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modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity recognition model

Computers, materials & Continua 2023年第5期75卷 4009-4024页

作者： Bareen Shamsaldeen Tahir Zainab Salih Ageed Sheren Sadiq Hasan subhi r.m.zeebaree Accounting Department College of Administration and EconomicsUniversity of DuhokDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq ITm Department Technical College of AdministrativeDuhok Polytechnic UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Traditional indoor human activity recognition(HAr)is a timeseries data classification problem and needs feature ***,considerable attention has been given to the domain ofHArdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older *** extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAr a vital subject in ubiquitous and mobile *** more commonly utilized inference and problemsolving technique in the HAr system have recently been deep learning(DL).The study develops amodifiedWild Horse Optimization withDLAided Symmetric Human Activity recognition(mWHODL-SHAr)*** major intention of the mWHODL-SHAr model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,*** the presented mWHODL-SHAr technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTm)model is applied for activity *** mWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTm *** experimental validation of the mWHODL-SHAr technique is simulated using a benchmark *** extensive comparison study revealed the betterment of themWHODL-SHAr technique over other recent approaches.

关键词： Human activity recognition symmetry deep learning machine learning pattern recognition time series classification

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Improved Transient Search Optimization with machine Learning Based Behavior recognition on Body Sensor Data

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Computers, materials & Continua 2023年第5期75卷 4593-4609页

作者： Baraa Wasfi Salim Bzar Khidir Hussan Zainab Salih Ageed subhi r.m.zeebaree ITm Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq Information System Engineering Department Erbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare *** though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAr)through body sensors gives useful information about the lifestyle and functionality of an *** article concentrates on the design of an Improved Transient Search Optimization with machine Learning based Behaviorrecognition(ITSOmLBr)technique using body sensor *** presented ITSOmL-Br technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and *** addition,the ITSOmL-Br technique extract features like variance,mean,skewness,and standard ***,the presented ITSOmL-Br technique executes a micro neural network(mNN)which can be employed for long term healthcare monitoring and ***,the parameters related to the mNN model are optimally selected via the ITSO *** experimental result analysis of the ITSOmL-Br technique is tested on the mHEALTH *** comprehensive comparison study reported a higher result for the ITSOmLBr approach over other existing approaches with maximum accuracy of 99.60%.

关键词： Behavior recognition transient search optimization machine learning healthcare sensors wearables

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Parameter Tuned Deep Learning Based Traffic Critical Prediction model on remote Sensing Imaging

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Computers, materials & Continua 2023年第5期75卷 3993-4008页

作者： Sarkar Hasan Ahmed Adel Al-Zebari rizgar r.Zebari subhi r.m.zeebaree Computer Networks Department Sulaimani Polytechnic UniversitySulaimaniIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

remote sensing(rS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related *** has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic *** article introduces an Optimal Deep Learning for Traffic Critical Prediction model on High-resolution remote Sensing Images(ODLTCP-HrrSI)to resolve these *** presented ODLTCP-HrrSI technique majorly aims to forecast the critical traffic in smart *** attain this,the presented ODLTCP-HrrSI model performs two major *** the initial stage,the ODLTCP-HrrSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic ***,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)*** experimental outcomes demonstrate the enhanced performance of the ODLTCP-HrrSI technique over recent approaches with maximum accuracy of 98.23%.

关键词： remote sensing images traffic prediction deep learning smart cities intelligent transportation systems

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Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

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Computers, materials & Continua 2023年第5期75卷 3677-3693页

作者： mohammed Jasim mohammed Jasim Bzar Khidir Hussan subhi r.m.zeebaree Zainab Salih Ageed Engineering College Al-Kitab UniversityKirkukIraq Information System Engineering Department Erbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various *** specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer *** is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss *** automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical ***,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative *** manuscript devises a new Northern Goshawk Optimization with Transfer Learning model for Colonic Polyp Detection and Classification(NGOTL-CPDC)*** NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp *** accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing ***,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO ***,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC *** comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

关键词： Biomedical imaging artificial intelligence colonic polyp classification medical image classification computer-aided diagnosis

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Solar radiation Prediction Using Satin Bowerbird Optimization with modified Deep Learning

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Computer Systems Science & Engineering 2023年第9期46卷 3225-3238页

作者： Sheren Sadiq Hasan Zainab Salih Agee Bareen Shamsaldeen Tahir subhi r.m.zeebaree ITm Department Technical College of AdministrativeDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Accounting Department College of Adminstration and EconomicsUniversity of DuhokDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Solar energy will be a great alternative to fossil fuels since it is clean and *** photovoltaic(PV)mechanism produces sunbeams’green energy without noise or *** PV mechanism seems simple,seldom malfunctioning,and easy to *** energy productivity significantly contributes to smart grids through many small PV *** solar radiation(Sr)prediction could substantially reduce the impact and cost relating to the advancement of solar *** recent times,several Sr predictive mechanism was formulated,namely artificial neural network(ANN),autoregressive moving average,and support vector machine(SVm).Therefore,this article develops an optimal modified Bidirectional Gated recurrent Unit Driven Solar radiation Prediction(OmBGrU-SrP)for energy *** presented OmBGrU-SrP technique mainly aims to accomplish an accurate and time Sr prediction *** accomplish this,the presented OmBGrU-SrP technique performs data preprocessing to normalize the solar ***,the mBGrU model is derived using BGrU with an attention mechanism and skip *** last,the hyperparameter tuning of the mBGrU model is carried out using the satin bowerbird optimization(SBO)algorithm to attain maximum prediction with minimum error *** SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the *** experiments were conducted to demonstrate the enhanced Sr prediction *** experimental values highlighted the supremacy of the OmBGrU-SrP algorithm over other existing models.

关键词： Solar radiation prediction deep learning parameter optimization energy management sustainability

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Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3831-3845页

作者： mohammed Jasim mohammed Jasim Shakir Fattah Kak Zainab Salih Ageed subhi r.m.zeebaree Engineering College Al-Kitab UniversityKirkukIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Engineering Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such ***-drug interaction(DDI)significantly contributed to drug administration and *** continues as the main obstacle in offering inexpensive and safe *** normally happens for patients with extensive medication,leading them to take many drugs *** may cause side effects,either mild or severe health *** reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery *** efforts were made to formulate new methods for DDI prediction to overcome this *** this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data *** the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for *** input data is preprocessed at the primary level to improve its ***,the salp swarm optimization algorithm(SSO)is used to select *** this study,the deep belief network(DBN)model is exploited to predict the DDI *** SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the *** experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

关键词： Drug-drug interaction deep learning spotted hyena optimization feature selection classification

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Privacy Preserving Blockchain with Optimal Deep Learning model for Smart Cities

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Computers, materials & Continua 2022年第12期73卷 5299-5314页

作者： K.Pradeep mohan Kumar Jenifer mahilraj D.Swathi r.rajavarman subhi r.m.zeebaree rizgar rZebari Zryan Najat rashid Ahmed Alkhayyat Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Information Technology School of Engineering and TechnologyKebridehar UniversityKebridehar250Ethiopia Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Computer Network Department Technical College of InformaticsSulaimani Polytechnic UniversitySulaimaniIraq College of Technical Engineering The Islamic UniversityNajafIraq

recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available *** the advantages of smart cities,security remains a huge challenge to be ***,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this ***,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and *** order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)*** proposed PPSFBODL model includes the collection of primary data using sensing ***,z-score normalization is also utilized to transform the actual data into useful ***,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short Termmemory(ABiLSTm)model is employed for detection and classification of *** is employed for optimal hyperparameter tuning of ABiLSTm *** the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud *** cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

关键词： Blockchain smart city security intrusion detection system chameleon swarm optimization deep learning parameter tuning

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Billiards Optimization with modified Deep Learning for Fault Detection in Wireless Sensor Network

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Computer Systems Science & Engineering 2023年第11期47卷 1651-1664页

作者： Yousif Sufyan Jghef mohammed Jasim mohammed Jasim subhi r.m.zeebaree rizgar r.Zebari Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Engineering College Al-Kitab UniversityKirkukIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

Wireless Sensor Networks(WSNs)gather data in physical environments,which is some *** ubiquitous sensors face several challenges responsible for corrupting them(mostly sensor failure and intrusions in external agents).WSNs were disposed to error,and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely *** learning(mL)was extremely utilized for detecting faults in ***,this study proposes a billiards optimization algorithm with modified deep learning for fault detection(BIOmDL-FD)in *** BIOmDLFD technique mainly concentrates on identifying sensor faults to enhance network *** do so,the presented BIOmDL-FD technique uses the attention-based bidirectional long short-term memory(ABLSTm)method for fault *** the ABLSTm model,the attention mechanism enables us to learn the relationships between the inputs and modify the probability to give more attention to essential *** the same time,the BIO algorithm is employed for optimal hyperparameter tuning of the ABLSTm model,which is stimulated by billiard games,showing the novelty of the *** analyses are made to affirm the enhanced fault detection outcomes of the BIOmDL-FD *** simulation results demonstrate the improvement of the BIOmDL-FD technique over other models with a maximum classification accuracy of 99.37%.

关键词： Wireless sensor network fault detection reliability deep learning metaheuristics

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Ensemble of Handcrafted and Deep Learning model for Histopathological Image Classification

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Computers, materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi majety N.Sharmili Chinmaya ranjan Pattanaik ELaxmi Lydia subhi r.m.zeebaree Sarmad Nozad mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGrU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

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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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