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Enhancing Bed Safety: Monitoring Sleeping Positions, Bed-Exits, and Falls Using Grid-EYE Infrared Array Sensors 1

引用

16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Comai, Sara Lambruschi, Matteo Ravasio, Federica Masciadri, Andrea Pomante, Luigi Salice, Fabio Politecn Milan Pzza L Vinci 32 Milan Italy Univ Aquila Pzza S Margherita 2 Laquila Italy

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Detecting bed-exit events, monitoring sleeping positions, and identifying potential falls can help ensure proper care for patients, elderly or individuals with disabilities, allowing caregivers or medical personnel to intervene promptly. This work presents a system for detecting such events employing two low-cost and low-resolution Infrared Array Grid-EYE sensors. The primary sensor, mounted on the ceiling, detects bed-exit events and identifies proper and critical sleeping positions using a neural network trained and validated on a dataset of 3000 images. The secondary sensor, positioned perpendicularly on a vertical wall, differentiates between proper bed-exit events and falls through a second neural network. The system achieved a success rate of up to 82% on a testing dataset with a total of 180 images under different conditions, demonstrating its potential for enhancing safety and well-being in smart living and healthcare settings.

关键词： Sleep Bed Falls Well-being Health Quality of life Ageing Smart Living Healthcare Safety Social Care Privacy-preserving Non-intrusive IoT

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Application of Deep Q Learning with Simulation Results for Elevator Optimization

Application of Deep Q Learning with Simulation Results for E...

引用

Intelligent systems Conference (IntelliSys)

作者： Cao, Zheng Guo, Raymond Tuguinay, Caesar M. Pock, Mark Gao, Jiayi Wang, Ziyu Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9783031477201;9783031477218

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a comprehensive list of features including capacity, acceleration, and maximum wait time thresholds to adequately model realistic circumstances. Using the same evaluation framework, we proceed to develop a Deep Q Learning model in an attempt to match a naive algorithmic approach for elevator control. Throughout the majority of the paper, we work under a Markov Decision Process (MDP) schema, but later explore how the assumption fails to characterize the highly stochastic overall Elevator Group Control System (EGCS).

关键词： Deep Q learning Optimization Simulation Markov decision process Temporal difference Elevator group control systems

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Transmission Lines Fault Identification Based on Cluster Tendency Visualization Techniques 5th

Transmission Lines Fault Identification Based on Cluster Ten...

引用

5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Guadalupe Coria-Acosta, Jose Cerda, Jaime R Fuerte-Esquivel, Claudio Solis-Ramos, Nabucodonosor Univ Michoacana Morelia 58030 Michoacan Mexico

ISBN: (纸本)9789819720521;9789819720538

In the present investigation, a clustering identification method to identify transmission lines faults is proposed. An important feature of this contribution relies in using real measurements stored in COMTRADE files of a three-phase power system, where the type of fault is not known. The VAT and iVAT algorithms are used as methods to determine if there is any pattern within the set of measurements that show if these have a clustering tendency. To this end, a strategy based on preprocessing and feature engineering is proposed to improve the clustering of faults usingkk-Means. Finally, a labeling of the dataset based on seven well-known types of electrical faults is proposed. The implemented methodology has allowed to cluster and classify the fault events using only the variances of the currents in each phase.

关键词： Computational geometry Graph theory Hamilton cycles

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Unified Deep Ensemble Architecture for Multiple Classification Tasks

Unified Deep Ensemble Architecture for Multiple Classificati...

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Intelligent systems Conference

作者： Mistry, Kody A. J. Mandal, Bappaditya Keele Univ Sch Comp Sci & Math Newcastle Under Lyme ST5 5BG Staffs England

ISBN: (纸本)9783031663284;9783031663291

Banks face regular challenges in making decisions for ever increasing need for bank loans. Most banks use applicant's financial situations, their past history, affordability checks, credit score and risk assessment, which are time consuming, challenging, tedious process and prone to errors. Although many existing machine learning algorithms are employed to extract crucial information involving pattern/behaviour of the loan applicants, significance challenges still exist. In this work, we propose a unified deep ensemble architecture for multiple classification tasks (DEAMT) solving problems that are diverse in nature with a focus on financial datasets for bank loan approval. Traditional machine learning algorithms focus on domain specific problems for classification task ignoring their generalisation capability across multiple domain applications. DEAMT is a novel architecture that uses concatenated decision trees and convolution neural networks configured in both sequential and parallel architectures for optimising multiple classification tasks across multiple domains. The proposed architecture is very versatile for both large and smaller datasets across multiple domains. Extensive experimental results, analysis and ablation studies on various diverse datasets handling various classification problems show the superiority of our proposed architecture as compared to the baseline and other popular emerging methods.

关键词： Ensemble architecture Twin network Multiple classification

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IMAGEN Data Analytics Platform for Animal Phenotype Detection 6th

IMAGEN Data Analytics Platform for Animal Phenotype Detectio...

引用

6th International Conference on Cloud Computing and Artificial Intelligence-Technologies and Applications

作者： Agarwal, Manu Dovdon, Enkhzol Hailu, Haftom Meles Barge, Luis Roma Dajsuren, Yanja de Vlieg, Jakob Eindhoven Univ Technol Math & Comp Sci Dept Eindhoven Netherlands

ISBN: (纸本)9783031786976;9783031786983

In Precision Livestock Farming (PLF), deep learning-based approaches are increasingly employed to study animal behavior on farms. These behavioral studies enable animal phenotyping, which can be used for genetic selection and social network analysis in large animal groups. While considerable attention is often given to models in the deep learning field, the models themselves do not function in isolation. Efficient deep learning workflows require systems that bridge research and prototyping with production operations. In this chapter, we introduce the IMAGEN Data Analytics Platform. This platform supports the development of deep learning models for animal phenotyping and addresses various data challenges through a DataOps approach. Although initially designed for the animal phenotype detection domain, the platform is domain-neutral and can be applied to similar cases in other application domains.

关键词： Data analytics platform Deep learning HPC Pilot-job framework DataOps Animal phenotype detection Precision livestock farming

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Approximate Solution of Newell-Whitehead-Segel Equation Using Deep Learning Method 4th

Approximate Solution of Newell-Whitehead-Segel Equation Usin...

引用

4th Congress on Intelligent systems (CIS)

作者： Kumar, Harender Yadav, Neha Natl Inst Technol Dept Math & Sci Comp Hamirpur 177005 HP India Dr BR Ambedkar Natl Inst Technol Dept Math Jalandhar 144011 Punjab India

ISBN: (纸本)9789819990429;9789819990436

In this paper, the approximation solution of the Newell-Whitehead-Segel equation (NWSE) is found by a deep learning technique. NWSE is a well-known partial differential equation (PDE) in fluid mechanics, and it depicts the dynamic behavior of dual blend fluid around the Rayleigh-Benard convection (RBC) bifurcation point of a binary fluid mixture. Deep-Galerkin Method (DGM) with GRU network is used to define a deep learning technique. The key benefit of the proposed technique is that a deep neural network that is comparable to GRU network is used that satisfies the initial conditions (ICs), boundary conditions (BCs), and differential operator (DO) without constructing a mesh. Adam optimizer is used to optimize the parameters of the DNN. The proposed experiment yielded extremely promising results when compared to recent methods such as: trigonometric cubic B-spline (TCBS), extended cubic uniform B-spline (ECBS), uniform Cubic B-spline (UCBS), and exponential B-spline collocation method (ExBSM).

关键词： Deep learning technique GRU network Newell-whitehead-segel equation Adam optimizer Rayleigh-Benard convection

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Understanding the Attitude of Teacher Education Students Toward Utilizing ChatGPT as a Learning Tool: A Quantitative Analysis 4th

Understanding the Attitude of Teacher Education Students Tow...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Fuentes, Justine O. Clorion, Frenz Djaxxas D. Abequibe, Bernadeth Valerio, Aldrin Sebastian Alieto, Ericson O. Western Mindanao State Univ Zamboanga Philippines

ISBN: (纸本)9783031686498;9783031686504

This study provides a comprehensive discussion regarding the use of ChatGPT as a learning tool among second-year students from the College of Teacher Education and will educate educational institutions, policymakers, and educators about how to effectively incorporate ChatGPT as a learning tool in CTE programs. In total of 225 respondents 183 (81.3%) females composed a large sample. The respondents of this study are pursuing courses in education, namely: Bachelor of Early Childhood Education (BECEd), Bachelor of Elementary Education (BEED), Bachelor of Secondary Education (BSED), Bachelor of Culture and Arts Education (BCAEd), and Bachelor of Special Needs Education (BSNEd). This study employed a quantitative research design. To ensure accurate data collection, the study adopted a research instrument [1] exhibiting a very good reliability score for interpretation of 0.93 Cronbach's alpha. Furthermore, the collected data were subjected to independent sample T tests and Pearson correlation. The investigation yielded a moderately positive result regarding the use of ChatGPT as a learning tool in affective, cognitive, and behavioral components of the ATUC scale. Additionally, the study revealed that there is a significant relationship between the affective and cognitive component, cognitive and behavioral component, behavioral and affective component in the ATUC scale. Last, the results show that gender has a moderate agreement on the results and does not pose any significant difference. This study suggest that ChatGPT can be a valuable learning tool for teacher education students as it can positively impact their learning outcomes.

关键词： ChatGPT Chatbots Artificial Intelligence Learning Tool Cognitive Component Affective Component Behavioral Component

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An Empirical Study on Insider Threats Towards Crime Prevention Through Environmental Design (CPTED) : A Student Case Study

An Empirical Study on Insider Threats Towards Crime Preventi...

引用

2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Beni, Robert Makila Mbaherya, Landry Mbale Kasunzi Muhau, Jean-Auguste Kuwita, Godwill Ilunga Wa Univ Nouveaux Horizons Fac Comp Sci Route Kasapa Lubumbashi DEM REP CONGO Univ Nouveaux Horizons Fac Sci & Technol Route Kasapa Lubumbashi DEM REP CONGO

ISBN: (纸本)9783031744426;9783031744433

In this research paper, we examine the framework of Crime Prevention Through Environmental Design (CPTED) to investigate the potential association between social engineers and an insider threat actors at Universite Nouveaux Horizons (UNH) in Lubumbashi, in the Democratic Republic of Congo. Students use social engineering techniques to get access by convincing their colleagues, who become insider threat actors, to share their student ID cards to get access to school premises. In comparison, social engineers employ psychological manipulation to exploit human behavior, whereas insider threat actors utilize their authorized access to carry out harmful actions. Attempting to evaluate the impact of social engineering through insider threat actors in a crime prevention through an environmental design solution by scrutinizing the organization's physical and social environment. This involves examining data from existing access control system and system monitoring practices related to information security to identify possible vulnerabilities that could be exploited. This research clarifies the mechanisms by which a social engineer can evolve into an insider threat actions and recommends preventive measures based on CPTED strategies to reduce these vulnerabilities. By integrating insights from criminology, psychology, and organizational behavior, this investigation deepens our grasp of insider threat actors and offers practical advice for enhancing organizational security. The study employs binary logistic regression analysis to explore the influence of gender, method of entry, student classification, and faculty associated on a status as threat or not. The findings reveal notable impacts of gender, entry method and student category with a significance level of 5%, while the affiliation of the faculty does not demonstrate a significant effect.

关键词： Insider threat Social engineering Organizational security Crime Prevention Through Environmental Design (CPTED) Binary Logistic Regression System Vulnerability

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Proposal to Improve Risk Management in New Product Development Projects in an Automotive Company 12th

Proposal to Improve Risk Management in New Product Developme...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Laranjeiro, Ines Tereso, Anabela Faria, Joao Univ Minho Masters Ind Engn & Management Guimaraes Portugal Bosch Car Multimedia Portugal SA Rua Max Grundig Braga Portugal Univ Minho ALGORITMI Res Ctr LASI P-4710057 Guimaraes Portugal

ISBN: (纸本)9783031602207;9783031602214

Risk Management is a crucial domain within Project Management for organizations, and it plays a fundamental role in the automotive sector, ensuring a competitive position in today's dynamic and constantly evolving environment. This research was carried out at Bosch Car Multimedia Portugal, with the main objective of analyzing and identifying areas for improvement in Risk Management practices in New Product Development projects of the company. As a result of the study, several measures were proposed to enhance and optimize the Risk Management process. The research strategy employed was a case study, incorporating various techniques for gathering the necessary information for both the theoretical foundation and the analysis of the current situation in the company, for the project under analysis. The research aimed to foster a culture of Risk Management within the project team, considering their needs, limitations, and expectations. It is important to promote Risk Management routines, ensure team involvement, and implement good practices that make the entire process effective, practical, and smooth. Following the comparative analysis between the observed reality and the theory, a model approach for Risk Management, tailored to the specific project, was proposed. Additionally, other suggestions were presented to facilitate and enhance the implementation of Risk Management practices within the company, as well as to broaden the overall knowledge of teams on the subject matter. The importance of cooperative and proactive work, the promotion of a risk-aware culture, and continuous investment in process improvement and team dynamics were highlighted.

关键词： Project Management Risk Management New Product Development

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Failure Modeling of Industrial Electric Motors Using Unsupervised Learning Methods 28th

Failure Modeling of Industrial Electric Motors Using Unsuper...

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2024 International Conference Automation

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Natl Univ Food Technol UA-01033 Kyiv Ukraine Natl Univ Life & Environm Sci Ukraine UA-03041 Kyiv Ukraine State Natl Univ Trade & Econ UA-02156 Kyiv Ukraine

ISBN: (纸本)9783031782657;9783031782664

The task of detecting breakdowns helps to improve the reliability of industrial devices through early identification of problems and their timely resolution, which helps to improve the resource and energy efficiency of production as a whole. When modeling breakdowns of industrial electric motors, the following characteristics are primarily taken into account: average load, rotational frequency, power supply voltage, current consumption, leakage current, collected from data from a traditional automated control system and breakdown logs of repair teams of an industrial enterprise. The model for detecting reliability and breakdowns of electric motors in the work is built on an anomaly detection algorithm using unsupervised machine learning methods. Its effectiveness has been successfully tested using many evaluation metrics. It can be used for a range of tasks and applications in equipment management and maintenance, visualization, forecasting and management decision support.

关键词： unsupervised learning Gaussian Mixture Model motor modelling breakdown reliability

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