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The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students' responses to the course exercises. We applied item response theory (IRT) analysis and a latent trait mode (LTM) to identify the most difficult exercises .To evaluate the quality of the course exercises we applied IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention., 6 pages,5 figures

The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology. The term "forms of communication" refers to the actions used by hearing-impaired people to communicate. These actions are difficult for ordinary people to comprehend. The recognition of Arabic Sign Language (ArSL) has become a difficult study subject due to variations in Arabic Sign Language (ArSL) from one territory to another and then within states. The Convolution Neural Network has been encapsulated in the proposed system which is based on the machine learning technique. For the recognition of the Arabic Sign Language, the wearable sensor is utilized. This approach has been used a different system that could suit all Arabic gestures. This could be used by the impaired people of the local Arabic community. The research method has been used with reasonable and moderate accuracy. A deep Convolutional network is initially developed for feature extraction from the data gathered by the sensing devices. These sensors can reliably recognize the Arabic sign language's 30 hand sign letters. The hand movements in the dataset were captured using DG5-V hand gloves with wearable sensors. For categorization purposes, the CNN technique is used. The suggested system takes Arabic sign language hand gestures as input and outputs vocalized speech as output. The results were recognized by 90% of the people.

Coronavirus disease-2019 (COVID-19) is a viral infection that rose in a city in the Chinese province of Hubei, Wuhan. The world did not wait too long until the virus spread to reach Europe, Africa, and America to be a global pandemic. Due to the lack of information about the behaviour of the virus, several prediction models are in use all over around the world for decision making and taking precautionary actions. Therefor, in this paper, a new model named MSIR based on SIR model is proposed. The model is used to predict the spread of the disease in three cities Riyadh, Hufof and Jeddah in the kingdom of Saudi Arabia. Also the estimation of disease propagation with and without containment measure is carried out. We think that the results could be used to enhance the predictability of the pandemic outbreaks in other cities and to build long term artificial intelligence prediction model.

Most of the current techniques for face recognition require the presence of a full face of the person to be recognized, and this situation is difficult to achieve in practice, the required person may appear with a part of his face, which requires prediction of the part that did not appear. Most of the current forecasting processes are done by what is known as image interpolation, which does not give reliable results, especially if the missing part is large. In this work, we adopted the process of stitching the face by completing the missing part with the flipping of the part shown in the picture, depending on the fact that the human face is characterized by symmetry in most cases. To create a complete model, two facial recognition methods were used to prove the efficiency of the algorithm. The selected face recognition algorithms that are applied here are Eigenfaces and geometrical methods. Image stitching is the process during which distinctive photographic images are combined to make a complete scene or a high-resolution image. Several images are integrated to form a wide-angle panoramic image. The quality of the image stitching is determined by calculating the similarity among the stitched image and original images and by the presence of the seam lines through the stitched images. The Eigenfaces approach utilizes PCA calculation to reduce the feature vector dimensions. It provides an effective approach for discovering the lower-dimensional space. In addition, to enable the proposed algorithm to recognize the face, it also ensures a fast and effective way of classifying faces. The phase of feature extraction is followed by the classifier phase.

Aspect-based sentiment analysis (ABSA) is a textual analysis methodology that defines the polarity of opinions on certain aspects related to specific targets. The majority of research on ABSA is in English, with a small amount of work available in Arabic. Most previous Arabic research has relied on deep learning models that depend primarily on context-independent word embeddings (e.g. word2vec), where each word has a fixed representation independent of its context. This article explores the modeling capabilities of contextual embeddings from pre-trained language models, such as BERT, and making use of sentence pair input on Arabic aspect sentiment polarity classification task. In particular, we develop a simple but effective BERT-based neural baseline to handle this task. Our BERT architecture with a simple linear classification layer surpassed the state-of-the-art works, according to the experimental results on three different Arabic datasets. Achieving an accuracy of 89.51% on the Arabic hotel reviews dataset, 73.23% on the Human annotated book reviews dataset, and 85.73% on the Arabic news dataset.

The care of new born babies are the most important and sensitive part of bio-medical domain. Some new born babies have a higher risk of mortality due to their gestational age or their birth weight. Most of the premature babies born on 32-37 weeks of gestation and are deceased due to their unmet need for warmth. The neonatal incubator is a device used to nourish the premature babies by providing a controlled and closed environment. This incubator provides the babies with optimum temperature, relative humidity, optimum light and appropriate level of oxygen which are same as that in the womb. But babies in the incubators have a risk of losing those babies lives due to the improper monitoring of the it which causes accidents like gas leakage and short circuits due to overheating which leads to bursting of incubators. Thus, the objective of this paper is to overcome the drawbacks of an unmonitored incubator and develops an affordable and safe device for real-time monitoring of the neonatal incubator. a low cost yet effective apparatus for monitoring the important parameters like pulse rate, temperature, humidity, gas and light of the premature baby inside an incubator. The sensed data are passed to the doctors or nurses wirelessly by the Arduino UNO via Internet of Things (IoT) so as to take necessary actions at times to maintain an appropriate environment for the safety of the lives of premature babies.