MAP selection algorithms based on future movement prediction capability

2007 ◽  
Author(s):  
Andrej Vilhar ◽  
Roman Novak ◽  
Gorazd Kandus
Keyword(s):  
Movement Prediction ◽  
Selection Algorithms ◽  
Prediction Capability

scholarly journals MAP Selection Algorithms Based on Future Movement Prediction Capability in Synthetic and Realistic Environment

2008 ◽  
Vol 4 (2) ◽  
pp. 122 ◽  
Author(s):  
Andrej Vilhar ◽  
Roman Novak ◽  
Gorazd Kandus
Keyword(s):  
Mobility Management ◽  
Evaluation Methodology ◽  
Absolute Accuracy ◽  
Mobile Nodes ◽  
The Past ◽  
Movement Prediction ◽  
The Future ◽  
Selection Algorithms ◽  
Prediction Capability ◽  
Mobility Anchor Point

Efficient mobility management involves micromobilityprinciples. The performance of the Hierarchical MobileIPv6 (HMIPv6) protocol, a representative micro-mobilityapproach, is affected by the Mobility Anchor Point (MAP)selection. In this paper, we propose a new selection method based on a prediction of the future movements of Mobile Nodes (MNs). The proposed algorithms exploit the information about the future availability of MAPs and choose those MAPs that assure a better service. An improvement to the evaluation methodology is also proposed. The algorithms are compared to each other notonly in synthetic but also in realistic internet topologies, which has not been a practice in the past. The simulation results show promising improvements in terms of distance from chosen MAPs and frequency of MAP changes. Moreover, we showed that, for perceivable improvement of MAP selection, absolute accuracy of movement prediction is not required. As pioneers in the mobility management analysis in realistic environment, we ascertain that offering MAP services over more than one Autonomous System (AS) proves beneficial.

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

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