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Defence Technology（防务技术） 2023年第1期19卷 1-11页

作者： Elango Natarajan Lídio Inacio Freitas M.s.santhosh Kalaimani Markandan Ammar abdulaziz Majeed Al-Talib C.s.Hassan Faculty of Engineering Technology and Built EnvironmentUCSI UniversityKuala LumpurMalaysia Dili Institute of Technology DiliTimor-Leste selvam Composite Materials Research Laboratory Department of Mechanical EngineeringSelvam College of TechnologyTamil NaduIndia

suitability of s-Glass/carbon fiber reinforced polymer composite for submarine hull subjected to hydrostatic pressure has been investigated in the present *** materials have raised concerns owing to their decomposition due to low resistance towards salinity and hence polymer composites have been explored to showcase their mechanical stability to withstand transverse and impact *** this end,the mechanical properties of s-Glass/carbon fiber reinforced polymer composite were experimentally investigated and higher specific strength and stiffness of the composite in comparison to many metallic materials used for submarine hull were *** obtained experimental values were used for the static and dynamic crash analysis of the bow,stern and foil through Finite Element Analysis(FEA);where depth of travel was varied from sea surface level of 0-7000 *** assembly was later developed with the optimum shape and thickness of each *** also report the nonlinear crash analysis upon impact at velocity ranging from 3 to 21 m/***,kinetic energy,acceleration peak and internal energy in struck submarine revealed that travel depth 1750 m and 3500 m is recommendable,more particularly,crash safety factor of the submarine is found to be within limit when submarine encounters crash at 1750 m.

关键词： Crash Impact Design submarine Hull Bow stern Foil

Managing security-Risks for Improving security-Durability of Institutional Web-Applications: Design Perspective

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Computers, Materials & Continua 2021年第2期66卷 1849-1865页

作者： abdulaziz Attaallah Abdullah Algarni Raees Ahmad Khan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknowUttar PradeshIndia

The advanced technological need,exacerbated by the flexible time constraints,leads to several more design level unexplored *** is an extremely vital component in software development;we must take charge of security and therefore analysis of software security risk assumes utmost *** order to handle the cyber-security risk of the web application and protect individuals,information and properties effectively,one must consider what needs to be secured,what are the perceived threats and the protection of *** preparation plans,implements,tracks,updates and consistently develops safety risk management *** management must be interpreted as the major component for tackling security *** particular,during application development,security is considered as an add-on but not the main *** is important for the researchers to stress on the consideration of protection right from the earlier developmental stages of the *** approach will help in designing software which can itself combat threats and does not depend on external security ***,it is essential to evaluate the impact of security risks during software *** this paper the researchers have used the hybrid Fuzzy AHPTOPsIs method to evaluate the risks for improving security durability of different Institutional Web *** addition,the e-component of security risk is measured on software durability,and vice *** paper’s findings will prove to be valuable for enhancing the security durability of different web applications.

关键词： Web applications durability cyber-security risk fuzzy logic decision-making approach

A Deep Learning Model for EEG-Based Lie Detection Test Using spatial and Temporal Aspects

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Computers, Materials & Continua 2022年第12期73卷 5655-5669页

作者： Abeer abdulaziz AlArfaj Hanan Ahmed Hosni Mahmoud Department of Computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Lie detection test is highly significant task due to its impact on criminology and *** lie detection test model using electroencephalogram(EEG)signals is studied in *** this paper we studied deep learning framework in lie detection test ***,we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole *** model describes a temporal feature map of the EEG signals measured during the lie detection test.A deep learning attention model(V-TAM)extracts the temporal map vector during the learning *** technique reduces computational time and lessens the overfitting in Deep Learning *** propose a Cascading attention model with a deep learning convolutional neural network(CNN).V-TAM model extracts local features and global features in separate paths spatial and ***,to enhance the EEG segmentation precision,a novel Visual-Temporal AttentionModel(V-TAM)is *** accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection *** compared our model with three recent published *** proposed model attained the highest performance of(98.5%)with(psual-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time *** investigationwere performed to prove the correctness and reliability of the proposed method through sizing of the input data,proving its effectiveness in attaining satisfactory performance by using only a smaller size input data.

关键词： Lie detection EEG image deep learning machine learning

An Intelligent Tree Extractive Text summarization Deep Learning

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Computers, Materials & Continua 2022年第11期73卷 4231-4244页

In recent research,deep learning algorithms have presented effective representation learning models for natural *** deep learningbased models create better data representation than classical *** are capable of automated extraction of distributed representation of *** this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory issues that were not addresses *** proposed model employs a tree structured mechanism to generate the phrase and text *** proposed architecture mimics the tree configuration of the text-texts and provide better feature *** also incorporates an attention mechanism that offers an additional information source to conduct better summary *** novel model addresses text summarization as a classification process,where the model calculates the probabilities of phrase and text-summary *** model classification is divided into multiple features recognition such as information entropy,significance,redundancy and *** model was assessed on two datasets,on the Multi-Doc Composition Query(MCQ)and Dual Attention Composition dataset(DAC)*** experimental results prove that our proposed model has better summarization precision *** models by a considerable margin.

关键词： Neural network architecture text structure abstractive summarization

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

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Digital Communications and Networks 2019年第4期5卷 205-213页

作者： abdulaziz Alzubaidi swarup Roy Jugal Kalita Department of Computer science Umm Alqura UniversityAl-Qunfudah28821Saudi Arabia Department of Computer Applications Sikkim UniversityGangtok737102India Department of Information Technology North Eastern Hill UniversityShillong793022India Department of Computer science University of ColoradoColorado SpringsCO80918USA

smartphones are vulnerable to fraudulent use despite having strong authentication *** authentication based on behavioral biometrics is a solution to protect the privacy of data in smart ***-based frameworks are effective for active ***,the success of any machine-learningbased techniques depends highly on the relevancy of the data in hand for *** addition,the training time should be very *** in view both issues,we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate *** hypothesized that every user has a unique pattern hidden in his/her usage of *** by this observation,we’ve designed a way to obtain training data,which can be used by any machine learning model for effective *** achieve better accuracy with reduced training time,we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority *** information theoretic app ranking scheme was used to prepare a user-targeted apps priority *** of each instance related to a candidate app was calculated by using a knockout ***,a weighted rank was calculated for each app specific to every *** with low ranked apps were removed to derive the reduced training *** datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.

关键词： Fraudulent user Machine learning Classification Behavioral biometric smartphone security

Automatic Detection and Classification of Human Knee Osteoarthritis Using Convolutional Neural Networks

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Computers, Materials & Continua 2022年第3期70卷 4279-4291页

作者： Mohamed Yacin sikkandar s.sabarunisha Begum abdulaziz AAlkathiry Mashhor shlwan NAlotaibi Md Dilsad Manzar Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Chemical Engineering Sethu Institute of TechnologyKariapatti626115TamilnaduIndia Department of Physical Therapy College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Nursing College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

Knee Osteoarthritis(KOA)is a degenerative knee joint disease caused by‘wear and tear’of ligaments between the femur and tibial ***,KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired *** this space between bones through the anterior view of a knee X-ray image is solely subjective and *** classification of this process helps in the selection of suitable treatment processes and customized knee *** this research,a new automatic classification of KOA images based on unsupervised local center of mass(LCM)segmentation method and deep siamese Convolutional Neural Network(CNN)is ***-order statistics and the GLCM matrix are used to extract KOA anatomical Features from segmented *** network is trained on our clinical data with 75 iterations with automaticweight updates to improve its validation *** assessment performed on the LCM segmented KOA images shows that our network can efficiently detect knee osteoarthritis,achieving about 93.2%accuracy along with multi-class classification accuracy of 72.01%and quadratic weighted Kappa of 0.86.

关键词： Osteoarthritis segmentation intensity value unsupervised neural network

Predicting Energy Consumption Using stacked LsTM snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah s.A.L-Malaise AL-Ghamdi Mahmoud Ragab Information systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long short-Term Memory(LsTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean square Error(RMsE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LsTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed s.Alshuhri sheeraz Akram Faculty of Computer science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer science National University of TechnologyIslamabad45000Pakistan Department of Radiological sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented surface Normal Vector (HOsNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

Design new chaotic maps based on dimension expansion

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Chinese Physics B 2019年第2期28卷 147-155页

作者： abdulaziz O A Alamodi Kehui sun Wei Ai Chen Chen Dong Peng school of Physics and Electronics Central South University

Based on the high-dimensional(HD) chaotic maps and the sine function, a new methodology of designing new chaotic maps using dimension expansion is proposed. This method accepts N dimensions of any existing HD chaotic map as inputs to generate new dimensions based on the combined results of those inputs. The main principle of the proposed method is to combine the results of the input dimensions, and then performs a sine-transformation on them to generate new *** characteristics of the generated dimensions are totally different compared to the input dimensions. Thus, both of the generated dimensions and the input dimensions are used to create a new HD chaotic map. An example is illustrated using one of the existing HD chaotic maps. Results show that the generated dimensions have better chaotic performance and higher complexity compared to the input dimensions. Results also show that, in the most cases, the generated dimensions can obtain robust chaos which makes them attractive to usage in a different practical application.

关键词： chaos sine-transform dimension expansion permutation entropy

Parallel Building: A Complex system Approach for smart Building Energy Management

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IEEE/CAA Journal of Automatica sinica 2019年第6期6卷 1452-1461页

作者： abdulaziz Almalaq Jun Hao Jun Jason Zhang Fei-Yue Wang Department of Electrical Engineering Engineering CollegeUniversity of HailHail 55476Saudi Arabia Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of DenverDenverCO 80208 USA state Key Laboratory for Management and Control of Complex systems Institute of AutomationChinese Academy of SciencesBeijing 100190 and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

These days' smart buildings have high intensive information and massive operational parameters, not only extensive power consumption. With the development of computation capability and future 5 G, the ACP theory(i.e., artificial systems,computational experiments, and parallel computing) will play a much more crucial role in modeling and control of complex systems like commercial and academic buildings. The necessity of making accurate predictions of energy consumption out of a large number of operational parameters has become a crucial problem in smart buildings. Previous attempts have been made to seek energy consumption predictions based on historical data in buildings. However, there are still questions about parallel building consumption prediction mechanism using a large number of operational parameters. This article proposes a novel hybrid deep learning prediction approach that utilizes long short-term memory as an encoder and gated recurrent unit as a decoder in conjunction with ACP theory. The proposed approach is tested and validated by real-world dataset, and the results outperformed traditional predictive models compared in this paper.

关键词： ACP theory artificial intelligence data acquisition deep learning(DL) energy consumption machine learning parallel energy prediction prediction algorithms smart grid