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Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

Computers, materials & Continua 2022年第10期73卷 801-815页

作者： mahmoud ragab sultanah m.alshamm.r. Amer H.Asseri Waleed K.Almutiry Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center for Artificial Intelligence in Precision medicines King Abdulaziz UniversityJeddah21589Saudi Arabia mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of mathematics College of Science and Arts in Ar RassQassim UniversityBuryadah52571Saudi Arabia

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(mrI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist *** the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided *** study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer *** proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain *** proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of mrI *** addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the mrI ***,a fusion of handcrafted with deep features using residual Network(resNet)is utilized as feature ***,HGSO algorithm with kernel extreme learning machine(KELm)model was utilized for identifying the presence of brain *** examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BrATS 2015 dataset.

关键词： Brain cancer medical imaging deep learning fusion model metaheuristics feature extraction handcrafted features

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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction model

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Computers, materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan mahmoud ragab sultanah m.alshamm.r. Abdullah S.Al-malaise Al-Ghamdi Ports and maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTm)model for predicting traffic *** order to enhance the performance of the ABLSTm model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(mSE),root mean square error(rmSE),and mean absolute percentage error(mAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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modified metaheuristics with Weighted majority Voting Ensemble Deep Learning model for Intrusion Detection System

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Computer Systems Science & Engineering 2023年第11期47卷 2497-2512页

作者： mahmoud ragab sultanah m.alshamm.r. Abdullah S.Al-malaise Al-Ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data *** system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT *** IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive ***,machine learning(mL)-related techniques were used for detecting intrusion in IoTs ***,the IoT IDS mechanism faces significant challenges because of physical and functional *** IoT features use every attribute and feature for IDS self-protection unrealistic and *** study develops a modified metaheuristics with Weighted majority Voting Ensemble Deep Learning(mm-WmVEDL)model for *** proposed mm-WmVEDL technique aims to discriminate distinct kinds of attacks in the IoT *** attain this,the presented mm-WmVEDL technique implements min-max normalization to scale the input *** feature selection purposes,the mm-WmVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDm)*** the presented mm-WmVEDL technique,a Bi-directional long short-term memory(BiLSTm),extreme learning machine(ELm)and an ensemble of gated recurrent unit(GrU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the mm-WmVEDL *** comparison study pointed out the supremacy of the mm-WmVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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The influence of β_2Ar polymorphisms as a predictor of successful short acting β_2-agonist nebulization during asthmatic exacerbation

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海南医学院学报 2010年第7期16卷 842-846页

作者： Nasir m Nor Hidayah AB rashidi A rusli I Department of Emergency medicine School of Medical SciencesUniversiti Sains Malaysia Health Campus Department of Pathology Hospital Sultanah Zainab IIKota Bharu Pharmacogenetics research Group Institute for Research in Molecular MedicineUniversiti Sains Malaysia Health Campus16150 Kubang Kerian

Aim:In this study,we would like to determine associations between β2-Adrenergic receptor(β2Ar)polymorphisms at codon 16 and 27 and the response to short acting β2-agonist during asthmatic ***:This was a prospective cross-sectional study of one year *** hundred and thirty two asthmatic patients were *** mls of venous blood was taken for DNA extraction and then genotyped for the β2Ar polymorphisms using multiplex ***'s clinical responses to β2-agonist nebulization were then compared to their genotype to determine the ***:We found that there was no association between β2Ar polymorphisms at both codon 16 and 27 with response towards short acting β2-agonist,P=0.315 and P=0.706 ***:We suggested that β2Ar polymorphisms at both codon 16 and 27 had no influent on the response to short acting β2-agonist.

关键词：曲马多镇痛效果剂量治疗方法

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