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| 1 | Solvent effects on the initial and transition states upon solvolysis oftrans-dichlorotetra(4-ethylpyridine)cobalt(III) perchlorate in water+methanol mixtures显示文摘 | Ibrahim M. Sidahmed Amel M. Ismail | 1987 | Transition Metal Chemistry1987，，： | 1 |
| 2 | Acylated flavonol glycosides from Eugenia jambolana leaves显示文摘 | Ibrahim I Mahmoud Mohamed S.A Marzouk Fatma A Moharram Mohamed R El-Gindi Amel M.K Hassan | 2001 | Phytochemistry2001，，8： | 1 |
| 3 | Assessing the cost effectiveness of robotics in urological surgery – a systematic review显示文摘 | Kamran Ahmed Amel Ibrahim Tim T. Wang Nuzhath Khan Ben Challacombe Muhammed Shamim Khan Prokar Dasgupta | 2012 | BJU International2012，，10： | 1 |
| 4 | 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 |
| 5 | 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 |
| 6 | 鲊辣椒生香酵母的分离筛选鉴定及其应用研究显示文摘从自然发酵鲊辣椒中以嗅闻法结合总酯含量作为生香酵母的筛选依据分离筛选到1株生香酵母,经鉴定并分类命名为库德里阿兹威氏毕赤酵母菌(Pichia kudriavzevii)Y50,为探究该菌株混菌发酵生产鲊辣椒的品质提升作用,将Y50与植物乳杆菌混合发酵鲊辣椒(C),以自然发酵的鲊辣椒(A)以及接种植物乳杆菌发酵的鲊辣椒(B)为对比研究对象,对3种鲊辣椒的基本理化指标、辣椒碱、有机酸和挥发性成分进行比较分析。结果表明,3种鲊辣椒pH值、总酸、亚硝酸盐、胡萝卜素含量较为接近,但鲊辣椒C中有机酸含量达(25.46±0.32)g/kg,比鲊辣椒A和鲊辣椒B分别高出57.8%和32.2%。鲊辣椒A、B、C中分别检出挥发性风味物质90种、67种和72种,其中鲊辣椒C的挥发性物质含量最高,为(10101.11±99.04)μg/kg,酯类、醇类、醛类、酸类挥发性物质含量均有提高,相比A、B分别提升了75%、358%、106%、23%和102%、59%、68%、51%,赋予鲊辣椒更浓郁的酯香、花香和果香,说明添加生香酵母Y50能提升鲊辣椒整体的风味品质,通过气味活度值(odor activity value,OAV)的计算,共检出22种OAV>1的香气化合物,这些关键香味物质赋予了鲊辣椒特有的香气特征,其中β-紫罗兰酮、2-甲基丁酸乙酯、己酸己酯、正庚醇、(+)-柠檬烯、庚醛、愈创木酚是构成鲊辣椒C风味的关键物质。试验表明Y50具有进一步应用于鲊辣椒生产的潜力。 | 姚红 尹小庆 颜宇鸽 阚建全 武运 戚晨晨 王治国 Sameh AWAD Amel IBRAHIM 杜木英 | 2023 | 食品与发酵工业2023，49，17： | 0 |
| 7 | 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 |
| 8 | 模糊数学感官评价结合响应面法优化鲊辣椒混菌发酵工艺显示文摘以小米椒和玉米粉为原料,植物乳植杆菌XZ3和生香酵母菌Y50为菌种,制作一款混菌发酵鲊辣椒。以鲊辣椒的模糊感官评分为响应值,采用Box-Behnken中心组合设计试验对鲊辣椒混菌发酵工艺进行优化,并测定其产品的功能性成分。结果表明,混菌发酵鲊辣椒的最优发酵条件为:植物乳植杆菌XZ3与生香酵母菌Y50的比例为1∶1、混菌添加量为2.5%、食盐添加量为4%、发酵时间6 d。在此优化条件下,鲊辣椒的感官评分为8.45分。与不接种自然发酵鲊辣椒和利用植物乳植杆菌XZ3纯种发酵的鲊辣椒进行对比,发现优化后的混菌发酵鲊辣椒的香气更突出。进一步测定其功能性成分发现,混菌发酵鲊辣椒中多酚组分含量最高且种类检出最多,其总酸含量、辣椒碱含量和总黄酮含量都显著高于自然发酵鲊辣椒。除此之外,相比于其他两种,混菌发酵鲊辣椒中甜味氨基酸的含量上升,苦味氨基酸的比例降低。该研究为混菌发酵鲊辣椒的生产提供了有益参考。 | 黄璐晗 尹小庆 阚建全 武运 戚晨晨 彭芸 SAMEH Awad AMEL Ibrahim 杜木英 | 2024 | 食品与发酵工业2024，50，7： | 0 |
| 9 | Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram显示文摘Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%. | Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy | 2023 | Computer Systems Science & Engineering2023，45，5： | 0 |
| 10 | Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm显示文摘Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields.Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization problems.Hence novel optimization methodologies are still needed.The Al-Biruni earth radius(BER)search optimization algorithm is proposed in this paper.The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals.The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems.The results show that BER can both explore and avoid local optima.BER has also been tested on an engineering design optimization problem.The results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms. | El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga | 2023 | Computer Systems Science & Engineering2023，45，5： | 0 |
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