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.

Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic factors associated with specific traits. However, ethical constraints prevent the direct exchange of genetic information, prompting the need for privacy preservation solutions. To address these issues, earlier works are based on cryptographic mechanisms such as homomorphic encryption, secure multi-party computing, and differential privacy. Very recently, federated learning has emerged as a promising solution for enabling secure and collaborative GWAS computations. This work provides an extensive overview of existing methods for GWAS privacy preserving, with the main focus on collaborative and distributed approaches. This survey provides a comprehensive analysis of the challenges faced by existing methods, their limitations, and insights into designing efficient solutions.

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 segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground-background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS.

Background modelling is a critical case for background‐subtraction‐based approaches and also for a wide range of applications. The background generation becomes difficult when the scene is complex or an object stays for a long time in the scene. Here, the authors propose a block‐based background initialisation, using the sum of absolute difference (SAD), and modelling, using a block‐based entropy evaluation, with a low computational cost which making them feasible for embedded platform. In general, many background‐subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modelling approach analyses the illumination change problem. The moving object detection mask is developed using a threshold selected by computing the mean of the SAD between the blocks background and the blocks of the current frame. From the qualitative and quantitative results obtained by the authors approach compared with some existing methods, the authors approach is effective for background generation and moving objects detection.

This paper presents a new algorithm for automatic segmentation of moving objects in video based on spatiotemporal saliency and laplacian coordinates (LC). Our algorithm exploits the saliency and the motion information to build a spatio-temporal saliency map, used to extract a moving region of interest (MRI). This region is used to provide automatically the seeds for the segmentation of the moving object using LC. Experiments show a good performance of our algorithm for moving objects segmentation in video without a user interaction, especially on Segtrack dataset.

Motion detection based on background subtraction approaches require a background model generation before extracting the moving objects. This extraction consists to subtract the static scene from the current image. The result of subtraction will be segmented in order to represent the moving object by a binary image using a threshold. In this paper a new background subtraction approach is presented. Firstly, each gray-level image of the sequence will be decomposed on two components, structure and texture/noise by applying the Osher and Vese algorithm. The structure component of each image will be taken to generate the background model. The background model development uses a threshold in order to decide if a pixel belongs to the background or to the foreground. The absolute difference is used to subtracting the background before compute the binary image of the moving objects using a proposed threshold selection operation. The experimental results demonstrate that our approach is effective and accurate moving objects detection comparing with the results of two existing methods.

Detecting and segmenting moving objects in video is a challenging and essential task in a number of applications. This paper presents a new algorithm of moving objects detection and segmentation. Firstly, we extract Selective Spatio-Temporal Interest Points (SSTIPs). The next step is to partition the SSTIPs into a set of moving clusters. To reduce the impact of the choice of a clustering method and its parameters on the quality of the result, we propose to integrate the concept of collaborative clustering of two clustering algorithms without requiring a user-defined number of clusters: Xmeans and Expectation-Maximization (EM) clustering. Finally, the segmentation of the objects associated to the given clusters is performed using an automatic maximal similarity based region merging (MSRM) method. Our algorithm is evaluated on several sequences and experimental results show a good performance for automatic detection and segmentation of moving objects.

In the present article, we will focus on the 3D Reconstruction of objects from flat images. The method we propose allows the realization of the 3D Reconstruction of objects from images based on estimating the Affine Projection Matrix made from different existing relationships between the three methods of Three-dimensional Reconstruction and without the passage through the calibration phase and self calibration of the cameras.

The Discrete Cosine Transform (DCT) based watermarking scheme is a common and popular technique that has been used for long time for image watermarking. This technique is based on embedding watermark information in the middle frequency band of the DCT blocks of the host image. In this work, we aimed to further improve the commonly used DCT based watermarking method by combining the DCT with the PDE (Partial Differential Equation) method, while the PDE is a method based on decomposing an image into its structures, textures, and noise components. To test the robustness of our developed combined method we evaluate the proposed method against Gaussian noise, JPEG compression, Salt&Pepper and Averagefilter. Our experimental results and comparison analysis demonstrated that our method has better performance in terms of invisibility and the robustness of the watermark compared to the traditional DCT based watermarking scheme.

Space-Time Interest Points (STIP) are among all the interesting features which can be extracted from videos; they are simple, robust and they allow a good characterization of a set of regions of interest corresponding to moving objects in a three-dimensional observed scene. In this paper, we show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for tracking. For a good detection of moving objects, we propose to apply the algorithm of the detection of spatiotemporal interest points on both components of the decomposition which is based on a partial differential equation (PDE): a geometric structure component and a texture component. Proposed results are obtained from very different types of videos, namely sport videos and animation movies.

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.

Extrusion, bevel, chamfer, and lathe tools are widely used in a large variety of computer graphics applications such as architectural designs and industrial prototyping of manufactured goods. However, such modeling is usually based on polygonal meshes which often require a considerable amount of graphics primitives. In this article, we present a new image-based approach for rendering extruded and revolved surfaces. We use only a single RGBA texture which stores a binary map (i.e. The profile curve), its Euclidean distance transform, and the two components of the unit gradient vector of the distance fields. All rendering algorithms are based on a ray-tracing procedure performed in texture space. The use of the distance fields allows culling of empty space and thus minimizes the number of ray-tracing steps. The extrusion and revolution mapping techniques produce very convincing models, and both are rendered at interactive frame rates.