Recognition of Different Modes of Human Thinking during Processing Visual Images of Histograms and Scenes

2013 ◽  
Vol 333-335 ◽  
pp. 1328-1331
Author(s):  
Mi Li ◽  
Sheng Fu Lu ◽  
Xue Tan ◽  
Yu Zhou ◽  
Ning Zhong
Keyword(s):  
Eye Movement ◽  
Pupil Diameter ◽  
Visual Images ◽  
Support Vector ◽  
Classification Methods ◽  
Classification Analysis ◽  
Svm Classification ◽  
Movement Data ◽  
Human Thinking

To investigate the different modes of human thinking, we designed an eye tracking experiment during people recognized two category images of histograms and scenes, and used the support vector machine (SVM) classification algorithm to classify these eye movement data. The results of statistical analysis showed that there were significant differences in saccade distance and pupil diameter between these two category images. By the feature selection, normalization of data preprocessing, and SVM classification, the results of classification analysis showed that there was a better performance on the classification of the histograms and scenes. These results suggest we can identify the modes of human thinking through the SVM classification methods based on the eye movement data.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

scholarly journals Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments

2020 ◽  
Vol 31 (3) ◽  
pp. 675-691 ◽  
Author(s):  
Jella Pfeiffer ◽  
Thies Pfeiffer ◽  
Martin Meißner ◽  
Elisa Weiß
Keyword(s):  
Virtual Reality ◽  
Recommender Systems ◽  
Eye Movement ◽  
Information Search ◽  
Search Behavior ◽  
Movement Data ◽  
Conversion Rates ◽  
Shopping Environments ◽  
Information Search Behavior

How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives without explicit user input? We demonstrate that eye movement data allow building reliable prediction models for identifying goal-directed and exploratory shopping motives. Our approach is validated in a real supermarket and in an immersive virtual reality supermarket. Several managerial implications of using gaze-based classification of information search behavior are discussed: First, the advent of virtual shopping environments makes using our approach straightforward as eye movement data are readily available in next-generation virtual reality devices. Virtual environments can be adapted to individual needs once shopping motives are identified and can be used to generate more emotionally engaging customer experiences. Second, identifying exploratory behavior offers opportunities for marketers to adapt marketing communication and interaction processes. Personalizing the shopping experience and profiling customers’ needs based on eye movement data promises to further increase conversion rates and customer satisfaction. Third, eye movement-based recommender systems do not need to interrupt consumers and thus do not take away attention from the purchase process. Finally, our paper outlines the technological basis of our approach and discusses the practical relevance of individual predictors.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

scholarly journals Contact-Free Cognitive Load Recognition Based on Eye Movement

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Liu ◽  
Tong Chen ◽  
Guoqiang Xie ◽  
Guangyuan Liu
Keyword(s):  
Cognitive Load ◽  
Eye Movement ◽  
Cognitive Load Theory ◽  
Recognition Rate ◽  
Support Vector ◽  
Load Theory ◽  
Whole Process ◽  
Movement Data ◽  
Mental Diseases ◽  
Contact Free

The cognitive overload not only affects the physical and mental diseases, but also affects the work efficiency and safety. Hence, the research of measuring cognitive load has been an important part of cognitive load theory. In this paper, we proposed a method to identify the state of cognitive load by using eye movement data in a noncontact manner. We designed a visual experiment to elicit human’s cognitive load as high and low state in two light intense environments and recorded the eye movement data in this whole process. Twelve salient features of the eye movement were selected by using statistic test. Algorithms for processing some features are proposed for increasing the recognition rate. Finally we used the support vector machine (SVM) to classify high and low cognitive load. The experimental results show that the method can achieve 90.25% accuracy in light controlled condition.

scholarly journals DIRECT AND INDIRECT CLASSIFICATION OF HIGH FREQUENCY LNA GAIN PERFORMANCE – A COMPARISON BETWEEN SVMS AND MLPS

2014 ◽  
pp. 24-31
Author(s):  
Peter C. Hung ◽  
Seán F. McLoone ◽  
Ronan Farrell
Keyword(s):  
Performance Measures ◽  
High Frequency ◽  
Low Noise ◽  
Support Vector ◽  
Functional Testing ◽  
Svm Classification ◽  
Direct Support ◽  
Noise Amplifier ◽  
High Frequency Performance

The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip. One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter. An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP classifiers marginally outperforming SVMs.

scholarly journals Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree

2020 ◽  
Vol 7 (6) ◽  
pp. 1253
Author(s):  
Jajang Jaya Purnama ◽  
Hendri Mahmud Nawawi ◽  
Susy Rosyida ◽  
Ridwansyah Ridwansyah ◽  
Risnandar Risnandar
Keyword(s):  
Decision Tree ◽  
Supervised Learning ◽  
Theory And Practice ◽  
Support Vector ◽  
Classification Methods ◽  
Tertiary Institution ◽  
Accuracy And Precision ◽  
Improve Accuracy ◽  
Kernel Model

<p>Mahasiswa di setiap perguruan tinggi dituntut untuk memperoleh pengetahuan dan keterampilan yang memenuhi syarat dengan prestasi akademik. Hasil dari pembelajaran mahasiswa didapat dari ujian teori dan praktek, setiap mahasiswa wajib menuntaskan nilai sesuai kriteria kelulusan minimum dari masing-masing dosen pengajar, jika dibawah batas minimum maka mahasiswa mengikuti her. Her adalah salah satu cara untuk menuntaskan kriteria kelulusan minimum. Mahasiswa yang mengikuti her setiap semesternya hampir mencapai angka yang relatif tinggi dari jumlah seluruh mahasiswa. Untuk mengurangi jumlah mahasiswa yang mengikuti her maka dibutuhkan sebuah metode yang dapat mengurangi hal tersebut, dengan metode <em>Support Ve</em><em>c</em><em>tor Machine</em> (SVM) dan <em>Decision Tree </em>(DT). SVM dan DT adalah salah satu metode klasifikasi <em>supervised learning</em>. Oleh karena itu, dalam penelitian ini menggunakan SVM dan DT. SVM dapat menghilangkan hambatan pada data, memprediksi, mengklasifikasikan dengan sampling kecil dan dapat meningkatkan akurasi dan mengurangi kesalahan. Klasifikasi data siswa yang melakukan her/peningkatan dengan mengimprovisasi model kernel untuk visualisasi termasuk bar, histogram, dan sebaran<em> </em>begitu juga<em> Decision Tree </em>mempunyai kelebihan tersendiri. Dari hasil penelitian ini telah didapatkan akruasi dan presisi model DT lebih besar dibandingkan dengan SVM, akan tetapi untuk <em>recall </em>DT lebih kecil dibandingkan SVM.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><strong><br /></strong></em></p><p class="Abstract"><em>Students in each tertiary institution are required to obtain knowledge and skills that meet the requirements with academic achievement. The results of student learning are obtained from the theory and practice exams, each student is required to complete grades according to the minimum graduation criteria of each teaching lecturer, if below the minimum limit then students take remedial. Remedial is one way to complete the minimum passing criteria. Students who take remedial every semester almost reach a relatively high number of the total number of students. To reduce the number of students who take remedial, a method that can reduce this is needed, with the Support Vector Machine (SVM) and Decision Tree (DT) methods. SVM and DT are one of the supervised learning classification methods. Therefore, in this study using SVM and DT. SVM can eliminate barriers to data, predict, classify with small sampling and can improve accuracy and reduce errors. Data classification of students who do remedial/improvements by improving the kernel model for visualization including bars, histograms, and distributions as well as the Decision Tree has its own advantages. From the results of this study it has been obtained that the accuracy and precision of DT models is greater than that of SVM, but for recall DT is smaller than SVM.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Research on Cognitive Effects of Narrative Rhetoric in Print Advertisement Based on Eye Movement

2021 ◽  
Vol 236 ◽  
pp. 05062
Author(s):  
Yubo Li ◽  
Gong Wang ◽  
Quan Gan
Keyword(s):  
College Students ◽  
Eye Movement ◽  
Pupil Diameter ◽  
Experimental Results ◽  
Cognitive Effects ◽  
Movement Data ◽  
Print Advertisement ◽  
Narrative Rhetoric

This article uses eye movement experiments to study the cognitive effects of consumer groups on different narrative-quality advertisements. The experiment selects typical advertising cases, takes college students as subjects, and uses computers to track and analyze eye movement data. The experimental results show that the quality of narrative rhetoric directly affects the number of attention, duration, and pupil diameter of the subjects, and the subjects’ browsing time and memory of advertisements are positively related to the quality of narrative rhetoric of advertisements. Among them, in the Low-involvement/Thinking product advertisements, consumers' eye movement data for advertisements with better narrative quality is relatively more significant.

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