scholarly journals Comparison Analysis of Gait Classification For Human Motion Identification Using Embedded Computer

2018 ◽  
Vol 8 (6) ◽  
pp. 5014
Author(s):  
Agung Nugroho Jati ◽  
Astri Novianty ◽  
Nanda Septiana ◽  
Leni Widia Nasution
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Human Motion ◽  
Human Gait ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Classification Methods ◽  
Comparison Analysis ◽  
Gait Classification ◽  
Embedded Computer

In this paper, it will be discussed about comparison between two kinds of classification methods in order to improve security system based of human gait. Gait is one of biometric methods which can be used to identify person. K-Nearest Neighbour has parallelly implemented with Support Vector Machine for classifying human gait in same basic system. Generally, system has been built using Histogram and Principal Component Analysis for gait detection and its feature extraction. Then, the result of the simulation showed that K-Nearest Neighbour is slower in processing and less accurate than Support Vector Machine in gait classification.

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

scholarly journals An Improved Dual Clustering Method for Classification of Microarray Image Segementation

2019 ◽  
Vol 8 (4) ◽  
pp. 2514-2519
Keyword(s):  
Support Vector Machine ◽  
Ct Scanning ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Resonance Imaging ◽  
Computerised Tomography ◽  
Medical Field ◽  
Microarray Image ◽  
Image Segementation

Microarray is a fast and rapid growing technology which plays dynamic role in the medical field. It is an advanced than MRI (Magnetic Resonance Imaging) and CT scanning (Computerised Tomography). The purpose of this work is to make fine perfection against the gene expression. In this study the two clustering are used which fuzzy c means and k means and also it classifies with better results. The microarray data base indicates the classification in support vector machine. Segmentation is most important step in microarray image. The classification in support vector machine is compared with other two classifiers which means the k nearest neighbour and with the Bayes classifiers.

scholarly journals Integration of synthetic minority oversampling technique for imbalanced class

2019 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Noviyanti Santoso ◽  
Wahyu Wibowo ◽  
Hilda Hikmawati
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Original Data ◽  
Support Vector ◽  
Classification Methods ◽  
Problematic Issue ◽  
Imbalanced Class ◽  
F Measure

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

scholarly journals Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8017
Author(s):  
Nurfazrina M. Zamry ◽  
Anazida Zainal ◽  
Murad A. Rassam ◽  
Eman H. Alkhammash ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Principal Component ◽  
Support Vector ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Memory Utilization

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with (𝑛𝑑) memory utilization and no communication overhead.

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