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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-Sup期10卷 S49-S49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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THERMAL FACE RECOGNITION UNDER DISGUISED CONDITIONS 18

THERMAL FACE RECOGNITION UNDER DISGUISED CONDITIONS

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International Conference on machine Learning and Cybernetics (IMLC)

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

ISBN: (纸本)9781728128160

A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear support vector machine classifier (Linear SVC), Convolutional Neural Network (CNN) and Ordinary Least Square (OLS). A grid of 22 thermal face points based on physiological information is extracted for training Linear SVC. The support vectors of testing images using Linear SVC are calculated to find the hyperplane for classification. The novelty is that we firstly apply temperature information in face recognition. In CNN model, the multiple layers of convolutional layer are utilized to extract more effective features. The fully connected layer (FC layer) is trained using the feature matrix of the last convolutional layer. This FC layer is then a classifier to identify the category of the test image. In the training phase, the predicted values from above two approaches are provided to the OLS fir linear regression. The OLS assigns weighting values to these two approaches. This can effectively compensate for the advantages and disadvantages of two approaches. In addition to the comparison with the traditional thermal face recognition, an experiment under disguised conditions was conducted. Experimental results of the proposed method outperform the existing methods.

关键词： Thermal face recognition Thermal image support vector machine classifier Convolutional neural network Ordinary least square

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Predicting Power Outages from Ice Storms using machine Learning Models with SHAP Interpretability 56

Predicting Power Outages from Ice Storms using Machine Learn...

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56th North American Power Symposium

作者： Rahman, Farishta Hassan, Tanzim Jim Ranganathan, Prakash Univ North Dakota Sch Elect Engn & Comp Sci SEECS Grand Forks ND 58202 USA

ISBN: (纸本)9798331521042;9798331521035

Ice storms pose significant challenges, causing extensive damage and widespread power outages. Accurate prediction of outages during such events and their impact on distribution infrastructure is critical for mitigation efforts. This study presents a machine learning-based approach to predict the likelihood of distribution line power outages due to ice storms. Utilizing a comprehensive Ice Storm dataset from the cities of Hope and Mayville of North Dakota, containing meteorological and power outage information, features such as temperature, dew point, humidity, wind gust, wind speed, wind direction and precipitation metrics are used in the dataset. Various machine learning (ML) algorithms, including Random Forest classifier (RFC), Logistic Regression classifier (LRC), support vector machine classifier (SVC), and Gradient Boosting classifier (GBC), are used to develop predictive models. The preliminary results indicate that RFC has the highest precision of 1.000. Feature importance using Shapley additive explanations (SHAP) analysis highlights wind gust, and wind direction as critical variables. This work can aid utility companies and emergency management agencies, enabling proactive measures to reduce the impact of ice storms.

关键词： ice storms power transmission lines temperature classifier power outage Random Forest classifier Gradient Boosting classifier Logistic Regression classifier support vector machine classifier

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SPECTRAL-SPATIAL CLASSIFICATION BASED ON INTEGRATED SEGMENTATION

SPECTRAL-SPATIAL CLASSIFICATION BASED ON INTEGRATED SEGMENTA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ghamisi, P. Benediktsson, J. A. Couceiro, M. S. Fauvel, M. Univ Iceland Fac Elect & Comp Engn IS-101 Reykjavik Iceland Robo Corp Inst Engn Coimbra P-3030199 Coimbra Portugal Univ Toulouse INRA DYNAFOR Lab F-31042 Toulouse France

ISBN: (纸本)9781479911141

A new spectral-spatial method for the classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Segmentation and one clustering method, K-means. In parallel, the input data set is classified by support vector machines (SVM). Furthermore, the result of the segmentation and clustering steps are combined with the result of SVM through majority voting within each object. The final classification map is made by using majority voting between three produced classification maps. Experimental results indicate that the proposed method can significantly improve SVM and other studied methods in terms of accuracies.

关键词： Multilevel Segmentation Swarm Optimization Mean Shift Segmentation support vector machine classifier Remote Sensing Hyperspectral Image Analysis

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