One of the common techniques to reduce the scalability problem in collaborative filtering (CF)-based recommender systems is the clustering technique, which accelerates finding the nearest neighbor users in the recommendation process. Different clustering algorithms lead to improved accuracy and diversity in recommender systems. It is challenging to develop recommender systems based on clustering with decreasing scalability and simultaneously increasing accuracy. This article proposes a new clustering-based recommender system that takes into account the theoretical properties of the Learning Automata (LA) technique. The presented clustering novelty lies in the fact that employing LA technique for the user clustering in a CF-based recommender system has not been set forth so far in a way that addresses the scalability while improving accuracy. In addition, a novel similarity metric is embedded in the proposed algorithm to measure the similarity value between users. This metric is developed as like/dislike (LD) which can significantly improve the accuracy by reducing the computational cost. Extensive simulations have been performed on real-world datasets such as MovieLens and FilmTrust, which confirm the effectiveness of the proposed algorithm. In this regard, the proposed algorithm has improved the precision between 5 and 16% on average compared to the existing state-of-the-art methods such as GA-GELS, NUSCCF, NNMF, and KL-KM.

Summary Today, large amounts of data are generated in various applications such as smart cities and social networks, and their processing requires a lot of time. One of the methods of processing data types and reducing computational time on data is the use of dimension reduction methods. Reducing dimensions is a problem with the optimization approach and meta‐heuristic methods can be used to solve it. Namib beetles are an example of intelligent insects and creatures in nature that use an interesting strategy to survive and collect water in the desert. In this article, the behavior of Namib beetles has been used to collect water in the desert to model the Namib beetle optimization (NBO) algorithm. In the second phase of a binary version, this algorithm is used to select features and reduce dimensions. Experiments on CEC functions show that the proposed method has fewer errors than the DE, BBO, SHO, WOA, GOA, and HHO algorithms. In large dimensions such as 200, 500, and 1000 dimensions, the NBO algorithm of meta‐heuristic algorithms such as HHO and WOA has a better rank in the optimal calculation of benchmark functions. Experiments show that the proposed algorithm has a greater ability to reduce dimensions and feature selection than similar meta‐heuristic algorithms. In 87.5% of the experiments, the proposed method reduces the data space more than other compared methods.

Abstract In biometric systems, template protection is a vital issue for preventing from identity theft. Fuzzy commitment scheme is a template cryptographic method providing secure templates by binding a uniform random key to a template. Fuzzy commitment scheme suffers from its lacks in privacy, in cancelability, and also in robustness against cross‐matching attacks. To improve both the security and cancelability properties simultaneously, we present a novel template protection approach for face recognition systems based on fuzzy commitment scheme, permutated features, and chaos symmetric key. To permute feature vectors, we produce pseudo random numbers by using nonlinear chaos function to fill the control array of GRP permutation method. Even if the permutated template is compromised, it is possible to substitute it with a new permutated template by changing the initial conditions of chaos map. To evaluate our proposed approach, a series of experiments have been conducted on two well‐known face databases ORL and Yale. We showed that the new feature permutation leads to more secure protected templates against decodability based on cross‐matching attacks. The experimental results also showed that our proposed approach outperforms existing fuzzy commitment methods both in security and privacy aspects without influencing the accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

ABSTRACT Steganography is the art and science of hiding a message in a carrier, in such a manner that no one, except the sender and intended recipient, can guess the existence of the message; it is a form of security through obscurity. Often, steganography algorithms using discrete cosine transform are detectable through steganalysis attacks. To reduce detectability of the stego, we propose an image steganography algorithm in transform domain based on morphological associative memory, permutation approach, and matrix encoding. In our proposed algorithm, in order to insert a secret message in the cover image, first, we map the cover image to a morphological representation containing morphological coefficients, and then bits of secret message are inserted into the image by permutation approach and matrix encoding. Permutation approach leads to unique distribution of secret message in the carrier, and the use of matrix encoding minimizes the number of coefficients needed to be changed because of inserting secret message. In our experiments, we compared the quality of stego, time complexity, and robustness of our method with state‐of‐art image steganography algorithms (F5 and morphological steganography) with an equal payload. The experimental results showed that the proposed method is able to produce insignificant visual distortion compared with other traditional approaches. To test the robustness of our proposed algorithm, we used wavelet‐based and block‐based steganalysis methods. The obtained results showed a high level of robustness of our algorithm against steganalysis attacks. Copyright © 2014 John Wiley & Sons, Ltd.