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| 1 | Changing dietary n-6:n-3 ratio using different oil sources affects performance,behavior,cytokines mRNA expression and meat fatty acid profile of broiler chickens显示文摘Typical formulated broiler diets are deficient in n-3 poly-unsaturated fatty acids(PUFA) due to widening n-6:n-3 PUFA ratio which could greatly affect performance,immune system of birds and,more importantly,meat quality.This study was conducted to evaluate the effect of modifying dietary n-6:n-3 PUFA ratio from plant and animal oil sources on performance,behavior,cytokine m RNA expression,antioxidative status and meat fatty acid profile of broiler chickens.Birds(n=420) were fed 7 diets enriched with different dietary oil sources and ratios as follows: sunflower oil in control diet(C); fish oil(FO); 1:1 ratio of sunflower oil to FO(C1 FO1); 3:1 ratio of sunflower oil to fish oil(C3 FO1); linseed oil(LO); 1:1 ratio of sunflower oil to linseed oil(C1 LO1); 3:1 ratio of sunflower oil to linseed oil(C3 LO1),resulting in dietary n-6:n-3 ratios of approximately 40:1,1.5:1,4:1,8:1,1:1,2.5:1 and 5:1,respectively.The best final body weight,feed conversion ratio as well as protein efficiency ratio of broilers were recorded in the C1 FO1 and C1 LO1 groups.Compared with the control group,the dressing percentage and breast and thigh yield were highest in the C1 FO1 and C1 LO1 groups.Narrowing the dietary n-6:n-3 ratio increased(P < 0.05) n-3 PUFA content of breast meat.Moreover,the breast meat contents of eicosapentaenoic acid and docosahexaenoic acid increased(P < 0.05) with increasing dietary FO whereas a-linolenic acid content was higher with LO supplementation.Also,enriching the diets with n-3 PUFA from FO and LO clearly decreased(P < 0.05) serum total cholesterol,triglycerides and very low-density lipoproteins and enhanced antioxidative status.The feeding frequency was decreased(P < 0.05) in the C1 FO1 and C1 LO1 groups.Likewise,n-3 PUFA-enriched diets enhanced the frequency of preening,wing flapping and flightiness.Animal oil source addition,compared to plant oil,to broiler diets enhanced the relative m RNA expression of interferon gamma,interleukin-1 beta,interleukin-2 and interleukin-6 genes,especially at low n-6:n-3 ratios.This study has clearly shown that narrowing n-6:n-3 ratio through the addition of FO or LO improved performance and immune response of broilers and resulted in healthy chicken meat,enriched with long chain n-3 PUFA. | Doaa Ibrahim Rania El-Sayed Safaa I.Khater Enas N.Said Shefaa A.M.El-Mandrawy | 2018 | Animal Nutrition2018，，1： | 5 |
| 2 | Fault location scheme for combined overhead line with underground power cable 显示文摘 | El Sayed Tag El Din Mohamed Mamdouh Abdel Aziz Doaa khalil Ibrahim | 2006 | Electric Power Systems Re- search2006，76，11： | 1 |
| 3 | A New Relay and Jammer Sel- ection Schemes for Secure One-Way Cooperative Netwo- rks显示文摘 | H Doaa Ibrahim S Emad | 2014 | Wireless Personal Communications2014，75，1： | 1 |
| 4 | Estimation of the lifetime of electrical components in distribution networks显示文摘 | Mamdouh Abd El Aziz M Doaa Khalil Ibrahim | 2010 | The Online Journal on Electronics and Electrical Engineering2010，2，3： | 1 |
| 5 | Traveling-Wave-Based Fault-Location Scheme for Multiend-Aged Underground Cable System显示文摘 | Mahmoud Gilany Doaa khalil Ibrahim El Sayed Tag Eldin | 2007 | IEEE Transactions on Power Delivery2007，1，22： | 1 |
| 6 | Traveling-wave-based Fault-location Scheme for Multiend-aged Underground Cable System显示文摘 | Mahmoud Gilany Doaa khalil Ibrahim EI Sayed Tag Eldin | 2007 | IEEE Transactions on Power Delivery2007，22，1： | 1 |
| 7 | Traveling-wave-based fault-location scheme for multiend-aged underground cable system 显示文摘 | Mahmoud Gilany Doaa khall Ibrahim Ei sayed Tag Elgin | 2007 | IEEE Transactions on Power Delivery2007，22，1： | 1 |
| 8 | Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets显示文摘Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments. | Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh | 2023 | Computers, Materials & Continua2023，，2： | 1 |
| 9 | Traveling wave based fault-location scheme for multi end-aged underground cable system显示文摘 | Mahmoud Gilany Doaa Khalil Ibrahim | 2007 | IEEE Trans on Power Del2007，22，1： | 1 |
| 10 | Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution显示文摘Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%). | Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 11 | Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization显示文摘Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks. | Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 12 | Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems显示文摘The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%). | Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 13 | Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection显示文摘Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection. | Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 14 | Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer显示文摘One of the most common kinds of cancer is breast cancer.The early detection of it may help lower its overall rates of mortality.In this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images.The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest.In addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling ratios.On the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are employed.The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy.The selected features are used to train a neural network to finally classify the thermal images of breast cancer.To achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm.Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature.Moreover,several experiments were conducted to compare the performance of the proposed approach with the other approaches.The results of these experiments emphasized the superiority of the proposed approach. | Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 15 | Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM显示文摘The design of an antenna requires a careful selection of its parameters to retain the desired performance.However,this task is time-consuming when the traditional approaches are employed,which represents a significant challenge.On the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance.In this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna.The proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep network.This optimized network is used to retrieve the metamaterial bandwidth given a set of features.In addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models. | Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 16 | Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets显示文摘In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard approaches.Consequently,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been proposed.When using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to instability.Because of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance vs.eleven other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and bGAmethods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection. | Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 17 | Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs显示文摘In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and development.As more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the architecture.However,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole attacks.The proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering technique.MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor features.This hybridmethod is primarily designed to combat active black-hole assaults.Using the LEACH clustering phase,however,can also detect passive black-hole attacks.The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach.For diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution time.Furthermore,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign. | Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 18 | Facial Expression Recognition Model Depending on Optimized Support Vector Machine显示文摘In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score. | Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 19 | Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia显示文摘Rainfall plays a significant role in managing the water level in the reser-voir.The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir.Many individuals,especially those in the agricultural sector,rely on rain forecasts.Forecasting rainfall is challenging because of the changing nature of the weather.The area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting model.To estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),respectively.Moreover,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed approach.On the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance. | El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga | 2023 | Computer Systems Science & Engineering2023，45，6： | 0 |
| 20 | Al-Biruni Earth Radius Optimization for COVID-19 Forecasting显示文摘Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,2019.The world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi Arabia.The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread.Methodology:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)algorithm.Results:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),2022.In addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach.The achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base models.On the other hand,a statistical analysis is performed to measure the significance of the proposed approach.Conclusions:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19. | El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
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