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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in medical Data

Computers, materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed m.El-kenawy Nagwan Abdel Samee mona m.jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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Computers, materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed m.El-Kenawy Ghada Atteia mona m.jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

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Computers, materials & Continua 2023年第1期74卷 1457-1473页

作者： Zahid Ullah mona jamjoom Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Alzheimer’s disease(AD)is a chronic and common form of dementia that mainly affects elderly *** disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear,and there is nomedicinal or surgical treatment available yet *** loss of memory and functionality control in multiple degrees according to AD’s progression ***,early diagnosis of AD can hinder its *** imaging tools such as magnetic resonance imaging(mRI),computed tomography(CT)scans,positron emission tomography(PET),*** help in medical diagnosis of ***,computer-aided diagnosis(CAD)such as deep learning applied to brain images obtained with these tools,has been an established strategic methodology that is widely used for clinical assistance in prognosis of *** this study,we proposed an intelligent methodology for building a convolutional neural network(CNN)from scratch to detect AD stages from the brain mRI images dataset and to improve patient *** is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting ***,for better understanding of classifiers and to overcome the model overfitting problem,we applied data augmentation to the minority classes in order to increase the number of mRI images in the *** experiments were conducted using Alzheimer’s mRI dataset consisting of brain mRI scanned *** performance of the proposed model determines detection of the four stages of *** results show high performance of the proposed model in that the model achieved a 99.38%accuracy rate,which is the highest so ***,the proposed model performance in terms of accuracy,precision,sensitivity,specificity,and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

关键词： Dementia mRI images CNN model detection data augmentation decision support system

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Securing Copyright Using 3D Objects Blind Watermarking Scheme

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Computers, materials & Continua 2022年第9期72卷 5969-5983页

作者： Hussein Abulkasim mona jamjoom Safia Abbas Faculty of Science New Valley UniversityEl-Kharga 72511Egypt Department of Computer Sciences College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia

Recently,securing Copyright has become a hot research topic due to rapidly advancing information *** a host cover,watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary *** media covers may often take any form,including audio,video,photos,even DNA data *** this work,we present a new methodology for watermarking to hide secret data into 3-D *** technique of blind extraction based on reversing the steps of the data embedding process is *** implemented technique uses the features of the 3-D object vertex’discrete cosine transform to embed a grayscale image with high *** coefficient of vertex and the encrypted picture pixels are used in the watermarking ***,the extraction approach is fully blind and is dependent on the backward steps of the encoding procedure to get the hidden *** distance,Euclidean distance,manhattan distance,and the Cosine distance are used to evaluate and test the performance of the proposed *** visibility and imperceptibility of the proposed method are assessed to show the efficiency of our work compared to previous corresponding methods.

关键词： Discrete cosine transform 3-D object copyright watermarking

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Plant Leaf Diseases Classification Using Improved K-means Clustering and SVm Algorithm for Segmentation

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Computers, materials & Continua 2023年第7期76卷 367-382页

作者： mona jamjoom Ahmed Elhadad Hussein Abulkasim Safia Abbas Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt Faculty of Science New Valley UniversityEl-Kharga72511Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt

Several pests feed on leaves,stems,bases,and the entire plant,causing plant *** a result,it is vital to identify and eliminate the disease before causing any damage to *** detecting plant disease and treating it is pretty challenging in this *** processing is employed to detect plant disease since it requires much effort and an extended processing *** main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf ***,this work uses the Support vector machine(SVm)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and *** gray level co-occurrence matrix(GLCm)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant *** to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.

关键词： SVm machine learning GLCm algorithm K-means clustering LBP

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Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in mobile Cloud

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Computers, materials & Continua 2020年第12期65卷 1925-1943页

作者： Nithya Rekha Sivakumar Sara Ghorashi mona jamjoom mai Alduaili Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

In today’s world,smart phones offer various applications namely face detection,augmented-reality,image and video processing,video gaming and speech *** the increasing demand for computing resources,these applications become more *** Computing(CC)environment provides access to unlimited resource pool with several features,including on demand self-service,elasticity,wide network access,resource pooling,low cost,and ease of *** Cloud Computing(mCC)aimed at overcoming drawbacks of smart phone *** task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant *** remote execution,recent studies suggested downloading all or part of mobile application from mobile *** the other hand,in offloading process,mobile device energy consumption,Central Processing Unit(CPU)utilization,execution time,remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks *** address the issues,a Heuristic and Bent Key Exchange(H-BKE)method can be considered by both ways to optimize energy consumption as well as to improve security during ***,an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where,the secondary users are allocated with the unused primary users’***,a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’unused spectrum by the secondary *** are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption,energy consumption along with the security aspects covered during offloading in mCC.

关键词： Cloud computing mobile cloud computing heuristic bent key exchange reactive offloading

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Statistical Analysis of a Class of Secure Relay Assisted Cognitive Radio Networks

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China Communications 2018年第12期15卷 174-189页

作者： mona Shokair Waleed Saad Shady m.Ibraheem Electronic and Electrical Comm.Dep. Faculty of Electronic EngineeringMeno.a UniversityEgypt Telecom Egypt Egypt Electrical Engineering Department College of EngineeringShaqra UniversityDawadmiAr RiyadhKingdom of Saudi Arabia

In this paper, the problem of communication security in an underlay wiretap cognitive radio network is addressed and statistically investigated. We rely on a simple dual-hop communication model of decode and forward relay assisted network. Regarding the interference from primary users, interference power and maximum transmit power constraints; this network is subjected to multiple eavesdropping attacks which employ a specific interception strategy. To confound this eavesdropping, proposed selection schemes are exploited that aim at maximizing the minimum of the dual secrecy rates in order to strengthen the physical layer security. moreover, exact and asymptotic closed form expressions are derived for specific performance metrics over independent and identically distributed Rayleigh fading channels. At high signal to interference noise ratio(SINR), tangential system bounds are also derived and discussed. monte Carlo simulation results emphasize our assumption. It is found out that at the full diversity of the system, any additional node that enters the cooperative eavesdropping system becomes significantly of no effect.

关键词： relay selection non-colluding or colluding eavesdroppers outage probability secrecy rate power constraints.

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Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G

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Computers, materials & Continua 2022年第8期72卷 3751-3762页

作者： Ahmed.A.Rosas mona Shokair m.I.Dessouky Department of Electronics and Electrical Communication Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration *** this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular *** genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each *** simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes.

关键词： 5G D2D communication spectrum allocation power allocation genetic algorithm optimization BAT-optimization particle swarm optimization

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Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

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Computers, materials & Continua 2023年第6期75卷 5659-5674页

作者： mohammad Yamin Tom Gedeon Saleh Bajaba mona m.Abusurrah Department of management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Optus Centre for AI Curtin UniversityPerth6102Australia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of management Information Systems College of Business AdministrationTaibah UniversityAl-Madinah42353Saudi Arabia

In recent years,the demand for biometric-based human recog-nition methods has drastically increased to meet the privacy and security *** prints,palm veins,finger veins,fingerprints,hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition *** the available biometric recognition techniques,Finger Vein Recognition(FVR)is a general technique that analyzes the patterns of finger veins to authenticate the *** Learning(DL)-based techniques have gained immense attention in the recent years,since it accomplishes excellent outcomes in various challenging domains such as computer vision,speech detection and Natural Language Processing(NLP).This technique is a natural fit to overcome the ever-increasing biomet-ric detection problems and cell phone authentication issues in airport security *** current study presents an Automated Biometric Finger Vein Recognition using Evolutionary Algorithm with Deep Learning(ABFVR-EADL)*** presented ABFVR-EADL model aims to accomplish bio-metric recognition using the patterns of the finger ***,the presented ABFVR-EADL model employs the histogram equalization technique to pre-process the input *** feature extraction,the Salp Swarm Algorithm(SSA)with Densely-connected Networks(DenseNet-201)model is exploited,showing the proposed method’s ***,the Deep-Stacked Denoising Autoencoder(DSAE)is utilized for biometric *** proposed ABFVR-EADL method was experimentally validated using the benchmark databases,and the outcomes confirmed the productive performance of the proposed ABFVR-EADL model over other DL models.

关键词： Biometric authentication finger vein recognition deep learning evolutionary algorithm security privacy

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Gas–liquid flow mass transfer in a T-shape microreactor stimulated with 1.7 mHz ultrasound waves

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Chinese Journal of Chemical Engineering 2017年第9期25卷 1143-1152页

作者： mona Akbari masoud Rahimi mahboubeh Faryadi CFD Research Center Chemical Engineering Department Razi University

This paper describes the application of ultrasound waves on hydrodynamics and mass transfer characteristics in the gas–liquid flow in a T-shape microreactor with a diameter of 800 μm. A 1.7 mHz piezoelectric transducer(PZT) was employed to induce the vibration in this microreactor. Liquid side volumetric mass transfer coefficients were measured by physical and chemical methods of CO_2 absorption into water and Na OH solution. The approach of absorption of CO_2 into a 1 mol·L^(-1) Na OH solution was used for analysis of interfacial areas. With the help of a photography system, the fluid flow patterns inside the microreactor were analyzed. The effects of superficial liquid velocity, initial concentration of Na OH, superficial CO_2 gas velocity and length of microreactor on the mass transfer rate were investigated. The comparison between sonicated and plain microreactors(microreactor with and without ultrasound) shows that the ultrasound wave irradiation has a significant effect on kLa and interfacial area at various operational conditions. For the microreactor length of 12 cm, ultrasound waves improved kLa and interfacial area about 21% and 22%, respectively. From this study, it can be concluded that ultrasound wave irradiation in microreactor has a great effect on the mass transfer rate. This study suggests a new enhancement technique to establish high interfacial area and kLa in microreactors.

关键词： Ultrasound waves microreactor Gas–liquid flow mass transfer Absorption

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