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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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Color texture-based object detection: An application to license plate localization 1st

Color texture-based object detection: An application to lice...

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1st international workshop on pattern recognition with Support Vector machines, SVM 2002

作者： In Kim, Kwang Jung, Keechul Kim Hyung, Jin Artificial Intelligence Lab CS Department Korea Advanced Institute of Science and Technology Taejon305-701 Korea Republic of Pattern Recognition and Image Processing Lab CS and Engineering Department Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)354044016X

This paper presents a novel color texture-based method for object detection in images. To demonstrate our technique, a vehicle license plate (LP) localization system is developed. A support vector machine (SVM) is used to analyze the color textural properties of LPs. No external feature extraction module is used, rather the color values of the raw pixels that make up the color textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, LP regions are identified by applying a continuously adaptive meanshift algorithm (CAMShift) to the results of the color texture analysis. The combination of CAMShift and SVMs produces not only robust and but also efficient LP detection as time-consuming color texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be analyzed. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Object detection

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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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Accidental Face recognition and Detection Using machine learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st international Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the states and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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Generation of memes to engage audience in social media 1

Generation of memes to engage audience in social media

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

作者： Kurochkin, Andrew Bokhan, Kostiantyn Ukrainian Catholic University Applied Sciences Faculty Lviv Ukraine Whirl Software Kyiv Ukraine

In digital marketing, memes have become an attractive tool for engaging online audience. Memes have an impact on buyers and sellers online behavior and information spreading processes. Thus, the technology of generating memes is a significant tool for social media engagement. The primary purpose of the project is to develop a new approach and compare it to the existing baselines in the field of social media content generation, more precisely - meme generation. A meme is an image superimposed with text, which has humoristic or sarcastic sense;a meme is just another type of visual online content. This project is aimed at applying state of the art Deep learning techniques as Transformer architecture to the meme generation problem. To achieve project objectives, we are going to collect dataset;create a model for generation of memes and titles based on the input text;create a model for defining optimal time to make a post;measure and analyze system performance in terms of social network audience engagement. 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： Deep learning

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Determining sentiment and important properties of Ukrainian language user reviews 1

Determining sentiment and important properties of Ukrainian ...

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

作者： Babenko, Dmytro Dyomkin, Vsevolod Ukrainian Catholic University Lviv Ukraine

The main idea of this project to develop a model, which can recognize emotions from user review text. Many websites contains review text with marks (stars or another representation) about some products or services. Sometimes user can write only text without a mark, in this case, such a model can help us to understand the estimation of this user. It can be a review of some special product, about booking a hotel or renting a car or something like that. Furthermore, another purpose of this project to detect which characteristics were influenced for such review. Here, the model should detect sentiments from text and classify for positive and negative. Moreover, it will understand which reasons were caused by these emotions and why the user wrote such texts. As this project mostly works with text processing, it is classical natural language processing task using deep learning. Probably, as a result, it would be some neural network, which was selected as the best from several experiments. 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： Deep learning

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Investigation of the complex data distributions for their efficient generation 1

Investigation of the complex data distributions for their ef...

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

作者： Kofman, Philipp Dobosevych, Oles Hryniv, Rostyslav Ukrainian Catholic University Lviv Ukraine University of Rzeszw Rzeszw Poland

Currently, the active development of image processing methods requires large amounts of correctly labeled data. The lack of quality data makes it impossible to use various machine learning methods. In case of limited possibilities for collecting real data, used methods for their synthetic generation. In practice, we can formulate the task of the high-quality generation of synthetic images as an efficient generation of complex data distributions, which is the object of study of this work. Generating high-quality synthetic data is an expensive and complicated process in terms of existing methods. We can distinguish two main approaches that are used to generate synthetic data: image generation based on rendered 3-D scenes and the use of GANs for simple images. These methods have some drawbacks, such as a narrow range of applicability and insufficient distribution complexity of the obtained data. When using GANs to generate complex distributions, in practice, we face a visible increase in the complexity of the model architecture and training procedure. A deep understanding of the real data complex distributions can be used to improve the quality of synthetic generation. Minimizing the differences in the real and synthetic data distributions can improve not only the generation process but also develop tools for solving the problem of data lack in the field of image processing. 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： Generative adversarial networks

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1st international Congress on Blockchain and Applications, BLOCKCHAIN 2019

1st International Congress on Blockchain and Applications, B...

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1st international Congress on Blockchain and Applications, BLOCKCHAIN 2019

ISBN: (纸本)9783030238124

The proceedings contain 21 papers. The special focus in this conference is on Blockchain and Applications. The topics include: Anticipatory policy as a design challenge: Experiments with stakeholders engagement in blockchain and distributed ledger technologies (bdlts);the electronic bill of lading: Challenges of paperless trade;a methodology for a probabilistic security analysis of sharding-based blockchain protocols;the “Tokenization” of the eparticipation in public governance: An opportunity to hack democracy;blockchain approach to solve collective decision making problems for swarm robotics;prediction of transaction confirmation time in ethereum blockchain using machine learning;improving event monitoring in iot network using an integrated blockchain-distributed pattern recognition scheme;on value preservation with distributed ledger technologies, intelligent agents, and digital preservation;blockchain technology: A review of the current challenges of cryptocurrency;lightning network: A comparative review of transaction fees and data analysis;a blockchain- and ai-based platform for global employability;blockchain and biometrics: A first look into opportunities and challenges;do smart contract languages need to be turing complete?;towards integration of blockchain and iot: A bibliometric analysis of state-of-the-art;clinicappchain: A low-cost blockchain hyperledger solution for healthcare;smart contracts are more than objects: Pro-activeness on the blockchain;blockchain based informed consent with reputation support;privacy centric collaborative machine learning model training via blockchain;fuzzy rules based solution for system administration security management via a blockchain.

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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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A comparison study on algorithms of detecting long forms for short forms in biomedical text

A comparison study on algorithms of detecting long forms for...

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1st international workshop on Text mining in Bioinformatics

作者： Torii, Manabu Hu, Zhang-zhi Song, Min Wu, Cathy H. Liu, Hongfang Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20057 USA Georgetown Univ Med Ctr Dept Biochem & Mol & Cell Biol Washington DC 20007 USA Univ Hts New Jersey Inst Technol Dept Informat Syst Newark NJ 07102 USA

Motivation: With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e., detecting definitions of acronyms, abbreviations, and symbols in biomedical text. We denote acronyms, abbreviations, and symbols as short forms (SFs) and their corresponding definitions as long forms (LFs). The study was designed to answer the following questions;i) how well a system performs in detecting LFs from novel text, ii) what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii) how to combine results from various SF knowledge bases. Method: We evaluated the following three publicly available detection systems in detecting LFs for SFs: i) a handcrafted pattern/rule based system by Ao and Takagi, ALICE, ii) a machine learning system by Chang et al., and iii) a simple alignment-based program by Schwartz and Hearst. In addition, we investigated the conceptual coverage of two terminological knowledge bases: i) the UMLS (the Unified Medical Language System), and ii) the BioThesaurus (a thesaurus of names for all UniProt protein records). We also implemented a web interface that provides a virtual integration of various SF knowledge bases. Results: We found that detection systems agree with each other on most cases, and the existing terminological knowledge bases have a good coverage of synonymous relationship for frequently defined LFs. The web interface allows people to detect SF definitions from text and to search several SF knowledge bases.

关键词： learning systems

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