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| 1 | A secure image steganography algorithm based on least significant bit and integer wavelet transform显示文摘The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding secret data from an unauthorized access into a cover object in such a way that it is invisible to human eyes. The cover object can be image, text, audio,or video. This paper proposes a secure steganography algorithm that hides a bitstream of the secret text into the least significant bits(LSBs) of the approximation coefficients of the integer wavelet transform(IWT) of grayscale images as well as each component of color images to form stego-images. The embedding and extracting phases of the proposed steganography algorithms are performed using the MATLAB software. Invisibility, payload capacity, and security in terms of peak signal to noise ratio(PSNR) and robustness are the key challenges to steganography. The statistical distortion between the cover images and the stego-images is measured by using the mean square error(MSE) and the PSNR, while the degree of closeness between them is evaluated using the normalized cross correlation(NCC). The experimental results show that, the proposed algorithms can hide the secret text with a large payload capacity with a high level of security and a higher invisibility. Furthermore, the proposed technique is computationally efficient and better results for both PSNR and NCC are achieved compared with the previous algorithms. | ELSHAZLY Emad ABDELWAHAB Safey ABOUZAID Refaat ZAHRAN Osama ELARABY Sayed ELKORDY Mohamed | 2018 | Journal of Systems Engineering and Electronics2018，29，3： | 2 |
| 2 | Suboptimal Control Design of Active and Passive Suspensions Based on a Full Car Model 显示文摘 | Elbeheiry D C Karnopp M E Elaraby M E | 1996 | Vehicle System Dynamics1996，，： | 1 |
| 3 | Simulationg and Analysis of IAEA Benchmark Transiednts显示文摘 | Mohamed A Gaheen Sayed Elaraby M Naguib Aly | 2007 | Progresss in Nuclear Energy2007，，： | 1 |
| 4 | Overtreatment and Undertreatment of Hyperlipidemia in the Outpatient Setting显示文摘 | Ashish Verma Paul Visintainer Mohamed Elarabi Siddharth Wartak Michael B. Rothberg | 2012 | Southern Medical Journal2012，，7： | 1 |
| 5 | Simulation and analysis of IAEA benchmark transients 显示文摘 | Mohamed A Gaheen Sayed Elaraby M Naguib Aly | 2007 | Process in Nuclear Energy2007，49，： | 1 |
| 6 | Optimization of Deep Learning Model for Plant Disease Detection Using Particle Swarm Optimizer显示文摘Plant diseases are a major impendence to food security,and due to a lack of key infrastructure in many regions of the world,quick identification is still challenging.Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities,motivating our mission.Because of the large range of diseases,identifying and classifying diseases with human eyes is not only time-consuming and labor intensive,but also prone to being mistaken with a high error rate.Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis.The proposed work describes a deep learning approach for detection plant disease.Therefore,we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases.In our research,we focused on detecting plant diseases in five crops divided into 25 different types of classes(wheat,cotton,grape,corn,and cucumbers).In this task,we used a public image database of healthy and diseased plant leaves acquired under realistic conditions.For our work,a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics(accuracy,specificity,Sensitivity,precision,and Fscore)of the tested deep learning networks achieves an accuracy of 98.83%,specificity of 98.56%,Sensitivity of 98.78%,precision of 98.67%,and F-score of 98.47%,demonstrating the feasibility of this approach. | Ahmed Elaraby Walid Hamdy Madallah Alruwaili | 2022 | Computers, Materials & Continua2022，，5： | 1 |
| 7 | Advanced ground vehicle suspension systems-a classified bibliography显示文摘 | Elbeheiry E M Kamopp D C Elaraby M E | 1995 | Vehicle System Dynamics1995，24，3： | 1 |
| 8 | Suboptimal Control Design of Active and Passive Suspensions Based on a Full Car Model 显示文摘 | Elbeheiry D C Karnopp M E Elaraby M E | 1996 | Vehicle System Dynamics1996，26，3： | 1 |
| 9 | Handling Capabilities of Vehicles in Emergencies Using Coordinated AFS and ARMC Systems 显示文摘 | E M Elbeheiry Y F Zeyada M E Elaraby | 2001 | Vehicle System Dynamics (S0042-3114)2001，35，3： | 1 |
| 10 | The effect of transversely aligned fibers on the axial tensile strength of carbon epoxy composites显示文摘 | ELARABI S M YU Weidong | | 0，，： | 1 |
| 11 | A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks显示文摘Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks(CNNs)for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample,the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition,training with binary crossentropy loss sometimes leads to poor margins.In this paper,a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition(HCR)tasks.The novelties of the proposed network are in.1)Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2)Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6%and 82%in few-and zero-shot learning respectively.In addition,a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments. | Nagwa Elaraby Sherif Barakat Amira Rezk | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 12 | Evaluation of Subsoil Corrosivity Condition around Baracaia Area Using the Electrical Resistivity Method--A Case Study from the Muglad Basin, Southwestern Sudan显示文摘 | Hussein Elarabi Tarig Elkhawad | 2014 | Journal of Earth Science and Engineering2014，4，5： | 0 |
| 13 | Construction of Micropiles Using Pressure Techniques显示文摘 | Hussein Elarabi Amin Ahmed Abbas Soorkty | 2015 | Journal of Civil Engineering and Architecture2015，9，1： | 0 |
| 14 | Preliminary Evaluation of Some Engineering Properties of Laterite as Foundation and Construction Materials in Muglad Basin in Sudan显示文摘 | Hussein Elarabi Tarig Elkhawad | 2013 | Journal of Environmental Science and Engineering(B)2013，2，12： | 0 |
| 15 | Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model显示文摘COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be classified.Convolutional Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy.Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results.In this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and pneumonia.In this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia infection.In this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the dataset.The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the datasets.It achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images. | Aymen Saad Israa SKamil Ahmed Alsayat Ahmed Elaraby | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 16 | Overlap of diabetic ketoacidosis and hyperosmolar hyperglycemic state显示文摘Diabetic ketoacidosis(DKA)and hyperosmolar hyperglycemia state(HHS)are two life-threatening metabolic complications of diabetes that significantly increase mortality and morbidity.Despite major advances,reaching a uniform consensus regarding the diagnostic criteria and treatment of both conditions has been challenging.A significant overlap between these two extremes of the hyperglycemic crisis spectrum poses an additional hurdle.It has well been noted that a complete biochemical and clinical patient evaluation with timely diagnosis and treatment is vital for symptom resolution.Worldwide,there is a lack of large-scale studies that help define how hyperglycemic crises should be managed.This article will provide a comprehensive review of the pathophysiology,diagnosis,and management of DKA-HHS overlap. | Esraa Mamdouh Hassan Hisham Mushtaq Esraa Elaraby Mahmoud Sherley Chhibber Shoaib Saleem Ahmed Issa Jain Nitesh Abbas B Jama Anwar Khedr Sydney Boike Mikael Mir Noura Attallah Salim Surani Syed A Khan | 2022 | World Journal of Clinical Cases2022，10，32： | 0 |
| 17 | Role of octreotide in small bowel bleeding显示文摘Gastrointestinal bleeding accounts for a drastic negative impact on the quality of the patients’lives as it requires multiple diagnostic and therapeutic interventions to identify the source of the bleeding.Small bowel bleeding is the least common cause of gastrointestinal bleeding.However,it is responsible for the majority of complaints from patients with persisting or recurring bleeding where the primary source of bleeding cannot be identified despite investigation.A somatostatin analog known as octreotide is among the medical treatment modalities currently used to manage small bowel bleeding.This medication helps control symptoms of gastrointestinal bleeding by augmenting platelet aggregation,decreasing splanchnic blood flow,and antagonizing angiogenesis.In this review article,we will highlight the clinical efficacy of octreotide in small bowel bleeding and its subsequent effect on morbidity and mortality. | Anwar Khedr Esraa Elaraby Mahmoud Noura Attallah Mikael Mir Sydney Boike Ibtisam Rauf Abbas B Jama Hisham Mushtaq Salim Surani Syed A Khan | 2022 | World Journal of Clinical Cases2022，10，26： | 0 |
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