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Dynamic Human Activity recognition with Vision-Based Pose Estimation and machine learning for Various Age Groups 6

Dynamic Human Activity Recognition with Vision-Based Pose Es...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Patil, Varsha K. Dhamange, Shristi Bhandurge, Shraddha Gaikwad, Samruddhi Patil, Aditya AISSMS Institute of Information Technology Pune India Bits Pilani Goa Campus India

ISBN: (纸本)9798350306613

Human activity recognition is evident in medical institutions, survey systems, security monitoring, and human-computer interaction study fields, this article is related to classifying six basic human-centered behaviors (Punch, stand, sitting, squat, wave, and kick) by implementing machine learning algorithms, Logistic Regression CV and Random Forest algorithm, and SVM classifier machine learning algorithms are used for the human activity classification. We have used a vision-based algorithm using skeleton pose estimation for feature extraction. We have presented results across age groups of adolescence, adult, and middle age. the dataset used is obtained from a public repository and handled using Open Pose (Kaggle). Human activity computing is an up-and-coming research to interpret individual behavior and make an effort to understand its societal context. the results show the mapping of face and body coordinates on the raw camera feed and produce a probabilistic output and a single output defining one of the six activities (Punch, squat, kick, sit, stand, and wave) on the screen. the result is shown in a screen feed of a fixed rate pixel and works simultaneously as the work runs as an infinite loop till the work is closed hence the results are dynamic and keep changing as per real-time scenarios precise, demanding, and agreeable application of collecting context by detecting human body motion information and thereby making intelligent decisions on real-time data is implemented. Here, activity recognition databases are considered publicly available as repositories. We get an on-screen result based on probability which is displayed on the video frame screening where six actions and three age groups (children, young adults. elderly persons) are displayed. © 2024 IEEE.

关键词： feature selection HAR Human activity recognition Kaggle Logistic Regression. Random forest machine learning Mediapipe Open Pose skeleton pose estimation SVM

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A Survey of Few-Shot Image Classification Based on Transfer learning 6

A Survey of Few-Shot Image Classification Based on Transfer ...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wei, Haifeng Jiao, Lianmeng Northwestern Polytechnical University School of Automation Xi'an China

ISBN: (纸本)9798350325485

Although the application scenarios of image classification are very extensive, it is difficult to collect enough data to train the deep learning model in many scenarios. Transfer learning is the main approach for image classification with few-shot samples. Using transfer learning, the knowledge and experience learned by the model in the source domain are transferred to the target domain, so that the model can be quickly learned and generalized in the target domain, reducing the dependence on the target domain data. this paper conducts a systemic survey for few-shot image classification algorithms based on transfer learning. According to the differences in distribution, feature space and label space of source domain and target domain, the reviewed algorithms are roughly divided into three categories: the first category is that the distribution is different, but the feature space and label space are the same, which can be solved by distribution adaptation;the second type is that the distribution and feature space are the same, but the label space is different, in which case the meta-learning method is used;the last one is with different feature spaces, which uses heterogeneous data to assist image classification. this survey introduces the three categories of algorithms to help readers better understand the current research status. Finally, in order to demonstrate the performance of different transfer learning algorithms in few-shot image classification, we conducted experiments on Office-31 and Mini-ImageNet datasets. © 2023 IEEE.

关键词： Image classification

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Song, Danming Gao, Yipeng Yan, Junkai Sun, Wei Zheng, Wei-Shi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Self-supervised contrastive learning methods have shown promising transferability in pretraining by maximizing the mutual information between two cropped regions as views from the same image. In order to effectively extract mutual information between views, the cropped regions need to be the same instance as prior hypothesis. However, the data collected in general scenes usually have multiple instances, so the two cropped regions probably contain different instances which will mislead the contrastive learning process. In this paper, we make the first attempt to exploit the spatial position relationships of the two cropped regions in self-supervised contrastive learning with images that include multiple instances. then, we propose an effective method called Space-correlated Contrastive learning (SpaceCL). Specifically, given two randomly cropped regions as contrastive pairs from the same image, we implement self-supervised contrastive learning by optimizing a space correspondence contrastive similarity loss. As a result, our method achieves state-of-the-art performance and remarkably outperforms other counterparts when pretrained on the COCO dataset of which images contain multiple instances. Experiments show our method outperforms ReSim with 2.6%AP on PASCAL VOC object detection, 0.8%AP(bb) and 0.6%AP(mk) on COCO object detection and instance segmentation, 1.3%AP(mk) on Cityscapes instance segmentation.

关键词： learning systems Representation learning Image segmentation Semantics Object detection data mining Task analysis

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LSTM Based Analysis of Sequential Usage pattern for Electrical Appliance 6

LSTM Based Analysis of Sequential Usage Pattern for Electric...

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6th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Lee, Joa Hyoung Doh, Yoonmee Heo, Tae-Wook Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9798350344349

Electrical appliances used at home are sometimes used independently, but there are also many cases where they are sequentially used in connection with other home appliances. Home appliances used sequentially and continuously have a characteristic that consumes a lot of energy and takes a long time compared to devices used alone. In this paper, we seek a way to find the correlation between home appliances that are used sequentially in succession by using LSTM, a kind of machine learning technique. Since the LSTM has the characteristic of maintaining the past value for a long period by transferring the cell value, it is possible to determine the correlation between the data used by providing a certain interval. Using these characteristics, we tried to analyze the correlation between home appliances, which correlates with the order of use in time. To verify the method, we analyzed the electric usage record in the UK with the LSTM algorithm. © 2024 IEEE.

关键词： Domestic appliances

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Deep Sparse Representation learning for Multi-class Image Classification 10th

Deep Sparse Representation Learning for Multi-class Image Cl...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Arya, Amit Soni thakur, Shreyanshu Mukhopadhyay, Sushanta Indian Inst Technol ISM Dhanbad Jharkhand India

ISBN: (纸本)9783031451690;9783031451706

this paper presents a novel deep sparse representation learning for multi-class image classification (DSRLMCC). In our proposed DSRLMCC, we use dictionary learning for sparse representation to train the deep convolutional layers to work as coding layers. the dictionary-learning algorithm uses input training data to learn an exhaustive dictionary and sparse representation. the deep sparse coding layer enforces locality constraints for activated dictionary bases to achieve high convergence. With the second deep learning layer, fine-grained components are learned, which, in turn, are shared by all atoms in the input dictionary;thus, a low-level representation of the dictionary atoms can be learned that is more informative and discriminatory. Comparing the proposed model with several prominent dictionary learning strategies and deep learning models, we found that the proposed method outperforms them. We have executed the proposed method on three prominent datasets, and the results are satisfactory.

关键词： Deep dictionary learning Multiclass classification Sparse representation Deep sparse representation

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A New Business Proposal for Attainment of the Sustainable Development Goals Based on data mining 6

A New Business Proposal for Attainment of the Sustainable De...

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6th international conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Xia, Runpeng Yamaguchi, Mae Matsumoto, Miyuki Hayashi, Hiromichi Ogawa, Reo The University of Tokyo Systems Innovation Tokyo Japan

ISBN: (纸本)9781665491259

the sustainable development goals (SDGs) are international goals aimed at achieving a sustainable and better world by 2030. As the importance of the SDGs grows these years, more and more people are becoming aware of them. However, compared to the high awareness, progress towards the SDGs in Japan is still low due to many people not putting the SDGs into practice. To address this issue, this paper adopts data from ADK General Consumer Survey 2020 and conducts analysis on it based on data mining techniques. We first conduct factor analysis on the consumption tendency of people who show low affirmations towards the SDGs, then construct a prediction model to predict people's affirmations towards the SDGs using machine learning methods. Based on these endeavors, we propose a new business aiming to encourage people to buy more sustainable products and contribute to the society's attainment of the SDGs. this paper also devises the mobile app for our proposed business and illustrates its application in reality. Although our new business mainly targets the Japanese market, it can also be introduced to other regions to promote the realization of a sustainable society. © 2023 IEEE.

关键词： data mining

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Classification of Recurrent Concepts with Metafeature-based Model Selection 24

Classification of Recurrent Concepts with Metafeature-based ...

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24th IEEE international conference on data mining Workshops, ICDMW 2024

作者： Komorniczak, Joanna Ksieniewicz, Pawel Wroclaw University of Science and Technology Pattern Recognition Team Department of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9798331530631

Processing non-stationary data streams has gained a significant research interest in the recent years. Most of the current state-of-the-art approaches in the literature exhibit hybrid solutions based on an ensemble learning paradigm, extending its parallel architecture with a drift detector. Such integrated mechanism of concept drift detection often relies on the metadescription of incoming data. the article presents a proposition of a novel hybrid classifier - the Metafeature Concept Selector. the method employs the analysis of statistical metafeatures calculated on subsequent data batches to identify the currently processed concept. the work is concluded with an extensive experimental analysis of synthetic data streams with recurrent concepts, various dimensionality and various concept change frequencies, showing that the proposed approach brings improvement when integrated with state-of-the-art base incremental learners. © 2024 IEEE.

关键词： data streams

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Understanding Cybersecurity Challenges and Detection Algorithms for False data Injection Attacks in Smart Grids 6th

Understanding Cybersecurity Challenges and Detection Algori...

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6th IFIP international conference on Internet of things, IFIP IoT 2023

作者： Zambare, Pallavi Liu, Ying Texas Tech University Lubbock United States

ISBN: (纸本)9783031458811

In Smart grid (SG), cyber-physical attacks (CPA) are the most critical hurdles to the use and development. False data injection attack (FDIA) is a main group among these threats, with a broad range of methods and consequences that have been widely documented in recent years. To overcome this challenge, several recognition processes have been developed in current years. these algorithms are mainly classified into model-based algorithms or data-driven algorithms. By categorizing these algorithms and discussing the advantages and disadvantages of each group, this analysis provides an intensive overview of them. the Chapter begins by introducing different types of CPA as well as the major stated incidents history. In addition, the chapter describes the use of machine learning (ML) techniques to distinguish false injection attacks in Smart Grids. A few remarks are made in the conclusion as to what should be considered when developing forthcoming recognition algorithms for fake data injection attacks. © 2024, IFIP international Federation for Information Processing.

关键词： Smart power grids

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Comparative Study on Handwritten Digit recognition Classifier Using CNN and machine learning Algorithms 6

Comparative Study on Handwritten Digit Recognition Classifie...

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6th international conference on Computing Methodologies and Communication, ICCMC 2022

作者： Kumari, Tanuja Vardan, Yatharth Giridhar Shambharkar, Prashant Gandhi, Yash Delhi Technological University Department of CSE Delhi India

ISBN: (纸本)9781665410281

Digit recognition is essential for interpreting image processing and pattern recognition since a machine cannot classify handwritten digits. Many real-time applications include OCR (Optical Character recognition), which recognizes characters and digitizes printed texts. Converting handwritten digits to digital characters has been a challenging problem since the past. the physical documents cannot be efficiently processed without converting them to digital copies and it requires a lot of time and efforts. To provide a solution to handwritten classification, several algorithms and techniques have been proposed over the years. the objective of this research is to use Convolutional Neural Networks (CNN), K-Nearest Neighbor, and Support Vector machine to recognize isolated handwritten digits. After implementing and training the models on the same dataset and comparing the results obtained for three different models, the results show that CNN is the most optimal machine learning technique to classify handwritten digits with an accuracy of 99.59 percent. © 2022 IEEE.

关键词： Convolutional neural networks

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Chimp Optimization Algorithm Based Feature Selection with machine learning for Medical data Classification 6

Chimp Optimization Algorithm Based Feature Selection with Ma...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Abosinnee, Ali S. Saeed Issa, Samer Alkhayyat, Ahmed Latef Naser, Zamen Jaffer, Zain Ramadan, Ghazi Mohamad Altoosi University College Najaf Iraq Al Rafidain University College Department of Computer Science Bagdad Iraq The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Technical College Baghdad Iraq Ahl Al Bayt University College of MLT Karbala Iraq

ISBN: (纸本)9798350303339

data mining is discovering interesting knowledge, namely associations, patterns, changes, significant structures, and anomalies, from enormous quantities of data stored in data warehouses, databases, or other data repositories. Association is the discovery of association correlations or relationships amongst a set of items. Associate rule mining is a method for identifying frequent patterns, correlations, associations, or causal structures in data sets found in numerous relational databases, transactional databases, and other data repositories. the algorithm performed is a fundamental process for mining association rules called a priori. In the healthcare field, association rules are useful as they offer the possibility to build important knowledge bases, conduct intelligent diagnoses, and extract invaluable information automatically and quickly. this study develops a machine learning based Association Rule mining and Classification for Healthcare data Management System (MLARMC-HDMS). the presented MLARMC-HDMS technique performs both classification and ARM processes. Initially, the MLARMC-HDMS technique employs the chimp optimization algorithm-based feature selection (COAFS) technique for attribute selection. Next, stochastic gradient descent with a multilayer perceptron (SGD-MLP) model is applied for the classification process. Moreover, the Apriori algorithm is used to determine the relationship between the attributes. To illustrate the enhanced performance of the presented MLARMC-HDMS model, a detailed experimental validation is performed on a benchmark medical dataset. the experimental values indicated the enhancements of the MLARMC-HDMS model over other methods with maximum accuracy of 99.75%. © 2023 IEEE.

关键词： Stochastic systems

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