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1st International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Yun, Yixiao Gu, Irene Y. H. Bolbat, Mikhail Khan, Zulfiqar Hasan Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

ISBN: (纸本)9781479928668

This paper addresses two issues for mitigating driver distraction/inattention by using novel video analysis techniques: (a) inside an ego vehicle, driver inattention is monitored through first tracking drivers face/eye region using Riemannian manifold-based particle filters, followed by recognition of dynamic eye states using PPCA (probabilistic principal component analysis) and SVM (support vector machine) classifier. Frequencies of eye blinking and eye closure are used as the indication of sleepy and warning sign is then generated for recommendation;(b) outside an ego vehicle, road traffic is also analyzed. Surrounding vehicles (in both directions) are tracked, and their states are analyzed by self-calibrated cameras using view-geometries and road information. Parameters (e.g. distance, velocity, number) of tracked vehicles are estimated on the road ground plane in the 3D world coordinate system. These pieces of information are provided for mitigating drivers inattention. The main novelties of the proposed scheme include facial geometry based eye region detection for eye closure identification, combined tracking and detection of vehicles, new formulae derived in camera self-calibration, and the hybrid system that handles both daytime and nighttime scenarios. Experiments have been conducted on video data in two different types of camera settings, i.e., captured inside and outside a vehicle. Preliminary tests have been conducted, results and performance evaluation have indicated the effectiveness of the proposed methods.

关键词： cameras driver information systems eye gaze tracking geometry particle filtering (numerical methods) principal component analysis support vector machines video signal processing PPCA Riemannian manifold-based particle filters SVM classifier driver distraction driver inattention dynamic eye state recognition ego vehicle eye blinking eye closure probabilistic principal component analysis road information self-calibrated cameras support vector machine classifier video-based analysis video-based detection view-geometries Cameras Face Monitoring Roads support vector machines Three-dimensional displays Vehicles

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THERMAL FACE RECOGNITION BASED ON PHYSIOLOGICAL INFORMATION 26

THERMAL FACE RECOGNITION BASED ON PHYSIOLOGICAL INFORMATION

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26th IEEE International Conference on Image Processing (ICIP)

作者： Lin, Shinfeng D. Chen, Kuanyuan Chen, Wensheng Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien Taiwan

ISBN: (纸本)9781538662496

In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear support vector machine classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification. A feature vector of testing image is inputted to the classifier for face recognition. Our contribution is that the proposed method firstly applies temperature information in face recognition. Experimental results prove the effectiveness of the proposed method.

关键词： Face Recognition Thermal Image Bayesian Framework support vector machine classifier

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Distributed Model Architecture for Cuff-Less Blood Pressure Measurement System

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SN Computer Science 2022年第6期3卷 503页

作者： Dave, Tejal Joshi, Maulin Pandya, Utpal Department of Instrumentation and Control Sarvajanik College of Engineering and Technology Sarvajanik University Surat India Department of Electronics and Communication Sarvajanik College of Engineering and Technology Sarvajanik University Surat India

Continuous monitoring of blood pressure (BP) can control hypertension and cardiac diseases prevailing in many people due to luxurious lifestyle. Blood pressure measurement using cuff-based technique provides intermittent measurement and inconvenient for long-term monitoring. This work is focused on estimation of continuous blood pressure from electrocardiogram (ECG) and photoplethysmogram (PPG). The distributed model architecture is proposed to estimate blood pressure from time domain features of ECG and PPG to overcome problems with single model-based approach. The proposed work uses support vector regression and back propagation neural network for training distributed models. During testing phase, extracted features are first passed through trained pre-classifier to divert the signal to one of the trained distributed models that carries out blood pressure estimation. The proposed work is tested on 193 record segments of Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) database and also on wireless data captured from 87 subjects using prototype device. According to the British Hypertension Society (BHS) standard, the proposed method achieves grade A in the estimation of systolic and diastolic pressure for wireless data. For MIMIC database, proposed model achieves grade B for systolic, whereas grade A for diastolic pressure estimation. The values of mean error and standard deviation by proposed method are within limits of Association for the Advancement of Medical Instrumentation (AAMI) standards for wireless as well as MIMIC data. The results show improvement in the accuracy of blood pressure estimation compared to similar work and indicate high acceptability according to BHS and AAMI standards. The proposed work is helpful in wireless monitoring of patients to track the physiological conditions without interrupting their routine activities. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cuff-less blood pressure monitoring Electrocardiogram (ECG) Photoplehysmogram (PPG) support vector machine classifier support vector regression (SVR)

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Heart Rate Variability: Analysis and Classification of Healthy Subjects for Different Age Groups 2

Heart Rate Variability: Analysis and Classification of Healt...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Poddar, M. G. Kumar, Vinod Sharma, Yash Paul Indian Inst Technol Dept Elect Engn Roorkee Uttarakhand India Post Grad Inst Med Educ & Res Chandigarh India

ISBN: (纸本)9789380544168

Heart rate and its variability contain subtle information about cardiac health, which are very much difficult to extract without a suitable computer based computation technique. The objective of this study is to evaluate the HRV analysis of healthy subjects of different age groups by features computed from time domain, frequency domain and non-linear methods and classify those using SVM, KNN and PNN multi classifier modules with PCA as a feature space reduction technique. For short term HRV analysis, 60 normal and healthy male volunteers were chosen for ECG recording. 20 subjects in each age group of 18 to 30 years: named as young, 30 to 45 years: named as middle and 45 to 60 years: named as old were considered. In this study, analysis results show that, young age group has higher values in all features than other two groups and PCA-PNN classifier module classifies with a better accuracy of 70% and sensitivity 70% with all age groups.

关键词： Heart rate variability Age group Time domain Frequency domain Non-linear Principal component analysis support vector machine classifier K-nearest neighbor classifier Probabilistic neural network classifier

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Manufacturing Services Classification in a Decentralized Supply Chain Using Text Mining 1

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17th International Conference on Hybrid Intelligent Systems (HIS)

作者： Akhtar, M. D. Manupati, V. K. Varela, M. L. R. Putnik, G. D. Madureira, A. M. Abraham, Ajith VIT Univ Sch Mech Engn Div Mfg Vellore Tamil Nadu India Univ Minho Sch Engn Dept Prod & Syst Guimaraes Portugal Polytech Inst Porto ISEP IPP Porto Portugal Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Washington DC 98071 USA

ISBN: (数字)9783319763514

ISBN: (纸本)9783319763514;9783319763507

With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naive Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.

关键词： Classification Naive bayes classifier support vector machine classifier Small and medium scale enterprises

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Multiple masks based pixel comparison steganalysis method for mobile Imaging

Multiple masks based pixel comparison steganalysis method fo...

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Conference on Mobile Multimedia/Image Processing for Military and Security Applications

作者： Agaian, Sos S. Peterson, Gilbert L. Rodriguez, Benjamin M. Univ Texas Dept Elect & Comp Engn Multimedia & Mobile Signal Proc Lab San Antonio TX 78285 USA Grad Sch Engn & Management Air Force Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH 45433 USA

ISBN: (纸本)081946306X

Steganalysis has many challenges;which include the accurate and efficient detection of hidden content within digital images. This paper focuses on the development of a new multi pixel comparison method used for the detection of steganographic content within digital images transmitted over mobile channels. The sensitivity of detecting hidden information within a digital image can be increased or decreased to determine if slight changes have been made to the digital image for the target of blind steganalysis. The key thought of the presented method is to increase the sensitivity of features when alterations are made within the bit planes of a digital image. The differences between the new method and existing pixel comparison methods are;multiple masks of different sizes are used to increase the sensitivity and weighted features are used to improve the classification of the feature sets. Weights are also used with the various pixel comparisons to ensure proper sensitivity when detecting small changes. The article also investigates the reliability of detection and estimation length of hidden data within wireless digital images with potential for military applications emphasizing on defense and security.

关键词： steganography steganalysis feature selection support vector machine classifier mobile imaging

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Quality assessment of tandem mass spectra using support vector machine (SVM)

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BMC BIOINFORMATICS 2009年第1期10卷 1-11页

作者： Zou, An-Min Wu, Fang-Xiang Ding, Jia-Rui Poirier, Guy G. Univ Saskatchewan Dept Mech Engn Saskatoon SK S7N 59A Canada Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 59A Canada Univ Laval Med Res Ctr CHUL Fac Med Hlth & Environm Unit Quebec City PQ G1V 4G2 Canada

Background: Tandem mass spectrometry has become particularly useful for the rapid identification and characterization of protein components of complex biological mixtures. Powerful database search methods have been developed for the peptide identification, such as SEQUEST and MASCOT, which are implemented by comparing the mass spectra obtained from unknown proteins or peptides with theoretically predicted spectra derived from protein databases. However, the majority of spectra generated from a mass spectrometry experiment are of too poor quality to be interpreted while some of spectra with high quality cannot be interpreted by one method but perhaps by others. Hence a filtering algorithm that removes those spectra with poor quality prior to the database search is appealing. Results: This paper proposes a support vector machine (SVM) based approach to assess the quality of tandem mass spectra. Each mass spectrum is mapping into the 16 proposed features to describe its quality. Based the results from SEQUEST, four SVM classifiers with the input of the 16 features are trained and tested on ISB data and TOV data, respectively. The superior performance of the proposed SVM classifiers is illustrated both by the comparison with the existing classifiers and by the validation in terms of MASCOT search results. Conclusion: The proposed method can be employed to effectively remove the poor quality spectra before the spectral searching, and also to find the more peptides or post-translational peptides from spectra with high quality using different search engines or de novo method.

关键词： support vector machine True Positive Rate support vector machine classifier Tandem Mass Spectrum Ammonia Molecule

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classifier Based Text Simplification for Improved machine Translation 2

Classifier Based Text Simplification for Improved Machine Tr...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Tyagi, Shruti Chopra, Deepti Mathur, Iti Joshi, Nisheeth Banasthali Univ Rajasthan Dept Comp Sci Vanasthali India

ISBN: (纸本)9781467369114

machine Translation is one of the research fields of Computational Linguistics. The objective of many MT Researchers is to develop an MT System that produce good quality and high accuracy output translations and which also covers maximum language pairs. As internet and Globalization is increasing day by day, we need a way that improves the quality of translation. For this reason, we have developed a classifier based Text Simplification Model for English-Hindi machine Translation Systems. We have used support vector machines and Naive Bayes classifier to develop this model. We have also evaluated the performance of these classifiers.

关键词： machine Translation Text Simplification Naive Bayes classifier support vector machine classifier

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Identifying potential millennial customers for financial institutions using SVM

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JOURNAL OF FINANCIAL SERVICES MARKETING 2022年第4期27卷 335-345页

作者： Anand, Swati Mishra, Kushendra Babasaheb Bhimrao Ambedkar Univ Sch Management Studies Dept Rural Management Rae Bareilly RdNear RamaBai Ambedkar Maidan Lucknow 226025 Uttar Pradesh India

In order to survive in this complex economic business environment with fierce competition among various players of the finance sector, the need is to understand the even more complex financial behaviour of the customers. We apply the support vector machine classifier, a machine learning algorithm to construct a nonlinear model which classifies the customers into good and bad class based on their respective positive and negative saving behaviour. With the help of web-based survey, a sample of urban banking millennial was collected and preprocessed to apply the support vector machine classifier technique. Pattern recognition from data and its prediction for the financial behaviour are based on the machine learning forecasts. Moreover, the comparative analysis of the weightage of the three attributes, namely income level, financial literacy and behavioural characteristic, is carried out and it is analysed for the savings/wealth accumulation of the millennial generation to understand the financial distress among the said generation in context.

关键词： support vector machine classifier machine learning Nonlinear pattern recognition Financial literacy

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Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images 11

Pedestrian detection and vehicle type recognition using CENT...

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11th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Irhehhude, Martins E. Ali, Mohammad Athar Edirisinghe, Eran A. Univ Loughborough Deparment Comp Sci Loughborough Leics England

ISBN: (纸本)9781467382007

This paper proposes a feature-based technique to detect pedestrians and recognize vehicles within thermal images that have been captured during nighttime. The proposed technique applies the support vector machine (SVM) classifier on CENsus Transformed histogRam Oriented Gradient (CENTROG) features in order to classify and detect humans and/or vehicles. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable for nighttime image capturing and subsequent analysis. Since contour is the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better detection and classification accuracy for both pedestrian and detection and vehicle type recognition.

关键词： image classification image resolution infrared imaging object detection object recognition pedestrians support vector machines CENTROG features SVM classifier census transformed histogram oriented gradient features low image resolution nighttime thermal images pedestrian detection support vector machine classifier vehicle type recognition Accuracy Computed tomography Feature extraction Histograms Image edge detection Vehicles Pedestrians support vector Network vehicle Edge detection histograms image resolution Infrared imaging Object detection Image classification object recognition Feature extraction model cars X-Ray Computed Tomography images Nighttime

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