A New Method Used for Traveling salesman problem Based on Discrete Artificial Bee Colony Algorithm

We propose a new method based on discrete Artificial Bee Colony algorithm (DABC) for traveling salesman problem(TSP). We redefine the searching strategy and transforming mechanism of leading bees, following bees and scout bees according to discrete variables. The transition of swarm role is based on ratio factor of definition. leading bees use 2-Opt operator and learning operator to accelerate the convergence speed and to search the neighborhood. The searching of following bees introduce tabu table to improve the local refinement ability of the algorithm. Scouts bees define exclusive operation to maintain the diversity of population, so it is better to balance the exploration and exploitation ability of the algorithm. Finally, the experimental results show that the new algorithm can find relatively satisfactory solution in a short time, and improve the efficiency of solving the TSP

Novel DV-hop Method Based on Krill Swarm Algorithm Used for Wireless Sensor Network Localization

Wireless sensor network (WSN) is self-organizing network; it consists of a large number of sensor nodes with perception, calculation ability and communication ability. As we all know, the floor, walls or people moving has an effect on indoor localization, so it will result in multi-path phenomena and decrease signal strength, also the received signal strength indicator (RSSI) is unable to gain higher accuracy of positioning. When using multilateral measurement method to calculate the unknown node coordinates, it will generate big error in range-free distance vector-hop (DV-hop) localization algorithm of WSN. In order to improve the WSN positioning accuracy in indoor condition, more reasonable distribute network resources, in this paper, we firstly propose krill swarm algorithm used for WSN localization. First, we detailed analyze the multilateral measurement method in DV-hop localization algorithm. The position problem can be transformed into a global optimization problem. Then, we adequately utilize the advantage of calculating optimization problem. We apply the krill swarm algorithm into the stage of estimating unknown node coordinates in DV-hop algorithm to realize localization. Finally, the simulation experience results show that the localization with krill swarm algorithm has an obviously higher positioning precision and accuracy stability with different anchor node proportion and nodes. We also make comparison with DV-hop algorithm and the newest localization algorithm

An Improved Artificial Bee Colony Algorithm for Staged Search

Artificial Bee Colony(ABC) or its improved algorithms used in solving high dimensional complex function optimization issues has some disadvantages, such as lower convergence, lower solution precision, lots of control parameters of improved algorithms, easy to fall into a local optimum solution. In this letter, we propose an improved ABC of staged search. This new algorithm designs staged employed bee search strategy which makes that employed bee has different search characters in different stages. That reduces probability of falling into local extreme value. It defines the escape radius which can guide precocious individual to jump local extreme value and avoid the blindness of flight behavior. Meanwhile, we adopt initialization strategy combining uniform distribution and backward learning to prompt initial solution with uniform distribution and better quality. Finally, we make simulation experiments for eight typical high dimensional complex functions. Results show that the improved algorithm has a higher solution precision and faster convergence rate which is more suitable for solving high dimensional complex functions

A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application

Biclustering algorithm is proposed for discovering matrix with biological significance in gene expression data matrix and it is used widely in machine learning which can cluster the row and column of matrix. In order to further improve the performance of biclustering algorithm, this paper proposes a semi-supervised clustering algorithm based on variational Bayesian. Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. In addition, it estimates the parameter of joint distribution probability model based on variational Bayesian learning method. Finally, it estimates the performance of proposed algorithm through synthesized data and real gene expression data set. Experiments show that normalized mutual information of this paper’s new method is better than relevant biclustering algorithms for biclustering analysis

An Improved Chaos Electromagnetism Mechanism Algorithm for Path Optimization Problem

As we all know, traditional electromagnetism mechanism (EM) algorithm has the disadvantage with low solution precision, lack of mining ability and easily falling into precocity. This paper proposes a new chaos electromagnetism mechanism algorithm combining chaotic mapping with limited storage Quasi-Newton Method (EM-CMLSQN). Its main idea is that it adopts limit quasi-Newton operator to replace the local optimization operator in EM algorithm for local searching in the late of algorithm. In the process of algorithm, the chaos mapping is introduced into optimization processes, and it generates new individuals to jump out of local to maintain the population diversity according to characteristics of chaos mapping random traversal. Finally, the experiments show that the new algorithm can effectively jump out of local optimal solution through comparing three continuous space test functions. The new algorithm has obvious advantages in terms of convergence speed compared to traditional EM algorithm, in addition, it is more accuracy than particle swarm optimization (PSO) algorithm. We compare the new chaos electromagnetism mechanism algorithm with ant colony optimization (ACO) algorithm, PSO algorithm, the results represent that new scheme can obtain the optimal path in the path optimization process, which shows that the new method has better applicability in the discrete domain problem

Mutual Coupling Optimization of Compact Microstrip Array Antenna

In this paper, we perfect the mutual coupling of compact microstrip array antenna by designing a new defected ground structure. When the resonant frequency is 2.45GHz, array element spacing is 0.1 times of free space wavelength, we introduce new defected ground structure into antenna array. Then we use HFSS to make simulation and compare the changing of antenna's parameters before and after adding defected ground structure. The results demonstrate that the parameters representing mutual coupling in new model can reduce by 30dB, which effectively perfects the mutual coupling of compact microstrip array antenna

A facial expression recognition method based on face texture feature fusion

Aiming at facial expression recognition, the recognition rate is not high due to noise and occlusion. A hybrid approach of facial expression has been presented by combining local and global features. First, feature extraction is performed to fuse the histogram of oriented gradients (HOG) descriptor with the compounded local ternary pattern (C-LTP) descriptor. Second, features extracted by HOG and C-LTP are fused into a single feature vector. Third, the feature vector is sent to a multi-class support vector machine classifier for facial classification. Finally, the proposed method is compared with the existing facial expression recognition methods in three public facial expression image databases, and the results show that the recognition rates of the proposed method in MMI, JAFFE and CK[KG-*2]+ databases are 98.28%, 95.75% and 99.64%, respectively. The average recognition rate is 10% higher than other methods, which is better than other existing methods. The results of this study provide a reference for the research of facial expression recognition in many situations. The method of facial expression recognition proposed can effectively promote the development of human-computer interaction system and the study of computer image understanding. It is of great significance to realize the fusion of human language and natural language, as well as the establishment and implementation of the connection model between language and expression

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

Abstract With the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security

Clinicopathological and Prognostic Significance of PRAME Overexpression in Human Cancer: A Meta-Analysis

Numerous studies have demonstrated that preferentially expressed antigen in melanoma (PRAME) is abnormally expressed in various solid tumours. However, the clinicopathological features and prognostic value of the PRAME expression in patients with cancer remain unclear. Accordingly, we performed a meta-analysis to accurately assess the association of the expression level of PRAME with clinicopathological features and cancer prognosis. Relevant study collection was performed in PubMed, Web of Science, and Embase until 28 February 2020. A total of 14 original studies involving 2,421 patients were included. Our data indicated that the PRAME expression was significantly associated with tumour stage (OR=1.99, 95% CI: 1.48–2.67, P<0.001) and positive lymph node metastasis (OR=3.14, 95% CI: 1.99–4.97, P<0.001). Pooled results showed that overexpression of PRAME is positively correlated with poor disease-free survival (HR=1.60, 95% CI: 1.36–1.88, P<0.001), progression-free survival (HR=1.88, 95% CI: 1.02–3.46, P=0.042), metastasis-free survival (HR=1.86, 95% CI: 1.05–3.31, P=0.034), and overall survival (HR=1.75, 95% CI: 1.53–1.99, P<0.001). In summary, these data are suggesting that PRAME is tumorigenic and may serve as a prognostic biomarker for cancer