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Feature selection is an active research area in data mining and machine learning, especially with the increase in the amount of numerical data. FS is a search strategy to find the best subset of features among a large number of subsets of features. Thus, FS is applied in most modern applications and in various domains, which requires the search for a powerful FS technique to process and classify high-dimensional data. In this paper, we propose a new technique for dimension reduction in feature selection. This approach is based on a recent metaheuristic called Archimedes’ Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy. The idea of the AOA is based on the steps of Archimedes' principle in physics. It explains the behavior of the force exerted when an object is partially or fully immersed in a fluid. AOA optimization maintains a balance between exploration and exploitation, keeping a population of solutions and studying a large area to find the best overall solution. In this study, AOA is exploited as a search technique to find an optimal feature subset that reduces the number of features to maximize classification accuracy. The K-nearest neighbor (K-NN) classifier was used to evaluate the classification performance of selected feature subsets. To demonstrate the superiority of the proposed method, 16 benchmark datasets from the UCI repository are used and also compared by well-known and recently introduced meta-heuristics in this context, such as: sine-cosine algorithm (SCA), whale optimization algorithm (WOA), butterfly optimization algorithm (BAO), and butterfly flame optimization algorithm (MFO). The results prove the effectiveness of the proposed algorithm over the other algorithms based on several performance measures used in this paper.

In this work, we propose a novel approach to enhancing the security of passwords before storing them in databases. Our method utilizes Braille transformation to encrypt the password after generating the corresponding hash. The hash is divided into multiple blocks, each representing a character treated as a transformation unit. Each character is then associated with its corresponding Braille code, which consists of 6 digits. To further enhance security, we randomly replace each occurrence of “0” in the generated string with one of the digits 7, 8, or 9. The final string, six times larger than the original hash, is then stored in the database. To evaluate our approach, we conducted several experiments and comparisons. The results demonstrate that Braille transformation is resistant to brute-force attacks, statistical attacks, and differential attacks. These results were justified using various evaluation criteria, such as execution time and memory space occupied. Braille transformation is susceptible to any modification made to the hash or the generated string, further reinforcing its security. Our Braille-based approach offers an effective solution to strengthen the security of database passwords. It provides advantages in terms of protection against different attacks and offers a robust evaluation based on relevant criteria.

Stereo camera self-calibration is a complex challenge in computer vision applications such as robotics, object tracking, surveillance and 3D reconstruction. To address this, we propose an efficient, fully automated End-To-End AI-Based system for automatic stereo camera self-calibration with varying intrinsic parameters, using only two images of any 3D scene. Our system combines deep convolutional neural networks (CNNs) with transfer learning techniques and fine-tuning. First, our end-to-end convolutional neural network optimized model begins by extracting matching points between a pair of stereo images. These matching points are then used, along with their 3D scene correspondences, to formulate a non-linear cost function. Direct optimization is subsequently performed to estimate the intrinsic camera parameters by minimizing this non-linear cost function. Following this initial optimization, a fine-tuning layer refines the intrinsic parameters for increased accuracy. Our hybrid approach is characterized by a special optimized architecture that leverages the strengths of end-to-end CNNs for image feature extraction and processing, as well as the pillars of our nonlinear cost function formulation and fine-tuning, to offer a robust and accurate method for stereo camera self-calibration. Extensive experiments on synthetic and real data demonstrate the superior performance of the proposed technique compared to traditional camera self-calibration methods in terms of precision and faster convergence.

In this study, we introduce a refined approach to encrypting 32-bit color images, leveraging the potential of four 1D chaotic maps – the Logistic map, Tent map, Chebyshev map, and Sine map. These chaotic maps intricately populate the four matrices within our encryption system, assigning exclusive integers ranging from 0 to 255. Our proposed methodology employs 16 × 16 matrices to represent the four channels (red, green, blue, and alpha) of a 32-bit color image, strategically utilizing specific grids for channel encryption. The top-left and bottom-right grids facilitate the encryption of the red and alpha channels, respectively, while the top-right and bottom-left grids are employed for encrypting the green and blue channels. The algorithm initiates by extracting decimal values from each pixel in the source image, mapping them to their corresponding positions in the matrices. A subsequent right circular shift operation on each pixel, determined by its row and column coordinates, is performed to prevent the encryption of areas with uniform color. To enhance security further, we employ the Four-square cipher method to encrypt the decimal values of the pixels. In the confusion stage, we apply the Arnold Cat Map transformation to strategically rearrange the position of all pixels, introducing an additional layer of complexity. Rigorous assessments using various security criteria were conducted to evaluate our algorithm's performance against common attacks, yielding consistently excellent results. Our method demonstrated superior outcomes, including a 25 % to 44 % increase in resistance to common attacks compared to existing methods.

With the growing need for secure multimedia data transmission, image encryption has become an important research area. Traditional encryption algorithms like RSA are not well-suited for this purpose, leading researchers to explore new approaches such as chaotic maps. The present study introduces a new image encryption algorithm that utilizes an improved Rossler system as a keystream generator. The improved Rossler system is an enhanced version of the original Rossler system, which has been optimized for better chaotic behavior and improved security. For the confusion part, we combine DNA encoding techniques with Baker maps to ensure high levels of security. Various performance metrics, including NPCR, UACI, correlation coefficient, histogram analysis, and key sensitivity analysis, were used to evaluate the proposed scheme. The results showed that the proposed method surpassed several existing image encryption methods in terms of both security and efficiency.

Creating 3D computer-generated surfaces has long been a difficult challenge in computer graphics, particularly when portraying massive landscapes with extremely detailed surfaces in real-time. Despite significant advances in computer vision in recent years, there is still a great demand for improved realism and the capacity to edit computer-generated 3D surfaces in real-time. We propose three scalable and faster algorithms for creating extended, beveled, and chamfered patterns using only two textures and a simple shape box. The proposed techniques produce visually pleasing results in real-time while retaining optimal rendering performance and without increasing the mesh density of the shape box.

Per-pixel displacement mapping is a texture mapping technique that adds the microrelief effect to 3D surfaces without increasing the density of their corresponding meshes. This technique relies on ray tracing algorithms to find the intersection point between the viewing ray and the microrelief stored in a 2D texture called a depth map. This intersection makes it possible to determine the corresponding pixel to produce an illusion of surface displacement instead of a real one. Cone tracing is one of the per-pixel displacement mapping techniques for real-time rendering that relies on the encoding of the empty space around each pixel of the depth map. During the preprocessing stage, this space is encoded in the form of top-opened cones and then stored in a 2D texture, and during the rendering stage, it is used to converge more quickly to the intersection point. Cone tracing technique produces satisfactory results in the case of flat surfaces, but when it comes to curved surfaces, it does not support the silhouette at the edges of the 3D mesh, that is to say, the relief merges with the surface of the object, and in this case, it will not be rendered correctly. To overcome this limitation, we have presented two new cone tracing algorithms that allow taking into consideration the curvature of the 3D surface to determine the fragments belonging to the silhouette. These two algorithms are based on a quadratic approximation of the object geometry at each vertex of the 3D mesh. The main objective of this paper is to achieve a texture mapping with a realistic appearance and at a low cost so that the rendered objects will have real and complex details that are visible on their entire surface and without modifying their geometry. Based on the ray-tracing algorithm, our contribution can be useful for current graphics card generation, since the programmable units and the frameworks associated with the new graphics cards integrate today the technology of ray tracing.

This paper proposes a new and efficient color image encryption algorithm based on multiple chaotic maps (Logistic map, Sine map, and Chebyshev map) and the intersecting planes method inside a cube. All maps were used to fill the three adjacent faces of the cube. These faces represent the three channels of the color image (red, green, and blue). The first phase of our method begins with extracting all the pixels from the original image and looking for the corresponding values of each pixel on the three faces of the cube. Then, we used a circular rotation operation based on the position of each pixel (row, column). This rotation prevents two identical pixels to have the same encrypted value. Afterward, we used the intersecting planes method with the corresponding face to encrypt the pixels. For the confusion part, we chose the 2D transformation Arnold Cat Map to shuffle all the pixels and change their positions according to the parameters calculated from all the pixels of the original image. We evaluated the performance of our algorithm against various differential and statistical attacks and based on multiple factors, such as histogram, entropy, correlation coefficient, UACI, NPCR, and PSNR. We compared the proposed method with several efficient methods in the field. The various results got by our method show its performance and reliability in terms of safety, precision, confidentiality, and robustness.

In this article we proposed a novel approach to self- calibrate a camera with variable focal length. We show that the estimation of camera’s intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera’s intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera’s intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence.

In recent years and following the spread of internet using, the way of life in our society as well as the management of the main production sectors have changed towards digitalization (E-Commerce, E-Surgery, road traffic management, etc.) to minimize the. This change challenged researchers and organizations to come up with solutions to manage, maintain, classify and make the best decision from the gigantic flows of data created daily while reducing environmental Damage (Consumption of Energical resources….), From which was born the Collaborative filtering approach which facilitate decision making based on the experience feedback of Internet users. This approach is used generally the big Data Analytics Algorithm to predict and classify data