A Gaussian error correction multi‐objective positioning model with NSGA‐II

Summary The performance of active contour model is limited on retinal vessel segmentation as vessel images are usually corrupted with intensity inhomogeneity, low contrast, and weak boundary, which severely affect the segmentation results of retinal vessels. A new active contour model combining the local and global information is proposed in this paper to facilitate the vessel segmentation. In our model, the fuzzy conception is firstly introduced as fuzzy methods generally provide more accurate and robust clustering and the concept of fuzziness in fuzzy clustering, which is represented by membership, can reflect the intensity distribution of the image. Then, we define local energy based on Maximum a Posterior Probability and use spatially varying parameters, mean and stand deviation, to describe the local Gaussian distribution in order to better deal with intensity inhomogeneity. Furthermore, we combine local and global energy based on fuzzy clustering, with a weight coefficient. The coefficient is computed by a weight function according to contrast ratio of the image. Experiments on synthetic and real images and comparisons with other state‐of‐the‐art active contour models show that the proposed model can detect objects more accurate and robust, especially for vessels on retinal angiogram.

Section: Implementation and Resultsmentioning

confidence: 99%

Active contours with local and global energy based‐on fuzzy clustering and maximum a posterior probability for retinal vessel detection

Wang

Jiang

et al. 2019

“…The matrix V is initialized by using a random number less than 1, the matrix U is found from Formula (8), and then the matrix V is recalculated by the calculated U according to Formula (9). The aforementioned two steps are repeated until the set maximum number of iterations (numIterations)…”

Section: Matrix Factorization Modelmentioning

confidence: 99%

“…We note that the Collaborative filtering (CF) technique is widely used in E‐commerce and it utilizes a set of ratings made by users on items to generate recommendations, in which items can be of any type. Besides, Matrix Factorization (MF) technique provides a way to decompose the user‐item rating matrix into two matrices that represent users and items, respectively.…”

Section: Introductionmentioning

confidence: 99%

Collaborative filtering and association rule mining‐based market basket recommendation on spark

Wang

Wen

Guo

et al. 2019

Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.

“…One of its biggest features is being data‐centric, which means that the entire network will not be affected by the failure of a few of nodes or other reasons. Due to the advantages of easy deployment, high reliability, and low cost, WSN is widely used in military, medical, transportation, and other fields . Since WSN has the high practical value, many scholars have conducted in‐depth research on it.…”

Section: Introductionmentioning

confidence: 99%

A unified heuristic bat algorithm to optimize the LEACH protocol

Cai

Geng

et al. 2019

Summary Wireless sensor networks (WSN) have high value in the field of wireless communications. As the earliest WSN clustering protocol, Low Energy Adaptive Clustering Hierarchy (LEACH) can effectively reduce the energy consumption of data transmission in sensor networks. However, LEACH has some problems such as cluster head nodes are unevenly distributed. In this paper, a unified heuristic bat algorithm (UHBA) is proposed to optimize elections in cluster heads. This algorithm guarantees that the election of cluster heads can freely transform both global search and local search. Meanwhile, comparing with several other variants of the bat algorithm in CEC2013 test suite, it can be seen from results that UHBA has better performance. Moreover, the application of the algorithm on LEACH is better than other algorithms, which further proves that the algorithm has better results.