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| 1 | Biosynthesis of glycyrrhetinic acid 3-O-mono-β- d -glucuronide by free and immobilized Aspergillus terreus β- d -glucuronidase显示文摘 | Hala Abdel Salam Amin Hassaan A. El-Menoufy Adel A. El-Mehalawy Eslam S. Mostafa | 2011 | Journal of Molecular Catalysis B Enzymatic2011，，1： | 1 |
| 2 | Microalgae-based wastewater treatment:Mechanisms,challenges,recent advances,and future prospects显示文摘The rapid expansion of both the global economy and the human population has led to a shortage of water resources suitable for direct human consumption.As a result,water remediation will inexorably become the primary focus on a global scale.Microalgae can be grown in various types of wastewaters(WW).They have a high potential to remove contaminants from the effluents of industries and urban areas.This review focuses on recent advances on WW remediation through microalgae cultivation.Attention has already been paid to microalgae-based wastewater treatment(WWT)due to its low energy requirements,the strong ability of microalgae to thrive under diverse environmental conditions,and the potential to transform WW nutrients into high-value compounds.It turned out that microalgae-based WWT is an economical and sustainable solution.Moreover,different types of toxins are removed by microalgae through biosorption,bioaccumulation,and biodegradation processes.Examples are toxins from agricultural runoffs and textile and pharmaceutical industrial effluents.Microalgae have the potential to mitigate carbon dioxide and make use of the micronutrients that are present in the effluents.This review paper highlights the application of microalgae in WW remediation and the remediation of diverse types of pollutants commonly present in WW through different mechanisms,simultaneous resource recovery,and efficient microalgae-based co-culturing systems along with bottlenecks and prospects. | Abdallah Abdelfattah Sameh Samir Ali Hassan Ramadan Eslam Ibrahim El-Aswar Reham Eltawab Shih-Hsin Ho Tamer Elsamahy Shengnan Li Mostafa M.El-Sheekh Michael Schagerl Michael Kornaros Jianzhong Sun | 2023 | Environmental Science and Ecotechnology2023，，1： | 0 |
| 3 | Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference显示文摘Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images. | Mohamed Ezz Waad Alanazi Ayman Mohamed Mostafa Eslam Hamouda Murtada K.Elbashir Meshrif Alruily | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 4 | A Transfer Learning Approach Based on Ultrasound Images for Liver Cancer Detection显示文摘The convolutional neural network(CNN)is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions;Hemangioma and hepatocellular carcinoma(HCC).Ultrasound images,which are commonly available and have low cost and low risk compared to computerized tomography(CT)scan images,will be used as input for the model.A total of 350 ultrasound images belonging to 59 patients are used.The number of images with HCC is 202 and 148,respectively.These images were collected from ultrasound cases.info(28 Hemangiomas patients and 11 HCC patients),the department of radiology,the University of Washington(7 HCC patients),the Atlas of ultrasound Germany(3 HCC patients),and Radiopedia and others(10 HCC patients).The ultrasound images are divided into 225,52,and 73 for training,validation,and testing.A data augmentation technique is used to enhance the validation performance.We proposed an approach based on ensembles of the best-selected deep transfer models from the on-the-shelf models:VGG16,VGG19,DenseNet,Inception,InceptionResNet,ResNet,and EfficientNet.After tuning both the feature extraction and the classification layers,the best models are selected.Validation accuracy is used for model tuning and selection.The accuracy,sensitivity,specificity and AUROC are used to evaluate the performance.The experiments are concluded in five stages.The first stage aims to evaluate the base model performance by training the on-the-shelf models.The best accu-racy obtained in the first stage is 83.5%.In the second stage,we augmented the data and retrained the on-the-shelf models with the augmented data.The best accuracy we obtained in the second stage was 86.3%.In the third stage,we tuned the feature extraction layers of the on-the-shelf models.The best accuracy obtained in the third stage is 89%.In the fourth stage,we fine-tuned the classification layer and obtained an accuracy of 93%as the best accuracy.In the fifth stage,we applied the ensemble approach using the best three-performing models and obtained an accuracy,specificity,sensitivity,and AUROC of 94%,93.7%,95.1%,and 0.944,respectively. | Murtada K.Elbashir Alshimaa Mahmoud Ayman Mohamed Mostafa Eslam Hamouda Meshrif Alruily Sadeem M.Alotaibi Hosameldeen Shabana Mohamed Ezz | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 5 | Fast adaptive regression-based model predictive control显示文摘Model predictive control(MPC)is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required reference.The computations of the MPC are performed at pre-determined sample instances over a finite time horizon.The number of sample instances and the horizon length determine the performance of the MPC and its computational cost.A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time,which results in better performance but at the expense of high computational cost.However,this long horizon is not always necessary,especially for slowly-varying states.In this case,a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational cost.In this paper,we propose an adaptive regression-based MPC that predicts the best minimum horizon length and the sample count from several features extracted from the time-varying changes of the states.The proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the prediction.The proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear models.The proposed technique shows a superior reduction in computational time with a reduction of about 35–65%compared with the other techniques without introducing a noticeable loss in performance. | Eslam Mostafa Hussein A.Aly Ahmed Elliethy | 2023 | Control Theory and Technology2023，21，4： | 0 |
| 6 | Innovative Hetero-Associative Memory Encoder(HAMTE)for Palmprint Template Protection显示文摘Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems.Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification.This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication.A HAMTE-Siamese network is constructed,which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users.The HAMTE is generated for each user during the enrollment phase,which is responsible for generating a secure template for the enrolled user.The proposed network secures the person’s Palmprint template by translating it into an irreversible template(different features space).It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person’s template from being stolen.Experimental results are conducted on the CASIA database,where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates.The recognition accuracy deviated by around 3%,and the equal error rate(EER)by approximately 0.02 compared to the original data,with appropriate performance(approximately 13 ms)while preserving the irreversibility property of the secure template.Moreover,the brute-force attack has been analyzed under the new Palmprint protection scheme. | Eslam Hamouda Mohamed Ezz Ayman Mohamed Mostafa Murtada K.Elbashir Meshrif Alruily Mayada Tarek | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
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