Neural network clustering for crops thermal mapping

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.

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Intrusion Detection Based on Dynamic Self-organizing Map Neural Network Clustering

Advances in Neural Networks – ISNN 2005 - Lecture Notes in Computer Science ◽

10.1007/11427469_69 ◽

2005 ◽

pp. 428-433 ◽

Cited By ~ 2

Author(s):

Yong Feng ◽

Kaigui Wu ◽

Zhongfu Wu ◽

Zhongyang Xiong

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Network Clustering ◽

Self Organizing Map ◽

Self Organizing

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189037 ◽

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.

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Fuzzy Hopfield neural network clustering for single-trial motor imagery EEG classification

Expert Systems with Applications ◽

10.1016/j.eswa.2011.07.106 ◽

2012 ◽

Vol 39 (1) ◽

pp. 1055-1061 ◽

Cited By ~ 27

Author(s):

Wei-Yen Hsu

Keyword(s):

Neural Network ◽

Motor Imagery ◽

Hopfield Neural Network ◽

Network Clustering ◽

Single Trial ◽

Eeg Classification

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

International Journal of Neural Systems ◽

10.1142/s0129065705000013 ◽

2005 ◽

Vol 15 (01n02) ◽

pp. 1-11 ◽

Cited By ~ 26

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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