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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem s.ifedine kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer science University of ZakhoZakhoKurdistan RegionIraq Department of Computer science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Pattern Recognition of Modulation signal Classification Using Deep Neural Networks

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Computer systems science & Engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan s.Ramesh s.Janupriya sangsoon Lim s.ifedine kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CsM-FFDNN).The goal of the CsM-FFDNN model is to classify the different types of digitally modulated *** proposed CsM-FFDNN model involves two major processes namely FE and *** proposed model uses sevcik Fractal Dimension(sFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CsM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CsM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： Pattern recognition signal modulation communication signals deep learning feature extraction

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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Computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia s.ifedine kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with seagull Optimization(TORA-DLsGO)*** proposed TORA-DLsGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLsGO technique uses the deep belief network(DBN)model for optimum offloading ***,the sGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLsGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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semi/Fully-Automated segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images

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Computers, Materials & Continua 2022年第2期70卷 4087-4105页

作者： Venkatesan Rajinikanth shabnam Mohamed Aslam s.ifedine kadry Orawit Thinnukool Department of Electronics and Instrumentation Engineering St.Joseph’s College of Engineering Chennai600119India Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded systems and Mobile Application in Health science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved *** and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its *** proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better *** proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed *** experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIs-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed *** outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.

关键词： Endoscopic/colonoscopic-image gastric polyp aquila-optimizationalgorithm fuzzy entropy chan-vese segmentation

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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Computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.s.s.Anupama G.Jose Moses E.Laxmi Lydia s.ifedine kadry sangsoon Lim Department of Computer science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer science Engineering Malla Reddy Engineering College500100India Department of Computer science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOssO)algorithm for selecting the CHs and construct *** QOssO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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Computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri s.srinivasan Mohamed Yacin sikkandar T.s.Balaji s.ifedine kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia saveetha school of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia saveetha school of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with shannon’s function(AFA-sF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,squeezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(ssAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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Computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.s.s.Anupama Raed Alsini N.supriya E.Laxmi Lydia s.ifedine kadry sang-soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PsNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PsNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

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Intelligent Automation & soft Computing 2023年第8期37卷 1453-1468页

作者： C.s.s.Anupama Rafina Zakieva Afanasiy sergin E.Laxmi Lydia s.ifedine kadry Chomyong Kim Yunyoung Nam Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Candidate of Pedagogical sciences Department of Industrial Electronics and Lighting EngineeringKazan State Power Engineering UniversityKazan420066Russia Department of Theories and Principles of Physical Education and Life safety North-Eastern Federal University Named After M.K.AmmosovYakutsk677000Russia Department of Computer science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Gait is a biological typical that defines the method by that people *** is the most significant performance which keeps our day-to-day life and physical *** electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any *** classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery *** approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)*** study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG *** EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is *** addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait *** last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR *** results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

关键词： Feature fusion human gait recognition deep learning electromyography signals artificial algae algorithm

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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Computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.sheryl Oliver P.suresh A.Mohanarathinam s.ifedine kadry Orawit Thinnukool Department of Computer science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded systems and Mobile Application in Health science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

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Computers, Materials & Continua 2022年第1期70卷 619-635页

作者： Javaria Amin Muhammad Almas Anjum Muhammad sharif s.ifedine kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMsATs University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

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