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Violent Video Event Detection: A Local Optimal Oriented pattern Based Approach 1st

Violent Video Event Detection: A Local Optimal Oriented Patt...

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1st international Conference on Applied Intelligence and Informatics (AII)

作者： Lohithashva, B. H. Aradhya, V. N. Manjunath JSS Sci & Technol Univ Dept Comp Applicat Mysuru Karnataka India

ISBN: (纸本)9783030822699;9783030822682

Video/Camera-based monitoring is a prominent and difficult research problem in the field of machine learning and pattern recognition and posed much interest in our safety in the private and public sectors. Therefore, surveillance cameras have been deploying to control suspicious activity. Consequently, many researchers have worked on developing an automatic surveillance system to detect violent events and assists security guards to take the right decision at the right time. still, violent event detection is difficult to detect because of illumination, complex background, scale variation, blurriness, occlusion, and low resolution in a surveillance camera. In this paper, the Local Optimal Oriented pattern (LOOP) texture-based feature descriptor is proposed. Eventually, eminent features are used with a support vector machine (SVM) classier for violent event detection. Experiments are conducted on the Hockey Fight dataset and Violent-Flows dataset. The five-Fold Cross-Validation approach is used to analyze the performance of the proposed method. The data and results are promising and encouraging.

关键词： Violent event Texture features Spatio-temporal LOOP descriptor

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Optimistic Multi-granulation Rough Set-Based Classification for Neonatal Jaundice Diagnosis 6th

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6th international workshop on Soft Computing Applications (SOFA)

作者： Kumar, S. Senthil Inbarani, H. Hannah Azar, Ahmad Taher Own, Hala S. Balas, Valentina Emilia Olariu, Teodora Periyar Univ Dept Comp Sci Salem 636011 India Benha Univ Fac Comp & Informat Banha Egypt Natl Res Inst Astron & Geophys Helwan Egypt Aurel Vlaicu Univ Arad Arad Romania Vasile Goldis West Univ Arad Satu Mare Romania

ISBN: (纸本)9783319182964

Neonatal jaundice diagnosis has been approached by various machine learning techniques. pattern recognition algorithms are capable of improving the quality of prediction, early diagnosis of diseases, and disease classification. pattern recognition algorithm results in Neonatal jaundice diagnosis or description of jaundice treatment by the medical specialist. This research focuses on applying rough set-based data mining techniques for Neonatal jaundice data to discover locally frequent identification of jaundice diseases. This work applies Optimistic Multi-granulation rough set model (OMGRS) for Neonatal jaundice data classification. Multi-granulation rough set provides efficient results than single granulation rough set model and soft rough set-based classifier model. The performance of the proposed Multi-granulation rough set-based classification is compared with other Naive bayes, Back Propagation Neural Networks (BPN), and Kth Nearest Neighbor (KNN) approaches using various classification measures.

关键词： Rough set Optimistic multi-granulation rough set Neonatal jaundice data Classification Comparative analysis of classification measures

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Deep learning and Change Detection for Fall recognition 20th

Deep Learning and Change Detection for Fall Recognition

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： Tasoulis, Sotiris K. Mallis, Georgios I. Georgakopoulos, Spiros V. Vrahatis, Aristidis G. Plagianakos, Vassilis P. Maglogiannis, Ilias G. Univ Thessaly Dept Comp Sci & Biomed Informat Volos Greece Univ Piraeus Dept Digital Syst Piraeus Greece

ISBN: (纸本)9783030202576;9783030202569

Early fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for daily-life activities tracking, able to detect immediately a high-risk fall via a machine learning framework. Towards this direction, accelerometer devices are used widely for the assessment of fall risk. Although there is a plethora of studies under this perspective with promising results, several challenges still remain such as the extremely demanding data and power management as well as the discovery of false positive falls. In this work we propose a complete methodology based on the combination of the computationally demanding convolutional neural networks along with a lightweight change detection method. Our basic assumption is that it is possible to control computational resources for the operation of a classifier, suffice to be activated when a strong change in user's movements is identified. The proposed methodology was applied to real experimental data providing reliable results that justify the original hypothesis.

关键词： Deep learning Change detection Fall detection Wearable devices

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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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Building a feature taxonomy of the terms extracted from a text collection 1

Building a feature taxonomy of the terms extracted from a te...

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Moiseyenko, Svitlana Vasileyko, Alexander Ermolayev, Vadim Department of Computer Science Zaporizhzhia National University Zhukovskogo st. 66 Zaporizhzhia Ukraine

This position paper presents an approach for feature grouping and taxonomic relationship extraction with the further objective to build a feature taxonomy of a learned ontology. The approach needs to be developed as a part of the OntoElect methodology for domain ontologies refinement. The paper contributes a review of the related work in taxonomic relationships extraction from natural language texts. Within this review, the research gaps and remaining challenges are analyzed. The paper proceeds with outlining the envisioned solution. It presents the approach to this solution starting with the research questions, followed by the initial research hypotheses to be tested. Consequently, the plan of research is presented, including the potential research problems, the rationale to use and re-use existing components, and evaluation plan. Finally, the proposed solution, and the project, are placed in the broader context of the overall OntoElect workflow. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Ontology

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

To Recognize Families In the Wild: A Machine Vision Tutorial

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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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Cloud Cultivation: Optimizing Agricultural Automation Practices through Deep learning 1

Cloud Cultivation: Optimizing Agricultural Automation Practi...

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1st international Conference on Smart Automation and Robotics for Future Industry, SMARTINDUstRY 2024

作者： Pandey, Abhishek Ramesh, V. SCSVMV University Tamil Nadu Kanchipuram India

Background: Modern agricultural operations collect data from a variety of sources that provide a better knowledge of the constantly shifting conditions of the crop, soil, and environment. This suggests that the processes involved in agriculture will become more and more data-driven. The goal of this study is to demonstrate how to handle diverse data and information from actual datasets that gather physiological in nature, biochemical processes. The agricultural industry only seems to be resistant to digital technological advances, and the "smart farm" concept is becoming increasingly common by using time-series data and the Internet of Things (IoT) paradigm to apply environmental and historical information. In recent years, deep programming has been effectively used for voice recognition, picture recognition, and processing of natural language. Aim: By examining cloud data with crop development trends, examine the potential of deep learning algorithms to optimise the use of agricultural resources, such as water, fertilisers, and pesticides. Method: The present study focuses on the design and implementation of real-world tasks, such as predicting agricultural harvest or recreating data from missing or incorrect sensors, by comparing and using different machine learning algorithms to recommend which way to spend efforts and resources. Results: The results of this study demonstrate the manner in which there are plenty of potential possibilities for innovation to coexist with requests and requirements from businesses who want to establish an optimised and sustainable agriculture industrial use business, making investments not only in technology but also in the expertise and skilled employees necessary to make the most of it. Conclusion: The conclusions presented in this study suggest that better accuracy and faster inference times may be attained by using novel deep learning techniques, and that applications in reality can benefit from the models. Lastly, a few suggesti

关键词： 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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