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intelligent Daycare: Enhancing Child Safety with IoT and Machine learning Innovations 9

Intelligent Daycare: Enhancing Child Safety with IoT and Mac...

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9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Prathyanga, Amisha Shyaminda, Pasindu Chamikara, Pasan Lakshan, Sachin thelijjagoda, Samantha Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350377972

Child safety and well-being in daycare settings are important, thus it is vital that the persons in charge be attentive to them. this research tries to solve the problem by introducing a smart and detailed monitoring system that utilizes new technology to enhance safety and support caretakers. the system has various innovative features, such as child missing detection, behavior and facial expression analysis, cry analysis and categorization, and fall detection. Using high-resolution cameras and on-device Edge AI capabilities, it ensures real-time surveillance of children's activities while protecting privacy. the approach integrates behavior and facial expression analysis to assess emotional states, cry recognition to discover childcare needs, as well as a useful fall detection system that can tell the difference between falls and other movements. All these parts synergize to hand over the necessary information to the caregivers on time and in a way that they can act upon. the quantitative experiments in real-time childcare centers show high accuracy through all the components, attesting to the system's reliability and durability. this research has important significance in improving the safety and the frequency response in daycare centers because it gives caregivers automated tools to quickly address the needs of children. While promising, the system has drawbacks, such as potential biases in data models and a reliance on ideal environmental circumstances. Future work intends to improve flexibility across varied daycare situations, improve accuracy through larger datasets, and investigate ethical frameworks for wider application. © 2024 IEEE.

关键词： Fall detection

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A Fast Recommendation Algorithm for data Streams Based on Differential Privacy 11

A Fast Recommendation Algorithm for Data Streams Based on Di...

引用

11th international conference on Advanced Cloud and Big data, CBD 2023

作者： Guo, Chang Wang, Xiujun Zheng, Xiao Shao, Xun Mo, Lei Yang, Gaoming AnHui University of Technology School of Computer Science and Technology Ma'Anshan243032 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi4418580 Japan Anhui University of Science and Technology School of Computer Science and Engineering Huainan232001 China

ISBN: (纸本)9798350345346

Recommendation systems are an essential tools for people to access interesting information from vast amounts of data and have a wide range of applications. However, current recommendation algorithms encounter persistent challenges, including significant time overhead and limitations in safeguarding user privacy effectively. these challenges stem from their reliance on static clustering algorithms to compress data streams. To tackle these issues, we introduce the Fast Recommendation Algorithm based on Differential Privacy (FRDP) for data streams. FRDP leverages the growing learning vector quantization technique for clustering and compressing data streams in a single pass. Additionally, FRDP dynamically introduces noise based on clustering error to protect individuals' privacy while using differentially private clustering data for recommendation analysis. Finally, we compare the FRDP algorithm with the best existing recommendation algorithms based on two real datasets. Extensive experimental results demonstrate that, while maintaining similar recommendation accuracy under the same privacy protection constraints, the FRDP algorithm achieves substantial time savings, with an average reduction in running time ranging from 55.82% to 59.89%. © 2023 IEEE.

关键词： Recommender systems

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Which Features Are Useful in Machine learning-Based COVID-19 Prognostication? A Meta-Analysis 11

Which Features Are Useful in Machine Learning-Based COVID-19...

引用

11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Mahmud, Minhaz Hasan, Md Rakibul Najjar, Reabal Hossain, Md Zakir Bangladesh Army International University of Science & Technology Department of Electrical and Electronic Engineering Cumilla Bangladesh Curtin University Optus Centre for Artificial Intelligence Perth Australia National University School of Computing Australian Canberra Australia Canberra Hospital Canberra Health Services Canberra Australia

ISBN: (纸本)9798350302639

COVID-19 brings devastating impacts on society and shows how fragile our current healthcare system is. To aid the healthcare system, machine learning (ML) models have shown strong potential in combating COVID-19 across several areas, including diagnosis, complication prediction, mortality risk prediction, and severity assessment. Feature selection is an essential step in developing efficient machine-learning models. In this research, we screened 143 COVID-19 research articles, where 26 articles had enough data to calculate the effect size. We used Cohen's d to calculate the effect size of the features, which reveals several highly significant features yet explored in only a few studies and several less significant features yet explored in many studies. Lymphocyte (%) and hypersensitive c-reactive protein are comparatively less used features but showed a very large effect size. On the other hand, lymphocyte count, c-reactive protein, D-dimer, and creatinine are utilized in several studies but are not necessarily significant. Considering these high-effect-sized features and discarding the low-effect-sized features would benefit future ML model developments for combating COVID-19. © 2023 IEEE.

关键词： COVID-19

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Iterative learning Model Predictive Control for Lateral Control of Autonomous Vehicles 11

Iterative Learning Model Predictive Control for Lateral Cont...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Chen, Yanfang Li, Xuefang Sun Yat-Sen University School of Intelligent Systems Engineering Guangzhou510275 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the work proposes a novel iterative learning model predictive control method for lateral tracking control of au-tonomous vehicles. It endows the traditional model predictive controller with the ability of learning from previous experiences. In contrast to the existent iterative learning controllers that mainly concern the control performance in the iteration domain, the proposed control algorithm also takes the dynamical variations of the controlled system along the time axis to expedite the learning speed while ensuring the driving safety. To further improve the robustness of the control system under uncertain driving environments, a control-affine feedforward neural network is incorporated to the proposed controller to deal with the system un-certainties and external disturbances. the convergence of the ILMPC method is rigorously analyzed, and a numerical simulation is illustrated to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Model predictive control

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intelligent Fault Classification of a Misaligned Geared-Rotor Machine Equipped with Active Magnetic Bearings 11th

Intelligent Fault Classification of a Misaligned Geared-Roto...

引用

11th IFToMM international conference on Rotordynamics, IFToMM 2023

作者： Das, Pantha Pradip Tiwari, Rajiv Bordoloi, Dhruba Jyoti Indian Institute of Technology Guwahati Assam India

ISBN: (纸本)9783031404542

Gear can be considered as one of the most vital components in any rotating machine. Like any mechanical system, geared-rotor systems can experience various types of faults or failures. One of the most commonly experience faults is shaft misalignment, which can cause due to improper installation or wear. In recent years, machine learning and deep learning techniques have sparked great interest in accurately diagnosing geared-rotor faults. therefore, in this paper a fault detection intelligent method is implemented to predict the type of fault class provided with a labeled set of input vibration and current data. A multi-class classification artificial neural network (ANN) model is developed with statistical features extracted from time-domain vibration. the vibration dataset is built by conducting an experiment on a geared-rotor test rig where angular misalignment is deliberately introduced with four fault conditions: no misalignment and three severity levels of misalignment. Transverse vibration data are recorded with proximity probes. the set-up is also equipped with two active magnetic bearings (AMBs) mounted on the input and output rotors, which are used for vibration suppression. the control current signal running through the AMB coils and time-domain vibration signal at three different speeds are used for misalignment diagnosis. Features like mean, root mean square and entropy have been found to perform the best. the optimum tuning of the hyper parameters of the ANN model is done to achieve about 98.33% prediction accuracy. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fault detection

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Streaming-data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties 11

Streaming-Data-Driven Distributionally Robust Joint Operatio...

引用

11th international conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Li, Longyan Ning, Chao Qiu, Haifeng Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798350399660

this paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). the proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. the day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study. © 2022 IEEE.

关键词： Real time control

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MULTIVARIATE TIME-SERIES IMPUTATION WIth DISENTANGLED TEMPORAL REPRESENTATIONS 11

MULTIVARIATE TIME-SERIES IMPUTATION WITH DISENTANGLED TEMPOR...

引用

11th international conference on learning Representations, ICLR 2023

作者： Liu, Shuai Li, Xiucheng Cong, Gao Chen, Yile Jiang, Yue School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore No.6 Pingshan 1st Road Nanshan District Guangdong Shenzhen518055 China

Multivariate time series often faces the problem of missing value. Many time series imputation methods have been developed in literature. However, they all rely on an entangled representation to model dynamics of time series, which may fail to fully exploit the multiple factors (e.g., periodic patterns) presented in the data. Moreover, the entangled representations usually have no semantic meaning, and thus they often lack interpretability. In addition, many recent models are proposed to deal with the whole time series to identify temporal dynamics, but they are not scalable to long time series. Different from existing approaches, we propose TIDER, a novel matrix factorization-based method with disentangled temporal representations that account for multiple factors, namely trend, seasonality, and local bias, to model complex dynamics. the learned disentanglement makes the imputation process more reliable and offers explainability for imputation results. Moreover, TIDER is scalable to long time series. Empirical results show that our method outperforms existing approaches on three typical real-world datasets, especially on long time series, reducing mean absolute error by up to 50%. It also scales well to long datasets on which existing deep learning based methods struggle. Disentanglement validation experiments further highlight the robustness and accuracy of our model. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Time series

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Security Enhancement Using Blockchain for Internet of things 11th

Security Enhancement Using Blockchain for Internet of Things

引用

11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Dhamodaran, S. Talukdar, Veera Ramesh, J.V.N. Sardana, Kamal Talukdar, Suryansh Bhaskar Sathappan, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India RNB Global University Rajasthan Bikaner India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur Vaddeswaram India Department of Electronics and Communication Engineering Technological Institute of Textile and Sciences Haryana Bhiwani India School of Computer Science and Engineering VIT Bhopal Madhya Pradesh Bhopal India Department of Information Technology Chaitanya Bharathi Institute of Technology Andhra Pradesh Hyderabad Gandipet India

ISBN: (纸本)9789819717231

this research concentrates on the security concerns raised by the IoT. there have been many research conducted on the topic of security;however, the issue with these studies is that they are not particularly effective. In addition, the results of these examinations did not provide an acceptable level of protection. To save both time and storage space, the suggested work employs a lossless compression approach to compress data. After that, information is encrypted and stored on the blockchain. To store encrypted and compressed data over a blockchain, a hash key is first used to initialize the nonce and timestamp. then, the receiver takes the data it needs from the blockchain and decompresses and decrypts it. Outputs of simulations present hybrid blockchain mechanism that is incorporating compression and encryption, is significantly increasing the level of security. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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Preliminary Study on Unexploded Ordnance Classification in Underwater Environment Based on the Raw Magnetometry data 24th

Preliminary Study on Unexploded Ordnance Classification in...

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24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Blachnik, Marcin Ściegienka, Piotr DĄbrowski, Daniel Department of Industrial Informatics Silesian University of Technology Gliwice44-100 Poland SR Robotics Sp. z o.o. Lwowska 38 Katowice40-389 Poland

ISBN: (纸本)9783031482311

Unexploded ordnance (UXO) dumped in water reservoirs pose a serious environmental and human safety hazard. Various ways of economically solving this problem are being sought. One of them is the use of machine learning methods for the automatic classification of dangerous objects based on the recorded signals. the paper presents the preliminary results on the use of machine learning methods applied to raw magnetometry data generated in a virtual environment based on the concept of a digital twin. this introduces a different approach to a standard approach, which is based on the inverse problem, where the signals are mapped to the magnetic dipole model. Conducted research points out that the highest performance can be obtained with neural networks, and a direct classification based on the raw signals allows to achieve accuracy of up to 93% when no remanent magnetization is present. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Recognition of Tobacco Yellowing Degree in Curing Process Based on Deep learning 4

Recognition of Tobacco Yellowing Degree in Curing Process Ba...

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4th international conference on Consumer Electronics and Computer engineering, ICCECE 2024

作者： Song, Xuan Cheng, Zhao Guo, Weimin Cao, Shuzhen School of Cyberspace Security Zhengzhou University Henan Zhengzhou China Zhengzhou Tobacco Research Institute of Cntc Henan Zhengzhou China

ISBN: (纸本)9798350384062

In order to identify the Browning degree of tobacco leaves in the curing process, image acquisition equipment was installed in the curing houses of 11 tobacco producing areas in China, and the image data of tobacco leaves in curing process were obtained. the improved lightweight model EfficientNet-GECA is used for training and testing, the ordinary convolution in the MBConv module is replaced by the Ghost module, and the ECA attention mechanism is used to replace the SE attention mechanism, which reduces the amount of parameters and calculation of the model and improves the running speed. the recognition accuracy of the improved model in the test set reaches 88.74%, which is 1.61 percentage points higher than that before improvement. Compared with VGG16, ResNet50 and other models, the accuracy is increased by 9.76%and 5.27%respectively. the number of model parameters is 2.97M, which is only 56.14%and 11.62%of EfficientNet and ResNet-50, and the recognition speed of a single image is only 16.86ms. the improved EfficientNet-GECA model can recognize the Browning degree of tobacco in the baking process, which provides important support for the development of intelligent tobacco baking. © 2024 IEEE.

关键词： Tobacco

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