Mohamed Loey | Benha University (original) (raw)

Papers by Mohamed Loey

ACM Transactions on Asian and Low-Resource Language Information Processing

Generating accurate high-resolution images from text representations is a difficult problem in co... more Generating accurate high-resolution images from text representations is a difficult problem in computer vision that has a wide range of functional applications. Text-to-image conversion is not dissimilar to the difficulties inherent in language processing. For example, each text meaning can be encoded in two distinct human languages, while photographs and text are two distinct encoding languages for similar data. However, these are two distinct issues, since text-to-image or image-to-text conversions are extremely multimodal in nature. The proposed model for creating 256 × 256 realistic images from Arabic text descriptions is discussed in this article. The relationship between an Arabic word in a sentence and its component in a picture as introduced in this paper using the DAMSM model. This model teaches two neural networks how to map the Arabic picture and word sub-regions of a full sentence to a shared semantic model. It performs well as an Arabic-text encoder and a picture encode...

Cognitive Computation, 2021

Blockchain Technologies, 2022

Arabic Handwritten Characters DatasetAstractHandwritten Arabic character recognition systems face... more Arabic Handwritten Characters DatasetAstractHandwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and large public databases. In this work, we model a deep learning architecture that can be effectively apply to recognizing Arabic handwritten characters. A Convolutional Neural Network (CNN) is a special type of feed-forward multilayer trained in supervised mode. The CNN trained and tested our database that contain 16800 of handwritten Arabic characters. In this paper, the optimization methods implemented to increase the performance of CNN. Common machine learning methods usually apply a combination of feature extractor and trainable classifier. The use of CNN leads to significant improvements across different machine-learning classification algorithms. Our proposed CNN is giving an average 5.1% misclassification error on testing data.ContextThe motivation of this study is to use cross knowledge learned from mu...

Arabic Handwritten Digits DatasetAbstractIn recent years, handwritten digits recognition has been... more Arabic Handwritten Digits DatasetAbstractIn recent years, handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. The paper provided a deep learning technique that can be effectively apply to recognizing Arabic handwritten digits. LeNet-5, a Convolutional Neural Network (CNN) trained and tested MADBase database (Arabic handwritten digits images) that contain 60000 training and 10000 testing images. A comparison is held amongst the results, and it is shown by the end that the use of CNN was leaded to significant improvements across different machine-learning classification algorithms.The Convolutional Neural Network was trained and tested MADBase database (Arabic handwritten di...

Blockchain Technologies, 2022

Advances in Intelligent Systems and Computing, 2019

Over the last few years, MENA region became an attractive target for cyber-attacks perpetrators. ... more Over the last few years, MENA region became an attractive target for cyber-attacks perpetrators. Hackers focus on governmental high valued sectors (i.e. oil and gas) alongside with other critical industries. MENA nations are increasingly investing in Information and Communication Technologies (ICTs) sector, social infrastructure, economic sector, schools and hospitals in the area are now completely based on the Internet. Currently, the position of ICTs became an essential phase of the domestic future and global security structure in the MENA Region, emphasizing the real need for a tremendous development in cybersecurity at a regional level. This environment raises questions about the developments in cybersecurity and offensive cyber tactics; this paper examines and investigates (1) the essential cybersecurity threats in MENA region, (2) the major challenges that faces both governments and organizations (3) the main countermeasures that governments follow to achieve the protection and business continuity in the region. It stresses the need for the importance of cybercrime legislation and higher defenses techniques towards cyberterrorism for MENA nations. It argues for the promotion of a cybersecurity awareness for the individuals as an effective mechanism for facing the current risks of cybersecurity in MENA region.

International Journal of Service Science, Management, Engineering, and Technology, 2020

Deep learning has brought a huge improvement in the area of machine learning in general and most ... more Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the us...

Symmetry, 2020

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unpreced... more The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for resear...

Acta Informatica Medica, 2019

Early detection of cancer increases the probability of recovery. This paper presents an intellige... more Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accu...

Advances in Intelligent Systems and Computing, 2016

2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15), 2015

In the handwriting recognition field, the deep learning is becoming the new trend thanks to their... more In the handwriting recognition field, the deep learning is becoming the new trend thanks to their ability to deal with unlabeled raw data especially with the huge size of raw data available nowadays. In this paper, we investigate Deep Belief Neural Network (DBNN) for Arabic handwritten character/word recognition. The proposed system takes the raw data as input and proceeds with a grasping layer-wise unsupervised learning algorithm. The approach was tested on two different databases. For the character level one, the results were promising with an error classification rate of 2.1% on the HACDB database. Unlike, the character level, the evaluation on the ADAB database to deal with word level shows an error rate which exceeds the 40%. Hence, the proposed DBNN structure is not already able to deal with high-level dimensional data and thus has to be improved.

In recent years, GPS has rise as major application in military and civilian devices, but some per... more In recent years, GPS has rise as major application in military and civilian devices, but some person’s misuse of using GPS. So Anti-GPS has rise as major application to prevent connection between satellites and GPS receiver. So we improve the performance of GPS jamming signal using new technology of jamming. We use new technology called multi-band limit white noise. We identify jamming design issues. How those issues may affect jamming system performance. It addresses fabrication issues, data requirements, error handling, local and remote operations, how to attain high accuracy, and repeatability during the generation and measurement of jamming. We simulate this GPS jamming signal using matlab, and jamming controlling system using fuzzy logic. Finally make comparisons between all jamming technologies that use in simulation

Computers in Biology and Medicine, 2022

Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the glob... more Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Intelligence. Recent advances in visual recognition are based on image classification and artefacts detection within these images. The purpose of this study is to classify chest X-ray images of COVID-19 artefacts in changed real-world situations. A novel Bayesian optimization-based convolutional neural network (CNN) model is proposed for the recognition of chest X-ray images. The proposed model has two main components. The first one utilizes CNN to extract and learn deep features. The second component is a Bayesian-based optimizer that is used to tune the CNN hyperparameters according to an objective function. The used large-scale and balanced dataset comprises 10,848 images (i.e., 3616 COVID-19, 3616 normal cases, and 3616 Pneumonia). In the first ablation investigation, we compared Bayesian optimization to three distinct ablation scenarios. We used convergence charts and accuracy to compare the three scenarios. We noticed that the Bayesian search-derived optimal architecture achieved 96% accuracy. To assist qualitative researchers, address their research questions in a methodologically sound manner, a comparison of research method and theme analysis methods was provided. The suggested model is shown to be more trustworthy and accurate in real world.

Enabling AI Applications in Data Science

The era of Deep Learning (DL) and Big data have a great enhancement in the area of artificial int... more The era of Deep Learning (DL) and Big data have a great enhancement in the area of artificial intelligence in generic and most especially in the human vision framework. The lead of DL has been applied to several scopes leading to massive fulfillment in the field of artificial intelligence and computer vision. Human society needs nutrition production and nutrition plant to meet the growing and increasing population. Automatic leaf plant malady detection using plant picture was originally look over using classical machine learning and image processing approaches outcoming in limited miss-classification rate. Using DL in plant malady classification made it possible to produce lower prediction error rates as well as broaden the scope of classifying diseases. This chapter introduces a survey on research papers on leaf plant diseases detection based on DL, and analyze in terms of the database used, transfer models, and miss-classification achieved.

Agriculture is one of the most important sources for human food throughout the history of humanki... more Agriculture is one of the most important sources for human food throughout the history of humankind. In many countries, agriculture is the foundation of its economy, and more than 90% of its population deriving their livelihoods from it. Insect pests are one of the main factors affecting agricultural crop production. With the advances of computer algorithms and artificial intelligence, accurate and speedy recognition of insect pests in early stages may help in avoiding economic losses in short and long term. In this paper, an insect pest recognition based on deep transfer learning models will be presented. The IP102 insect pest dataset was selected in this research. The IP102 dataset consists of 27500 images and contains 102 classes of insect pests, it is considered one the biggest dataset for insect pest and was launched in 2019. Through the paper, AlexNet, GoogleNet, and SqueezNet were the selected deep transfer learning models. Those models were selected based on their small numb...

Skin cancer is the most common type of cancer. One in every three cancers diagnosed is a skin can... more Skin cancer is the most common type of cancer. One in every three cancers diagnosed is a skin cancer according to skin cancer foundation statistics globally. The early detection of this type of cancer would help in raising the opportunities of curing it. The advances in computer algorithms such as deep learning would help doctors to detect and diagnose skin cancer automatically in early stages. This paper introduces an empirical study and enhancement on deep transfer learning for skin lesions detection. The study selects different pre-trained deep convolutional neural network models such as resnet18, squeezenet, google net, vgg16, and vgg19 to be applied into two different datasets. The datasets are MODE-NODE and ISIC skin lesion datasets. Data augmentation techniques have been adopted in this study to enlarge the total number of images in the datasets to be 5 times larger than the original datasets. The adopted augmentation techniques make the DCNN models more robust and prevent ov...

Computers in Biology and Medicine

ACM Transactions on Asian and Low-Resource Language Information Processing

Cognitive Computation, 2021

Blockchain Technologies, 2022

Advances in Intelligent Systems and Computing, 2019

International Journal of Service Science, Management, Engineering, and Technology, 2020

Symmetry, 2020

Acta Informatica Medica, 2019

Advances in Intelligent Systems and Computing, 2016

2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15), 2015

Computers in Biology and Medicine, 2022

Enabling AI Applications in Data Science