Image memorability represents the degree to which images are remembered or forgotten after a period of time. Studying image memorability in computer vision is the task of finding special characteristics in memorable images, in order to develop a representative model of this type of images. Several approaches have been realised to examine features that can affect image memorability. In this study, the authors use bag‐of‐features as another kind of visual feature descriptor to assess image memorability. The authors’ method based on bag‐of‐visual‐words (BoVWs) technique involves four main steps. First, the authors extract local image features from regions/points of interest which are automatically detected. Then, they encode these local features by mapping them to a created visual vocabulary. Later, the authors apply features pooling and normalisation techniques to obtain image BoVW representation. Finally, the authors use this representation to examine image memorability as a problem of classification. They present different implementation choices for each step and compare reached results. The authors’ method performs best significant results in comparison with other approaches found in literature.

Photos are becoming more spread with digital age. Cameras, smart phones and Internet provide large dataset of images available to a wide audience. Assessing memorability of these photos is becoming a challenging task. Besides, finding the best representative model for memorable images will enable memorability prediction. The authors develop a new approachbased rule of photography to evaluate image memorability. In fact, they use three groups of features: image basic features, layout features and image composition features. In addition, they introduce a diversified panel of classifiers based on some data mining techniques used for memorability analysis. They experiment their proposed approach and they compare its results to the state-of-the-art approaches dealing with image memorability. Their approach experiment's results prove that models used in their approach are encouraging predictors for image memorability.

Worldwide, cardiac arrhythmia disease has become one of the most frequent heart problems, leading to death in most cases. In fact, cardiologists use the electrocardiogram (ECG) to diagnose arrhythmia by analyzing the heartbeat signals and utilizing electrodes to detect variations in the heart rhythm if they show certain abnormalities. Indeed, heart attacks depend on the treatment speed received, and since its risk is increased by arrhythmias, in this chapter the authors create an automatic system that can detect cardiac arrhythmia by using deep learning algorithms. They propose a deep convolutional neural network (CNN) to automatically classify five types of arrhythmias then evaluate and test it on the MIT-BIH database. The authors obtained interesting results by creating five CNN models, testing, and comparing them to choose the best performing one, and then comparing it to some state-of-the-art models. The authors use significant performance metrics to evaluate the models, including precision, recall, sensitivity, and F1 score.

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