Students engagement level detection in online e-learning has become a crucial problem due to the rapid advance of digitalization in education. In this paper, a novel Videos Recorded for Egyptian Students Engagement in E-learning (VRESEE) dataset is introduced for students engagement level detection in online e-learning. This dataset is based on an experiment conducted on a group of Egyptian college students by video recording them during online e-learning sessions. Each recorded video is labeled with a value from 0 to 3 representing the level of engagement of each student during the online session. Moreover, three new hybrid end-to-end deep learning models have been proposed for detecting student’s engagement level in an online e-learning video. These models are evaluated using the VRESEE dataset and also using a public Dataset for the Affective States in E-Environment (DAiSEE). The first proposed hybrid model uses EfficientNet B7 together with Temporal Convolution Network (TCN) and achieved an accuracy of 64.67% on DAiSEE and 81.14% on VRESEE. The second model uses a hybrid EfficientNet B7 along with Long Short Term Memory (LSTM) and reached an accuracy of 67.48% on DAiSEE and 93.99% on VRESEE. Finally, the third hybrid model uses EfficientNet B7 along with a Bidirectional LSTM and achieved an accuracy of 66.39% on DAiSEE and 94.47% on VRESEE. The results of the first, second and third proposed models outperform the results of currently existing models by 1.08%, 3.89%, and 2.8% respectively in students engagement level detection.

Automatic facial expression recognition (AFER), sometimes referred to as emotional recognition, is important for socializing. Automatic methods in the past two years faced challenges due to Covid-19 and the vital wearing of a mask. Machine learning techniques tremendously increase the amount of data processed and achieved good results in such AFER to detect emotions; however, those techniques are not designed for masked faces and thus achieved poor recognition. This paper introduces a hybrid convolutional neural network aided by a local binary pattern to extract features in an accurate way, especially for masked faces. The basic seven emotions classified into anger, happiness, sadness, surprise, contempt, disgust, and fear are to be recognized. The proposed method is applied on two datasets: the first represents CK and CK +, while the second represents M-LFW-FER. Obtained results show that emotion recognition with a face mask achieved an accuracy of 70.76% on three emotions. Results are compared to existing techniques and show significant improvement.

Liver cancer (Hepatocellular carcinoma (HCC)) is considered one of the life threatening diseases that causes death. Early detection of HCC is considered as alive saving process. One of the important tools that helps in the early detection of HCC is image processing. This paper introduces a new size selection region growing based segmentation algorithm for highly accurate liver extraction from CT images. Our work was done in two main steps. We began by preprocessing the data set that we are going to start our segmentation process with. The data set consists of 69 case studies of different patients, among these studies we concentrate on 10 cases of different patients, liver positions, normal and up normal liver tissues. The second stage was extracting the liver from the CT images which was done in three consecutive steps thresholding, region growing and size selection morphological operations.