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Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

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Computers, Materials & Continua 2022年第8期72卷 4143-4155页

作者： Mahmoud ragab Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt

Rainfall prediction becomes popular in real time environment due to the developments of recent *** and fast rainfall predictive models can be designed by the use of machine learning(ML),statistical models,***,feature selection approaches can be derived for eliminating the curse of dimensionality *** this aspect,this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression(CSMO-OKRR)model for accurate rainfall *** goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather *** proposed CSMO-OKRR technique encompasses three major processes namely feature selection,prediction,and parameter ***,the CSMO algorithm is employed to derive a useful subset of features and reduce the computational *** addition,the KRR model is used for the prediction of rainfall based on weather ***,the symbiotic organism search(SOS)algorithm is employed to properly tune the parameters involved in it.A series of simulations are performed to demonstrate the better performance of the CSMO-OKRR technique with respect to different *** simulation results reported the enhanced outcomes of the CSMO-OKRR technique with existing techniques.

关键词： Rainfall prediction statistical techniques machine learning kernel ridge regression symbiotic organism search parameter tuning

A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures

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Computers, Materials & Continua 2022年第7期72卷 1579-1592页

作者： Mahmoud ragab Ali Altalbe Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

Due to the drastic increase in the number of critical infrastructures like nuclear plants,industrial control systems(ICS),transportation,it becomes highly vulnerable to several *** become the major targets of cyberattacks due to the increase in number of interconnections with other *** research works have focused on the design of intrusion detection systems(IDS)using machine learning(ML)and deep learning(DL)*** the same time,Blockchain(BC)technology can be applied to improve the security *** order to resolve the security issues that exist in the critical infrastructures and ICS,this study designs a novel BC with deep learning empowered cyber-attack detection(BDLE-CAD)in critical infrastructures and *** proposed BDLE-CAD technique aims to identify the existence of intrusions in the *** addition,the presented enhanced chimp optimization based feature selection(ECOA-FS)technique is applied for the selection of optimal subset of ***,the optimal deep neural network(DNN)with search and rescue(SAR)optimizer is applied for the detection and classification of ***,a BC enabled integrity checking scheme(BEICS)has been presented to defend against the misrouting *** experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying *** simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the accuy of 92.63%.

关键词： BC internet of critical infrastructure ids deep learning security deep neural network machine learning

Low Complexity Encoder with Multilabel Classification and Image Captioning Model

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Computers, Materials & Continua 2022年第9期72卷 4323-4337页

作者： Mahmoud ragab Abdullah Addas Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Landscape Architecture Department Faculty of Architecture&PlanningKing Abdulaziz UniversityJeddah21589Saudi Arabia

Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT,which are commonly employed in diverse application areas, especially forcapturing remote sensing (RS) images. At the same time, the interpretationof the captured RS image also plays a crucial issue, which can be addressedby the multi-label classification and Computational Linguistics based imagecaptioning techniques. To achieve this, this paper presents an efficient lowcomplexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves thelow complexity encoder using the Neighborhood Correlation Sequence (NCS)with a burrows wheeler transform (BWT) technique called LCE-BWT forencoding the RS images captured by the UAV. The application of NCS greatlyreduces the computation complexity and requires fewer resources for imagetransmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presentedto determine the multiple class labels of the RS image, shows the novelty ofthe work. Finally, the Computational Linguistics based Bidirectional EncoderRepresentations from Transformers (BERT) technique is applied for imagecaptioning, to provide a proficient textual description of the RS image. Theperformance of the presented technique is tested using the UCM dataset. Thesimulation outcome implied that the presented model has obtained effectivecompression performance, reconstructed image quality, classification results,and image captioning outcome.

关键词： Image captioning unmanned aerial vehicle low complexity encoder remote sensing images image classification

Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第9期72卷 5577-5591页

作者： Mahmoud ragab Ashwag Albukhari Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large *** histopathologist generally investigates the colon biopsy at the time of colonoscopy or *** detection of colorectal cancer is helpful to maintain the concept of accumulating cancer *** medical practices,histopathological investigation of tissue specimens generally takes place in a conventional way,whereas automated tools that use Artificial Intelligence(AI)techniques can produce effective results in disease detection *** this background,the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification(AAI-CCDC)*** proposed AAICCDC technique focuses on the examination of histopathological images to diagnose colorectal ***,AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation,Median Filtering(MF)-based noise removal,and contrast *** addition,Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature ***,Glowworm Swarm Optimization(GSO)with Stacked Gated Recurrent Unit(SGRU)model is used for the detection and classification of colorectal *** proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.

关键词： Colorectal cancer medical data classification noise removal data classification artificial intelligence biomedical images deep learning optimizers

Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model

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Computers, Materials & Continua 2022年第8期72卷 4185-4200页

作者： Mahmoud ragab Diaa Hamed Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Mineral Resources and Rocks Faculty of Earth SciencesKing Abdulaziz UniversityJeddah 21589Saudi Arabia Geology Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt

Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision ***,the advances of machine learning(ML)techniques assist to perform the effective classification *** this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data *** proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification ***,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification ***,the OSVM model investigates the clustered medical data to perform classification ***,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM *** comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.

关键词： Clustering medical data classification machine learning parameter tuning support vector machines

Metaheuristic Lightweight Cryptography for Security Enhancement in Internet of Things

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Computers, Materials & Continua 2022年第8期72卷 3009-3023页

作者： Mahmoud ragab Ehab Bahaudien Ashary Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia

The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different ***,data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure *** this background,the current paper proposes a novel lightweight cryptography method for enhance the security in *** proposed encryption algorithm is a blend of Cross Correlation Coefficient(CCC)and Black Widow Optimization(BWO)*** the presented encryption technique,CCC operation is utilized to optimize the encryption process of cryptography *** projected encryption algorithm works in line with encryption and decryption *** key selection is performed with the help of Artificial Intelligence(AI)tool named BWO *** the combination of AI technique and CCC operation,optimal security operation is improved in *** different sets of images collected from databases,the projected technique was validated in MATLAB on the basis of few performance metrics such as encryption time,decryption time,Peak Signal to Noise Ratio(PSNR),CC,Error,encryption time and decryption *** results were compared with existing methods such as Elliptical Curve cryptography(ECC)and Rivest-Shamir-Adleman(RSA)and the supremacy of the projected method is established.

关键词： Security images encryption time correlation coefficient black widow optimization artificial intelligence decryption time

Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

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Computers, Materials & Continua 2022年第10期73卷 1783-1797页

作者： Mahmoud ragab Adel A.Bahaddad Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Due to drastic increase in the generation of data,it is tedious to examine and derive high level knowledge from the *** rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning *** feature selection procedure establishes a generally encountered issue of global combinatorial *** FS process can lessen the number of features by the removal of unwanted and repetitive *** this aspect,this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning(IHSFS-ODL)enabled classification *** proposed IHSFS-ODL technique intends to reduce the curse of dimensionality and enhance classification *** addition,the IHSFSODL technique derives an IHSFS technique by the use of local search method with traditional harmony search algorithm(HSA)for global ***,ODL based classifier including quantum behaved particle swarm optimization(QPSO)with gated recurrent unit(GRU)is applied for data classification *** utilization of HSA for the choice of features and QPSO algorithm for hyper parameter tuning processes helps to accomplish maximum classification *** order to demonstrate the enhanced outcomes of the IHSFS-ODL technique,a series of simulations were carried out and the results reported the betterment over its recent state of art approaches.

关键词： Data classification feature selection global optimization deep learning metaheuristics

Optimal Deep Transfer Learning Model for Histopathological Breast Cancer Classification

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Computers, Materials & Continua 2022年第11期73卷 2849-2864页

作者： Mahmoud ragab Alaa F.Nahhas Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival *** of the commonly utilized imaging modalities for breast cancer is histopathological *** manual inspection of histopathological images is a challenging task,automated tools using deep learning(DL)and artificial intelligence(AI)approaches need to be *** latest advances of DL models help in accomplishing maximum image classification performance in several application *** this view,this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer(DTLRO-HCBC)*** proposed DTLRO-HCBC technique aims to categorize the existence of breast cancer using histopathological *** accomplish this,the DTLRO-HCBC technique undergoes pre-processing and data augmentation to increase quantitative ***,optimal SqueezeNet model is employed for feature extractor and the hyperparameter tuning process is carried out using the Adadelta ***,rider optimization with deep feed forward neural network(RO-DFFNN)technique was utilized employed for breast cancer *** RO algorithm is applied for optimally adjusting the weight and bias values of the DFFNN *** demonstrating the greater performance of the DTLRO-HCBC approach,a sequence of simulations were carried out and the outcomes reported its promising performance over the current state of art approaches.

关键词： Breast cancer histopathological images machine learning biomedical analysis deep learning computer vision

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2309-2322页

作者： Mahmoud ragab Jaber Alyami Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Diagnostic Radiology Faculty of Applied Medical SciencesKing AbdulazizJeddah21589Saudi Arabia Imaging Unit King Fahd Medical Research CentreKing AbdulazizJeddah21589Saudi Arabia

Liver cancer is one of the major diseases with increased mortality in recent years,across the *** detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its ***,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response ***,there is a need exists to develop automated tools for liver cancer detection in a precise *** this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)*** proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation ***,Kapur’s entropy-based segmentation technique is used to identify the affected regions in ***,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature *** last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer *** order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different *** comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.

关键词： Liver cancer image segmentation artificial intelligence deep learning CT images parameter tuning