One of the most common diseases in the world is the chronic diabetes. Diabetes has a direct impact on the lives of millions of people worldwide. Diabetes can be controlled and improved with early diagnosis, but the majority of patients continue to live with it. There is a dispirit need to a system to anticipate and select the people who are most likely to be diabetes in the future. Diagnosing the future diseased person without taking any blood or glucose screening tests, is the main goal of this study. This paper proposed a deep-learning model for diabetes disease prediction. The proposed model consists of three main phases, data pre-processing, feature selection and finally different classifiers. Initially, during the data pre-processing stage, missing values are handled, and data normalization is applied to the data. Then, three techniques are used to select the most important features which are mutual information, chi-squared and Pearson correlation. After that, multiple machine learning classifiers are used. Four experiments are then conducted to test our models. Additionally, the effectiveness of the proposed model is evaluated against that of other well-known machine learning techniques. The accuracy, AUC, sensitivity, and F-measure of the linear regression classifier are higher than those of the other methods, according to experimental data, which show that it performs better. The suggested model worked better than traditional methods and had a high accuracy rate for predicting diabetic disease.

Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy.

Passive image forgery detection has attracted many researchers in the recent years. Image manipulation becomes easier than before because of the fast development of digital image editing software. Image splicing is one of the most widespread methods for tampering images. Research on detection of image splicing still carries great challenges. In this paper, an algorithm based on deep learning approach and wavelet transform is proposed to detect the spliced image. In the deep learning approach, Convolution Neural Network (CNN) is employed to automatically extract features from the spliced image. CNN is applied and then Haar Wavelet Transform (HWT) is used. Support Vector Machine (SVM) is used later for classification. Additional experiments are performed. That is, Discrete Cosine Transform (DCT) replaces HWT and then Principle Component Analysis (PCA) is applied. The proposed algorithm is evaluated on a publicly available image splicing datasets (CASIA v1.0 and CASIA v2.0). It achieves high accuracy while using a relatively low dimension feature vector. Our results demonstrate that the proposed algorithm is effective and accomplishes better performance for detecting the spliced image.