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Deep learning Model for Detection and recognition of Fire based on Virtual Reality Video Images 6

Deep Learning Model for Detection and Recognition of Fire ba...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Prasad, Kanakam Siva Rama Rao, N. Srinivasa Babu, T. Kishore Pranav, A. Sai Naga Bobby, Gosu Haribulla, Shaik Department of CSE Pace Institute of Technology and Sciences India Department of CSIT Pace Institute of Technology and Sciences India

ISBN: (纸本)9798350398496

Fire detection and recognition is an important aspect of fire safety, and the use of virtual reality video images and deep learning (DL) methods can help to optimize this process. Deep learning (DL) is the sub-field of machine learning (ML) which utilizes the artificial neural networks (ANN) to train and analyze predictions. these networks are more suitable for processing enormous amounts of data which is better for image recognition. Based on the fire status and immersive view, the detection and recognition of fire are detected. Deep learning algorithms can be trained using these images to recognize patterns and identify fires, smoke, and other indicators of fire. this paper introduced the new fire detection model which detects the fire from video footage and also images collected various online sources. the proposed model used the pre-trained model RESNET-50 to train the fire affected videos. To detect the fire affected region the feature extraction method Histogram of Oriented Gradients and Radial Basis Function Networks (RBFNs) used to detect the fire affectedimages. © 2023 IEEE.

关键词： Radial basis function networks

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Grassmann Graph Embedding for Few-Shot Class Incremental learning 6th

Grassmann Graph Embedding for Few-Shot Class Incremental Lea...

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6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Gu, Ziqi Xu, Chunyan Cui, Zhen Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Sch Comp Sci & EngnPCA Lab Nanjing Peoples R China

ISBN: (纸本)9789819985425;9789819985432

Few-shot class incremental learning (FSCIL) poses a significant challenge in machine learning as it involves acquiring new knosstabwledge from limited samples while retaining previous knowledge. However, the scarcity of data for new classes not only leads to overfitting but also exacerbates catastrophic forgetting during incremental learning. To tackle these challenges, we propose the Grassmann Graph Embedding framework for Few-shot Class Incremental learning (GGE-FSCIL), which effectively preserves the geometric properties and structural relationships of learned knowledge. Unlike most existing approaches that optimize network parameters in the Euclidean space, we leverage manifold space for optimizing the incremental learning network. Specifically, we embed the approximated characteristics of each class onto a Grassmann manifold, enabling the preservation of intra-class knowledge and enhancing adaptability to new tasks with few-shot samples. Recognizing that knowledge exists within interconnected relationships, we construct a neighborhood graph on the Grassmann manifold to maintain interclass structure information, thereby alleviating catastrophic forgetting. We extensively evaluated our method on CIFAR100, miniImageNet, and CUB200 datasets, and the results demonstrate that our approach surpasses the state-of-the-art methods in few-shot class incremental learning.

关键词： shot Class Incremental learning Grassmann Manifold Graph Embedding

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Research on data mining Methods in the Field of Quality Problem Analysis Based on BERT Model 8th

Research on Data Mining Methods in the Field of Quality Prob...

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8th international conference on data mining and Big data, DMBD 2023

作者： Ding, Zihuan Zhao, Guangyan Beihang University Beijing100191 China

ISBN: (纸本)9789819708369

In the current quality problem analysis work, there are problems such as large amount of data with scattered distribution, isolated data and difficult machine understanding. Most of the current data mining work in the field is based on deep learning models, which is difficult to be integrated into the characteristics of the data in the field. Also, there are still some deficiencies in its accuracy rate and training speed. therefore, this paper carries out the research of data mining methods in the field of quality problem analysis and incorporates the characteristics of data in the field on the basis of the existing model construction research. Focusing on the named entity recognition task and the relationship extraction task, the data are preprocessed by sequence annotation method to form the data sets of the two tasks. For the first task, a BERT-based recognition method is adopted, where the input is processed by word-level segmentation and the meaning features are learned. For the second task, a method also based on the BERT model is used. the models trained by the two tasks are used to achieve data mining work in the field of quality problem analysis. Comparative analysis by examples shows that the training results based on BERT model are better than those based on LSTM model in both tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data mining

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recognition of Endotracheal Suctioning Activities: A Feature Extraction and Ensemble learning Approach based on Pose Estimation data

Recognition of Endotracheal Suctioning Activities: A Feature...

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6th international conference on Activity and Behavior Computing (ABC)

作者： Hoang Khang Phan Tu Nhat Khang Nguyen Truong Vi Bui Khuong Cong Duy Nguyen Tuan Phong Nguyen Nhat Tan Le Vietnam Natl Univ Dept Biomed Engn Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Dept Comp Sci & Engn Ho Chi Minh City Univ Technol Ho Chi Minh City Vietnam

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510;9798350375503

Endotracheal suctioning is a critical yet intricate procedure in healthcare, often associated with potential complications due to its invasive nature. the escalating demand for certified professionals proficient in this technique, particularly in the context of home healthcare, underscores the necessity for comprehensive research. However, there exists a dearth of studies investigating automated recognition and analysis of nursing activities during endotracheal suctioning. this study utilizes skeleton pose data to establish correlations between nursing activities and pose dynamics, utilizing a dataset comprising 26 nurses and nurse students. Feature extraction encompassed a diverse array derived from pose characteristics, movement patterns, and activity sequences. For classification purposes, three ensemble learning pipelines were proposed, incorporating both original features and synthetic features generated by the Synthetic data Vault model. the optimal pipeline, a voting classification approach, achieved noteworthy performance metrics, attaining 89.9% and 90.4% for overall weighted F1 score and accuracy, respectively. Furthermore, ANOVA f-test results revealed the significance of statistical features such as relative position and movement in activity discernment, with sequential features significantly augmenting model efficacy. Notably, disparities in activity execution were observed between experienced nurses and students, particularly evident in high-skill invasive maneuvers during endotracheal suctioning. these findings offer valuable insights for nurse training applications and highlight the potential of automated analysis in healthcare settings.

关键词： Nurse care activity Endotracheal Suctioning Activity recognition Pose Estimation machine learning

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Identifying Users based on their Activity pattern using machine learning 23

Identifying Users based on their Activity Pattern using Mach...

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6th international conference on Electronics, Communications and Control Engineering, ICECC 2023

作者： Shin, Jungpil Al Mehedi Hasan, Md. Maniruzzaman, Md. Pattern Processing Lab School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Fukushima965-8580 Japan

ISBN: (纸本)9798400700002

Biometric technology is a very advanced security method that is difficult to duplicate. Recently, fingerprint and face recognition have become popular with the public through their practical use in unlocking smartphones. Research on activity-based biometrics using data from smartphones, smartwatches, and small sensors has been conducted. this study presents a machine learning (ML)-based approach for automatically identifying users based on their daily activity patterns. We used a publicly available human activity dataset, which was collected from eight subjects using on-body three wearable sensors (accelerometer, gyroscope, and magnetometer). First, we extract 21 time and frequency-domain features. Secondly, more efficient features were selected using a minimum redundancy-maximum relevance (mRMR)-based feature selection method. thirdly, four ML-based approaches, namely random forest, decision tree, 1-dimensional convolutional network, and extra tree (ET) were implemented for user identification. Our experimental results illustrated that the mRMR-based ET classifier produced a higher recognition accuracy rate of 99.5%. Our results indicate that our proposed method can more effectively identify users based on their daily living activities. © 2023 ACM.

关键词： Smartphones

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Rotation Augmented Distillation for Exemplar-Free Class Incremental learning with Detailed Analysis 6th

Rotation Augmented Distillation for Exemplar-Free Class Incr...

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6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Chen, Xiuwei Chang, Xiaobin Sun Yat Sen Univ Sch Artificial Intelligence Guangzhou Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Class incremental learning (CIL) aims to recognize both the old and new classes along the increment tasks. Deep neural networks in CIL suffer from catastrophic forgetting and some approaches rely on saving exemplars from previous tasks, known as the exemplar-based setting, to alleviate this problem. On the contrary, this paper focuses on the Exemplar-Free setting with no old class sample preserved. Balancing the plasticity and stability in deep feature learning with only supervision from new classes is more challenging. Most existing Exemplar-Free CIL methods report the overall performance only and lack further analysis. In this work, different methods are examined with complementary metrics in greater detail. Moreover, we propose a simple CIL method, Rotation Augmented Distillation (RAD), which achieves one of the top-tier performances under the Exemplar-Free setting. Detailed analysis shows our RAD benefits from the superior balance between plasticity and stability. Finally, more challenging exemplar-free settings with fewer initial classes are undertaken for further demonstrations and comparisons among the state-of-the-art methods.

关键词： Class Incremental learning Catastrophic Forgetting Exemplar Free

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Artificial Intelligence Techniques for the Performance Analysis of Indian Mutual Fund Industry and Market Trends 6

Artificial Intelligence Techniques for the Performance Analy...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priscila, S. Silvia Sakthivanitha, M. Yogeshwari, M. Devakirubai, T. Shirley Sirajudeen, M. Mohamed Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu 600126 India Department of Information Technology VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Data Analyst Saveetha Liberal Arts and Science College Tamil Nadu Chennai India Department of Commerce Madras Christian College Tamil Nadu Chennai India Thaloor Tamil Nadu The Nilgiris643239 India

ISBN: (纸本)9798331522667

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. this study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including data mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. the purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques. © 2025 IEEE.

关键词： Decentralized finance

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Using data mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th international conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. these motion vectors are then used to analyze facial expressions using some data mining algorithms. this analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. the facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep learning (DL), Support Vector machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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dataset Bias in Deception Detection 26

Dataset Bias in Deception Detection

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

作者： Mambreyan, Ara Punskaya, Elena Gunes, Hatice Univ Cambridge Dept Engn Cambridge England Univ Cambridge Dept Comp Sci & Technol Cambridge England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

With the advances in machine learning, lie detection technology gained significant attention. In recent years, several multi-modal techniques achieved as high as 99% accuracy results using the Real-life Trial dataset with only 121 data points. this led to considerable media hype and research interest in lie detection with machine learning. In this paper, we analyze the effect of dataset bias in deception detection. More specifically, we train a classifier to predict the sex of the identity appearing in the video. On a test data point, we use the sex predictor to predict sex which we use as a proxy for predicting deception, predicting lie for females and truth for males. this lie predictor simulates a classifier that uses nothing but dataset bias. Nevertheless, we find that the performance of this biased classifier is comparable to those of state-of-the-art papers. More specifically, when using IDT features, our biased classifier achieves 64.6% and 59.3% AUC while a classifier trained normally on truth/lie labels achieves 57.4% accuracy and 69.3% AUC. We perform similar experiments on the Bag-of-Lies dataset and show that it too is biased with respect to sex. In addition, we apply the state-of-the-art techniques on an unbiased dataset and show that their performance is no better than chance. Our experiments strongly suggest that the results of recent deception detection techniques can be explained by the bias inherent in the datasets.

关键词： Measurement Industries Ethics machine learning algorithms Government machine learning Detectors

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Speeding up Heterogeneous Federated learning with Sequentially Trained Superclients 26

Speeding up Heterogeneous Federated Learning with Sequential...

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

作者： Zaccone, Riccardo Rizzardi, Andrea Caldarola, Debora Ciccone, Marco Caputo, Barbara Politecnico Torino Turin Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. this approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, Le. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.(1)

关键词： Training Performance evaluation Federated learning Computational modeling Neural networks Statistical distributions data models

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