Multiple classifier combination technique for sensor drift compensation using ANN &amp;amp; KNN

Adhikari, Saswati; Saha, Sankhadip

doi:10.1109/iadcc.2014.6779495

Cited by 17 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…k ‐nearest neighbors (k‐NN) are an excellent statistical machine learning algorithm, which is very straightforward in using for data classification. In k ‐NN, there is a need to calculate the distance between all the training and testing samples, and for the classifications, there is a need to take the neighbors with greater distances . The number of nearest neighbors is a constant value depending on the user.…”

Section: Features Extraction and Classificationmentioning

confidence: 99%

“…In k-NN, there is a need to calculate the distance between all the training and testing samples, and for the classifications, there is a need to take the neighbors with greater distances. 42 The number of nearest neighbors is a constant value depending on the user. Consider k stands for the number of neighbor and X is the training set with n numbers of samples and Y stands for the test pattern.…”

Section: K-nearest Neighbors (K-nn)mentioning

confidence: 99%

“…9 k-nearest neighbor lazy learning, non-parametric, simplest, and mostly used method of machine learning and it is used to predict the single point from several classes in classification. 10 Decision tree (DT) is also a supervised machine learning method, used to represent any Boolean function on discrete aspects. 11 The motivation of the study comes from the lack of explicit and specific comparisons of these three features reduction methods using classification methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gait signals classification and comparison

Barua

Yang

Ren

et al. 2019

Int J Numerical Modelling

View full text Add to dashboard Cite

Use of wireless signal technology in sensing of human gait activity is a satisfactory example of device‐free sensing and effective in medical science to detect human motion–related diseases. Some prior research showed some potential detecting process of human walking gait from wireless channel information (WCI) using wireless signals. In this paper, we present comparison of three popular features reduction methods such as principal component analysis (PCA), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA) using three classifications methods, support vector machine (SVM), k‐nearest neighbor (k‐NN), and decision tree (DT) in an absolutely equivalent situation for identifying walking gait signals. The analysis was carried out on the WCI‐based dataset where dataset was divided into four classes (normal gait, small gait, fast gait, and turn gait). Using dataset with the combination of methods (features reduction and classification), experimental results shows that all the combinations of PCA, KPCA, and LDA with three classifications achieve an average accuracy of gait identification is accordingly 86%, 79%, and 95%.

show abstract

Section: Features Extraction and Classificationmentioning

confidence: 99%

Section: K-nearest Neighbors (K-nn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gait signals classification and comparison

Barua

Yang

Ren

et al. 2019

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…The UWB based location system helps to define the wandering behavior by using the instruments, and with machine learning algorithms, we classify the measured data for recognizing every wandering behavior. SVM and k-NN are two popular machine learning algorithms where SVM used for classification and regression tasks, and k-NN used to determine the single point from classes in classification tasks [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

et al. 2020

View full text Add to dashboard Cite

Wireless localization systems have significant impact in the field of human-driven edge computing (HEC). It became very attractive among the researchers and used in applications of numerous areas such as medical, industrial, public safety, logistics, and so on. Ultra-wideband (UWB) technology used in localization systems owing to achieving high accuracy in real-time. In this paper, we exhibit a UWB based localization system based on the edge computing (EC) paradigm to analyze the wandering behavior of the patients who are suffering from dementia disease in the large-scale form. Physical changes in the brain are responsible for dementia disease. The appearance of wandering behavior is a common manner of the patients, which also a threat and interference for caregivers. We used the UWB standard appliance to symbolize various sorts of wandering patterns, including pacing, lapping, and two random movements in the large 2D map. The flow of all the movements illustrated in the X and Y-axis. Support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms used to classify all the patterns and accuracy result is above 99%. The result shows that the proposed system can achieve high accuracy in classification and satisfactory for applications in the medical area.

show abstract

“…The aim was to assess if the application of one of the most used computational systems, i.e., ANN, could enhance overall performances. To the best of our knowledge, such a machine learning approach has not been applied to chemical sensors, although it has been shown to yield promising results in other fields, such as classification of clouds through images [23] or disease like diabetes and cancer [24]; finally, it differs from the method proposed by [25], i.e., an ensemble of k-NN and ANN classifiers used to counteract sensors drift.…”

Section: Introductionmentioning

confidence: 99%

k-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances Affected by Drift Caused by Early Life Aging

et al. 2020

View full text Add to dashboard Cite

The drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important topics in this field. The work aims to test the performance of MOX gas sensors over the aging process. Firstly, sensors were tested with ethanol to understand their behavior and response changes. In parallel, beers with different alcoholic content were analyzed to assess what happened in a real application scenario. With ethanol analysis, it was possible to quantify drift of the baseline of the sensors and changes that could affect their responses over time (from day 1 to day 51). Conversely, the beer dataset has been exploited to evaluate how two different classifiers perform the classification task based on the alcohol content of the samples. A hybrid k-nearest neighbors artificial neural network (k-NN-ANN) approach and “standard” k-NN were used to evaluate to distinguish among the samples when the measures were affected by drift. To achieve this goal, data acquired from day one to day six were used as training to predict data collected up to day 51. Overall, performances of the two methods were similar, even if the best result in terms of accuracy is reached by k-NN-ANN (96.51%).

show abstract

Multiple classifier combination technique for sensor drift compensation using ANN & KNN

Cited by 17 publications

References 13 publications

Gait signals classification and comparison

Gait signals classification and comparison

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

k-NN and k-NN-ANN Combined Classifier to Assess MOX Gas Sensors Performances Affected by Drift Caused by Early Life Aging

Contact Info

Product

Resources

About