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Competitive Swarm Optimization with Encryption Based Steganography for Digital Image Security

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Computers, Materials & Continua 2022年第8期72卷 4173-4184页

作者： Ala’A.Eshmawi Suliman A.Alsuhibany sayed abdel-khalek Romany F.Mansour Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Mathematics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Digital image security is a fundamental and tedious process on shared communication *** methods have been employed for accomplishing security on digital image transmission,such as encryption,steganography,and *** stenography and encryption are commonly used models to achieve improved ***,optimal pixel selection process(OPSP)acts as a vital role in the encryption *** this motivation,this study designs a new competitive swarmoptimization with encryption based stenographic technique for digital image security,named CSOES-DIS *** proposed CSOES-DIS model aims to encrypt the secret image prior to the embedding *** addition,the CSOES-DIS model applies a double chaotic digital image encryption(DCDIE)technique to encrypt the secret image,and then embedding method was ***,the OPSP can be carried out by the design of CSO algorithm and thereby increases the secrecy *** order to portray the enhanced outcomes of the CSOES-DIS model,a comparative examination with recent methods is performed and the results reported the betterment of the CSOES-DIS model based on different measures.

关键词： Image security optimal pixel selection encryption metaheuristics image steganography

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

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Computers, Materials & Continua 2022年第4期71卷 1189-1203页

作者： Wafaa Alsaggaf Felwa Abukhodair Amani Tariq Jamal sayed abdel-khalek Romany F.Mansour Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly *** use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud ***,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT *** the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision *** this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC *** proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease *** addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic *** COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN *** order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI *** experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

关键词： Intelligent models healthcare systems disease diagnosis internet of things cloud computing clustering deep learning

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

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Computers, Materials & Continua 2022年第7期72卷 1921-1938页

作者： Ahmed Althobaiti Abdullah Alhumaidi Alotaibi sayed abdel-khalek Suliman A.Alsuhibany Romany F.Mansour Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics College of ScienceP.O.Box 11099Taif UniversityTaif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate *** have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision *** scene classification has attracted much attention in the examination of earth data captured by *** paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)*** MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class *** MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)*** fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification *** addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization *** AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of ***,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature *** proposedMMF-EDCmodel has been tested using UCM and WHU-RS *** proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

关键词： Remote sensing unmanned aerial vehicles deep learning artificial intelligence scene classification

Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media;

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Computers, Materials & Continua 2022年第12期73卷 5985-5999页

作者： Hanan M.Alghamdi Saadia H.A.Hamza Aisha M.Mashraqi sayed abdel-khalek Department of Computer Science College of Computing Al-QunfidhahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science and HumanitiesSlayelPrince Sattam Bin AbdulAziz UniversitySaudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UnivesityNajran61441Saudi Arabia Department of Mathematics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,*** Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and ***,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of ***,there is a need exists to develop a proper technique for both identification and classification of sentiments in social *** get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning *** current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social *** presented SOADL-SAC model involves the proper identification and classification of sentiments in social *** order to attain this,SOADL-SAC model carries out data preprocessing to clean the input *** addition,Glove technique is applied to generate the feature ***,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the ***,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier *** order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark *** experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.

关键词： Sentiment analysis classification of sentiment social media seeker optimization algorithm glove embedding natural language processing

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

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Computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： V.Thanikachalam S.Shanthi K.Kalirajan sayed abdel-khalek Mohamed Omri Lotfi M.Ladhar Sri Sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of Scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OGSO algorithm for SqueezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

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Computers, Materials & Continua 2022年第5期71卷 2543-2557页

作者： Ala’A.Eshmawi Hesham Alhumyani sayed abdel khalek Rashid A.Saeed Mahmoud Ragab Romany F.Mansour Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of SciencesTaif UniversityTaif21944Saudi Arabia Centre for Artificial Intelligence in Precision Medicine King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram ***, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

关键词： Opinion mining sentiment analysis fuzzy neural network metaheuristics feature extraction classification

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

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Journal of Software Engineering and Applications 2014年第1期7卷 62-67页

作者： Samy Sadek sayed abdel-khalek Department of Mathematics Faculty of Science Sohag University Sohag Egypt Mathematics Department Faculty of Science Taif Univesity Taif KSA

In 1953, Rènyi introduced his pioneering work (known as α-entropies) to generalize the traditional notion of entropy. The functionalities of α-entropies share the major properties of Shannon’s entropy. Moreover, these entropies can be easily estimated using a kernel estimate. This makes their use by many researchers in computer vision community greatly appealing. In this paper, an efficient and fast entropic method for noisy cell image segmentation is presented. The method utilizes generalized α-entropy to measure the maximum structural information of image and to locate the optimal threshold desired by segmentation. To speed up the proposed method, computations are carried out on 1D histograms of image. Experimental results show that the proposed method is efficient and much more tolerant to noise than other state-of-the-art segmentation techniques.

关键词： α-Entropy Cell Image Entropic Image Segmentation

Entropy and Entanglement of Moving Two Atoms in a Squeezed Field via Four-Photon Process

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Natural Science 2014年第7期6卷 487-494页

作者： sayed abdel-khalek Saraa Hussein Halawani Mathematics Department Faculty of Science Sohag University Sohag Egypt Mathematics Department Faculty of Science Taif University Taif Saudi Arabia

In this paper, the entanglement between two atoms and squeezed field via four photon process is investigated. The dynamical behavior of the entanglement between two atoms and a squeezed field is analyzed. In particular, the effects of the atomic motion, the initial atomic state and the field squeezing are examined. A high amount of entanglement is generated by increasing the field squeezing. Furthermore, we show that a sudden death and sudden birth emerge when the moving atoms are initially prepared in the excited state.

关键词： Von Neumann Entropy Entanglement Sudden Death and Sudden Birth Squeezed State

Some Features on Entropy Squeezing for Two-Level System with a New Nonlinear Coherent State

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Journal of Quantum Information Science 2014年第1期4卷 71-81页

作者： Salama I. Ali sayed abdel-khalek Mathematics Department Faculty of Science Al-Azher University Cairo Egypt Mathematics Department Faculty of Science Sohag University Sohag Egypt

Entropy squeezing is an important feature in performing different tasks in quantum information processing such as quantum cryptography and superdense coding. These quantum information tasks depend on finding the states in which squeezing can be created. In this article, a new feature on entropy squeezing for a two level system with a class of nonlinear coherent state (NCS) is observed. An interesting result on the comparison between the coherent state (CS) and NCS is explored. The influence of the Lamb-Dick parameter in both absence and presence of the Kerr medium is examined. A rich feature of entropy squeezing in the case of NCS, which is observed to describe the motion of the trapped ion, has been obtained.

关键词： Entropy Squeezing Kerr-Like Medium Nonlinear Coherent State