Informatica

Clinics and hospitals have already adopted more technological resources to provide a faster and more precise diagnostic for patients, health care providers, and institutes of medicine. Security issues get more and more important in medical services via communication resources such as Wireless-Fidelity (Wi-Fi), third generation of mobile telecommunications technology (3G), and other mobile devices to connect medical systems from anywhere. Furthermore, cloud-based medical systems allow users to access archived medical images from anywhere. In order to protect medical images, lossless data hiding methods are efficient and easy techniques. In this paper, we present a data hiding of two-tier medical images based on histogram shifting of prediction errors. The median histogram shifting technique and prediction error schemes as the two-tier hiding have high capacity and PSNR in 16-bit medical images.

This paper presents a non-iterative deep learning approach to compressive sensing (CS) image reconstruction using a convolutional autoencoder and a residual learning network. An efficient measurement design is proposed in order to enable training of the compressive sensing models on normalized and mean-centred measurements, along with a practical network initialization method based on principal component analysis (PCA). Finally, perceptual residual learning is proposed in order to obtain semantically informative image reconstructions along with high pixel-wise reconstruction accuracy at low measurement rates.

A $(k,n)$-threshold secret image sharing scheme is any method of distributing a secret image amongst n participants in such a way that any k participants are able to use their shares collectively to reconstruct the secret image, while fewer than k shares do not reveal any information about the secret image. In this work, we propose a lossless linear algebraic $(k,n)$-threshold secret image sharing scheme. The scheme associates a vector ${\mathbf{v}_{i}}$ to the ith participant in the vector space ${\mathbb{F}_{{2^{\alpha }}}^{k}}$, where the vectors ${\mathbf{v}_{i}}$ satisfy some admissibility conditions. The ith share is simply a linear combination of the vectors ${\mathbf{v}_{i}}$ with coefficients from the secret image. Simulation results demonstrate the effectiveness and robustness of the proposed scheme compared to standard statistical attacks on secret image sharing schemes. Furthermore, the proposed scheme has a high level of security, error-resilient capability, and the size of each share is $1/k$ the size of the secret image. In comparison with existing work, the scheme is shown to be very competitive.

Normalization and aggregation are two most important issues in multi-criteria analysis. Although various multi-criteria decision-making (MCDM) methods have been developed over the past several decades, few of them integrate multiple normalization techniques and mixed aggregation approaches at the same time to reduce the deviations of evaluation values and enhance the reliability of the final decision result. This study is dedicated to introducing a new MCDM method called Mixed Aggregation by COmprehensive Normalization Technique (MACONT) to tackle complicate MCDM problems. This method introduces a comprehensive normalization technique based on criterion types, and then uses two mixed aggregation operators to aggregate the distance values between each alternative and the reference alternative on different criteria from the perspectives of compensation and non-compensation. An illustrative example is given to show the applicability of the proposed method, and the advantages of the proposed method are highlighted through sensitivity analyses and comparative analyses.

To solve the problem of choosing the appropriate cloud computing vendors in small and medium-sized enterprises (SMEs), this paper boils it down to a group decision making (GDM) problem. To facilitate the judgment, this paper uses preference relation as the decision making technology. Considering the situation where uncertain positive and negative judgments exist simultaneously, interval-valued intuitionistic fuzzy preference relations (IVIFPRs) are employed to express the decision makers’ judgments. In view of the multiplicative consistency and consensus analysis, a new GDM algorithm with IVIFPRs is offered. To accomplish this goal, a new multiplicative consistency is first defined, which can avoid the limitations of the previous ones. Then, a programming model is built to check the consistency of IVIFPRs. To deal with incomplete IVIFPRs, two programming models are constructed to determine the missing values with the goal of maximizing the level of multiplicative consistency and minimizing the total uncertainty. To achieve the minimum adjustment of original preference information, a programming model is established to repair inconsistent IVIFPRs. In addition, programming models for getting the decision makers (DMs)’ weights and improving the consensus degree are offered. Finally, a practical decision making example is given to illustrate the effectiveness of the proposed method and to compare it with previous methods.

Emotion recognition from facial expressions has gained much interest over the last few decades. In the literature, the common approach, used for facial emotion recognition (FER), consists of these steps: image pre-processing, face detection, facial feature extraction, and facial expression classification (recognition). We have developed a method for FER that is absolutely different from this common approach. Our method is based on the dimensional model of emotions as well as on using the kriging predictor of Fractional Brownian Vector Field. The classification problem, related to the recognition of facial emotions, is formulated and solved. The relationship of different emotions is estimated by expert psychologists by putting different emotions as the points on the plane. The goal is to get an estimate of a new picture emotion on the plane by kriging and determine which emotion, identified by psychologists, is the closest one. Seven basic emotions (Joy, Sadness, Surprise, Disgust, Anger, Fear, and Neutral) have been chosen. The accuracy of classification into seven classes has been obtained approximately 50%, if we make a decision on the basis of the closest basic emotion. It has been ascertained that the kriging predictor is suitable for facial emotion recognition in the case of small sets of pictures. More sophisticated classification strategies may increase the accuracy, when grouping of the basic emotions is applied.

We consider a geographical region with spatially separated customers, whose demand is currently served by some pre-existing facilities owned by different firms. An entering firm wants to compete for this market locating some new facilities. Trying to guarantee a future satisfactory captured demand for each new facility, the firm imposes a constraint over its possible locations (a finite set of candidates): a new facility will be opened only if a minimal market share is captured in the short-term. To check that, it is necessary to know the exact captured demand by each new facility. It is supposed that customers follow the partially binary choice rule to satisfy its demand. If there are several new facilities with maximal attraction for a customer, we consider that the proportion of demand captured by the entering firm will be equally distributed among such facilities (equity-based rule). This ties breaking rule involves that we will deal with a nonlinear constrained discrete competitive facility location problem. Moreover, minimal attraction conditions for customers and distances approximated by intervals have been incorporated to deal with a more realistic model. To solve this nonlinear model, we first linearize the model, which allows to solve small size problems because of its complexity, and then, for bigger size problems, a heuristic algorithm is proposed, which could also be used to solve other constrained problems.

Data hiding technique is an important multimedia security technique and has been applied to many domains, for example, relational databases. The existing data hiding techniques for relational databases cannot restore raw data after hiding. The purpose of this paper is to propose the first reversible hiding technique for the relational database. In hiding phase, it hides confidential messages into a relational database by the LSB (Least-Significant-Bit) matching method for relational databases. In extraction and restoration phases, it gets the confidential messages through the LSB and LSB matching method for relational databases. Finally, the averaging method is used to restore the raw data. According to the experiments, our proposed technique meets data hiding requirements. It not only enables to recover the raw data, but also maintains a high hiding capacity. The complexity of our algorithms shows their efficiencies.

In data mining research, outliers usually represent extreme values that deviate from other observations on data. The significant issue of existing outlier detection methods is that they only consider the object itself not taking its neighbouring objects into account to extract location features. In this paper, we propose an innovative approach to this issue. First, we propose the notions of centrality and centre-proximity for determining the degree of outlierness considering the distribution of all objects. We also propose a novel graph-based algorithm for outlier detection based on the notions. The algorithm solves the problems of existing methods, i.e. the problems of local density, micro-cluster, and fringe objects. We performed extensive experiments in order to confirm the effectiveness and efficiency of our proposed method. The obtained experimental results showed that the proposed method uncovers outliers successfully, and outperforms previous outlier detection methods.

The crosstalk error is widely used to evaluate the performance of blind source separation. However, it needs to know the global separation matrix in advance, and it is not robust. In order to solve these problems, a new adaptive algorithm for calculating crosstalk error is presented, which calculates the crosstalk error by a cost function of least squares criterion, and the robustness of the crosstalk error is improved by introducing the position information of the maximum value in the global separation matrix. Finally, the method is compared with the conventional RLS algorithms in terms of performance, robustness and convergence rate. Furthermore, its validity is verified by simulation experiments and real world signals experiments.