Modeling observer happiness from facial hyperspectral sensor

2019 ◽  
Vol 37 (1) ◽  
pp. 161-180
Author(s):  
Min Hao ◽  
Guangyuan Liu ◽  
Desheng Xie ◽  
Ming Ye ◽  
Jing Cai
Keyword(s):  
Real Life ◽  
Health Concern ◽  
Tissue Oxygen Saturation ◽  
Support Vector ◽  
Local Dynamic ◽  
Daily Lives ◽  
Content Type ◽  
Linear Discriminant ◽  
Physiological Signal ◽  
Experimental Process

Purpose Happiness is an important mental emotion and yet becoming a major health concern nowadays. For this reason, better recognizing the objective understanding of how humans respond to event-related observations in their daily lives is especially important. Design/methodology/approach This paper uses non-intrusive technology (hyperspectral imaging [HSI]) for happiness recognition. Experimental setup is conducted for data collection in real-life environments where observers are showing spontaneous expressions of emotions (calm, happy, unhappy: angry) during the experimental process. Based on facial imaging captured from HSI, this work collects our emotional database defined as SWU Happiness DB and studies whether the physiological signal (i.e. tissue oxygen saturation [StO2], obtained by an optical absorption model) can be used to recognize observer happiness automatically. It proposes a novel method to capture local dynamic patterns (LDP) in facial regions, introducing local variations in facial StO2 to fully use physiological characteristics with regard to hyperspectral patterns. Further, it applies a linear discriminant analysis-based support vector machine to recognize happiness patterns. Findings The results show that the best classification accuracy is 97.89 per cent, objectively demonstrating a feasible application of LDP features on happiness recognition. Originality/value This paper proposes a novel feature (i.e. LDP) to represent the local variations in facial StO2 for modeling the active happiness. It provides a possible extension to the promising practical application.

An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arijit Maji ◽  
Indrajit Mukherjee
Keyword(s):  
Support Vector Machine ◽  
Control Chart ◽  
Manufacturing Process ◽  
Reference Sample ◽  
Real Life ◽  
Fine Tuning ◽  
Support Vector ◽  
Response Distribution ◽  
Content Type ◽  
One Class Classifier

PurposeThe purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.Design/methodology/approachThe step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.FindingsA comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.Research limitations/implicationsThe sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.Practical implicationsThe proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.Originality/valueVarious multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

What factors determine reviewer credibility?

Kybernetes ◽  
2019 ◽  
Vol 49 (10) ◽  
pp. 2547-2567 ◽  
Author(s):  
Himanshu Sharma ◽  
Anu G. Aggarwal
Keyword(s):  
Nearest Neighbor ◽  
Source Credibility ◽  
Personal Information ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Content Type ◽  
Linear Discriminant ◽  
Travel And Tourism ◽  
Hotel Booking

Purpose The experiential nature of travel and tourism services has popularized the importance of electronic word-of-mouth (EWOM) among potential customers. EWOM has a significant influence on hotel booking intention of customers as they tend to trust EWOM more than the messages spread by marketers. Amid abundant reviews available online, it becomes difficult for travelers to identify the most significant ones. This questions the credibility of reviewers as various online businesses allow reviewers to post their feedback using nickname or email address rather than using real name, photo or other personal information. Therefore, this study aims to determine the factors leading to reviewer credibility. Design/methodology/approach The paper proposes an econometric model to determine the variables that affect the reviewer’s credibility in the hospitality and tourism sector. The proposed model uses quantifiable variables of reviewers and reviews to estimate reviewer credibility, defined in terms of proportion of number of helpful votes received by a reviewer to the number of total reviews written by him. This covers both aspects of source credibility i.e. trustworthiness and expertness. The authors have used the data set of TripAdvisor.com to validate the models. Findings Regression analysis significantly validated the econometric models proposed here. To check the predictive efficiency of the models, predictive modeling using five commonly used classifiers such as random forest (RF), linear discriminant analysis, k-nearest neighbor, decision tree and support vector machine is performed. RF gave the best accuracy for the overall model. Practical implications The findings of this research paper suggest various implications for hoteliers and managers to help retain credible reviewers in the online travel community. This will help them to achieve long term relationships with the clients and increase their trust in the brand. Originality/value To the best of authors’ knowledge, this study performs an econometric modeling approach to find determinants of reviewer credibility, not conducted in previous studies. Moreover, the study contracts from earlier works by considering it to be an endogenous variable, rather than an exogenous one.

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ikhlaas Gurrib ◽  
Firuz Kamalov
Keyword(s):  
Sentiment Analysis ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Price Information ◽  
Content Type ◽  
Linear Discriminant ◽  
News Announcements ◽  
Svm Model ◽  
News Sentiment ◽  
Random Guess

Purpose Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis. Design/methodology/approach Concretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information. Findings Relative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction. Originality/value To the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis.

CLASSIFYING CREDIT CARD ACCOUNTS FOR BUSINESS INTELLIGENCE AND DECISION MAKING: A MULTIPLE-CRITERIA QUADRATIC PROGRAMMING APPROACH

2005 ◽  
Vol 04 (04) ◽  
pp. 581-599 ◽  
Author(s):  
YONG SHI ◽  
YI PENG ◽  
GANG KOU ◽  
ZHENGXIN CHEN
Keyword(s):  
Decision Making ◽  
Quadratic Programming ◽  
Portfolio Management ◽  
Business Intelligence ◽  
Credit Card ◽  
Real Life ◽  
Multiple Criteria ◽  
Programming Approach ◽  
Support Vector ◽  
Linear Discriminant

A major challenge in credit card portfolio management is to classify and predict credit cardholders' behaviors in a reliable precision because cardholders' behaviors are rather dynamic in nature. This is crucial for creditors because it allows them to take proactive actions and minimize charge-off and bankruptcy losses. Although the methods used in the area of credit portfolio management have improved significantly, the demand for alternative and sophisticated analytical tools is still strong. The objective of this paper is to propose a multiple criteria quadratic programming (MCQP) to classify credit card accounts for business intelligence and decision making. MCQP is intended to predict credit cardholders' behaviors from a nonlinear perspective that is justifiable because both the objective functions and constraints in credit card accounts classification may be nonlinear. Using a real-life credit card dataset from a major US bank, the MCQP method is compared with popular and similar classification methods: linear discriminant analysis, decision tree, multiple criteria linear programming, support vector machine, and neural network. The results indicate that MCQP is a promising business intelligence method in credit card portfolio management.

scholarly journals Activity recognition from Smartphone data using WSVM-HMM classification

2021 ◽  
Vol 12 (6) ◽  
pp. 0-0
Keyword(s):  
Activity Recognition ◽  
Regularization Parameter ◽  
Traditional Approach ◽  
Hybrid Approach ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sensing Applications

A lot of real-life mobile sensing applications are becoming available nowadays. The traditional approach for activity recognition employs machine learning algorithms to learn from collected data from smartphpne and induce a model. The model generation is usually performed offline on a server system and later deployed to the phone for activity recognition. In this paper, we propose a new hybrid classification model to perform automatic recognition of activities using built-in embedded sensors present in smartphones. The proposed method uses a trick to classify the ongoing activity by combining Weighted Support Vector Machines (WSVM) model and Hidden Markov Model (HMM) model. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our proposed method outperforms the state-of-the-art on a large benchmark dataset.

scholarly journals Optical Character Recognition of Amharic Documents

2007 ◽  
Vol 3 (2) ◽  
Author(s):  
Million Meshesha ◽  
C V Jawahar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Information Needs ◽  
Real Life ◽  
Principal Component ◽  
Support Vector ◽  
Extensive Literature ◽  
Large Set ◽  
Linear Discriminant ◽  
Optical Character

In Africa around 2,500 languages are spoken. Some of these languages have their own indigenous scripts. Accordingly, there is a bulk of printed documents available in libraries, information centers, museums and offices. Digitization of these documents enables to harness already available information technologies to local information needs and developments. This paper presents an Optical Character Recognition (OCR) system for converting digitized documents in local languages. An extensive literature survey reveals that this is the first attempt that report the challenges towards the recognition of indigenous African scripts and a possible solution for Amharic script. Research in the recognition of African indigenous scripts faces major challenges due to (i) the use of large number characters in the writing and (ii) existence of large set of visually similar characters. In this paper, we propose a novel feature extraction scheme using principal component and linear discriminant analysis, followed by a decision directed acyclic graph based support vector machine classifier. Recognition results are presented on real-life degraded documents such as books, magazines and newspapers to demonstrate the performance of the recognizer.

scholarly journals Activity Recognition From Smartphone Data Using WSVM-HMM Classification

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
M'hamed Bilal Abidine ◽  
Belkacem Fergani
Keyword(s):  
Activity Recognition ◽  
Regularization Parameter ◽  
Traditional Approach ◽  
Hybrid Approach ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sensing Applications

A lot of real-life mobile sensing applications are becoming available nowadays. The traditional approach for activity recognition employs machine learning algorithms to learn from collected data from smartphpne and induce a model. The model generation is usually performed offline on a server system and later deployed to the phone for activity recognition. In this paper, we propose a new hybrid classification model to perform automatic recognition of activities using built-in embedded sensors present in smartphones. The proposed method uses a trick to classify the ongoing activity by combining Weighted Support Vector Machines (WSVM) model and Hidden Markov Model (HMM) model. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our proposed method outperforms the state-of-the-art on a large benchmark dataset.

scholarly journals Mental Workload Estimation Based on Physiological Features for Pilot-UAV Teaming Applications

2021 ◽  
Vol 15 ◽  
Author(s):  
Gaganpreet Singh ◽  
Caroline P. C. Chanel ◽  
Raphaëlle N. Roy
Keyword(s):  
Classification Accuracy ◽  
Mental Workload ◽  
Subject Classification ◽  
Support Vector ◽  
Physiological Measures ◽  
Significant Drop ◽  
Linear Discriminant ◽  
Physiological Signal ◽  
Cooperative Team ◽  
The Impact

Manned-Unmanned Teaming (MUM-T) can be defined as the teaming of aerial robots (artificial agents) along with a human pilot (natural agent), in which the human agent is not an authoritative controller but rather a cooperative team player. To our knowledge, no study has yet evaluated the impact of MUM-T scenarios on operators' mental workload (MW) using a neuroergonomic approach (i.e., using physiological measures), nor provided a MW estimation through classification applied on those measures. Moreover, the impact of the non-stationarity of the physiological signal is seldom taken into account in classification pipelines, particularly regarding the validation design. Therefore this study was designed with two goals: (i) to characterize and estimate MW in a MUM-T setting based on physiological signals; (ii) to assess the impact of the validation procedure on classification accuracy. In this context, a search and rescue (S&R) scenario was developed in which 14 participants played the role of a pilot cooperating with three UAVs (Unmanned Aerial Vehicles). Missions were designed to induce high and low MW levels, which were evaluated using self-reported, behavioral and physiological measures (i.e., cerebral, cardiac, and oculomotor features). Supervised classification pipelines based on various combinations of these physiological features were benchmarked, and two validation procedures were compared (i.e., a traditional one that does not take time into account vs. an ecological one that does). The main results are: (i) a significant impact of MW on all measures, (ii) a higher intra-subject classification accuracy (75%) reached using ECG features alone or in combination with EEG and ET ones with the Adaboost, Linear Discriminant Analysis or the Support Vector Machine classifiers. However this was only true with the traditional validation. There was a significant drop in classification accuracy using the ecological one. Interestingly, inter-subject classification with ecological validation (59.8%) surpassed both intra-subject with ecological and inter-subject with traditional validation. These results highlight the need for further developments to perform MW monitoring in such operational contexts.

scholarly journals Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces

2021 ◽  
Vol 13 (8) ◽  
pp. 202
Author(s):  
David Balderas ◽  
Pedro Ponce ◽  
Diego Lopez-Bernal ◽  
Arturo Molina
Keyword(s):  
Eye Movement ◽  
Engineering Students ◽  
Real Life ◽  
Support Vector ◽  
Brain Computer Interfaces ◽  
Restricted Boltzmann Machines ◽  
Significant Information ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Scientists And Engineers

Education 4.0 is looking to prepare future scientists and engineers not only by granting them with knowledge and skills but also by giving them the ability to apply them to solve real life problems through the implementation of disruptive technologies. As a consequence, there is a growing demand for educational material that introduces science and engineering students to technologies, such as Artificial Intelligence (AI) and Brain–Computer Interfaces (BCI). Thus, our contribution towards the development of this material is to create a test bench for BCI given the basis and analysis on how they can be discriminated against. This is shown using different AI methods: Fisher Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Restricted Boltzmann Machines (RBM) and Self-Organizing Maps (SOM), allowing students to see how input changes alter their performance. These tests were done against a two-class Motor Image database. First, using a large frequency band and no filtering eye movement. Secondly, the band was reduced and the eye movement was filtered. The accuracy was analyzed obtaining values around 70∼80% for all methods, excluding SVM and SOM mapping. Accuracy and mapping differentiability increased for some subjects for the second scenario 70∼85%, meaning either their band with the most significant information is on that limited space or the contamination because of eye movement was better mitigated by the regression method. This can be translated to saying that these methods work better under limited spaces. The outcome of this work is useful to show future scientists and engineers how BCI experiments are conducted while teaching them the basics of some AI techniques that can be used in this and other several experiments that can be carried on the framework of Education 4.0.

Sign in / Sign up

Export Citation Format

Share Document