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Support vector learning for gender classification using audio and visual cues

Support vector learning for gender classification using audi...

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1st international workshop on pattern recognition with Support Vector machines

作者： Walavalkar, L Yeasin, M Narasimhamurthy, A Sharma, R Penn State Univ Dept Comp Sci Pond Lab 220 University Pk PA 16802 USA

ISBN: (纸本)354044016X

Computer vision systems for monitoring people and collecting valuable demographic information in a social environment is an important research problem. It is expected that such a system will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system can provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates gender classification using Support Vector machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principle component. analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment. The performance of the SVM was compared with two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. To further understand the robustness issues, the proposed approach has been applied on a large balanced (roughly equal distribution of gender, ethnicity and age group) data-base consisting of 8000 faces collected in real world environment. While, the results are very promising it indicates more to be done to make a statistically meaningful co

关键词： gender classification support vector machines (SVMs) PCA NMF mel-cepstrum linear and nonlinear classifies

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How Effective is the Salp Swarm Algorithm in data Classification 1st

How Effective is the Salp Swarm Algorithm in Data Classifica...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Panda, Nibedan Majhi, Santosh Kumar Department of Computer Science and Engineering Veer Surendra Sai University of Technology BurlaOdisha768018 India

ISBN: (纸本)9789811390418

In this paper, the Salp Swam Algorithm (SSA) is deployed in training the Multilayer Perceptron (MLP) for the task of data classification. The UCI machine learning repository standard datasets are used for evaluation of the proposed SSA based MLP. The performance of the proposed method is verified by considering various standard classification measures over the considered benchmark datasets. The result obtained by the proposed method is compared with other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimization (GWO), etc. From the simulation study, it has been verified that the proposed method shows supremacy in results as compared with other evolutionary algorithm based MLP. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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NCBI: A Novel Correlation Based Imputing Technique Using Biclustering 1st

NCBI: A Novel Correlation Based Imputing Technique Using Bic...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Chowdhury, Hussain A. Ahmed, Hasin A. Bhattacharyya, Dhruba Kumar Kalita, Jugal K. Computer Science and Engineering Tezpur University SonitpurAssam784028 India Department of Information and Computer Science Assam Women’s University JorhatAssam785004 India Computer Science University of Colorado Colorado SpringsCO80933-7150 United States

ISBN: (纸本)9789811390418

Presence of missing values (MV) in gene expression data is commonplace. It significantly affects the performance of statistical analysis and machine learning algorithms. Discarding objects or attributes with missing values and inappropriate estimation of MVs lead to high information loss and misleading results. So, it is necessary to have an accurate technique for missing value imputation. In this paper, we present a novel correlation based missing value imputation technique for gene expression datasets. We refer to our method as NCBI. We compare the estimation accuracy of our technique with two widely used methods such as KNNI and KMI, on four benchmark datasets by randomly knocking out data values as missing. Our technique can estimate missing values almost 20–25% more accurately than KNNI and KMI in all datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Turns analysis for automatic role recognition

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1st international workshop on Mobile Social Signal Processing, MSSP 2010

作者： Favre, Sarah Idiap Research Institute CP 592 1920 Martigny Switzerland Ecole Polytechnique Federale de Lausanne 1015 Lausanne Switzerland

ISBN: (纸本)9783642543241

This article presents approaches for recognizing automatically the roles people play in a wide range of interaction settings. The proposed role recognition approach includes two main steps. The first step aims at representing the individuals involved in an interaction with feature vectors accounting for their relationships with others. This step includes three main stages, namely segmentation of audio into turns (i.e. time intervals during which only one person talks), conversion of the sequence of turns into a social network, and use of the social network as a tool to extract features for each person. The second step uses machine learning methods to map the feature vectors into roles. The experiments have been carried out over roughly 90 hours of material. This is not only one of the largest databases ever used in literature on role recognition, but also the only one, to the best of our knowledge, including different interaction settings. In the experiments, the accuracy of the percentage of data correctly labeled in terms of roles is roughly 80% in production environments and 70% in spontaneous exchanges (lexical features have been added in the latter case). © 2014 Springer-Verlag Berlin Heidelberg.

关键词： learning systems

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From data Collection Merit to data Connection Merit for Smart Sustainable Cities 20

From Data Collection Merit to Data Connection Merit for Smar...

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1st international workshop on Intelligence Cross-data Analytics and Retrieval, ICDAR 2020

作者： Zettsu, Koji National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9781450375092

Smart data refers to the IoT data that has been processed to produce valuable data be turned into actionable information, which can be used for intelligence, planning, controlling and decision making efficiently and effectively by governments, industries and citizens. Unprecedentedly large amount and variety of sensory data can be collected to explore how these big data can become smart data and offer intelligence. Advanced data modeling and analytics, as well as data science solutions, are indispensable for transforming big data into smart data. For accelerating the utilization of smart data, NICT Real Space Information Analytics Project makes efforts to develop a cross-data analytics technology for utilizing data obtained from a variety of sensing technologies and different kinds of social big data to construct a platform that will help develop and expand smart services with a view towards smart sustainable cities. We are developing the xdata Platform on NICT's Integrated Testbed, which is a platform implementing functions of a data loader for data collection, retrieval and conversion from a variety of data sources, association mining for spatiotemporal data integration and discovery of association rules, machine learning for prediction of spatiotemporal association patterns, and creation and distribution of prediction result data in a GIS format for route search and alert notification. For accelerating open innovation using the xdata Platform, we conducted field experiments with participation of citizens. © ICDAR 2020.

关键词： data warehouses

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Sub-scene Target Detection and recognition Using Deep learning Convolution Neural Networks 1st

Sub-scene Target Detection and Recognition Using Deep Learni...

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1st international Conference on data Science, machine learning and Applications, 2019

作者： Merugu, Suresh Jain, Kamal Mittal, Anshul Raman, Balasubramanian R&D Centre CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811514197

Sub-scene recognition algorithm based on super resolution along with scene dependent neural network model and sub-scene dependent target detection for automating object information extraction is proposed. This work deals with large number of challenges possessed by classification problems. Some of the challenges in this problem are the low resolution satellite images, diverse pattern of each sub-scene causing the low level learning for classification and plethora of distinct object classes present in each sub-scene causes low accuracy of object detection. Objective of this paper presents an image super resolution technique for rectifying problems posed by low resolution images with color density variations of chromaticity coordinates. To eliminate the problem of diverse patterns, have divided various land cover types into separate groups based on maximum mixed fraction among these groups and corresponding sub-scene recognition disparate model parameters are used to recognize various scenes. To increase the accuracy for object detection has developed a sub-scene dependent Neural Network model for extracting the target/anomaly of object information. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN 1st

A 12-Lead ECG Arrhythmia Classification Method Based on 1D D...

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1st international workshop on machine learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted stenting, CVII-stENT 2019, held in conjunction with 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Wang, Chunli Yang, Shan Tang, Xun Li, Bin Chengdu Spaceon Electronics Co. Ltd. Chengdu China

ISBN: (纸本)9783030333263

In this work, we have proposed an electrocardiogram (ECG) arrhythmia classification method for short 12-lead ECG records to identify nine types (one normal type and eight abnormal types), using a 1D densely connected CNN which is a relatively novel convolutional neural network (CNN) model and shows outstanding performance in the field of pattern recognition. Firstly, noticing that ECG records are one dimensional time series with different noise levels, several wavelet-based shrinkage filtering methods were adopted to the ECG records for data augmentation. Secondly, each ECG record was divided into segments with a fixed length of 10 s, and the total number of segments for an ECG record is 10. And then, 10 segments were fed into an optimized 1D densely connected CNN for training. And lastly, a threshold vector was trained for the multi-label classification since each record may have more than one abnormal types. The approach has been validated against The First China ECG Intelligent Competition data set, obtaining a final F1 score of 0.873 and 0.863 on the validation set and test set, respectively. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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To Recognize Families In the Wild: A machine Vision Tutorial 18

To Recognize Families In the Wild: A Machine Vision Tutorial

引用

26th ACM Multimedia Conference (MM)

作者： Robinson, Joseph P. Shao, Ming Fu, Yun Northeastern Univ Boston MA 02115 USA UMass Dartmouth Dartmouth MA 02747 USA

ISBN: (纸本)9781450356657

Automatic kinship recognition has relevance in an abundance of applications. For starters, aiding forensic investigations, as kinship is a powerful cue that could narrow the search space (e.g., knowledge that the "Boston Bombers" were brothers could have helped identify the suspects sooner). In short, there are many beneficiaries that could result from such technologies: whether the consumer (e.g., automatic photo library management), scholar (e.g., historic lineage & genealogical studies), data analyzer (e.g., social-media-based analysis), investigator (e.g., cases of missing children and human trafficking- for instance, it is unlikely that a missing child found online would be in any database, however, more than likely a family member would be), or even refugees. Besides application-based problems, and as already hinted, kinship is a powerful cue that could serve as a face attribute capable of greatly reducing the search space in more general face-recognition problems. In this tutorial, we will introduce the background information, progress leading us up to these points, several current state-of-the-art algorithms spanning various views of the kinship recognition problem (e.g., verification, classification, tri-subject). We will then cover our large-scale Families In the Wild (FIW) image collection, several challenge competitions it as been used in, along with the top performing deep learning approaches. The tutorial will end with a discussion about future research directions and practical use-cases.

关键词： Visual kinship understanding family recognition deep learning familiar feature big data

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IFIP TC12 1st international Conference on Artificial Intelligence Applications and Innovations, AIAI 2004

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IFIP TC12 1st international Conference on Artificial Intelligence Applications and Innovations, AIAI 2004

ISBN: (纸本)1402081502

The proceedings contain 40 papers. The special focus in this conference is on Artificial Intelligence Applications and Innovations. The topics include: Artificial intelligence systems in micromechanics;integrating two artificial intelligence theories in a medical diagnosis application;artificial intelligence and law;virtual market environment for trade;an artificial neural networks approach to the estimation of physical stellar parameters;evolutionary robot behaviors based on natural selection and neural network;control of overhead crane by fuzzy-PID with genetic optimisation;creative design of fuzzy logic controller;on-line extraction of fuzzy rules in a wastewater treatment plant;finding manufacturing expertise using ontologies and cooperative agents;using agents in the exchange of product data;a pervasive identification and adaptation system for the smart house;deductive diagnosis of digital circuits;verification of nasa emergent systems;learning Bayesian metanetworks from data with multilevel uncertainty;using organisational structures emergence for maintaining functional integrity in embedded systems networks;efficient attribute reduction algorithm;using relative logic for pattern recognition;a multi-agent intelligent tutoring system;analysis and intelligent support of learning communities in semi-structured discussion environments;an adaptive assessment system to evaluate student ability level;forming the optimal team of experts for collaborative work;introducing a star topology into latent class models for collaborative filtering;an agency for semantic-based automatic discovery of web services;using genetic algorithms and tabu search parallel models to solve the scheduling problem;modelling document categories by evolutionary learning of text centroids;AIR - a platform for intelligent systems;data mining by MOUCLAS algorithm for petroleum reservoir characterization from well logging data;online possibilistic diagnosis based on expert knowledge for engine dyno

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Advances in Computational Intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Joan Cabestany Ignacio Rojas Gonzalo Joya

ISBN: (数字)9783642214981

ISBN: (纸本)9783642214974

This two-volume set LNCS 6691 and 6692 constitutes the refereed proceedings of the 11th international Work-Conference on Artificial Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers were carefully reviewed and selected from 202 submissions for presentation in two volumes. The second volume includes 76 papers organized in topical sections on video and image processing; hybrid artificial neural networks: models, algorithms and data; advances in machine learning for bioinformatics and computational biomedicine; biometric systems for human-machine interaction; data mining in biomedicine; bio-inspired combinatorial optimization; applying evolutionary computation and nature-inspired algorithms to formal methods; recent advances on fuzzy logic and soft computing applications; new advances in theory and applications of ICA-based algorithms; biological and bio-inspired dynamical systems; and interactive and cognitive environments. The last section contains 9 papers from the international workshop on Intelligent Systems for Context-Based Information Fusion, ISCIF 2011, held at IWANN 2011.

关键词： Computational Biology/Bioinformatics pattern recognition Artificial Intelligence data mining and Knowledge Discovery Models and Principles Bioinformatics

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