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Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

Computers, Materials & Continua 2022年第11期73卷 2371-2387页

作者： hussah nasser aleisa El-Sayed M.El-kenawy Amel Ali Alhussan Mohamed Saber Abdelaziz A.Abdelhamid Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Most children and elderly people worldwide die from pneumonia,which is a contagious illness that causes lung *** diagnosing pneumonia from chest X-ray images,many deep learning models have been put *** goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia *** varying the deep learning approach,three pre-trained models,GoogLeNet,ResNet18,and DenseNet121,are employed in this research to extract the main features of pneumonia and normal *** addition,the binary dipper throated optimization(DTO)algorithm is utilized to select the most significant features,which are then fed to the K-nearest neighbor(KNN)classifier for getting the final classification *** guarantee the best performance of KNN,its main parameter(K)is optimized using the continuous DTO *** test the proposed approach,six evaluation metrics were employed namely,positive and negative predictive values,accuracy,specificity,sensitivity,and ***,the proposed approach is compared with other traditional approaches,and the findings confirmed the superiority of the proposed approach in terms of all the evaluation *** minimum accuracy achieved by the proposed approach is(98.5%),and the maximum accuracy is(99.8%)when different test cases are included in the evaluation experiments.

关键词： Metaheuristic pneumonia dipper throated optimization KNN

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Novel Path Counting-Based Method for Fractal Dimension Estimation of the Ultra-Dense Networks

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Intelligent Automation & Soft Computing 2023年第4期36卷 561-572页

作者： Farid Nahli Alexander Paramonov Naglaa F.Soliman hussah nasser aleisa Reem Alkanhel Ammar Muthanna Abdelhamied A.Ateya Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of TelecommunicationsSaint Petersburg193232Russia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Engineering Zagazig UniversityZagazig44519SharqiaEgypt

Next-generation networks,including the Internet of Things(IoT),ﬁfth-generation cellular systems(5G),and sixth-generation cellular systems(6G),suf-fer from the dramatic increase of the number of deployed *** puts high constraints and challenges on the design of such *** changing of the network is one of such challenges that affect the network performance,includ-ing the required quality of service(QoS).The fractal dimension(FD)is consid-ered one of the main indicators used to represent the structure of the communication *** this end,this work analyzes the FD of the network and its use for telecommunication networks investigation and *** clus-ter growing method for assessing the FD is introduced and *** article proposes a novel method for estimating the FD of a communication network,based on assessing the network’s connectivity,by searching for the shortest *** the cluster growing method,the proposed method does not require multiple iterations,which reduces the number of calculations,and increases the stability of the results ***,the proposed method requires less compu-tational cost than the cluster growing method and achieves higher *** method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication *** developed method is evaluated for two different network structures and compared with the cluster growing *** validate the developed method.

关键词： Cluster growing connectivity dense networks fractal dimension network structure shortest route quality of service

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Optimization Ensemble Weights Model for Wind Forecasting System

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Computers, Materials & Continua 2022年第11期73卷 2619-2635页

作者： Amel Ali Alhussan El-Sayed M.El-kenawy hussah nasser aleisa M.El-SAID Sayed A.Ward Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Electrical Engineering Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Delta Higher Institute of Engineering and Technology(DHIET) Mansoura35111Egypt Department of Electrical Engineering Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Faculty of Engineering Delta University for Science and TechnologyGamasaMansoura11152Egypt

Effective technology for wind direction forecasting can be realized using the recent advances in machine ***,the stability and safety of power systems are expected to be significantly ***,the unstable and unpredictable qualities of the wind predict the wind direction a challenging *** paper proposes a practical forecasting approach based on the weighted ensemble of machine learning *** weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input *** conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight *** recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind *** addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its ***,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.

关键词： Guided Whale Optimization Algorithm(Guided WOA) forecasting machine learning weighted ensemble model wind direction

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman hussah nasser aleisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman hussah nasser aleisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

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Computer Systems Science & Engineering 2023年第5期45卷 2235-2247页

作者： Mostafa Nassar Anas M.Ali Walid El-Shafai Adel Saleeb Fathi E.Abd El-Samie Naglaa F.Soliman hussah nasser aleisa Hossam Eldin H.Ahmed Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the *** techniques have been suggested for detecting attacks using machine learning and deep *** significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of ***,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective *** histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification *** different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also *** accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 *** proposed model is compared with wrapper-based and filter-based Feature Selection(FS)*** proposed model reduced classification time and increased detection accuracy.

关键词： Feature selection DCH LSTM CNN security systems

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm Walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.Soliman hussah nasser aleisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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