scholarly journals Identifying Diseases and Diagnosis using Machine Learning

2019 ◽  
Vol 8 (6S2) ◽  
pp. 978-981
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Dimensionality Reduction ◽  
Disease Classification ◽  
High Dimensional ◽  
Classification Algorithms ◽  
Major Work ◽  
The Mathematical Model ◽  
Best Fit ◽  
Learning By Using

The method that is use to optimize the criterion efficiency that depend on the previous experience is known as machine learning. By using the statistics theory it creates the mathematical model, and its major work is to surmise from the examples gave. To take the data straightforwardly from the information the approach uses computational methods. For recognize and identify the disease correctly a pattern is very necessary in Diagnosis recognition of disease. for creating the different models machine learning is used, this model can use for prediction of output and this output is depend on the input that is related to the data which previously used. For curing any disease it is very important to identify and detect that disease. For classify the disease classification algorithms are used. It uses are many dimensionality reduction algorithms and classification algorithms. Without externally modified the computer can learn with the help of the machine learning. For taking the best fit from the observation set the hypothesis is selected. Multi-dimensional and high dimensional are used in machine learning. By using machine learning automatic and classy algorithms can build.

scholarly journals Isotropic Modeling of a Composite Panel of the Stern of a Fiberglass Boat Propelled by an Outboard Motor

2014 ◽  
Vol 7 (14) ◽  
pp. 9
Author(s):  
Patrick Townsend Valencia
Keyword(s):  
Mathematical Model ◽  
Finite Element ◽  
Mathematical Formulation ◽  
Composite Panel ◽  
Finite Element Program ◽  
Element Program ◽  
Material Conditions ◽  
Sandwich Materials ◽  
The Mathematical Model ◽  
Best Fit

We performed a theoretical and experimental study to define the best way to model the finite element sandwich structure aft of a fiberglass boat less than 15 meters in length, using an isotropic linear mathematical model that fits anisotropic material conditions. This is done by defining the properties of the ship’s fiberglass resin structure, which is representative of the influence of the forces acting during the glide on the geometry of the entire vessel. Formulation of the Finite Elements Method is presented, which works on the mathematical model to define the limitations of the results obtained. Isotropic material adjustment is calculated using Halpin-Tsai laws, developing its mathematical formulation for restrictions of modulus data entered as the finite element program experimentally calculated for each of the sandwich materials. The best-fit mathematical presentation to the modulus of the composite tool justifies the calculation thereof. 

THE APPLICA TION OF "FROZEN WALL " SOFTWARE IN SIMULA TION OF ARTIFICIAL GROUND FREEZING

2019 ◽  
Vol 4 (1) ◽  
pp. 269-282
Author(s):  
L.Y. Levin ◽  
◽  
M.A. Semin ◽  
A.V. Bogomyagkov ◽  
O.S. Parshakov ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Numerical Solution ◽  
General Information ◽  
Software Application ◽  
Ground Freezing ◽  
Artificial Ground Freezing ◽  
Frozen Wall ◽  
The Mathematical Model ◽  
Best Fit ◽  
Technical Measures

The paper presents general information about the software application “Frozen Wall ”, which was designed to simulate frozen wall formation around constructed vertical shafts. The main feature of the developed application is the possibility of calibrating the mathematical model for the best fit with the experimental temperature measurements by numerical solution of the inverse Stefan problem. In addition, it takes into account a number of technological processes that affect the state of the frozen wall. Based on calculations performed in the application, it is possible to develop technical measures aimed at ensuring the efficiency of mine shafts construction in difficult hydrogeological conditions.

Adjustment of a Rotor Model to Achieve Agreement between Calculated and Experimental Natural Frequencies

1979 ◽  
Vol 21 (6) ◽  
pp. 389-396 ◽  
Author(s):  
G. T. S. Done
Keyword(s):  
Mathematical Model ◽  
Natural Frequencies ◽  
Turbine Rotor ◽  
Beam Elements ◽  
Stiffness Properties ◽  
Standard Values ◽  
Lumped Masses ◽  
The Mathematical Model ◽  
Best Fit ◽  
Rotor Model

This paper is concerned with the problem of adjusting the mathematical model of a system such that the computed natural frequencies coincide with those measured experimentally. The particular system considered is a laboratory turbine-rotor model, modelled mathematically by 42 Timoshenko beam elements and lumped masses. Model adjustments are made by assuming, firstly, Young's modulus and the modulus of rigidity to be variable, a change from standard values representing overall stiffness deficiencies in the mathematical model. In this case, a best fit to the lowest six natural frequencies, as measured experimentally, is made. Secondly, stiffness diameters are assumed variable, thereby allowing for deficiencies in the model near discontinuous changes of section, and in this case, the lowest six natural frequencies are matched exactly, but an overall measure of the differences between the actual and the stiffness diameters is minimized. An analysis for the rates of change of natural frequency with the various stiffness properties (i.e. the sensitivities) is presented, and the results of the manipulation discussed.

scholarly journals Dimensionality Reduction Methods Used in Machine Learning

2020 ◽  
Vol 13 (1) ◽  
pp. 148-151
Author(s):  
Kristóf Muhi ◽  
Zsolt Csaba Johanyák
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Dimensionality Reduction ◽  
Feature Space ◽  
Data Preprocessing ◽  
Short Review ◽  
High Dimensional ◽  
Data Types ◽  
Reduction Methods ◽  
The Individual

AbstractIn most cases, a dataset obtained through observation, measurement, etc. cannot be directly used for the training of a machine learning based system due to the unavoidable existence of missing data, inconsistencies and high dimensional feature space. Additionally, the individual features can contain quite different data types and ranges. For this reason, a data preprocessing step is nearly always necessary before the data can be used. This paper gives a short review of the typical methods applicable in the preprocessing and dimensionality reduction of raw data.

scholarly journals Testing the classifier adapted to recognize the languages of works based on the Latin alphabet

2021 ◽  
pp. 83-94
Author(s):  
Zafar Usmanov ◽  
◽  
Abdunabi Kosimov ◽  
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Learning Algorithm ◽  
Automatic Recognition ◽  
Machine Learning Algorithm ◽  
The Third ◽  
Optimal Value ◽  
Minimum Distances ◽  
Homogeneity Hypothesis ◽  
The Mathematical Model

Using the example of a model collection of 10 texts in five languages (English, German, Spanish, Italian, and French) using Latin graphics, the article establishes the applicability of the γ-classifier for automatic recognition of the language of a work based on the frequency of 26 common Latin alphabetic letters. The mathematical model of the γ-classifier is represented as a triad. Its first component is a digital portrait (DP) of the text - the distribution of the frequency of alphabetic unigrams in the text; the second component is formulas for calculating the distances between the DP texts and the third is a machine learning algorithm that implements the hypothesis of “homogeneity” of works written in one language and “heterogeneity” of works written in different languages. The tuning of the algorithm using a table of paired distances between all products of the model collection consisted in determining an optimal value of the real parameter γ, for which the error of violation of the “homogeneity” hypothesis is minimized. The γ-classifier trained on the texts of the model collection showed a high, 100% accuracy in recognizing the languages of the works. For testing the classifier, an additional six random texts were selected, of which five were in the same languages as the texts of the model collection. By the method of the nearest (in terms of distance) neighbor, all new texts confirmed their homogeneity with the corresponding pairs of monolingual works. The sixth text in Romanian showed its heterogeneity in relation to all elements of the collection. At the same time, it showed closeness in minimum distances, first of all, to two texts in Spanish and then to two works in Italian.

Method of Mathematical Modeling Based on PSO Algorithms

2013 ◽  
Vol 347-350 ◽  
pp. 2447-2451
Author(s):  
Guo He Li ◽  
Xiang Yue ◽  
Wei Jiang Wu ◽  
Jiang Hui Zhao
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Mathematical Model ◽  
Seismic Data ◽  
Binary Tree ◽  
Seismic Inversion ◽  
Swarm Optimization ◽  
Input Variables ◽  
Set Up ◽  
The Mathematical Model

In order to set up universal and non-linear map of variables, a full binary tree is constructed as mathematical model. Leaf nodes of the full binary tree are linear combination of input variables, and used as inputs of next nodes. On the basis of weighting two inputs by selector for inner node, the inputs are again linearly combined and used as output for next node. The inputs and outputs of all the inner nodes are constructed in turn as the same, and the output of root node is the output of mathematical model, implementing segment-linear approximation. With the means of machine learning of particle swarm optimization for data from some areas, all the coefficients of mathematical model are achieved for the special. The mathematical model is applied to seismic inversion to interpret stratum by seismic data, approving it very practical.

The Analysis on Dimensionality Reduction Mathematical Model Based on Feedback Constraint for High-Dimensional Information

2013 ◽  
Vol 846-847 ◽  
pp. 1056-1059
Author(s):  
Peng Wu
Keyword(s):  
Mathematical Model ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Inverse Regression ◽  
Model Based ◽  
Cloud Resource ◽  
Constrained Environments ◽  
Data Coverage ◽  
Cloud Resource Scheduling

This paper proposes a dimensionality reduction mathematical model based on feedback constraint for High-dimensional information. It uses feedback restriction technique to construct dimensionality reduction model for multidimensional product data. The data obtained is with high latitudes, where a large number of data are under components involved standardized restrictions. High-dimensional data participating in operation will increase the complexity of operation, and hence, we need to reduce its dimension. In this paper multi-constrained inverse regression model is adopted to reduce the dimension of cloud resource scheduling data in multi-constrained environments. Experimental results show that the proposed method increases the data coverage rate of high-dimensional data mining by 66%, and has great optimizing effect.

scholarly journals A Review on Machine Learning Techniques to Predict Diseases

2019 ◽  
pp. 525-529
Author(s):  
Rohit A Nitnaware ◽  
Prof. Vijaya Kamble
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
The Past ◽  
Course Of Action ◽  
Learning Techniques ◽  
Past Data ◽  
Game Plan

In Disease Diagnosis, affirmation of models is so basic for perceiving the disease exactly. Machine learning is the field, which is used for building the models that can predict the yield relies upon the wellsprings of data, which are connected subject to the past data. Disease unmistakable verification is the most essential task for treating any disease. Classification computations are used for orchestrating the disease. There are a couple of classification computations and dimensionality decline counts used. Machine Learning empowers the PCs to learn without being changed remotely. By using the Classification Algorithm, a hypothesis can be looked over the course of action of decisions the best fits a game plan of recognition. Machine Learning is used for the high dimensional and the multi-dimensional data. Better and modified computations can be made using Machine Learning.

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