scholarly journals Neural Network Clustering Methods to Evaluate the Totality of Taxpayers According to Their Degree of Creditworthiness

2017 ◽  
Vol 12 ◽  
Author(s):  
Alexandr Biryukov
Keyword(s):  
Neural Network ◽  
Network Clustering ◽  
Clustering Methods

scholarly journals A review on data clustering using spiking neural network (SNN) models

2019 ◽  
Vol 15 (3) ◽  
pp. 1392
Author(s):  
Siti Aisyah Mohamed ◽  
Muhaini Othman ◽  
Mohd Hafizul Afifi
Keyword(s):  
Neural Network ◽  
Data Clustering ◽  
Network Clustering ◽  
Complex Data ◽  
Spiking Neural Network ◽  
Clustering Methods ◽  
Clustering Method ◽  
Static Data ◽  
Clustering Approach ◽  
Clustering Problems

The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.

scholarly journals Comprehension and Thinking of Complex Network Construction and Clustering Based on Neural Network Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03040
Author(s):  
Bing Shen
Keyword(s):  
Neural Network ◽  
Complex Networks ◽  
Complex Network ◽  
Small World ◽  
Network Clustering ◽  
Clustering Methods ◽  
Development History ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Application Fields

With the development of computer technology and the enhancement of people's cognition of the world, more and more scholars have been focusing on the research of complex networks. At the same time, the emerging machine learning neural network algorithm has become a powerful tool for various researchers. This paper mainly discusses the construction and clustering of complex networks based on neural network algorithm. Firstly, the development history and main application fields of neural network are introduced. Then, several common methods of complex network clustering are summarized, and then the limitations of these clustering methods are discussed. At last, it proposes to improve the construction of neural network through the concept of small world in complex network and enhance the effect of complex network clustering by the characteristics of neural network algorithm, including the accuracy, reliability, stability, speed, etc.

The integration of surface seismic and borehole data using artificial neural network clustering methods

2001 ◽  
Vol 2001 (1) ◽  
pp. 1-4
Author(s):  
Matthew Carr ◽  
Richard Cooper ◽  
Maggie Smith ◽  
M. Turhan Taner ◽  
Joel Walls
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Clustering ◽  
Clustering Methods ◽  
Borehole Data ◽  
Artificial Neural

scholarly journals ОПТИМІЗАЦІЯ ПРОГНОЗУВАННЯ РОЗВИТКУ АРТЕРІАЛЬНОЇ ГІПЕРТЕНЗІЇ НА ОСНОВІ КОМПЛЕКСНОГО ЗАСТОСУВАННЯ ІНФОРМАЦІЙНИХ МЕТОДИК ІЗ РОЗРОБКОЮ ДИФЕРЕНЦІАЛЬНО-ДІАГНОСТИЧНИХ КРИТЕРІЇВ ПРИ НАДАННІ ПЕРВИННОЇ МЕДИЧНОЇ ДОПОМОГИ

2016 ◽  
Author(s):  
V. P. Martsenyuk ◽  
P. R. Selskyy ◽  
B. P. Selskyy
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Primary Health Care ◽  
Roc Analysis ◽  
Correlation Coefficients ◽  
Network Clustering ◽  
Care Level ◽  
Primary Health ◽  
Initial Survey

The paper describes the optimization of the prediction of disease at the primary health care level with a complex phased application of information techniques. The approach is based on analysis of the average values of indicators, correlation coefficients, using multi-parameter neural network clustering, ROC-analysis and decision tree.The data of 63 patients with arterial hypertension obtained at teaching and practical centers of primary health care were used for the analysis. It has been established that neural network clasterization can effectively and objectively allocate patients into the appropriate categories according to the level of average indices of patient examination results. Determination of the sensitivity and specificity of hemodynamic parameters, including blood pressure, and repeated during the initial survey was conducted using ROC-analysis.The diagnostic criteria of decision-making were developed to optimize the prediction of disease at the primary level in order to adjust examination procedures and treatment based on the analysis of indicators of patient examination with a complex gradual application of information procedures.

A Framework for Self-Realizing Process Models for Additive Manufacturing

2011 ◽  
Author(s):  
Sungshik Yim ◽  
David W. Rosen
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Process Planning ◽  
Process Model ◽  
Selective Laser Sintering ◽  
Process Models ◽  
Clustering Methods ◽  
In Series ◽  
Design Requirements ◽  
Planning Problems

This research discusses a framework for automating process model realization for additive manufacturing. The models map relationships from design requirements to process variables and can be utilized for future process planning. A repository is employed to collect data and contains previous process plans and corresponding design requirements. The framework organizes data through a statistical clustering method and builds regression models using a multi-layer neural network. Hierarchical and k-means clustering methods are employed in series to manage the data. A two layer neural network and augmented training algorithm are employed to build process models. The framework has been tested with Stereolithography and Selective Laser Sintering process planning problems to demonstrate its usefulness.

Achievements analysis of mooc English course based on fuzzy statistics and neural network clustering

2020 ◽  
Vol 39 (4) ◽  
pp. 5559-5569
Author(s):  
Meichen Jin
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Language Processing ◽  
Learning Ability ◽  
Network Clustering ◽  
College English ◽  
English Curriculum ◽  
The Neural Network ◽  
Training Stage ◽  
Fuzzy Statistics

At present, the field of natural language will also introduce in-depth learning, using the concept of word vector, so that the neural network can also complete the work in the field of statistics. It can be said that the neural network has begun to show its advantages in the field of natural language processing. In this paper, the author analyzes the multimedia English course based on fuzzy statistics and neural network clustering. Different factors were classified, and scores were classified according to the number of characteristics of different categories. It can be seen that with the popularization of the Internet, MOOC teaching meets the requirements of the current college English curriculum, is a breakthrough in the traditional teaching mode, improves students’ participation, and enables students to learn independently. It not only conforms to the characteristics of College students, but also improves their learning effect. In the automatic scoring stage, the quantitative text features are extracted by the feature extractor in the pre-processing stage, and then the weights of network connections obtained in the training stage are used to score the weights comprehensively. This model can better reflect students’ autonomous learning ability and language application ability.

AUTOMATED DIAGNOSIS OF BRAIN TUMOURS ASTROCYTOMAS USING PROBABILISTIC NEURAL NETWORK CLUSTERING AND SUPPORT VECTOR MACHINES

2005 ◽  
Vol 15 (01n02) ◽  
pp. 1-11 ◽  
Author(s):  
DIMITRIS GLOTSOS ◽  
JUSSI TOHKA ◽  
PANAGIOTA RAVAZOULA ◽  
DIONISIS CAVOURAS ◽  
GEORGE NIKIFORIDIS
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Network Clustering ◽  
High Grade ◽  
Cell Nuclei ◽  
Decision Tree Classification ◽  
Average Accuracy ◽  
Malignancy Grading

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.

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