robust structure of a quantum size equal 3.13 nm mediated two-dimensional gum Arabic surfactant as characterized by scan tunnelling microscope （STM）. Strong blue-shifted absorption and emission bands are depicted by optical characterization even for the sample stored under ambient condition for two weeks. These enhancements can be attributed to the completely passivated surface trap states by Gum Arabic.

关键词： field emission molybdenum dioxide enhancement factor

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Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

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Computers, Materials & Continua 2023年第5期75卷 3743-3759页

作者： Emad Alsuwat Suhare Solaiman hatim Alsuwat Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)*** to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model *** to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and *** paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the *** goal of this architecture combination is to accurately represent data and create an effective cyber security prediction *** intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware *** data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)*** on the classification output,the decision for the detection of attackers and authorized users will be *** experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.

关键词： Concept drift machine learning DDOS cyber security SAW_WDA MLPGDT

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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

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Computers, Materials & Continua 2021年第6期67卷 3329-3348页

作者： Emad-ul-Haq Qazi Muhammad Hussain hatim A.AboAlsamh Visual Computing Lab Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The detection of alcoholism is of great importance due to its effects on individuals and *** alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a robust AADS is a challenging ***’current designs are based on conventional,hand-engineered methods and restricted *** by the excellent deep learning(DL)success in many recognition tasks,we implement an AAD system based on EEG signals using DL.A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD *** order to solve this problem,we propose a multi-channel Pyramidal neural convolutional(MP-CNN)network that requires a less number of learnable *** the deep CNN model,we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or *** validate the robustness and effectiveness of proposed AADS using KDD,a benchmark dataset for alcoholism detection *** order to find the brain region that contributes significant role in AAD,we investigated the effects of selected 19 EEG channels(SC-19),those from the whole brain(ALL-61),and 05 brain regions,i.e.,TEMP,OCCIP,CENT,FRONT,and *** results show that SC-19 contributes significant role in AAD with the accuracy of 100%.The comparison reveals that the state-of-the-art systems are outperformed by the *** proposed AADS will be useful in medical diagnosis research and health care systems.

关键词： Electroencephalogram convolutional neural network deep learning alcoholism

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Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

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Intelligent Automation & Soft Computing 2023年第3期35卷 2941-2962页

作者： Raqinah Alrabiah Muhammad Hussain hatim A.AboAlSamh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia

The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications(e.g.,sur-veillance and human–computer interaction[HCI]).Images of varying levels of illumination,occlusion,and other factors are captured in uncontrolled *** and facial recognition technology cannot be used on these images because iris texture is unclear in these instances,and faces may be covered by a scarf,hijab,or mask due to the COVID-19 *** periocular region is a reliable source of information because it features rich discriminative biometric ***,most existing gender classiﬁcation approaches have been designed based on hand-engineered features or validated in controlled *** by the superior performance of deep learning,we proposed a new method,PeriGender,inspired by the design principles of the ResNet and DenseNet models,that can classify gender using features from the periocular *** proposed system utilizes a dense concept in a residual *** skip connections,it reuses features on different scales to strengthen dis-criminative *** of the proposed system on challenging datasets indicated that it outperformed state-of-the-art *** achieved 87.37%,94.90%,94.14%,99.14%,and 95.17%accuracy on the GROUPS,UFPR-Periocular,Ethnic-Ocular,IMP,and UBIPr datasets,respectively,in the open-world(OW)*** further achieved 97.57%and 93.20%accuracy for adult periocular images from the GROUPS dataset in the closed-world(CW)and OW protocols,*** results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features,and feminine features can be identiﬁed through the eyebrow,upper eyelids,and corners of the ***,using a whole region without cropping enhances PeriGender’s learning capability,improving its understanding of both eyes’global structure without discontinuity.

关键词： Gender recognition periocular region deep learning convolutional neural network unconstrained environment

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Effect of elevation on floristic diversity,life forms and chorotypes in the AlHada mountain escarpment,Saudi Arabia

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Journal of Mountain Science 2022年第10期19卷 2806-2816页

作者： Mohamed A.FADL hatim M.AL-YASI Emad A.ALSHERIF Department of Biology College of ScienceTaif UniversityTaif 21944Saudi Arabia Department of Biology College of Science and ArtsUniversity of JeddahJeddah 21921Saudi Arabia Department of Botany and Microbiology Faculty of ScienceBeni-Suef UniversityBeni Suef 62521Egypt

Mountains are biodiversity hotspots,and due to their unique microclimatic circumstances,they host a vast range of endemic *** are two main hypotheses,the Rapoport and the mid-domain effect hypotheses,which explain how elevation and species richness are *** current study was conducted in the Al-Hada escarpment,which is a unique area in the *** is located on the border of Eurasia and Africa,where there are a lot of plant species from both *** study aimed to detect the effect of elevation on the floristic composition of the study *** obtained results showed that the Al-Hada escarpment flora consists of 297 species belonging to 194 genera and fifty-seven distinct *** two families,Poaceae and Asteracae,had 22%of the whole recorded *** obtained results showed that with increasing elevation,the numbers of species,genera,and families increased,in accordance with the Rapoport *** numbers of families increased by 62%from the lowest elevation to the highest one,while the numbers of both species and genera increased by more than *** exhibited the maximum number,which was 44%,and Chamaephytes came in second with 25%.Phanerophytes and hemicryptophytes made up 13%and 11%of all life forms,respectively,while geophytes made up just 7%.Monoregional elements represented 33.2%of the total recorded species,where Saharo-Sindian species had the most monoregional species,accounting for around 13%of the total *** the highest elevation,succulents accounted for 7.6%of the research area,whereas Nfixing plants accounted for 6.2%.At the highest elevation,they had the lowest value,and at the middle elevation,they had the highest *** the rise in elevation,the neotropical,endemic,and Mediterranean elements *** and Nfixing species did not show a clear relationship with the elevation but exhibited the lowest value at the highest elevation,and vice *** and geophytes increased while th

关键词： Rapoport hypothesis Mid-domain effect Arid land Mountain Altitude Al-Hada escarpment

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The Buffering analysis to identify common geographical factors within the vicinity of severe injury related to motor vehicle crash in Malaysia

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World Journal of Emergency Medicine 2016年第4期7卷 278-284页

作者： Nik Hisamuddin Rahman Ruslan Rainis Syed hatim Noor Sharifah Mastura Syed Mohamad Department of Emergency Medicine School of Medical Sciences University Sains Malaysia Kota Bharu 16150 Malaysia School of Humanity University Sains Malaysia Penang Malaysia Unit of Biostatistics School of Medical Sciences University Sains Malaysia Kota Bharu 16150 Malaysia Unit of Health Informatics School of Health Sciences University Sains Malaysia Kota Bharu 16150 Malaysia

BACKGROUND:The main objective was to identify common geographical buildup within the100-meter buffer of severely injured based on injury severity score(ISS) among the motor vehicle crash(MVC) victims in ***:This was a prospective cohort study from July 2011 until June 2013 and involved all MVC patients attending emergency departments(ED) of two tertiary centers in a district in Malaysia.A set of digital maps was obtained from the Town Planning Unit of the district Municipal Office(local district map).Vector spaces were spanned over these maps using GIS software(ARCGIS 10.1licensed to the study center),and data from the identified severe injured cases based on ISS of 16 or more were *** analysis was performed and included all events occurring within a 100-meter perimeter around a reference ***:A total of 439 cases were recruited over the ten-month data collection *** two(11%) of the cases were categorized as severe cased based on ISS scoring of 16 and *** buffer analysis looking at the buildup areas within the vicinity of the severely injured locations showed that most of the severe injuries occurred at locations on municipal roads(15,29%),straight roads(16,30%) and within villages buildup(suburban) areas(18,35%).CONCLUSION:This study has successfully achieved its objective in identifying common geographical factors and buildup areas within the vicinity of severely injured road traffic cases.

关键词： Motor vehicle crash Emergency department Injury severity score

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On the Power Performance of Test Statistics for the Generalized Rayleigh Interval Grouped Data

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Open Journal of Statistics 2015年第5期5卷 474-482页

作者： hatim Solayman Migdadi Department of Mathematics Faculty of Science and Information Technology Jadara University Irbid Jordan

In this paper, the weighted Kolmogrov-Smirnov, Cramer von-Miss and the Anderson Darling test statistics are considered as goodness of fit tests for the generalized Rayleigh interval grouped data. An extensive simulation process is conducted to evaluate their controlling of type 1 error and their power functions. Generally, the weighted Kolmogrov-Smirnov test statistics show a relatively better performance than both, the Cramer von-Miss and the Anderson Darling test statistics. For large sample values, the Anderson Darling test statistics cannot control type 1 error but for relatively small sample values it indicates a better performance than the Cramer von-Miss test statistics. Best selection of the test statistics and highlights for future studies are also explored.

关键词： Generalized Rayleigh Distribution Interval Grouped Data Goodness of Fit Tests Empirical Type 1 Error Power Function

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