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Every major healthcare system is now under the throes of the Coronavirus disease outbreak as it is operating at its maximum capacity. There is an absolute need to establish an appropriate cure for this virus as quickly and efficiently as possible. Advances in deep learning models may play a critical role in SARS-2 discovery by locating a possible treatment. This article's objective is to demonstrate the machine learning and deep learning models approaches for classifying prospective coronavirus treatment on a single human cell. A partial dataset of RXRX.ai which is a publicly available dataset is used in this research. This work targeted to implement a strategy for automatically identifying a single human cell depending on the type of treatment and its concentration level. Throughout this study, we present a DCNN model along with an image processing approach. The systematic approach comprises translating the original dataset's numerical attributes to the image domain, and then incorporating them into DCNN model. In comparison to standard machine learning techniques including such Ensemble, Decision Tree and Support Vector Machine, the experimental findings indicate that the suggested DCNN model for treatment classification (32 categories) obtained a testing accuracy of 98.05 percent. The (Ensemble) algorithm achieves 98.5 percent for the accuracy test in treatment concentration level prognosis, whereas the suggested DCNN model reached 98.2 percent. The classification of treatments and assessing their concentration levels are considerably accurate due to the performance indicators obtained from the experiments. �� 2021 Little Lion Scientific.

Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.

Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.

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 overfitting. Moreover, seven accredited performance matrices in deep learning have been used to conclude an optimal selection of the most appropriate DCNN model that fits the nature of the skin lesions datasets. The study concludes that vgg19 is the most appropriate DCNN according to testing accuracy measurement and achieved 98.8%. The seven performance matrices strengthen this result. Also, a comparative result was introduced with related works. The vgg19 overcomes the related work in terms of testing accuracy and the performance matrices on both datasets. Finally, the vgg19 model was trained on a smaller number of images than the related work by 10 times, which proved that the choice of data augmentation techniques played an important role in achieving better results. That would reflect on reducing the training time, memory consumption and the calculation complexity.

The main objective of this thesis is to build an intelligent tutor system torecognize and diagnose children handwritten mistakes. Handwritten recognition system has been an important area due to its applications in severalfields such as optical character recognition, license Plate recognition, etc.This work is focusing on the recognition as a part of handwritten Arabiccharacters and digits recognition that faces several challenges, including theunlimited variation in human handwriting and the large public databases. Theproposed system was designed and developed for automatically recognizinghandwritten Arabic characters and digits using deep learning architectures.The system also detects and diagnoses Arabic preschool children handwrittencharacters based on immediate feedbacks. We show how to automate this task,creating a system that checks the handwriting errors such as stroke sequenceerrors, stroke direction error, stroke position error, and extra stroke error.For Arabic handwritten digits recognition , this work presents a deeplearning architecture called Convolutional Neural Network (CNN). LeNet-5model was implemented by two convolutional, two pooling layers ,and twoclassification layers. CNN based on LeNet-5 was used to train and test theMADBase database (Arabic handwritten digits images) that contains 10ktesting images and 60k training images. The CNN based LeNet-5 outcomesan average accuracy 88%. We improve the performance of the classificationusing unsupervised learning approach called Stacked AutoEncoder (SAE)for Arabic handwritten digits categorization. SAE shows that the use ofSAE leads to significant improvements across different machine-learningviiiclassification algorithms. Stacked autoencoder is giving an average accuracy of 98.5%. We improve the performance of handwritten digits using aconvolutional neural network (CNN). The optimization methods were implemented to increase the performance of CNN. A comparison is held amongstthe results, and it is shown by the end that the use of convolutional neuralnetwork led to significant improvements across different machine learningclassification algorithms. Convolutional neural nework was trained and testedon MADBase database. The proposed CNN approach is describe as follows: INPUT → CONV → RELU → POOL → CONV → RELU → POOL →FC → RELU → FC → Output. Moreover, the improved convolutional neuralnetwork is giving an average recognition accuracy of 99.15%.For Arabic handwritten characters recognition , this work presents anunsupervised learning approach using SAE designed for Arabic handwrittencharacters categorization. SAE was trained and tested using our proposeddataset that contains 3360 testing images and 13440 training images. Weshow that the use of SAE leads to bad classification outcomes. SAE is givingan average accuracy of 64%. This thesis presents a supervised learningapproach using convolutional neural network. The convolutional neuralnework was trained and tested using our database that contains 16800 dataset.The proposed CNN approach is described as follows : INPUT → CONV →RELU → POOL → CONV → RELU → POOL → FC → RELU → FC →Output. Moreover, the convolutional neural network is giving an averagerecognition accuracy of 94.85%.Finally, an intelligent tutor application was built for Arabic preschoolchildren called Intelligent Arab Teaching. Intelligent Arab Teaching systemallows Arab children to practice at anytime and anywhere. The IntelligentArab Teaching provides a useful feedback to Arab children to correct theirmistakes. Experimental results indicate that the proposed intelligent systemsuccessfully detect handwritten strokes errors with immediate feedback.

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 (in 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 accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection.

he 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 researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

In 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.

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 number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem by increasing the dataset images up to 4 times than original images. The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 89.33%. In addition, it has a minimum number of layers, which decreases the training time and computational complexity. Moreover, the choice of data augmentation techniques played an important role in achieving better results. Finally, A comparison results were carried out at the end of the research with related work which used the same dataset IP102. The presented work achieved a superior result than the related work in terms of testing accuracy, precision, recall, and F1 score.