scholarly journals An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3021 ◽  
Author(s):  
Jinsong Yu ◽  
Jie Yang ◽  
Diyin Tang ◽  
Jing Dai
Keyword(s):  
Feature Selection ◽  
Lithium Ion Batteries ◽  
Feature Selection Method ◽  
Lithium Ion ◽  
Cellular Phone ◽  
Relevance Vector Machine ◽  
Test Method ◽  
Selection Strategy ◽  
Feature Subset ◽  
Cellular Phones

The early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentiate between normal and weak batteries in short cycles prior to shipping them to the marketplace. A novel approach to determine the optimal burn-in policy using a feature selection strategy and relevance vector machine (RVM) is proposed. The sequential floating forward search (SFFS) is used as the feature selection method to find an optimal feature subset from the entire sequence of the batteries’ quality characteristics while preserving the original variables. Given the selected feature subset, the RVM is applied to classify batteries into two groups and simultaneously obtain the posterior probabilities. To achieve better discrimination performance with less risk, a new characteristic is extracted from the discharge profile. Subsequently, an optimization cost model is developed by introducing a classification instability penalty to ensure the stability of the optimal number of burn-in cycles. A case study utilizing cellular phone lithium-ion batteries randomly selected from manufactured lots is presented to illustrate the proposed methodology. Furthermore, we conduct a comparison with the cumulative degradation (CD) method and non-cumulative degradation (NCD) method based on the Wiener process. The results show that our proposed burn-in test method performs better than comparable methods.

scholarly journals iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongxu Zhao ◽  
Zhixia Teng ◽  
Yanjuan Li ◽  
Dong Chen
Keyword(s):  
Feature Selection ◽  
Amino Acid ◽  
Random Forest ◽  
Feature Selection Method ◽  
Selection Strategy ◽  
Feature Subset ◽  
Evolutionary Analysis ◽  
Anti Inflammatory ◽  
Optimal Feature Subset ◽  
Optimal Feature

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

scholarly journals Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chaolong Zhang ◽  
Yigang He ◽  
Lifeng Yuan ◽  
Sheng Xiang ◽  
Jinping Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Noise Pollution ◽  
Relevance Vector Machine ◽  
Wavelet Denoising ◽  
Remaining Useful Life ◽  
Electronic Systems ◽  
De Algorithm ◽  
Battery Capacity ◽  
Capacity Data

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.

scholarly journals Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jin-Jia Wang ◽  
Fang Xue ◽  
Hui Li
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Group Lasso ◽  
High Dimensional ◽  
Test Accuracy ◽  
Gradient Descent Method ◽  
Feature Subset ◽  
Eeg Signals ◽  
Sparse Group Lasso ◽  
Selection Of

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

scholarly journals MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arijit Dey ◽  
Soham Chattopadhyay ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Feature Selection ◽  
Golden Ratio ◽  
Medical Image Analysis ◽  
Feature Selection Method ◽  
World Health ◽  
Significant Feature ◽  
Feature Subset ◽  
Upper Respiratory Tract ◽  
Global Pandemic ◽  
Health Organization

AbstractCOVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.

Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface

2021 ◽  
Author(s):  
ShuRui Li ◽  
Jing Jin ◽  
Ian Daly ◽  
Chang Liu ◽  
Andrzej Cichocki
Keyword(s):  
Feature Selection ◽  
Brain Computer Interface ◽  
Feature Selection Method ◽  
Event Related Potentials ◽  
Selection Method ◽  
Computer Interface ◽  
Feature Subset ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Menger Curvature

Abstract Brain–computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.

scholarly journals A New Feature Selection Method for Text Classification Based on Independent Feature Space Search

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yong Liu ◽  
Shenggen Ju ◽  
Junfeng Wang ◽  
Chong Su
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Predictive Accuracy ◽  
Feature Selection Method ◽  
Feature Space ◽  
Selection Method ◽  
The Other ◽  
Feature Subset ◽  
Search Range ◽  
Text Documents

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.

An Embedded Feature Selection and Multi-Class Classification Method for Detection of the Progression from Mild Cognitive Impairment to Alzheimer's Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 370-379 ◽  
Author(s):  
Jie Cai ◽  
Lingjing Hu ◽  
Zhou Liu ◽  
Ke Zhou ◽  
Huailing Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Treatment Plan ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Feature Subset

Background: Mild cognitive impairment (MCI) patients are a high-risk group for Alzheimer's disease (AD). Each year, the diagnosed of 10–15% of MCI patients are converted to AD (MCI converters, MCI_C), while some MCI patients remain relatively stable, and unconverted (MCI stable, MCI_S). MCI patients are considered the most suitable population for early intervention treatment for dementia, and magnetic resonance imaging (MRI) is clinically the most recommended means of imaging examination. Therefore, using MRI image features to reliably predict the conversion from MCI to AD can help physicians carry out an effective treatment plan for patients in advance so to prevent or slow down the development of dementia. Methods: We proposed an embedded feature selection method based on the least squares loss function and within-class scatter to select the optimal feature subset. The optimal subsets of features were used for binary classification (AD, MCI_C, MCI_S, normal control (NC) in pairs) based on a support vector machine (SVM), and the optimal 3-class features were used for 3-class classification (AD, MCI_C, MCI_S, NC in triples) based on one-versus-one SVMs (OVOSVMs). To ensure the insensitivity of the results to the random train/test division, a 10-fold cross-validation has been repeated for each classification. Results: Using our method for feature selection, only 7 features were selected from the original 90 features. With using the optimal subset in the SVM, we classified MCI_C from MCI_S with an accuracy, sensitivity, and specificity of 71.17%, 68.33% and 73.97%, respectively. In comparison, in the 3-class classification (AD vs. MCI_C vs. MCI_S) with OVOSVMs, our method selected 24 features, and the classification accuracy was 81.9%. The feature selection results were verified to be identical to the conclusions of the clinical diagnosis. Our feature selection method achieved the best performance, comparing with the existing methods using lasso and fused lasso for feature selection. Conclusion: The results of this study demonstrate the potential of the proposed approach for predicting the conversion from MCI to AD by identifying the affected brain regions undergoing this conversion.

scholarly journals Intrusion Detection System using SMIFS and Multi class Multi layer Perceptron

2019 ◽  
Vol 8 (9) ◽  
pp. 2622-2628
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Mutual Information ◽  
New Technologies ◽  
Detection System ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Feature Subset ◽  
Classification Problems ◽  
Data Set

As the new technologies are emerging, data is getting generated in larger volumes high dimensions. The high dimensionality of data may rise to great challenge while classification. The presence of redundant features and noisy data degrades the performance of the model. So, it is necessary to extract the relevant features from given data set. Feature extraction is an important step in many machine learning algorithms. Many researchers have been attempted to extract the features. Among these different feature extraction methods, mutual information is widely used feature selection method because of its good quality of quantifying dependency among the features in classification problems. To cope with this issue, in this paper we proposed simplified mutual information based feature selection with less computational overhead. The selected feature subset is experimented with multilayered perceptron on KDD CUP 99 data set with 2- class classification, 5-class classification and 4-class classification. The accuracy is of these models almost similar with less number of features.

scholarly journals Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

2019 ◽  
Vol 8 (2) ◽  
pp. 910-916
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Mutation Operator ◽  
Knn Classifier ◽  
Optimal Feature Subset ◽  
Optimal Feature

The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

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