scholarly journals Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1405-1412 ◽  
Keyword(s):  
Classification Accuracy ◽  
Missing Values ◽  
Random Number ◽  
Human Detection ◽  
Classification Methods ◽  
Individual Person ◽  
Volatile Organic ◽  
Minimum Number ◽  
Accuracy Result ◽  
Human Odour

The main objective of this paper is to investigate the effects of replacing missing values of Volatile Organic Compounds (VOC) that represent gases emitted from human on the accuracy of classifying individual person. These missing values will be replaced with three possibilities, which include 0, 1, and Random number between 0 and 1. The effects of using these predefined values on the classification accuracy are investigated by conducting experiments that involve applying a list of classification methods to classify 15 humans using human odour. Each person is characterized by their own pre-selected 15 gases emitted from their sweats. In this paper, we also study and determine the minimum number of gases that is required to produce acceptable results to correctly classify an individual person based on the gases emitted from their bodies. Based on the results obtained from the conducted experiments, the maximum and minimum allowable numbers of missing gases in human odour samples in reference to human emitted gases are 4 and 3. The best accuracy result when missing values are introduced in the odour dataset is the ensemble Bagged Trees.

ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Social Touch Gesture Recognition Using Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Saad Albawi ◽  
Oguz Bayat ◽  
Saad Al-Azawi ◽  
Osman N. Ucan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Human Robot Interaction ◽  
Sensor Data ◽  
Social Touch ◽  
Minimum Number ◽  
Original Frame

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

scholarly journals Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6550 ◽  
Author(s):  
Chen Cheng ◽  
Ji Chang ◽  
Wenjun Lv ◽  
Yuping Wu ◽  
Kun Li ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Classification Accuracy ◽  
Concept Drift ◽  
Dynamic Environment ◽  
Temporal Correlation ◽  
Classification Methods ◽  
Terrain Classification ◽  
Localization Accuracy ◽  
Learning Framework ◽  
Control Scheme

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

A New Network Traffic Classification Method Based on Optimized Hadamard Matrix and ECOC-SVM

2014 ◽  
Vol 989-994 ◽  
pp. 1895-1900
Author(s):  
Hong Zhi Wang ◽  
Li Hui Yan
Keyword(s):  
Network Traffic ◽  
Classification Accuracy ◽  
Hadamard Matrix ◽  
Classification Method ◽  
Traffic Classification ◽  
Classification Methods ◽  
Computational Burden ◽  
Network Traffic Classification ◽  
Encrypted Traffic

The traditional network traffic classification methods have many shortcomings, the classification accuracy is not high, the encrypted traffic cannot be analyzed, and the computational burden is usually large. To overcome above problems, this paper presents a new network traffic classification method based on optimized Hadamard matrix and ECOC. Through restructuring the Hadamard matrix and erasing the interference rows and columns, the ECOC table is optimized while eliminating SVM sample imbalance, and the error correcting ability for classification is reserved. The experiments results show that the proposed method outperform in network traffic classification and improve the classification accuracy.

scholarly journals Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Nikolay V. Manyakov ◽  
Nikolay Chumerin ◽  
Adrien Combaz ◽  
Marc M. Van Hulle
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Methods ◽  
Linear Classifier ◽  
Typing System ◽  
The Individual ◽  
Lateral Sclerosis ◽  
Selection Of

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

Coupling Sentiment Dictionary and SVM Classification for Text Orientation Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 2444-2449
Author(s):  
Ming Ze Gao ◽  
Fang Fang Li ◽  
Zhe Yuan Ding ◽  
Wei Dong Xiao
Keyword(s):  
Classification Accuracy ◽  
Opinion Mining ◽  
Svm Classifier ◽  
Classification Methods ◽  
Initial Training ◽  
Training Set ◽  
Orientation Analysis ◽  
Second Stage ◽  
Sentiment Dictionary ◽  
Two Stages

Sentiment classification finds various applications in opinion mining, which can help users determine sentiment tendency of texts and information. In this paper, we consider the problem of text orientation analysis. In particular, we propose a two-stage approach by coupling sentiment dictionary and classification methods. In the first stage, we build sentiment dictionary and rules to obtain the texts whose emotional scores are ranked in the top 1/4 and the bottom 1/4. These texts are marked classified for supervising the second stage. In the second stage, we employ the SVM classifier to process the remaining texts. Finally, we combine the two stages to get the orientation analysis results for all the texts. Experimental results demonstrate that, in contrast to using sentiment dictionary and classification method separately, our proposed method achieves higher classification accuracy when an initial training set by manual tagging is unavailable.

scholarly journals Comparation Analysis of Ensemble Technique With Boosting(Xgboost) and Bagging (Randomforest) For Classify Splice Junction DNA Sequence Category

2019 ◽  
Vol 9 (1) ◽  
pp. 27-36
Author(s):  
Iswaya Maalik Syahrani
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Dna Sequence ◽  
Splice Junction ◽  
Classification Methods ◽  
Parameter Setting ◽  
Ensemble Technique ◽  
Junction Type ◽  
Accuracy Result ◽  
Complex Dataset

Bioinformatics research currently supported by rapid growth of computation technology and algorithm. Ensemble decision tree is common method for classifying large and complex dataset such as DNA sequence. By implementing two classification methods with ensemble technique like xgboost and random Forest might improve the accuracy result on classifying DNA Sequence splice junction type. With 96,24% of xgboost accuracy and 95,11% of Random Forest accuracy, our conclusions  the xgboost and random forest methods using right parameter setting are highly effective tool for classifying small example dataset. Analyzing both methods with their characteristics will give an overview on how they work to meet the needs in DNA splicing.

scholarly journals Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

2020 ◽  
pp. 55-60
Author(s):  
Triando Hamonangan Saragih ◽  
Diny Melsye Nurul Fajri ◽  
Alfita Rakhmandasari
Keyword(s):  
Expert System ◽  
Decision Tree ◽  
Comparative Study ◽  
Jatropha Curcas ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Accuracy Result

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

scholarly journals EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

2016 ◽  
Vol XLI-B7 ◽  
pp. 235-239 ◽  
Author(s):  
P. Karakus ◽  
H. Karabork
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Classification Accuracy ◽  
Multispectral Image ◽  
Classification Method ◽  
Support Vector ◽  
Classification Methods ◽  
Panchromatic Image ◽  
Object Based ◽  
Spot 5

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

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