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An Efficient Method for Heat Recovery Process and Temperature Optimization

Computers, Materials & Continua 2023年第4期75卷 1017-1031页

作者： Basim Kareem Naser Mohammed Dauwed Ahmed Alkhayyat mustafa musa jaber Shahad Alyousif Mohammed Hasan Ali Dentistry Department Al-Mustaqbal University CollegeHilla51001Iraq Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdad00964Iraq College of Technical Engineering The Islamic UniversityNajaf54001Iraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Research Centre University of MashreqBaghdad11001Iraq College of Engineering Department of Electrical&Electronic EngineeringGulf UniversityAlmasnad4363Kingdom of Bahrain Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afarAl-Sadiq UniversityNajaf10023Iraq

Flue gas heat loss accounts for a significant component of theoverall heat loss for coal-fired boilers in power plants. The flue gas absorbsmore heat as the exhaust gas temperature rises, which reduces boiler efficiencyand raises coal consumption. Additionally, if the exhaust gas temperatureis too high, a lot of water must be used to cool the flue gas for the wetflue gas desulfurization system to function well, which has an impact onthe power plant’s ability to operate profitably. It is consequently vital totake steps to lower exhaust gas temperatures in order to increase boilerefficiency and decrease the amount of coal and water used. Desulfurizationperformance may be enhanced and water use can be decreased by reasonableflue gas characteristics at the entry. This study analyzed the unit’s energyconsumption, investment, and coal savings while proposing four couplingstrategies for regulating flue gas temperature and waste heat recovery. Agraded flue gas conditioning and waste heat recovery plan was presentedunder the condition of ensuring high desulfurization efficiency, along withthe notion of minimizing energy loss owing to energy inflow temperaturedifference. Numerical results show that the proposed methods improved thesystem performance and reduced the water consumption and regulated theboiler temperature.

关键词： Heat exchange system boiler energy optimization temperature control

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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

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Computers, Materials & Continua 2022年第8期72卷 2175-2190页

作者： mustafa musa jaber Salman Yussof Amer S.Elameer Leong Yeng Weng Sura Khalil Abd Anand Nayyar Institute of Informatics and Computing in Energy Universiti Tenaga NasionalMalaysia Department of Computer Science Dijlah University CollageBaghdad10021Iraq Biomedical Informatics College University of Information Technology and CommunicationsBaghdadIraq Faculty of Information Technology Duy Tan UniversityDa Nang550000Viet Nam Graduate School Duy Tan UniversityDa Nang550000Viet Nam Department of Computer Science Al-turath University CollegeBaghdadIraq

Artificial intelligence plays an essential role in the medical and health *** convolution networks offer valuable services and help create automated systems to perform medical image ***,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected ***,an optimized deep convolution network is utilized for analyzing disease-affected regions in this *** disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning *** method identifies affected pixels by the gray wolf hunting *** convolution network uses an automatic learning function that predicts the disease affected by previous imaging *** optimized algorithm-based selected regions are further examined using the distribution pattern-matching *** pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation *** process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

关键词： Artificial intelligence medical field gray wolf-optimized deep convolution networks distribution pattern-matching rule

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Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

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Computers, Materials & Continua 2023年第5期75卷 3359-3374页

作者： Firas Abedi Hayder M.A.Ghanimi Mohammed A.M.Sadeeq Ahmed Alkhayyat Zahraa H.Kareem Sarmad Nozad Mahmood Ali Hashim Abbas Ali S.Abosinnee Waleed Khaild Al-Azzawi mustafa musa jaber Mohammed Dauwed Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq ITM Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq College of Technical Engineering The Islamic UniversityNajafIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkuk36013Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Altoosi University College NajafIraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of Medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

Recent economic growth and development have considerably raised energy consumption over the *** load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power *** order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid *** major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient *** proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature ***,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective ***,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO *** experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

关键词： Load forecasting smart grid energy storage system electricity load forecasting artificial intelligence clustering

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Severity Based Light-Weight Encryption Model for Secure Medical Information System

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Computers, Materials & Continua 2023年第3期74卷 5691-5704页

作者： Firas Abedi Subhi R.M.Zeebaree Zainab Salih Ageed Hayder M.A.Ghanimi Ahmed Alkhayyat Mohammed A.M.Sadeeq Sarmad Nozad Mahmood Ali S.Abosinnee Zahraa H.Kareem Ali Hashim Abbas Waleed Khaild Al-Azzawi mustafa musa jaber Mohammed Dauwed Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq College of Technical Engineering The Islamic UniversityNajafIraq ITM Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Altoosi University College NajafIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of Medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly ***,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been *** commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using ***,a deep learning classifier is used to classify records according to the degree of concealment they *** images that aren’t needed can be saved by using this method,which cuts down on security *** is one of the most effective methods for protecting medical images after this *** and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this *** splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the *** that,this research suggests a Region of Interest(ROI)technique based on selective picture *** the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent *** all of the blocks have been encrypted,they are combined to create encrypted *** investigational analysis is carried out to test the competence of the projected ideal with existing techniques.

关键词： Deep learning encryption medical images scrambling security skew tent map rotation zigzag pattern

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An Optimal Algorithm for Resource Allocation in D2D Communication

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Computers, Materials & Continua 2023年第4期75卷 531-546页

作者： Shahad Alyousif Mohammed Dauwed Rafal Nader Mohammed Hasan Ali mustafa musa Jabar Ahmed Alkhayyat Research Centre University of MashreqBaghdadIraq College of Engineering Department of Electrical&Electronic EngineeringGulf UniversityAlmasnadKingdom of Bahrain Medical Instrumentation Techniques Engineering Dijlah University CollegeBaghdadIraq Department of Pharmacy Al-Mustaqbal University CollegeHilla51001Iraq Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityNajaf10023Iraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq College of Technical Engineering The Islamic UniversityNajafIraq

The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency *** are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing ***-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.

关键词： D2D communication resource allocation latency optimization

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Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

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Computers, Materials & Continua 2022年第9期72卷 4267-4281页

作者： jaber S.Alzahrani Reem M.Alshehri Mohammad Alamgeer Anwer mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)*** FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the FSS-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO *** choice of IG and GSO approaches results in promising medical data classification *** experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

关键词： Medical data classification feature selection deep learning healthcare sector artificial intelligence

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Characteristic properties of date-palm fibre/sheep wool reinforced polyester composites

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Journal of Bioresources and Bioproducts 2023年第4期8卷 430-443页

作者： Mohammed Y.Abdellah mustafa Gamal Sadek Hamzah Alharthi G.T.Abdel-jaber Ahmed H.Backar Mechanical Engineering Department Faculty of EngineeringSouth Valley UniversityQena83523Egypt Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm AL-Qura UniversityMakkah21955Saudi Arabia New Assiut Technological University(NAUT) Assiut71684Egypt Production Engineering Department Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt

In this study,we attempted to characterise the effects of date-palm fibre(DPF)and a date-palm fibre/sheep wool hybrid in polyester to enhance high-performance and low-cost composite materials that can be used in insulation building systems,automotive parts,and home *** DPF was treated using 5%NaOH solution;and the sheep wool was cleaned with 50℃hot water and *** composite specimens were prepared with different fibre contents(0%,10%,20%,30%(w))using a compression moulding *** effect of fibre reinforcement was analysed in terms of the mechanical properties(tensile,flexural,impact,and hardness)and composite ***,scanning electron microscopy(SEM)was performed on the fibres before and after treatment,and the fractured surfaces of all composite specimens were examined after tensile *** results showed that the 20%DPF/sheep wool hybrid reinforced polyester produced the best *** ultimate tensile strength and modulus were 27 MPa and 3.69GPa,*** ultimate flexural strength and flexural modulus were 35.4 and 2507 MPa,*** impact strength was 39.5 kJ/m^(2)and the hardness was 64 *** density decreased to the lowest value of 1.02 g/cm^(3)with the 30%DPF/sheep wool *** SEM showed good adhesion and interfacial bonding between DPF/sheep wool hybrid fibres and the polyester matrix,particularly at 20%fibre content.

关键词： Date-palm fibre Polyester Compression moulding Mechanical property

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Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

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Computers, Materials & Continua 2022年第7期72卷 333-348页

作者： Anwer mustafa Hilal jaber SAlzahrani Ibrahim Abunadi Nadhem Nemri Fahd NAl-Wesabi Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen

In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be *** technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber *** blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs *** this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G *** proposed PPBDLIIoT technique aims at identifying the existence of intrusions in ***,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the ***,CGO technique is applied to fine tune the hyperparameters in BiGRNN *** algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN ***,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT *** performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various *** experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

关键词： 6G networks industrial iot blockchain security intrusion detection artificial intelligence

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Effects of astaxanthin on antioxidant parameters in ARPE-19 cells on oxidative stress model

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International Journal of Ophthalmology(English edition) 2019年第6期12卷 930-935页

作者： Yigit musa Günes Alime Uguz Cihangir Yalcin Tok Ozlem Tok Levent Oz Ahmi Naziroglu mustafa Department of Ophthalmology Süleyman Demirel University Research and Education HospitalCünür-Isparta 32200Turkey Department of Biophysics Faculty of MedicineSüleyman Demirel University Research and Education HospitalCünür-Isparta 32200Turkey

AIM: To observe the protective effect of astaxanthin(AST) against hydroquinone(HQ) mediated cell death in the apoptotic cascade and evaluate intracellular Ca2+ release, caspase-3, and-9 activation, reactive oxygen species(ROS) production in ARPE-19 ***: We cultured ARPE-19 cells in special mediums and performed MTT tests to determine protective effect of AST, before exposing the cells to HQ in an incubator. We analyzed intracellular Ca2+ release experiments, mitochondrial membrane depolarization, glutathione(GSH), glutathione peroxidase(GSH-Px) and ROS experiments, and apoptosis ***: ROS production ranges depend on the amount of cell death. We computed the correlation between ROS ranges and cell death by 20,70-dichlorofluorescein fluorescence, and Ca2+ levels by Fura-2-AM. HQ-induced cell death found out to rise ranges of caspase-3 and-9, and mitochondrial depolarization. These three steps were delayed by AST ***: ARPE-19 cells are avoided from HQinduced ROS production and caspase-3 and-9 activation by AST. AST may limit the range of caspase synthesis, Ca2+ release and excess production of ROS with antiapoptotic effect. This study proposes a new therapeutic approach for the treatment of age-related macular degeneration.

关键词： apoptosis ARPE-19 cell astaxanthin oxidative stress

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Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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Computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari jaber S.Alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed S.Salama Anwer mustafa Hilal Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： Biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

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