scholarly journals A Data Mining Method For Improving the Prediction Of Bioinformatics Data

2021 ◽  
Vol 2137 (1) ◽  
pp. 012067
Author(s):  
Tong Wang ◽  
Wenan Tan ◽  
Jianxin Xue
Keyword(s):  
Significant Feature ◽  
Mining Method ◽  
High Dimensions ◽  
Low Dimensions ◽  
Jackknife Test ◽  
Feature Vectors ◽  
Higher Dimensional ◽  
Primitive Feature ◽  
Encoding Method ◽  
The Given

Abstract The composition of proteins nearly correlated with its function. Therefore, it is very ungently important to discuss a method that can automatically forecast protein structure. The fusion encoding method of PseAA and DC was adopted to describe the protein features. Using this encoding method to express protein sequences will produce higher dimensional feature vectors. This paper uses the algorithm of predigesting the characteristic dimension of proteins. By extracting significant feature vectors from the primitive feature vectors, eigenvectors with high dimensions are changed to eigenvectors with low dimensions. The experimental method of jackknife test is adopted. The consequences indicate that the arithmetic put forwarded here is appropriate for identifying whether the given protein is a homo-oligomer or a hetero-oligomer.

THE GENERALIZED TIME-DELAYED HÉNON MAP: BIFURCATIONS AND DYNAMICS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350045
Author(s):  
SHAKIR BILAL ◽  
RAMAKRISHNA RAMASWAMY
Keyword(s):  
Lyapunov Exponents ◽  
Parameter Space ◽  
Periodic Motion ◽  
Period Doubling ◽  
Chaotic Attractors ◽  
Nonlinearity Parameter ◽  
High Dimensions ◽  
Low Dimensions ◽  
Onset Of Chaos ◽  
Generalized Time

We analyze the bifurcations of a family of time-delayed Hénon maps of increasing dimension and determine the regions where the motion is attracted to different dynamical states. As a function of parameters that govern nonlinearity and dissipation, boundaries that confine asymptotic periodic motion are determined analytically, and we examine their dependence on the dimension d. For large d these boundaries converge. In low dimensions both the period-doubling and quasiperiodic routes to chaos coexist in the parameter space, but for high dimensions the latter predominates and prior to the onset of chaos, the systems exhibit multistability. When the nonlinearity parameter is varied, the dimension of chaotic attractors in the systems changes smoothly with increasing number of non-negative Lyapunov exponents.

scholarly journals Protein Subnuclear Localization Based on Radius-SMOTE and Kernel Linear Discriminant Analysis Combined with Random Forest

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1566
Author(s):  
Liwen Wu ◽  
Shanshan Huang ◽  
Feng Wu ◽  
Qian Jiang ◽  
Shaowen Yao ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Random Forest ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant ◽  
Jackknife Test ◽  
Subnuclear Localization ◽  
Feature Vectors ◽  
Benchmark Datasets ◽  
Novel Method ◽  
Protein Datasets

Protein subnuclear localization plays an important role in proteomics, and can help researchers to understand the biologic functions of nucleus. To date, most protein datasets used by studies are unbalanced, which reduces the prediction accuracy of protein subnuclear localization—especially for the minority classes. In this work, a novel method is therefore proposed to predict the protein subnuclear localization of unbalanced datasets. First, the position-specific score matrix is used to extract the feature vectors of two benchmark datasets and then the useful features are selected by kernel linear discriminant analysis. Second, the Radius-SMOTE is used to expand the samples of minority classes to deal with the problem of imbalance in datasets. Finally, the optimal feature vectors of the expanded datasets are classified by random forest. In order to evaluate the performance of the proposed method, four index evolutions are calculated by Jackknife test. The results indicate that the proposed method can achieve better effect compared with other conventional methods, and it can also improve the accuracy for both majority and minority classes effectively.

A FAST FRACTAL ENCODING METHOD BASED ON FRACTAL DIMENSION

Fractals ◽  
2009 ◽  
Vol 17 (04) ◽  
pp. 459-465 ◽  
Author(s):  
XING-YUAN WANG ◽  
YAHUI LANG
Keyword(s):  
Fractal Dimension ◽  
Fractal Theory ◽  
Image Texture ◽  
Computationally Efficient ◽  
Box Counting ◽  
Coding Method ◽  
Fractal Encoding ◽  
Speedup Ratio ◽  
Encoding Method ◽  
The Given

In this paper a fast fractal coding method based on fractal dimension is proposed. Image texture is an important content in image analysis and processing which can be used to describe the extent of irregular surface. The fractal dimension in fractal theory can be used to describe the image texture, and it is the same with the human visual system. The higher the fractal dimension, the rougher the surface of the corresponding graph, and vice versa. Therefore in this paper a fast fractal encoding method based on fractal dimension is proposed. During the encoding process, using the fractal dimension of the image, all blocks of the given image first are defined into three classes. Then each range block searches the best match in the corresponding class. The method is based on differential box counting which is chosen specifically for texture analysis. Since the searching space is reduced and the classification operation is simple and computationally efficient, the encoding speed is improved and the quality of the decoded image is preserved. Experiments show that compared with the full search method, the proposed method greatly reduced the encoding time, obtained a rather good retrieved image, and achieved the stable speedup ratio.

International Views of Beijing in Authoritative Network Media

2014 ◽  
Vol 556-562 ◽  
pp. 6395-6398
Author(s):  
Ye Liang ◽  
Ning Ning Guo ◽  
Rui Yun Xu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Dimensional Space ◽  
Western Society ◽  
Support Vector ◽  
The Internet ◽  
Feature Vectors ◽  
Internet News ◽  
Higher Dimensional ◽  
Network Media

With the fact that the some media vilifies China, monitoring the Internet news about Beijing in authoritative network media is very important which could forecast the international views of Beijing in western society. An important task is to summarize a set of relevant features of the views of Beijing in order to obtain the feature vectors of the sample news. Based on these features, the random sample contents of a great deal of latest news are clustered, which investigates whether the news is a hot topic. On the basis of the selected robust and accurate classification algorithm, the support vector machine is used to map the vectors into a higher dimensional space to establish a hyperplane with the maximum margin, and then two parallel hyperplanes are established respectively on each side of the hyperplane which separates the data and maximizes the distance between the two parallel hyperplanes for the purpose of data classification. In the process of machine learning, the composition, the measurement and the weight of the feature vectors are modified and improve through trials and errors, thus to realize the accurate forecasting of international views of Beijing.

Automatically Proving Plane Geometry Theorems Stated by Text and Diagram

2019 ◽  
Vol 33 (07) ◽  
pp. 1940003 ◽  
Author(s):  
Wenbin Gan ◽  
Xinguo Yu ◽  
Ting Zhang ◽  
Mingshu Wang
Keyword(s):  
Theorem Proving ◽  
Experimental Results ◽  
Plane Geometry ◽  
Mining Method ◽  
High Confidence ◽  
Test Dataset ◽  
Model Method ◽  
The Given

This paper presents an algorithm for proving plane geometry theorems stated by text and diagram in a complementary way. The problem of proving plane geometry theorems involves two challenging subtasks, being theorem understanding and theorem proving. This paper proposes to consider theorem understanding as a problem of extracting relations from text and diagram. A syntax–semantics (S2) model method is proposed to extract the geometric relations from theorem text, and a diagram mining method is proposed to extract geometry relations from diagram. Then, a procedure is developed to obtain a set of relations that is consistent with the given theorem with high confidence. Finally, theorem proving is conducted by using the existing proving methods which take the extracted geometric relations as input. The experimental results show that the proposed theorem proving algorithm can prove 86% of plane geometry theorems in the test dataset of 200 theorems, which is all the theorems in the popular textbook. The proposed algorithm outperforms the existing algorithms mainly because it can extract relations not only from text but also from diagram.

LOCALIZATION VERSUS DAMAGE SPREADING IN IMMUNOLOGICAL SHAPE SPACE: A VECTORIZED ALGORITHM

1992 ◽  
Vol 03 (06) ◽  
pp. 1189-1194
Author(s):  
DIETRICH STAUFFER
Keyword(s):  
Shape Space ◽  
High Dimensions ◽  
Low Dimensions ◽  
Dimensional Lattice ◽  
Damage Spreading ◽  
The Many

The model of de Boer, Segel and Perelson, where the various shapes of antibodies are represented by sites on a d-dimensional lattice and where the antibody concentrations change with time only by constant factors, is simulated in one to ten dimensions, with millions of sites. The algorithm is fully vectorized in spite of the many if-conditions which allow one program to deal with all dimensions d. We find that an initially localized perturbation may spread over the whole lattice in high dimensions but prefers to remain localized in low dimensions.

ON PIERCING SETS OF AXIS-PARALLEL RECTANGLES AND RINGS

1999 ◽  
Vol 09 (03) ◽  
pp. 219-233 ◽  
Author(s):  
MICHAEL SEGAL
Keyword(s):  
Dimensional Space ◽  
Time Algorithm ◽  
Efficient Algorithms ◽  
Center Problem ◽  
Running Time ◽  
Higher Dimensional ◽  
Axis Parallel ◽  
The Given ◽  
Existing Result

We consider the p-piercing problem for axis-parallel rectangles. We are given a collection of axis-parallel rectangles in the plane and wish to determine whether there exists a set of p points whose union intersects all the given rectangles. We present efficient algorithms for finding a piercing set (i.e, a set of p points as above) for values of p=1,2,3,4,5. The result for 4 and 5-piercing improves an existing result of O(n  log 3 n) and O(n  log 4 n) to O(n  log  n) time. The result for 5-piercing can be applied find an O(n  log 2 n) time algorithm for planar rectilinear 5-center problem, in which we are given a set S of n points in the pane, and wish to find 5 axis-parallel congruent squares of smallest possible size whose union covers S. We improve the existing algorithm for general (but fixed) p to O(np-4 log  n) running time, and we also extend our algorithms to higher dimensional space. We also consider the problem of piercing a set of rectangular rings.

scholarly journals An Efficient Method for Similarity Measure in Independent PCA based Classification

2013 ◽  
Vol 9 (3) ◽  
pp. 1099-1109
Author(s):  
Dr. H. B. Kekre ◽  
Dr. Tanuja K. Sarode ◽  
Jagruti K. Save
Keyword(s):  
Coordinate System ◽  
Similarity Measure ◽  
Efficient Method ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Test Image ◽  
Nearest Neighbor Classifier ◽  
Feature Vectors ◽  
The Given ◽  
Neighbor Classifier

The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with  distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.

scholarly journals The Cohomology Groups of Tori in Finite Galois Extensions of Number Fields

1966 ◽  
Vol 27 (2) ◽  
pp. 709-719 ◽  
Author(s):  
J. Tate
Keyword(s):  
Field Theory ◽  
Multiplicative Group ◽  
Number Fields ◽  
Class Field Theory ◽  
Large Set ◽  
Cohomology Groups ◽  
Galois Extensions ◽  
Class Field ◽  
Higher Dimensional ◽  
The Given

Class field theory determines in a well-known way the higher dimensional cohomology groups of the idéies and idèle classes in finite Galois extensions of number fields. At the Amsterdam Congress in 1954 I announced [7] the corresponding result for the multiplicative group of the number field itself, but the proof has never been published. Meanwhile, Nakayama showed that results of this type have much broader implications than had been realized. In particular, his theorem allows us to generalize our result from the multiplicative group to the case of an arbitrary torus which is split by the given Galois extension. We also treat the case of “S-units” of the multiplicative group or torus, for a suitably large set of places S. It is a pleasure for me to publish this paper here, in recognition of Nakayama’s important contributions to our knowledge of the cohomological aspects of class field theory; his work both foreshadowed and generalized the theorem under discussion.

scholarly journals C&R Tree based Air Target Classification Using Kinematics

2014 ◽  
pp. 79-85
Author(s):  
Manish Garg ◽  
Upasna Singh
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Significant Feature ◽  
Target Classification ◽  
Target Class ◽  
Material Technology ◽  
Data Set ◽  
Counter Measures ◽  
Feature Vectors ◽  
Stealth Technology

Since the improvement in Anti Radar Material technology and stealth technology grows, there are immense counter measures that have opened to deny such technologies for classification to the adversary. At the same time it is observed that radar is continuously tracking the air target. This track data represents the kinematics which can be efficiently manipulated for effective classification without being deceived. The present study uses decision tree based classifier, specifically Classification and Regression Tree (CRT) algorithm over certain significant feature vectors. It classifies the data set of an air target into a target class where feature vectors are derived from the Radar Track Data using Matlab code. The work presented here aims to assess the performance of CRT. Although the methods and results presented here are for Air Target Classification, they may give insight for other applications.

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