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Yamunadevi, S.
- The Multiple Time Series Clinical Data Processing with Modified Artificial Bee Colony Algorithm and Artificial Neural Network
Authors
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology,Coimbatore, Tamilnadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 5 (2016), Pagination: 1-12Abstract
Objectives: The main objective of this research is to discover patient acuity or severity of illness for immediate practical use for clinicians by evaluating the use of multivariate time series modelling along with multiple models.
Methods/Statistical analysis: As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. In many situations, analyzing a time-series in isolation is reasonable. And also this scenario is used to increase the prediction accuracy and reducing the time complexity using optimization algorithm.
Findings: The various research works has been analyzed and evaluated. From the analysis, the multiple measurements support vector machine (MMSVM), multiple measurements random forest regression (MMRF) and improved particle swarm optimization (IPSO) algorithm, modified artificial bee colony algorithm (MABCA) to solve the multiple time series problems by maximizing the optimal feature information which found to be superior for higher performance in terms of accuracy, precision and recall. The proposed MABCA with transductive support vector machine (TSVM) and artificial neural network (ANN) is used to improve the classification performance.
Application/Improvements: The findings of this work prove that the graph search based method provides better result than other approaches.
Keywords
Data Mining, Multiple Measurements, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Modified Artificial Bee Colony Algorithm (MABCA) and Artificial Neural Network (ANN).Full Text
References
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- LBP-Top Descriptor for Detecting Interesting Events in Crowded Environments
Authors
1 Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 5 (2016), Pagination: 1-8Abstract
Objectives: To detect interesting events in crowded environments by introducing Local Binary Patterns from Three Orthogonal Planes (LBP-TOP).The mission of automatically detecting frames with anomalous or interesting events from long duration video sequences has become the research interest in the last decade.
Methods/Statistical analysis: The existing system introduced a Swarm Intelligence based approach for Detecting Interesting Events in Crowded Environments. In this system both appearance and motion are measured to detect the anomalies. The Histograms of Oriented Gradients (HOG) is used for capture the appearance information and Histograms of Oriented Swarms (HOS) is used for capture the frame dynamics. Both are combined to form a new descriptor that effectively characterizes each scene. However it does not considered dynamic texture to achieve high accuracy. To solve this problem the proposed system introduced histogram of LBP-TOP to represent dynamic texture.
Findings: In a time window of each frame average triplets of HOG, HOS and LBP-TOP are consecutively computed. Then, these features are passed as an input to classifier. Here proximal support machine is used for classification. Proximal Support Vector Machine is based on Support Vector Machine, it is simpler and faster than traditional Support Vector Machines algorithm, which is especially suitable for large amounts of data or image classification and operations.
Improvements/Applications: The experimental results show that the proposed system achieves better performance compared with existing system.
Keywords
Histograms of Oriented Gradients, Histograms of Oriented Swarms and Texture.Full Text
References
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- Improved and Ensemble Methods for Time Series Classification with Cote
Authors
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore-641028, Tamil Nadu, IN
2 Hindusthan Institute of Technology, Coimbatore-641028, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 6 (2016), Pagination: 1-9Abstract
Background/Objectives: To classify the time series data efficiently by introducing Collective of Transformation-Based Ensembles method (COTE).
Methods/Statistical analysis: In existing scenario, the method is introduced named as COTE. It is mainly used for increasing the classification accuracy than preceding research. Another algorithm is named as Time series classification (TSC) which is used for transformation process which is based on comparative features. COTE contains classifiers constructed in the time, frequency, change, and shapelet transformation domains combined in alternative ensemble structures. However it has issue with transformation process and hence accuracy of the classification is reduced significantly. To avoid this issue introduced the concept called as run length transformation to improve the classification accuracy higher than existing system.
Findings: The run length algorithm is improved along with genetic approach to produce the optimal features. In this scenario, the measures are considered as similarity coefficient, likelihood ratio and dynamic time warping (DTW). Based on the modified k- nearest neighbor distance concept the speed is increased and classification accuracy is improved prominently.
Improvements/Applications: From the experimental result we can conclude that our proposed scenario yields better classification performance rather than existing scenario.
Keywords
Collective of Transformation-Based Ensembles Method, K Nearest Neighbor, Periodogram Transformation, Heterogeneous Ensemble, Elastic Ensemble.Full Text
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