Showing papers in "Information Sciences in 2019"

TL;DR: A cosine-transform-based chaotic system (CTBCS) that can produce chaotic maps with complex dynamical behaviors and an image encryption scheme that provides a higher level of security than several advanced image encryption schemes.

TL;DR: GM is introduced into the basic MFO to improve neighborhood-informed capability, CM with a large mutation step is adopted to enhance global exploration ability and LM is embedded to increase the randomness of search agents’ movement.

TL;DR: This paper surveys the different approaches to automatic detection of fake news and rumours proposed in the recent literature and provides a comprehensive analysis on the various techniques used to perform rumour and fake news detection.

TL;DR: This paper proposes a parallel diffusion method that ensures the parallelism of diffusion to the utmost extent and achieves a qualitative improvement in efficiency over traditional streaming diffusion methods.

TL;DR: This paper transforms a document using three document representation methods: term frequency–inverse document frequency (TF–IDF) based on the bag-of-words scheme, topic distribution based on latent Dirichlet allocation (LDA), and neural-network-based document embedding known as document to vector (Doc2Vec).

TL;DR: This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose, and identifies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommendation mechanisms in complex social networks scenarios with uncertain knowledge.

TL;DR: A novel algorithm using synergetic neural networks for robustness and security of digital image watermarking is proposed, which obtains an optimal Peak Signal-to-noise ratio (PSNR) and can complete certain image processing operations with improved performance.

TL;DR: A new dataset named DDR is provided for assessing deep learning models and further exploring the clinical applications, particularly for lesion recognition, indicating that lesion segmentation and detection are quite challenging.

TL;DR: This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally.

TL;DR: An integrated methodology to address MCGDM problems based on the best-worst method (BWM) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique in an interval type-2 fuzzy environment is provided.

TL;DR: A new hybrid GSA-GA algorithm is presented for the constraint nonlinear optimization problems with mixed variables that is tuned up with the gravitational search algorithm and each solution is upgraded with the genetic operators such as selection, crossover, mutation.

TL;DR: This work proposes a secure digital evidence framework using blockchain (Block-DEF) with a loose coupling structure in which the evidence and the evidence information are maintained separately and the multi-signature technique is adopted for evidence submission and retrieval.

TL;DR: The overall experimental results suggest that HEFS performs best when it is integrated with Random Forest classifier, where the baseline features correctly distinguish 94.6% of phishing and legitimate websites using only 20.8% of the original features.

TL;DR: This work proposes a hyperspectral image unsupervised classification framework based on robust manifold matrix factorization and its out-of-sample extension and designs a novel Augmented Lagrangian Method (ALM) based procedure to seek the local optimal solution of the proposed optimization.

TL;DR: This paper presents a hybrid technique that combines supervised and unsupervised techniques to improve the fraud detection accuracy and shows that the combination is efficient and does indeed improve the accuracy of the detection.

TL;DR: A novel undersampling approach called cluster-based instance selection (CBIS) that combines clustering analysis and instance selection is introduced, and experimental results show that the CBIS approach can make bagging and boosting-based MLP ensemble classifiers perform significantly better than six state-of-the-art approaches.

TL;DR: A novel multiple attribute decision making (MADM) method to rank products based on OPRs is introduced, and some basic theories of probabilistic linguistic term sets (PLTSs) are reviewed.

TL;DR: A theoretical and experimental analysis of the SMOTE method, which explores the accuracy of how faithful it emulates the underlying density and the effect of the different factors on generation accuracy, such as the dimension, size of the training set and the considered number of neighbors K .

TL;DR: Comparative analysis shows that the two ranking results obtained by means of two different decision-making methods have a high consensus.

TL;DR: Experimental results indicate that the proposed stereo image zero- watermarking algorithm is strongly robust to various asymmetric and symmetric attacks and has superiority compared with other zero-watermarking algorithms.

TL;DR: The proposed MADM method can overcome the drawbacks of the existing MADM methods for MADM in intuitionistic fuzzy environments and the proposed modified VIKOR method.

TL;DR: This paper presents a comprehensive survey on the theoretical and practical aspects of SMPC protocols, including its security requirements and basic construction techniques, and summarizes the concrete application-oriented protocols that are currently available.

TL;DR: A novel GSA-based algorithm with evolutionary crossover and mutation operators is proposed to deal with feature selection (FS) tasks and the extensive results and comparisons demonstrate the superiority of the proposed algorithm in solving FS problems.

TL;DR: An analysis indicates that using word embeddings and its flavors is a very promising approach for HTC.

TL;DR: Two new simple but effective similarity models have been developed by considering all rating vectors of users to classify relevant neighborhoods and generate recommendations in a lower computation time by considering relevant Jaccard similarity.

TL;DR: A heuristic feature selection algorithm with low computational complexity is presented to improve the performance of cancer classification using gene expression data and outperforms other related methods in terms of the number of selected genes and the classification accuracy, especially as the size of the genes increases.

TL;DR: This paper investigates the interaction between the disease transmission and disease-related awareness spread, and proposes a new coupled disease spreading model on a two-layered multiplex network, where one layer denotes the underlying topology for the epidemics and the other one represents the corresponding topologies for the awareness spread.

TL;DR: This paper proposes a novel decision model based on the combination of three-way decisions and multi-criteria decision-making (MCDM), and proposes an aggregation method of loss functions to reflect the comprehensive losses of alternatives.

TL;DR: The problem of sliding-mode fault-tolerant control is addressed for a class of uncertain nonlinear systems with distributed delays and parameter perturbations by using interval type-2 Takagi–Sugeno (T–S) fuzzy models, of which uncertain parameters and distributed state delays are represented in a unifiedtype-2 fuzzy framework.

TL;DR: An innovative MCDM model for sustainable supplier selection is reported by integrating best-worst method (BWM) and alternative queuing method (AQM) within the interval-valued intuitionistic uncertain linguistic setting.