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Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

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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Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

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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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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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Challenges and Opportunities in Healthcare and Industrial IoT: A Comparative Analysis 1

Challenges and Opportunities in Healthcare and Industrial Io...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Berjis, Zaid Ameen, Siddeeq Y. Al-Jammas, Mohammed H. Electrical and Computer Engineering Department College of Engineering University of Duhok Duhok Iraq Energy Engineering Department College of Technical Engineering Duhok Polytechnic University Duhok Iraq Computer and Information Engineering Department College of Electronics Ninevah University Mosul Iraq

ISBN: (纸本)9798331533557

The Internet of Things (IoT) has changed many industries by enabling smart devices to transmit data, operate autonomously, and interact in real-time. Among its most prominent applications are in healthcare and industrial sectors, where IoT is classified into two classes: massive Machine-Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). While mMTC devices prioritize low energy consumption and can handle large-scale deployments with minimal data rates, URLLC devices are focused on mission-critical applications requiring ultra-low latency and high reliability. This paper examines the challenges and opportunities of IoT implementation in healthcare and industrial sectors, focusing on interoperability, data security, and scalability issues. The objective is to provide actionable solutions for the effective integration of IoT technologies. The investigation shows that in healthcare, mMTC is utilized for non-critical monitoring, while URLLC supports critical tasks like remote surgery. In industrial settings, mMTC improves processes like supply chain management, while URLLC enables automation and real-time control of machinery. Despite these opportunities, challenges such as data privacy, security, interoperability, and reliability remain. Addressing these issues is crucial for unlocking the full potential of IoT in healthcare and industrial sectors. © 2024 IEEE.

关键词： Data privacy

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Agility of in Deep Learning Algorithms in Developing Conflicting Attacks Protections and to Secure Vulnerabilities 4

Agility of in Deep Learning Algorithms in Developing Conflic...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Asaad, Renas Rajab Abdulrahman, Saman Mohammed Hani, Ahmed Alaa Ahmad, Hawar Bahzad Sallow, Amira Bibo Zeebaree, Subhi R. M. Nawroz University Department of Computer Science Duhok Iraq Nawroz University College of Science Department of Computer Science KRG Duhok Iraq Duhok Polytechnic University Technical College of Engineering Energy Engineering Department Duhok Iraq

ISBN: (纸本)9798350360165

Deep learning methods have brought revolution to a variety of applications: from image recognition to natural language processing, and many others. However, their most important vulnerability, when used in adversarial conditions, is a matter of concern that remains up to today. Most importantly, adversarial attacks insinuate the design of input in a way to be capable of bringing errors in the model, hence exposing significant vulnerabilities. This paper investigates the nature of such vulnerabilities and proposes the enhancement of model robustness through comprehensive methodologies. First of all, adversaries would be considered under the threat landscape and classified according to their complexity and impact, should one materialize. The second section would detail all available defense mechanisms, again classified based on strengths and limitations. We propose using the ensemble of adversaries, regularization methods, and dynamic network architecture. We validate this using the empirical testing of the technique over several datasets and models, subjected to a battery of rigorous tests. © 2024 IEEE.

关键词： Adversarial machine learning

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A Development of Edge Computing Method in Integration with IOT System for Optimizing and to Produce Energy Efficiency System 4

A Development of Edge Computing Method in Integration with I...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Asaad, Renas Rajab Hani, Ahmed Alaa Sallow, Amira Bibo Abdulrahman, Saman Mohammed Ahmad, Hawar Bahzad Subhi, R.M. Nawroz University Department of Computer Science Duhok Iraq Nawroz University Department of Computer Science College of Science KRG Duhok Iraq Duhok Polytechnic University Technical College of Engineering ZeebareeEnergy Engineering Department Duhok Iraq

ISBN: (纸本)9798350360165

The number of the Internet of Things (IoT) devices is growing, leading to increased generation of data at a level that has never been witnessed from the network edge. These pervading are through different industries: in the likes of healthcare, transportation, manufacturing, and smart cities, explicit needs call for effective data processing and analysis at the edge. This paper discusses the idea of edge computing in an IoT context, focusing on how this can best be optimized in terms of minimizing the two aforementioned areas to improve overall system performance. The traditional approach to data processing in IoT systems, which is cloud-centric,json, often experiences latency problems because data has to travel over distances to servers that are centralized for analysis. More so, the continuous transmission of voluminous raw data into the cloud is very consuming of the energy and weighs down the network bandwidth. That is where the edge computing comes into place, filling this gap through the relocation of computational tasks near the source, thereby reducing latency that translates to relieving the network from congestion. This paper tries to reflect key principles and benefits of edge computing in an IoT environment based on a comprehensive review of existing literature and relevant case studies. Near IoT devices, edge nodes enable computational capabilities. © 2024 IEEE.

关键词： Smart manufacturing

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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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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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