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Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

Computers, Materials & Continua 2022年第10期73卷 1159-1175页

作者： Mohammad Alamgeer Saud S.Alotaibi Shaha Al-Otaibi Nazik Alturki Anwer Mustafa hilal Abdelwahed Motwakel Ishfaq Yaseen Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge ***,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare *** this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare *** proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud *** proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT ***,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted *** addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter *** usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet *** examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried *** simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.

关键词： Internet of things healthcare decision making privacy preserving blockchain deep learning

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Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

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Computers, Materials & Continua 2022年第9期72卷 4589-4601页

作者： Souad Larabi-Marie-Sainte Eatedal Alabdulkreem Mohammad Alamgeer Mohamed K Nour Anwer Mustafa hilal Mesfer Al Duhayyim Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Electroencephalography(EEG)eye state classification becomes an essential tool to identify the cognitive state of *** can be used in several fields such as motor imagery recognition,drug effect detection,emotion categorization,seizure detection,*** the latest advances in deep learning(DL)models,it is possible to design an accurate and prompt EEG EyeState classification *** this view,this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification(CBADL-BEESC)*** major intention of the CBADL-BEESC technique aims to categorize the presence of EEG *** CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature *** addition,extreme learning machine autoencoder(ELM-AE)model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using *** experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.

关键词： Biomedical signals EEG EyeState classification deep learning metaheuristics

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Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

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Computers, Materials & Continua 2022年第6期71卷 6257-6273页

作者： Majdy M.Eltahir Ibrahim Abunadi Fahd NAl-Wesabi Anwer Mustafa hilal Adil Yousif Abdelwahed Motwakel Mesfer Al Duhayyim Manar Ahmed Hamza Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Novel coronavirus 2019(COVID-19)has affected the people’s health,their lifestyle and economical status across the *** application of advanced Artificial Intelligence(AI)methods in combination with radiological imaging is useful in accurate detection of the *** also assists the physicians to take care of remote villages *** current research paper proposes a novel automatedCOVID-19 analysismethod with the help ofOptimal Hybrid Feature Extraction(OHFE)and Optimal Deep Neural Network(ODNN)called OHFE-ODNN from chest x-ray *** objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray *** presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering(MF)-based pre-processed,feature extraction and finally,binary(COVID/Non-COVID)and multiclass(Normal,COVID,SARS)***,in OHFE-based feature extraction,Gray Level Co-occurrence Matrix(GLCM)and Histogram of Gradients(HOG)are integrated *** presented OHFE-ODNN model includes Squirrel Search Algorithm(SSA)for finetuning the parameters of *** performance of the presented OHFEODNN technique is conducted using chest x-rays *** presented OHFE-ODNN method classified the binary classes effectively with a maximumprecision of 95.82%,accuracy of 94.01%and F-score of 96.61%.Besides,multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%,accuracy of 95.60%and an F-score of 95.73%.

关键词： COVID-19 classification deep learning radiological images

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Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

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Computers, Materials & Continua 2022年第7期72卷 141-156页

作者： R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa hilal Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering RajaRajeswari College of EngineeringBengaluru560074India Department of Information Technology Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a15347Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation Systems(ITS)in today’s *** is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real *** is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in ***,effective identification and the classification of attacks have become the major requirements for secure data transmission in *** the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a *** this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for *** presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the *** algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel *** is applied to avoid congestion on ***,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the *** outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

关键词： VANET intelligent transportation systems congestion control attack detection dos attack deep learning

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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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Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

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Computers, Materials & Continua 2022年第5期71卷 3969-3983页

作者： CSS Anupama T.J.Benedict Jose Heba FEid Nojood O Aljehane Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa hilal Department Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Faculty of Science AL-Azhar UniversityCairo11651Egypt Faculty of Computers and Information Technology University of Tabuk47512Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia&Faculty of Computer and ITSana’a University31220Yemen Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman University11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or *** image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,*** the same time,cervical cancer becomes a major reason for increased women’s mortality *** cervical cancer is an identified at an earlier stage using regular pap smear *** this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)*** BPSIC-CDF technique enables the IoT devices for pap smear image *** addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)***,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear ***,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear *** order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev *** experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

关键词： Biomedical imaging pap smear images internet of things deep learning cervical cancer disease diagnosis

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Portal hypertension secondary to myelofibrosis with myeloid metaplasia:A study of 13 cases

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World Journal of Gastroenterology 2009年第25期15卷 3128-3133页

作者： Mohannad Abu-hilal Jayant Tawaker Department of Internal Medicine Division of GastroenterologyMayo Clinic College of MedicineRochester

AIM:To describe the clinical presentation and complications of portal hypertension (PH) secondary to myelofibrosis with myeloid metaplasia (MMM).METHODS: Medical records for 123 patients with MMM were ***: Thirteen patients with PH secondary to MMM were identified. Median ages at time of MMM and PH diagnosis were 61 and 66 years, respectively. The interval from MMM diagnosis to presentation with one of the PH features ranged from 1 to 11 years. Variceal bleeding and ascites were the most common presentations. Of the eight patients who presented with variceal bleeding, six patients underwent endoscopic variceal ligation (EVL) with no variceal recurrence or hematological worsening during a 12-mo follow up ***: Patients with MMM might develop PH. Exact mechanisms leading to PH in MMM are still controversial. As in other etiologies, variceal bleeding and ascites are the most common presentations. Anemia may correlate with, and/or predict, the severity of the PH presentation in these patients. EVL can successfully control variceal bleeding in MMM. Further clinical studies are required.

关键词：骨髓增生继发高压纤维化静脉曲张临床表现 PH值

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Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

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Computers, Materials & Continua 2022年第4期71卷 1079-1094页

作者： Mesfer Al Duhayyim Hadeel Alsolai Fahd N.Al-Wesabi Nadhem Nemri Hany Mahgoub Anwer Mustafa hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or *** crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial *** the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical *** IoT can be combined with the FCP model to examine the financial data from the users and perform decision making *** paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT *** MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and ***,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further *** addition,SMOTE technique is employed to handle class imbalance *** goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of ***,SAE model is utilized as a classification technique to determine the class label of the financial *** the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the *** extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct *** experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

关键词： Financial data financial crisis prediction class imbalance problem internet of things stacked autoencoder

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IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

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Computer Systems Science & Engineering 2023年第7期46卷 475-489页

作者： Mesfer Alduhayyem Mrim M.Alnfiai Nabil Almalki Fahd N.Al-Wesabi Anwer Mustafa hilal Manar Ahmed Hamza Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia King Salman Center for Disability Research RiyadhAl-Hayātim 16273Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Special Education College of EducationKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Science College of Science&ArtsKing Khaled UniversityAr-Riyad 12372Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Visual impairment is one of the major problems among people of all age groups across the *** Impaired Persons(VIPs)require help from others to carry out their day-to-day *** they experience several problems in their daily lives,technical intervention can help them resolve the *** this background,an automatic object detection tool is the need of the hour to empower VIPs with safe *** recent advances in the Internet of Things(IoT)and Deep Learning(DL)techniques make it *** current study proposes IoT-assisted Transient Search Optimization with a Lightweight RetinaNetbased object detection(TSOLWR-ODVIP)model to help *** primary aim of the presented TSOLWR-ODVIP technique is to identify different objects surrounding VIPs and to convey the information via audio message to *** data acquisition,IoT devices are used in this ***,the Lightweight RetinaNet(LWR)model is applied to detect objects ***,the TSO algorithm is employed for fine-tuning the hyperparameters involved in the LWR ***,the Long Short-Term Memory(LSTM)model is exploited for classifying *** performance of the proposed TSOLWR-ODVIP technique was evaluated using a set of objects,and the results were examined under distinct *** comparison study outcomes confirmed that the TSOLWR-ODVIP model could effectually detect and classify the objects,enhancing the quality of life of VIPs.

关键词： Visually impaired people deep learning object detection computer vision long short-term memory transient search optimization

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Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

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Computers, Materials & Continua 2022年第8期72卷 2783-2795页

作者： Fadwa Alrowais Ahmed S.Almasoud Radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Faculty of Computer and Information TechnologySana’a University61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation ***,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in *** study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)*** proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC ***,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC ***,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC *** examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： Data offloading mobile edge computing security machine learning artificial intelligence XGBoost salp swarm algorithm

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