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Fusion Strategy for Improving Medical Image Segmentation

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Computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 1235-1257页

作者： Javed Rashid Imran Khan Ghulam Ali Shafiq ur Rehman fahad Alturise Tamim Alkhalifah Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Department of CS University of OkaraOkara56310Pakistan Department of Botany University of OkaraOkara56310Pakistan Department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassQassim52571Saudi Arabia

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human *** is cultivated in vast areas of Asian and Non-Asian countries,including *** guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability *** existing plant leaf disease detection techniques can detect only one disease from a ***,a single leaf may contain symptoms of multiple *** study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several ***,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava *** proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder ***,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected *** the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava *** self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and *** proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and *** average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

关键词： Guava leaf diseases guava leaf segmentation guava patches segmentation multiple leaf diseases guava leaf diseases dataset

Artificial lift system optimization using machine learning applications

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Petroleum 2022年第2期8卷 219-226页

作者： fahad I.Syed Mohammed Alshamsi Amirmasoud K.Dahaghi S.Neghabhan University of Kansas&ADNOC Offshore Dept.of Chemical and Petroleum E…Lawrence66045USA

Currently,in the oil industry,artificial lift optimization(ALO)systems are dealing with different applications including well monitor and control,reservoir management,production optimization,predictive maintenance,artificial lift,and flow assurance,multiphase pumping systems,*** scope of this article is to provide a detailed analysis of ALO and predictive pump maintenance applications using machine learning(ML)and artificial intelligence(AI).The oil and gas industry has experienced a lot of improvements that have impacted the businesses and economies associated with the market in recent *** such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon ***,advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO *** paper covers several applications and techniques in which ML and AI have been applied to optimize hydrocarbon withdrawal from potentially depleted reservoirs that require some external supports to uplift the reservoir fluid from sub surface to surface using artificial lift *** a nutshell,the applications of AI and ML for the artificial lift selection,their predictive maintenance,equipment malfunctioning detection,*** a self-trained system are the main topics of this *** reviewing each of these techniques,the workflow is also explained along with the effectiveness of utilizing each application to the current operations.

关键词： Artificial lift optimization Predictive maintenance

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

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工程与科学中的计算机建模（英文） 2023年第4期135卷 755-778页

作者： Mohamed Hassan Essai Ali fahad Alraddady Mo’ath Y.Al-Thunaibat Shaima Elnazer Department of Electrical Engineering Faculty of EngineeringAl-Azhar UniversityQena83513Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Management Information Systems Taif UniversityTaif21944Saudi Arabia

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state ***,it utilizes pilots to offer more helpful information about the communication *** proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based *** CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based *** three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based *** BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)*** addition,the computational and learning time complexities for DNN-CSEs are *** estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

关键词： DLNNs channel state estimator 5G and beyond communication systems robust loss functions

Current Progress of Phytomedicine in Glioblastoma Therapy

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Current Medical Science 2020年第6期40卷 1067-1074页

作者： fahad Hassan shah Saad Salman Jawaria Idrees Fariha Idrees Syed Turab Ali shah Abid Ali Khan Bashir Ahmad Centre of Biotechnology and Microbiology University of PeshawarPeshawar 25120Pakistan Department of Pharmacy The University of LahoreIslamabad CampusIslamabad 45800Pakistan Islamia College University Peshawar Peshawar 25000Pakistan

Glioblastoma multiforme,an intrusive brain cancer,has the lowest survival rate of all brain *** chemotherapy utilized to prevent their proliferation and propagation is limited due to modulation of complex cancer signalling *** complex pathways provide infiltrative and drug evading properties leading to the development of chemotherapy ***,the development and discovery of such interventions or therapies that can bypass all these resistive barriers to ameliorate glioma prognosis and survival is of profound *** plants are comprised of an exorbitant range of phytochemicals that have the broad-spectrum capability to target intrusive brain cancers,modulate anti-cancer pathways and immunological responses to facilitate their eradication,and induce *** phytocompounds also interfere with several oncogenic proteins that promote cancer invasiveness and metastasis,chemotherapy resistance and *** plants are extremely vital for promising anti-glioma therapy to avert glioma proliferation and *** this review,we acquired recent literature on medicinal plants whose extracts/bioactive ingredients are newly exploited in glioma therapeutics,and also highlighted their mode of action and pharmacological profile.

关键词： phytomedicine phytochemicals glioblastoma chemotherapy resistance bioactive

Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

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Computers, Materials & Continua 2022年第9期72卷 4691-4707页

作者： Bassam A.Y.Alqaralleh fahad Aldhaban Esam A.AlQarallehs Ahmad H.Al-Omari MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan Faculty of Science Computer Science DepartmentNorthern Border UniversityArar91431Saudi Arabia

Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety *** learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite *** Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS *** developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high *** this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for *** proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of ***,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS ***,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the *** order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark *** experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.

关键词： Cyber-physical systems explainable artificial intelligence deep learning security intrusion detection metaheuristics

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

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Computers, Materials & Continua 2022年第9期72卷 5727-5740页

作者： Esam A.Al.Qaralleh fahad Aldhaban Halah Nasseif Bassam A.Y.Alqaralleh Tamer AbuKhalil School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal UniversityMa’an71111Jordan

Recent technological advancements have been used to improve the quality of living in smart *** the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public *** the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)*** this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city *** major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in *** effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline *** addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 ***,Tesseract based character recognition model is applied to effectively recognize the characters present in the *** experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

关键词： Smart city license plate recognition optimal deep learning metaheuristic algorithms parameter tuning

Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank

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Computers, Materials & Continua 2022年第2期70卷 2991-3004页

作者： Shibli Nisar Muhammad Asghar Khan fahad Algarni Abdul Wakeel M.Irfan Uddin Insaf Ullah Department of Electrical Engineering National University of Sciences and Technology(NUST)Islamabad44000Pakistan Hamdard Institute of Engineering&Technology Islamabad44000Pakistan College of Computing and Information Technology University of BishaBishaSaudi Arabia Institute of Computing Kohat University of Science and TechnologyKohatPakistan

One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a *** procedure,however,usually requires an appointment with an ophthalmologist,which is both time-consuming and expensive *** issues that can arise include a lack of appropriate equipment and trained practitioners,especially in rural *** on a cognitively motivated attribute extraction and speech recognition approach,this paper proposes a novel idea that immediately determines the eyesight *** proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature *** adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the *** performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth,established by the expert ophthalmologist’s *** overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%.The proposed method potentially offers a second opinion to ophthalmologists,and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.

关键词： Eyesight test speech recognition HMM SVM feature extraction

Hybrid Smart Contracts for Securing IoMT Data

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Computer Systems Science & Engineering 2023年第1期44卷 457-469页

作者： D.Palanikkumar Adel fahad Alrasheedi P.Parthasarathi S.S.Askar Mohamed Abouhawwash Department of Computer Science and Engineering Dr NGP Institute of TechnologyCoimbatore641048India Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathy638401India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Data management becomes essential component of patient *** of Medical Things(IoMT)performs a wireless communication between E-medical applications and human *** of consulting a doctor in the hospital,patients get health related information remotely from the *** main issues in the E-Medical application are lack of safety,security and priv-acy preservation of patient’s health care *** overcome these issues,this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured *** health data is collected from physician,smart devices *** main goal is to store this highly valuable health related data in a secure,safety,easy access and less cost-effective *** this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work(PBFT-PoW).The implementation is done using cloud technology setup with smart contracts(PBFT-PoW).The accuracy rate of PBFT is 90.15%,for PoW is 92.75%and our proposed work PBFT-PoW is 99.88%.

关键词： PoW byzantine fault tolerance IoMT cloud computing health care data