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Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network

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Advances in Manufacturing 2021年第1期9卷 115-129页

作者： Omar Ahmed Mohamed syed hasan masood Jahar Lal Bhowmik School of Engineering Deakin UniversityGeelongAustralia Department of Mechanical Engineering Higher Institute of Engineering TechnologyBani WalidLibya Department of Mechanical and Product Design Engineering Swinburne University of TechnologyHawthornVIC 3122Australia Department of Health Science and Biostatistics Swinburne University of TechnologyHawthornVIC 3122Australia

Additive manufacturing(AM)technologies such as fused deposition modeling(FDM)rely on the quality of manufactured products and the process ***,the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard,and variations are controlled without significantly affecting the functioning of processes,machines,and product *** study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical *** this study,a new class of experimental design techniques for integrated second-order definitive screening design(DSD)and an artificial neural network(ANN)are proposed for designing experiments to evaluate and predict the effects of six important operating *** determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts,the time consumption and number of complex experiments are reduced considerably in this *** optimum fabrication conditions generated through a second-order DSD are verified with experimental *** results indicate that the slice thickness,part print direction,and number of perimeters significantly affect the percentage of length difference,whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap,bead width,number of perimeters,and part print ***,the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

关键词： Artificial neural network(ANN) OptimizationDefinitive screening design(DSD) Analysis of variance(ANOVA) Fused deposition modeling(FDM) Dimensional accuracy

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Nickel-titanium shape memory alloys made by selective laser melting:a review on process optimisation

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Advances in Manufacturing 2022年第1期10卷 24-58页

作者： Omar Ahmed Mohamed syed hasan masood Wei Xu School of Engineering Deakin UniversityGeelongVIC 3216Australia Department of Mechanical Engineering The Higher Institute of Engineering TechnologyBani WaledLibya Department of Mechanical and Product Design Engineering Swinburne University of TechnologyHawthornVIC 3122Australia

Selective laser melting(SLM)is a mainstream powder-bed fusion additive manufacturing(AM)process that creates a three-dimensional(3D)object using a high power laser to fuse fine particles of various metallic powders such as copper,tool steel,cobalt chrome,titanium,tungsten,aluminium and stainless *** the past decade,SLM has received significant attention due to its capability in producing dense parts with superior mechanical *** a premier shape memory alloy,the nickel-titanium(NiTi)shape memory alloy is attractive for a variety of biomedical applications due to its superior mechanical properties,superelasticity,corrosion resistance and *** paper presents a comprehensive review of the recent progress in NiTi alloys produced by the SLM process,with a particular focus on the relationship between processing parameters,resultant microstructures and *** research gaps,challenges and suggestions for future research are also addressed.

关键词： Additive manufacturing(AM) Selective laser melting(SLM) Microstructure NiTi alloy Mechanical properties

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A Study of Melt Flow Analysis of an ABS-Iron Composite in Fused Deposition Modelling Process

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Tsinghua Science and Technology 2009年第S1期14卷 29-37页

作者： MOSTAFA Nikzad syed hasan masood IGOR Sbarski ANDREW Groth Faculty of Engineering and Industrial Sciences Swinburne University of Technology Hawthorn Melbourne VIC3122 Australia Departments of Health and Molecular Sciences CSIRO Melbourne VIC3122 Australia

Fused deposition modelling (FDM) is a filament based rapid prototyping system which offers the possibility of introducing new composite material for the FDM process as long as the new material can be made in feedstock filament form. Swinburne has been undertaking extensive research in development of new composite materials involving acrylonitrile-butadiene-styrene (ABS) and other materials including metals. In order to predict the behaviour of new ABS based composite materials in the course of FDM process, it is necessary to investigate the flow of the composite material in liquefier head. No such study is available considering the geometry of the liquefier head. This paper presents 2-D and 3-D numerical analysis of melt flow behaviour of a representative ABS-iron composite through the 90-degree bent tube of the liquefier head of the fused deposition modelling process using ANSYS FLOTRAN and CFX finite element packages. Main flow parameters including temperature, velocity, and pressure drop have been investigated. Filaments of the filled ABS have been fabricated and characterized to verify the possibility of prototyping using the new material on the current FDM machine. Results provide promising information in developing the melt flow modelling of metal-plastic composites and in optimising the FDM parameters for better part quality with such composites.

关键词： acrylonitrile-butadiene-styrene (ABS) composite fused deposition modelling (FDM) melt flow behaviour ANSYS

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A Novel Cryptocurrency Prediction Method Using Optimum CNN

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Computers, Materials & Continua 2022年第4期71卷 1051-1063页

作者： syed H.hasan syeda Huyam hasan Mohammed Salih Ahmed syed Hamid hasan Iowa State University AmesUSA University of Texas at Arlington USA Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

In recent years,cryptocurrency has become gradually more significant in economic regions *** cryptocurrencies,records are stored using a cryptographic *** main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social *** is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media ***,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet *** paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and *** results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

关键词： Cryptocurrency litecoin monero bitcoin ethereum twitter optimal CNN price prediction sentiment analysis

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Optimized Generative Adversarial Networks for Adversarial Sample Generation

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Computers, Materials & Continua 2022年第8期72卷 3877-3897页

作者： Daniyal M.Alghazzawi syed Hamid hasan Surbhi Bhatia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent *** few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this *** are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal *** this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh *** discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal *** model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the *** performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,*** superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection *** support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.

关键词： Aquila optimizer convolutional generative adversarial networks mine blast harmony search algorithm network traffic dataset adversarial artificial intelligence techniques

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Effect of Salicylic Acid on Salinity-induced Changes in Brassica juncea

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Journal of Integrative Plant Biology 2008年第9期50卷 1096-1102页

作者： Mohammad Yusuf syed Aiman hasan Barket Ali Shamsul Hayat Qazi Fariduddin Aqil Ahmad Plant Physiology Section Department of Botany Aligarh Muslim University Department of Applied Sciences Higher College of Technology

Seeds of Indian mustard （Brassicajuncea （L.） Czern. et Coss.） were exposed to 0, 50, 100 and 150 mmol/L NaCI for 8 h and seeds were sown in an earthen pot. These stressed seedlings were subsequently sprayed with 10μmol/L salicylic acid （SA） at 30 d and were sampled at 60 d to assess the changes in growth, photosynthesis and antioxidant enzymes. The seedlings raised from the seeds treated with NaCI had significantly reduced growth and the activities of carbonic anhydrase, nitrate reductase and photosynthesis, and the decrease was proportional to the increase in NaCI concentration. However, the antioxidant enzymes （catalase, peroxidase and superoxide dismutase） and proline content was enhanced in response to NaCI and/or SA treatment, where their interaction had an additive effect. Moreover, the toxic effects generated by the lower concentration of NaCI （50 mmol/L） were completely overcome by the application of SA. It was, therefore, concluded that SA ameliorated the stress generated by NaCl through the alleviated antioxidant system.

关键词： carbonic anhydrase catalase mustard net photosynthetic rate nitrate reductase peroxidase salicylic acid salinity superoxidedismutase.

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IoT Devices Authentication Using Artificial Neural Network

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Computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： syed Shabih Ul hasan Anwar Ghani Ikram Ud Din Ahmad Almogren Ayman Altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for Internet of Things(IoT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： Implicit authentication behavioral authentication artificial neural network processed pattern authentication

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Determination of correlation of Adjusted Blood Requirement Index with outcome in patients presenting with acute variceal bleeding

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World Journal of Gastroenterology 2009年第19期15卷 2372-2375页

作者： Naheed Akhtar Bader Faiyaz Zuberi syed Riazul hasan Raj Kumar Salahuddin Afsar Department of Medicine Dow University of Health SciencesKarachi 74000Pakistan

AIM： To determine the correlation of Adjusted Blood Requirement Index （ABRI） with the 7th day outcome in patients presenting with acute variceal ***： All patients presenting with acute variceal hemorrhage （AVH） were included. Patients with previous band ligation, sclerotherapy, gastrointestinal or hepatic malignancies were excluded. Patients were managed as per standard protocol for AVH with terlipressin and band ligation. ABRI scores were calculated using the formula outcome of alive or expired up to the 7th day after treatment. The correlation between ABRI and mortality was estimated and a receiver operative characteristic （ROC） curve was ***： A total of 113 patients （76 male; 37 female） were included. On assessment, 18 were in Child＇s Pugh Class A, 82 in Class B and 13 were in Class C. The median number of blood units transfused ± inter-quartile range was 3.0 ± 2.0. The median ± inter-quartile range for ABRI was 1.3 ± 1.1. The ROC curve of ABRI for expiry showed a significantly large area of 0.848 （P 〈 0.0001; 95% CI： 0.75-0.95）. A significant correlation of log transformation of ABRI with an outcome of mortality was present （P 〈 0.0001）.

关键词： Adjusted Blood Requirement Index Cirrhosis Mortality Portal hypertension Variceal hemorrhage

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul hasan Saoucene Mahfoudh syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

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Computers, Materials & Continua 2022年第7期72卷 1173-1193页

作者： Daniyal M.Alghazzawi Osama Bassam J.Rabie Surbhi Bhatia syed Hamid hasan Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

The Deep Neural Networks(DNN)training process is widely affected by backdoor *** backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning *** state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same *** overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)*** the ICNN-GBO model,we are injecting the triggers via a steganography and regularization *** are generating triggers using a single-pixel,irregular shape,and different *** performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and *** the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target *** proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M *** results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

关键词： Convolutional neural network gradient-based optimization steganography backdoor attack and regularization attack

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