565 research outputs found
A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set
Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS) theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory
Certainty of outlier and boundary points processing in data mining
Data certainty is one of the issues in the real-world applications which is
caused by unwanted noise in data. Recently, more attentions have been paid to
overcome this problem. We proposed a new method based on neutrosophic set (NS)
theory to detect boundary and outlier points as challenging points in
clustering methods. Generally, firstly, a certainty value is assigned to data
points based on the proposed definition in NS. Then, certainty set is presented
for the proposed cost function in NS domain by considering a set of main
clusters and noise cluster. After that, the proposed cost function is minimized
by gradient descent method. Data points are clustered based on their membership
degrees. Outlier points are assigned to noise cluster and boundary points are
assigned to main clusters with almost same membership degrees. To show the
effectiveness of the proposed method, two types of datasets including 3
datasets in Scatter type and 4 datasets in UCI type are used. Results
demonstrate that the proposed cost function handles boundary and outlier points
with more accurate membership degrees and outperforms existing state of the art
clustering methods.Comment: Conference Paper, 6 page
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models
Neutrosophic Multi-Criteria Decision Making
The notion of a neutrosophic quadruple BCK/BCI-number is considered in the first article (“Neutrosophic Quadruple BCK/BCI-Algebras”, by Young Bae Jun, Seok-Zun Song, Florentin Smarandache, and Hashem Bordbar), and a neutrosophic quadruple BCK/BCI-algebra, which consists of neutrosophic quadruple BCK/BCI-numbers, is constructed. Several properties are investigated, and a (positive implicative) ideal in a neutrosophic quadruple BCK-algebra and a closed ideal in a neutrosophic quadruple BCI-algebra are studied. Given subsets A and B of a BCK/BCI-algebra, the set NQ(A,B), which consists of neutrosophic quadruple BCK/BCInumbers with a condition, is established. Conditions for the set NQ(A,B) to be a (positive implicative) ideal of a neutrosophic quadruple BCK-algebra are provided, and conditions for the set NQ(A,B) to be a (closed) ideal of a neutrosophic quadruple BCI-algebra are given. Techniques for the order of preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE) are widely used methods to solve multicriteria decision-making problems. In the second research article (“Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I”), Muhammad Akram, Shumaiza, and Florentin Smarandache present the bipolar neutrosophic TOPSIS method and the bipolar neutrosophic ELECTRE-I method to solve such problems. The authors use the revised closeness degree to rank the alternatives in the bipolar neutrosophic TOPSIS method. The researchers describe the bipolar neutrosophic TOPSIS method and the bipolar neutrosophic ELECTRE-I method by flow charts, also solving numerical examples by the proposed methods and providing a comparison of these methods. In the third article (“Interval Neutrosophic Sets with Applications in BCK/BCI-Algebra”, by Young Bae Jun, Seon Jeong Kim and Florentin Smarandache), the notion of (T(i,j),I(k,l),F(m,n))-interval neutrosophic subalgebra in BCK/BCI-algebra is introduced for i,j,k,l,m,n infoNumber 1,2,3,4, and properties and relations are investigated. The notion of interval neutrosophic length of an interval neutrosophic set is also introduced, and the related properties are investigated
Model and Algorithm for Closed-loop Logistics System Considering Time-satisfaction Degree and Returns under E-commerce Environment
Facility location, inventory control and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. At the same time, the customer’s time-satisfaction degree had become one of the important factors for competition .A multi-objective integrated optimization model of location-inventory-routing problem (LIRP) taking the cost of the returned merchandises and time-satisfaction degree into account is proposed for closed-loop logistics system. An improved adaptive genetic algorithm (IAGA) is designed to solve the model. Finally, the real instance is presented to show the effectiveness of the model and algorithm
A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment
Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment
The Evaluation of E-commerce Efficiency in China using DEA-Tobit model: evidence from Taobao data
Using the analytical framework of DEA-Tobit, this paper investigates the efficiency of e-commerce in China\u27s provinces based on the cross-section data of 31 provinces in China and the data of e-commerce service providers from Taobao’s open platform. The data envelopment analysis (DEA) is used to calculate the technical efficiency and scale efficiency. Furthermore the paper gives an empirical test on the relationship between the scale efficiency and influencing factors by using the censored Tobit model. The results show there are significant regional differences in the efficiency of e-commerce services in provinces of China, and the Real GDP per capita, the seller number on e-commerce platform, the retail sales and wholesale are important reasons for the different efficiency in each province of China. This study provides a domain-specific, integrative approach in evaluating the E-commerce development combining macro data from National Bureau of Statistics of China and micro data from taobao.com
Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch
Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. The original version of FA usually traps into local optima like many other general swarm intelligence optimization algorithm. In order to overcome this drawback, the chaotic firefly algorithm(CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PS
Deep LSTM with Guided Filter for Hyperspectral Image Classification
Hyperspectral image (HSI) classification has been a hot topic in the remote sensing community. A large number of methods have been proposed for HSI classification. However, most of them are based on the extraction of spectral feature, which leads to information loss. Moreover, they rarely consider the correlation among the spectrums. In this paper, we see spectral information as a sequential data which should be relevant to each other. We introduce long short-term memory (LSTM) model, which is a typical recurrent neural network (RNN), to deal with HSI classification. To tackle the problem of overfitting caused by limited labeled samples, regularization strategy is introduced. For unbalance in different classes, we improve LSTM by weighted cost function. Also, we employ guided filter to smooth the HSI that can greatly improve the classification accuracy. And we proposed a method for modeling hyperspectral sequential data, which is very useful for future research work. Finally, the experimental results show that our proposed method can improve the classification performance as compared to other methods in three popular hyperspectral datasets
- …