An efficient Cluster Based Cab Recommender System (CBCRS) assists the cab drivers with the recommendations about passenger pickup location available at the shortest distance from him. To recommend drivers about the pa...
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Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-sou...
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Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and K-means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple-source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1-luc2 mouse model were conducted to assess the performance of the proposed method in multiple-source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications.
This research proposes to develop a systematic methodology for ranking a large number of alternatives in large-scale real-world problems under uncertainty, where the traditional individual ranking methods fail to prov...
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This research proposes to develop a systematic methodology for ranking a large number of alternatives in large-scale real-world problems under uncertainty, where the traditional individual ranking methods fail to provide meaningful and actionable insights. This paper introduces a novel framework for fuzzy large-scale decision-making (FLSDM) using triangular neutrosophic fuzzy numbers (TNFNs) to perform cluster-based ranking as a solution to these challenges. The proposed approach develops the Sugeno-Weber operator within the TNFN environment for data aggregation. An advanced K-means++ algorithm is designed to enable precise clustering of alternatives. Using the TOPSIS and DEA cross-efficiency model, extended for TNFNs, the clusters are ranked, and the alternatives are prioritized within each cluster. The practical use of the proposed approach is demonstrated through a real-world case study on rooftop solar photovoltaic (PV) site selection. Additionally, thorough analyses are conducted to validate its robustness and effectiveness.
With the rapid development of rail transit, station management faces increasing complexity and an explosive growth in data volume. To address the challenges posed by the high dimensionality and diversity of passenger ...
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With the advancement of technology, the use of machine learning techniques has increased. The need for the prevention of terrorist attacks has brought upon the use of machine learning techniques to explosive detection...
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With the advancement of technology, the use of machine learning techniques has increased. The need for the prevention of terrorist attacks has brought upon the use of machine learning techniques to explosive detection. Flammable liquids such as alcohol are easily available and widely used in various terrorist attacks. In this study, a new microwave measurement system is developed and a hybridclustering approach is proposed to classify liquids. With the proposed measurement system, the reflection coefficient (S-11 parameter) of liquids in bottles is measured at room temperature and these measurements are used as inputs by the proposed clusteringalgorithm. The results obtained using the proposed clusteringalgorithm are compared with the results obtained using a set of well-known clusteringalgorithms, that is, K-means, hierarchical clustering, farthest first, and fuzzy C-means, in order to make a fair comparison. The results show that the proposed clusteringalgorithm provides 100% accuracy and is superior to the well-known algorithms used in this study. The results will enable us to manufacture a low-cost liquid scanner for railway stations and shopping malls as well as small airports. The proposed liquid scanner's design was completed, and the manufacturing phase has been started.
In complex decision-making scenarios involving multiple stakeholders, the uncertainty and individual confidence of decision-makers (DMs) are crucial in determining the outcomes. A novel approach is proposed in this pa...
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In complex decision-making scenarios involving multiple stakeholders, the uncertainty and individual confidence of decision-makers (DMs) are crucial in determining the outcomes. A novel approach is proposed in this paper to improve decision-making processes within a large group of DMs operating under an "Intuitionistic Fuzzy Self-Confidence (IFN-SC)" setting. The research presents a hybrid clustering algorithm to categorize DMs based on their numerical similarities and psychological factors. A multi-objective nonlinear optimization problem is employed to determine the criteria weights in the IFN-SC environment when the weight vector is either partially or fully unknown. Using the max operator, we derive a single-objective nonlinear optimization problem, which is solved by the "Particle Swarm Optimization (PSO)" algorithm. Furthermore, extending the "Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)" for the IFN-SC environment significantly enhances the model's effectiveness in ranking alternatives. The study exemplified its capability in managing a large-scale decision-making problem based on health emergency strategy selection and presented various analyses highlighting its utility, adaptability, and robustness in practical situations.
In recent years, the clinical application of magnetic resonance (MR) images is more and more extensive and in-depth. However, image segmentation is a bottleneck to restrict the application of MR imaging in clinic, and...
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In recent years, the clinical application of magnetic resonance (MR) images is more and more extensive and in-depth. However, image segmentation is a bottleneck to restrict the application of MR imaging in clinic, and the segmentation of brain MR images now is confronted with the presence of uncertainty and noise, and various kinds of algorithms have been proposed to handle this problem. In this study, a hybrid clustering algorithm combined with a new intuitionistic fuzzy factor and local spatial information is proposed, where type-2 fuzzy logic can handle randomness, the rough set can deal with vagueness, and the intuitionistic fuzzy logic can address the external noises. Finally, the experimental tests have been done to demonstrate the superiority of the proposed technique.
Cellular manufacturing system plays a vital role in improving organizational productivity. The efficiency and effectiveness of machine-component cell formation of cellular manufacturing system are measured in terms of...
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Cellular manufacturing system plays a vital role in improving organizational productivity. The efficiency and effectiveness of machine-component cell formation of cellular manufacturing system are measured in terms of grouping efficiency and grouping efficacy, respectively. These measures are affected by inputs as well as methods used to form the machine-component cells. The input may be in the form of 0-1 matrix, which is called as machine-component incidence matrix or in terms of similarity coefficients among machines as well as among components. In this paper, the machine-component cell formation problem using "similarity coefficient" as an input is considered. The objective is to compare the effect of five different similarity coefficients on the grouping efficiency as well as on the grouping efficacy. Two different algorithms, viz. hybrid Principle Component Analysis and hybrid Agglomerative clusteringalgorithm are used to solve the machine-component cell formation problem. In each of these algorithms, Rank Order clusteringalgorithm is used as a local search algorithm to form the final machine-component cells. A complete factorial experiment with 10 problem sizes, five different similarity coefficients, and two different algorithms is designed with two replications under each of the experimental combinations to check the main effects of the factors and their interaction effects on the grouping measures of the machine-component cell formation problem. The inferences of the complete factorial experiment are reported. (C) 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).
Bioluminescence tomography (BLT) can reconstruct internal bioluminescent source from the surface measurements. However, multiple sources resolving of BLT is always a challenge. In this work, a comparative study on hyb...
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ISBN:
(纸本)9781510625808
Bioluminescence tomography (BLT) can reconstruct internal bioluminescent source from the surface measurements. However, multiple sources resolving of BLT is always a challenge. In this work, a comparative study on hybrid clustering algorithm, synchronization-based clusteringalgorithm and iterative self-organizing data analysis technique algorithm for multiple sources recognition of BLT is conducted. Simulation experiments on two and three sources reconstruction are demonstrated the performances of these three algorithms. The results show that the iterative self-organizing data analysis technique is more suitable for the closer multiple-targets and the other two algorithms are suitable for distant targets. Moreover, iterative self-organizing data analysis technique has the least computing time.
As data mining having attracted a significant amount of research attention, many clustering methods have been proposed in past decades. However, most of those techniques have annoying obstacles in precise pattern reco...
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ISBN:
(纸本)9783540681243
As data mining having attracted a significant amount of research attention, many clustering methods have been proposed in past decades. However, most of those techniques have annoying obstacles in precise pattern recognition. This paper presents a new clusteringalgorithm termed G-TREACLE, which can fulfill numerous clustering requirements in data mining applications. As a hybrid approach that adopts grid-based concept, the proposed algorithm recognizes the solid framework of clusters and, then, identifies the arbitrary edge of clusters by utilization of a new density-based expansion process, which named "tree-alike pattern". Experimental results illustrate that the new algorithm precisely recognizes the whole cluster, and efficiently reduces the problem of high computational time. It also indicates that the proposed new clusteringalgorithm performs better than several existing well-known approaches such as the K-means, DBSCAN, CLIQUE and GDH algorithms, while produces much smaller errors than the K-means, DBSCAN, CLIQUE and GDH approaches in most the cases examined herein.
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