Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient.

This paper classifies sentiment analysis in Arabic language and mining sentiment in relation to the COVID-19 pandemic in the period (2019 - 2021). Three large data sets are collected from tweets, hotel and restaurant reviews for building the proposed sentiment analysis model. We compared eight machine learning algorithms ,Multinomial Naïve Bayes (MNB), Bernoulli Naïve Bayes (BNB), Decision Tree (DT) ,K-nearest neighbor classifier (KNN), Support Vector Machines (SVM), Linear Support Vector Classifier (LSVC), Random Forest Classifier (RFC) and Stochastic Gradient Descent Classifier (SGD) on three cases: n-gram unigram, bigram, and trigram for each algorithm. The performance evaluations are compared according to precision, recall, and F-measure. The polarity prediction results in sentiment analysis models was achieved by linear SVC using hotel datasets with bigram case , with the accuracy of 0.966, precision of 0.967, recall of 0.966 and F-measure of 0.966 . The rest algorithms give average performance on all datasets . It can be concluded that the machine learning algorithms need the right morphological features to enhance the classification accuracy when dealing with different words that play different roles in the sentence with the same letters.