Informatica

ID-based cryptographic protocol is an extremely valuable apparatus in the field of cryptography and has numerous latent applications. The safety of conventional ID-based cryptographic protocol is entirely contingent in light of the safety of private keys. Revelation of private keys needs reissuing all beforehand doled out encryptions. This confinement turns out to be clearer today as key presentation is more regular with expanding utilization of unprotected gadgets and mobile technology. In this context, relieving the loss of key disclosure in ID-based cryptographic protocol is a critical issue. To manage this issue, we present to include onward security into ID-based cryptographic protocol. Besides, we propose another development of indistinguishability-ID-based cryptographic protocol using Integer Factorization Problem (IFP) and Generalized Discrete Logarithm Problem (GDLP) which is semantically protected against Chosen Plaintext Attack (CPA) in random oracle. We show that our presented protocol beats the other standing protocol as far as security, the length of public key and computational cost are concerned. We shed light on some applications and future scope.

In this paper, at first, we develop some new geometric distance measures for interval-valued intuitionistic fuzzy information, including the interval-valued intuitionistic fuzzy weighted geometric distance (IVIFWGD) measure, the interval-valued intuitionistic fuzzy ordered weighted geometric distance (IVIFOWGD) measure and the interval-valued intuitionistic fuzzy hybrid weighted geometric distance (IVIFHWGD) measure. Also, several desirable properties of these new distance measures are studied and a numerical example is given to show application of the distance measure to pattern recognition problems. And then, based on the developed distance measures a consensus reaching process with interval-valued intuitionistic fuzzy preference information for group decision making is proposed. Finally, an illustrative example with interval-valued intuitionistic fuzzy information is given.

Radiologists need to find a position of a slice of one computed tomography (CT) scan in another scan. The image registration is a technique used to transform several images into one coordinate system and to compare them. Such transversal plane images obtained by CT scans are considered, where ribs are visible, but it does not lessen the significance of our work because many important internal organs are located here: liver, heart, stomach, pancreas, lungs, etc. The new method is developed for registration based on the mathematical model describing the rib-bounded contour. Parameters of the mathematical model and of distribution of the bone tissue on the CT scan slice form a set of features describing a particular slice. The registration method applies translation, rotation, and scaling invariances. Several strategies of translation invariance and options of the unification of scales are proposed. The method is examined on real CT scans seeking for its best performance.

The Improved Artificial Bee Colony (IABC) algorithm is a variant of the well-known Artificial Bee Colony (ABC) algorithm. In IABC, a new initialization approach and a new search mechanism were added to the ABC for avoiding local optimums and a better convergence speed. New parameters were added for the new search mechanism. Specified values of these newly added parameters have a direct impact on the performance of the IABC algorithm. For better performance of the algorithm, parameter values should be subjected to change from problem to problem and also need to be updated during the run of the algorithm. In this paper, two novel parameter control methods and related algorithms have been developed in order to increase the performance of the IABC algorithm for large scale optimization problems. One of them is an adaptive parameter control which updates parameter values according to the feedback coming from the search process during the run of the algorithm. In the second method, the management of the parameter values is left to the algorithm itself, which is called self-adaptive parameter control. The adaptive IABC algorithms were examined and compared to other ABC variants and state-of-the-art algorithms on a benchmark functions suite. Through the analysis of the results of the experiments, the adaptive IABC algorithms outperformed almost all ABC variants and gave competitive results with state-of-the-art algorithms from the literature.

An adaptive multi-rate wideband (AMR-WB) speech codec with a sampling rate of 16 kHz is known as one of the speech codecs employed in handheld devices that support 4G mobile communication systems. When applied to smartphones, it provides a superior speech quality relative to conventional speech codecs. Nonetheless, a major disadvantage is that an algebraic codebook search occupies a significant computational load in an AMR-WB encoder. In other words, the high computational complexity accounts for the high power consumption on a smartphone battery. This paper presents an improved version of depth-first tree search (DF) algorithm as a means to considerably reduce the complexity of an algebraic codebook search in an AMR-WB speech codec. This proposed search algorithm firstly involves the choice of a specified number of candidate pulses according to a pulse contribution ranking. Subsequently, a DF search is performed on the candidate pulses for a set of best pulses. Consequently, the target of the search and computational complexity reduction can be reached as expected. With a well maintained speech quality, this proposal demonstrates a search performance superiority over a DF and a global pulse replacement approach. Furthermore, with DF as a benchmark, a computational load reduction above 73% is reached in all coding modes.

This paper proposes the concepts of a neutrosophic number and a trapezoidal neutrosophic number (TNN), the basic operational relations of TNNs, and the score function of TNN. Then, we develop a trapezoidal neutrosophic weighted arithmetic averaging (TNWAA) operator and a trapezoidal neutrosophic weighted geometric averaging (TNWGA) operator to aggregate TNN information and investigate their properties. Furthermore, a multiple attribute decision making method based on the TNWAA and TNWGA operators and the score function of TNN is established under a TNN environment. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.

The popularity of sharing data through cloud services has increased these days. As a result, the security of data sharing has become an important issue. The security mechanism has to ensure that the shared data would not be intercepted or altered by illegal members during transmission. A data sharing scheme for cloud services is proposed in this paper to achieve the following four security requirements: 1) forward secrecy and backward secrecy, 2) source authentication, 3) data integrity, and 4) confidentiality. In addition, message recovery is applied to improve the efficiency of encryption and signature computation. The computation cost is reduced by computing a common key for all data. Thus, the data owner only needs to encrypt the shared data once before sending it in this proposed scheme.

In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

In this paper, a new class of uncertain linguistic variables called 2-tuple linguistic hesitant fuzzy sets (2-TLHFSs) is defined, which can express complex multi-attribute decision-making problems as well as reflect decision makers’ hesitancy, uncertainty and inconsistency. Besides, it can avoid information and precision losing in aggregation process. Firstly, several new closed operational laws based on Einstein t-norm and t-conorm are defined over 2-TLHFSs, which can overcome granularity and logical problems of existing operational laws. Based on the new operational laws, 2-tuple linguistic hesitant fuzzy Einstein weighted averaging (2-TLHFEWA) operator and 2-tuple linguistic hesitant fuzzy Einstein weighted geometric (2-TLHFEWG) operator are proposed, and some of their properties are investigated. Then, a new model method based on similarity to ideal solution is proposed to determine weights of attribute, which takes both subjective and objective factors into consideration. Finally, a linguistic hesitant fuzzy multi-attribute decision making procedure is developed by means of 2-TLHFEWA and 2-TLHFEWG operators. An example is given to illustrate the practicality and efficiency of the proposed approach.

Clustering high-dimensional data is a challenging task in data mining, and clustering high-dimensional categorical data is even more challenging because it is more difficult to measure the similarity between categorical objects. Most algorithms assume feature independence when computing similarity between data objects, or make use of computationally demanding techniques such as PCA for numerical data. Hierarchical clustering algorithms are often based on similarity measures computed on a common feature space, which is not effective when clustering high-dimensional data. Subspace clustering algorithms discover feature subspaces for clusters, but are mostly partition-based; i.e. they do not produce a hierarchical structure of clusters. In this paper, we propose a hierarchical algorithm for clustering high-dimensional categorical data, based on a recently proposed information-theoretical concept named holo-entropy. The algorithm proposes new ways of exploring entropy, holo-entropy and attribute weighting in order to determine the feature subspace of a cluster and to merge clusters even though their feature subspaces differ. The algorithm is tested on UCI datasets, and compared with several state-of-the-art algorithms. Experimental results show that the proposed algorithm yields higher efficiency and accuracy than the competing algorithms and allows higher reproducibility.