This paper offers a comprehensive review of the synergy between artificial intelligence and DNA methylation analysis, encompassing machine learning, deep learning, natural language processing, and explainable artificial intelligence. In this study, we also highlighted the underexplored potential of signal processing and large language models-based models in DNA methylation research. Additionally, we discussed the challenges and limitations faced when managing and analyzing large and complex DNA methylation datasets. Furthermore, this article tries to shed light on the continuing evolution of this field and on the possible directions for future research.

The promotion of e-commerce platforms has changed the lifestyle of several people from traditional marketing to digital marketing where businesses are made online and the concurrence reached high levels. These platforms have helped the ease of purchases while providing more advantages to the customers such as benefiting from a wide range of high-quality products, low prices, buying at any time, and more importantly supplying information and reviews about the products, and so on. Unfortunately, a plethora of companies mislead the customers to buy their products or demote the competitors' by using deceptive opinion spams which has a negative impact on the decision and the behavior of the purchasers. Deceptive opinion spams are written deliberately to seem legitimate and authentic so that to misguide or delude the customer's purchases. Consequently, the detection of these opinions is a hard task due to their nature for both humans and machines. Most of the studies are based on traditional machine learning and sparse feature engineering. However, these models do not capture the semantic aspect of reviews. According to many researchers, it is the key to the detection of deceptive opinion spam. Besides, only a few studies consider using contextual information by adopting neural networks in comparison with plenty of traditional machine learning classifiers. These models face numerous shortcomings as long as their representations are obtained while mining each review considering only words, sentences, reviews, or a combination of them, thereby classifying them based on their representations. In fact, deceptive opinions are written by the same deceivers belonging to the same companies with similar aims to promote or demolish a product. In other words, Deceptive opinion spams tend to be semantically coherent with each other. To the best of our knowledge, no model tries to obtain a representation based on the contextual relationships between opinions. This article proposes to use a capsule neural network, bidirectional long short-term memory, attention mechanism, and paragraph vector distributed bag of words to detect deceptive opinion spam. Our model provides a powerful representation of the opinions since it centers on the preservation of their contexts and the relationships between them. The results show that our model significantly outperforms the existing state-of-the-art models.

Image registration plays a crucial role in several areas, yet iconic registration methods are more efficient than those in geometrical registration, but they require great execution time. Regarding reduction in the execution time of iconic registration, the authors have proposed a new method based on mutual information while exploiting adaptive multiresolution decomposition, bidimensional empirical mode decomposition (BEMD) in its fast and adaptive version fast and adaptive BEMD (FABEMD). The idea is that instead of registering two images, the authors proceed to registration of the bidimensional intrinsic mode functions (BIMFs) that results from the FABEMD decomposition. The BIMF selected by the authors’ algorithm is characterised by preservation of the general form of the image, and it contains a tone of grey levels lower than that of the original image, thus the number of combinations of the grey levels, used while calculating entropy is reduced, which in turn reduces execution time of the registration.