Overview of Movement Analysis and Gait Features

2011 ◽  
pp. 1-69 ◽  
Author(s):  
Russell Best ◽  
Rezaul Begg
Keyword(s):  
Computational Intelligence ◽  
Movement Analysis ◽  
Research Process ◽  
Machine Learning Techniques ◽  
Intelligence Analysis ◽  
Objective Data ◽  
Analysis Tools ◽  
Learning Techniques ◽  
Gait Features ◽  
Key Features

This chapter provides an overview of the commonly used motion analysis approaches and techniques and the key features that are extracted from movement patterns for characterizing gait. The ultimate goal of gait analysis should be to provide reliable, objective data on which to base clinical decisions (Kaufman, 1998). Thousands of gait features/parameters have been used over the years. Selection of the correct gait features forms an important part of the research process, and often the success of the research outcomes depends heavily on selecting the most appropriate gait features. Analysis tools based on both statistical and machine-learning techniques use various types of gait features, ranging from the basic and directly measurable parameters to parameters that have undergone significant data processing and treatments. In this chapter, we attempt to introduce the commonly used methods to extract these features for use with the various statistical and computational intelligence analysis tools.

A Review on Intrusion Detection System using Artificial Intelligence Approach

2018 ◽  
Vol 4 (8) ◽  
pp. 6
Author(s):  
Apoorva Deshpande
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
High Detection Rate ◽  
Learning Techniques

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by researchers at different times.

scholarly journals Model approach of a robotic manipulator 3DOF using Machine Learning

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Darielson Souza ◽  
Josias Batista ◽  
Laurinda Reis ◽  
Antonio De Souza Junior
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Computational Intelligence ◽  
Machine Learning Techniques ◽  
Robotic Arm ◽  
Learning Techniques ◽  
Three Phase ◽  
And Robotics ◽  
Model Approach

Applications of robotics have been steadily expanding in recent years, and robotics is evolving every day. Currently, robotics is seen as an important area in many applications. Robotics and computational intelligence are increasingly working in parallel with the goal of better performance and productivity. This work has the objective of making an modeling of a robotic arm with three phase induction motor through machine learning techniques to obtain a better model that represents the plant. The techniques used were Articial Neural Network (ANNs): MLP and ELM. The techniques obtained a good performance, and they were evaluated through the multi-correlation coecient for a comparative analysis.

scholarly journals Machine Learning and Applications in Education Science Research

2021 ◽  
Author(s):  
Nguyen Thi Kim Son ◽  
Chu Cam Tho ◽  
Bui Thi Thanh Huong ◽  
Pham Tuan Anh
Keyword(s):  
Machine Learning ◽  
Science Research ◽  
Research Process ◽  
Machine Learning Techniques ◽  
Research Directions ◽  
Learning And Teaching ◽  
Processing Data ◽  
Use Of Technology ◽  
Learning Techniques ◽  
Education Science

The article presents an overview of the application of machine learning techniques in education science research. The research process shows the use of technology in learning and teaching, collecting information, analyzing and processing data to provide high-accuracy answers or advice in solving educational issues is the trend and strength in education science research. Through this, the group of authors make recommendations on some research directions in the field of education approaching international publications.  

Finding decision support requirements for effective intelligence analysis tools

2005 ◽  
Author(s):  
William Elm ◽  
Scott Potter ◽  
James Tittle ◽  
David Woods ◽  
Justin Grossman ◽  
...  
Keyword(s):  
Decision Support ◽  
Intelligence Analysis ◽  
Analysis Tools

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document