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Hybrid Color Texture Features Classification Through ANN for Melanoma

Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani ahmed alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difﬁcult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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Computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani ahmed alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords etc.Therefore,automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net.Real time datasets,from local surveillance department’s test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani ahmed alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing demand.Container cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking failures.Cloud-based platforms usually entail users define application resource supplies for eco container virtualization.There is a constant problem of over-service in data centers for cloud service providers.Higher operating costs and incompetent resource utilization can occur in a waste of resources.Kubernetes revolutionized the orchestration of the container in the cloud-native age.It can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s contribution.Kubernetes clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the controller.Due to operational delays,the system will become unstable,and the service will be unavailable.This work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes cluster.RBACS allocation pattern is analyzed with the Kubernetes VPA.To estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site relocation.The experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco containers.Compared to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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Improved RC6 Block Cipher Based on Data Dependent Rotations

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Computers, Materials & Continua 2022年第1期70卷 1921-1934页

作者： Osama S.Faragallah Ibrahim F.Elashry ahmed alghamdi Walid El-Shafai S.El-Rabaie Fathi E.Abd El-Samie Hala S.El-sayed Mohamed A.Elaskily Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Communications Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt Department of Informatics Electronic Research Institute(ERI)CairoEgypt

This paper introduces an Improved RC6(IRC6)cipher for data encryption based on data-dependent rotations.The proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard(AES).Four parameters are used to characterize the proposed scheme.These parameters are the size of the word(w)in bits,the number of rounds(r),the length of the secret key(b)in bytes,and the size of the block(L)in bits.The main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6,resulting in a variable block size for plaintext and ciphertext.The IRC6 cipher is designed to improve the robustness against attacks by increasing the diffusion for each round and providing greater security with fewer rounds.The effectiveness of the proposed IRC6 scheme is verified against theoretical attacks.The proposed IRC6 scheme depends on full diffusion and confusion mechanisms regardless of the utilized block size.The proposed IRC6 scheme saves 70%of the encryption time and 64%of the decryption time of RC6.The simulation results prove that the IRC6 achieves a better encryption/decryption time compared to the traditionalRC6.Therefore,the proposed IRC6 is anticipated to fulfill the market needs and system security requirements.

关键词： Cryptography block cipher RC6,IRC6,AES

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Enhancement of Quality of Secondary Industrial Wastewater Effluent by Coagulation Process: A Case Study

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Journal of Environmental Protection 2011年第9期2卷 1250-1256页

作者： Hossam Altaher ahmed alghamdi 不详

The local wastewater treatment facility in Yanbu Industrial City receives 24,000 m3/day of industrial wastewater. This wastewater, mostly from refineries and petrochemical industries, goes through physical, biological and chemical stages of treatment. However, the treated water still fails to pass some of the permissible levels set by governmental agencies. This research paper investigated the enhancement of the treatment processes to reduce the turbidity of the effluent treated water. Ferric chloride, ferrous sulfate, alum and commercial synthetic cationic polymer were tried as coagulants. Different conditions (i.e., pH, temperature, dose, stirring rate) were searched. Ferrous sulfate and polymer reduced the final turbidity to acceptable values with very low doses compared with other coagulant.

关键词： Coagulation Turbidity Industrial Wastewater Treatment Polymer

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A Particle Swarm Optimization Based Deep Learning Model for Vehicle Classification

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Computer Systems Science & Engineering 2022年第1期40卷 223-235页

作者： Adi Alhudhaif Ammar Saeed Talha Imran Muhammad Kamran ahmed S.alghamdi ahmed O.Aseeri Shtwai Alsubai Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Cantt47010Pakistan University of Jeddah College of Computer Science and Engineering Department of CybersecurityJeddah21959Saudi Arabia

Image classification is a core field in the research area of image proces-sing and computer vision in which vehicle classification is a critical domain.The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security,traffic analysis,and self-driving and autonomous vehicles.The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional,and handcrafted means of solving image analysis problems.In this paper,a combina-tion of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme,particle swarm optimization(PSO),was employed for autonomous vehi-cle classification.The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented.The trained model was classified using several classifiers;however,the Cubic SVM(CSVM)classifier was found to out-perform the others in both time consumption and accuracy(94.8%).The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accu-racy(94.8%)but also in terms of training time(82.7 s)and speed prediction(380 obs/sec).

关键词： Vehicle classification intelligent transport system deep learning constrained machine learning particle swarm optimization CNN GoogleNet

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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Computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf ahmed Mohammed alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,etc.Urdu is the national language of Pakistan,and it is the fourth spoken language in the world.However,it is still challenging to recognize Urdu handwritten characters owing to their cursive nature.Our paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online characters.Our research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this field.For offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online systems.Moreover,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition.To this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums.Subsequently,we carried out detailed experiments for character recognition,as detailed in the results.The proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa ahmed Mohammed alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which comp

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Analysis of Flow Structure in Microturbine Operating at Low Reynolds Number

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Computers, Materials & Continua 2022年第4期71卷 961-977页

作者： Mohamed Omri Yusuf Al-Turki ahmed A.alghamdi Amrid Amnache Luc G.Fréchette King Abdulaziz University Deanship of Scientific ResearchJeddahSaudi Arabia King Abdulaziz University Electrical and Computer Engineering DepartmentJeddahSaudi Arabia Universitéde Sherbrooke Interdisciplinary Institute for Technological Innovation(3IT)SherbrookeCanada

In this paper,three-dimensional flows in laminar subsonic cascades at relatively low Reynolds numbers(Re<2500)are presented,based on numerical calculations.The stator and rotor blade designs are those for a MEMS-based Rankine microturbine power-plant-on-a-chip with 109-micron chord blades.Blade passage calculations in 3D were done for different Reynolds numbers,tip clearances(from 0 to 20%)and incidences(0◦to 15◦)to determine the impact of aerodynamic conditions on the flow patterns.These conditions are applied to a blade passage for a stationary outer casing.The 3D blade passage without tip clearance indicates the presence of two large symmetric vortices due to the interaction between hub/casing boundary layers and the blade.Opening the tip clearance introduces the tip vortex,which tends to become dominant above a tip clearance of 10%.In addition to providing a description and understanding of the 3D flow in a MEMS microturbine,these results suggest the importance of considering 3D flows in the design of microturbomachinery,even though the geometry is dominantly 2D.

关键词： Microturbomachinery turbine vortex internal flows secondary flows

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A Novel Database Watermarking Technique Using Blockchain as Trusted Third Party

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Computers, Materials & Continua 2022年第1期70卷 1585-1601页

作者： ahmed S.alghamdi Surayya Naz Ammar Saeed Eesa Al Solami Muhammad Kamran Mohammed Saeed Alkatheiri Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahKSA Abdul Wali Khan University MardanPakistan Department of Computer Science COMSATS University IslamabadWah CampusWah CanttPakistan

With widespread use of relational database in various real-life applications,maintaining integrity and providing copyright protection is gaining keen interest of the researchers.For this purpose,watermarking has been used for quite a long time.Watermarking requires the role of trusted third party and a mechanism to extract digital signatures(watermark)to prove the ownership of the data under dispute.This is often inefficient as lots of processing is required.Moreover,certain malicious attacks,like additive attacks,can give rise to a situation when more than one parties can claim the ownership of the same data by inserting and detecting their own set of watermarks from the same data.To solve this problem,we propose to use blockchain technology—as trusted third party—along with watermarking for providing a means of rights protection of relational databases.Using blockchain for writing the copyright information alongside watermarking helps to secure the watermark as changing the blockchain is very difficult.This way,we combined the resilience of our watermarking scheme and the strength of blockchain technology—for protecting the digital rights information from alteration—to design and implement a robust scheme for digital right protection of relational databases.Moreover,we also discuss how the proposed scheme can also be used for version control.The proposed technique works with nonnumeric features of relational database and does not target only selected tuple or portion(subset)from the database for watermark embedding unlike most of the existing techniques;as a result,the chances of subset selection containing no watermark decrease automatically.The proposed technique employs zerowatermarking approach and hence no intentional error(watermark)is added to the original dataset.The results of the experiments proved the effectiveness of the proposed scheme.

关键词： Watermarking blockchain digital copyright protection relational databases security

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