Protein Fold Recognition Using Neural Networks and Support Vector Machines

Abstract Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the characteristics of protein folds. However, the feature extraction methods are still the bottleneck of the performance improvement of these methods. In this paper, we proposed two new feature extraction methods called MotifCNN and MotifDCNN to extract more discriminative fold-specific features based on structural motif kernels to construct the motif-based convolutional neural networks (CNNs). The pairwise sequence similarity scores calculated based on fold-specific features are then fed into support vector machines to construct the predictor for fold recognition, and a predictor called MotifCNN-fold has been proposed. Experimental results on the benchmark dataset showed that MotifCNN-fold obviously outperformed all the other competing methods. In particular, the fold-specific features extracted by MotifCNN and MotifDCNN are more discriminative than the fold-specific features extracted by other deep learning techniques, indicating that incorporating the structural motifs into the CNN is able to capture the characteristics of protein folds.

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A comparison of artificial neural networks and support vector machines for short-term load forecasting using various load types

2017 IEEE Manchester PowerTech ◽

10.1109/ptc.2017.7980814 ◽

2017 ◽

Cited By ~ 6

Author(s):

Glen Mitchell ◽

Sanjay Bahadoorsingh ◽

Neil Ramsamooj ◽

Chandrabhan Sharma

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Machines ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Vector Machines ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Load Types

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Sensitivity analysis for time lag selection to forecast seasonal time series using Neural Networks and Support Vector Machines

The 2010 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2010.5596890 ◽

2010 ◽

Cited By ~ 14

Author(s):

Paulo Cortez

Keyword(s):

Neural Networks ◽

Time Series ◽

Sensitivity Analysis ◽

Support Vector Machines ◽

Time Lag ◽

Support Vector ◽

Seasonal Time Series ◽

Lag Selection ◽

Vector Machines

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Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

Volume 4: Processes ◽

10.1115/msec2018-6680 ◽

2018 ◽

Cited By ~ 1

Author(s):

Achyuth Kothuru ◽

Sai Prasad Nooka ◽

Rui Liu

Keyword(s):

Neural Networks ◽

Support Vector Machines ◽

Tool Wear ◽

Support Vector ◽

Workpiece Material ◽

Artificial Intelligent ◽

Tool Condition ◽

Hardness Variation ◽

Vector Machines ◽

Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

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