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GraphSAGE-Based Generative Adversarial Network for Short-Term Traffic Speed Prediction Problem

GraphSAGE-Based Generative Adversarial Network for Short-Ter...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Han Zhao Ruikang Luo Bowen Yao Yiyi Wang Shaoqing Hu Rong Su School of Electrical and Electronic Engineering Nanyang Technological University Singapore Lee Luan Yew School of Public Policy National University of Singapore School of Media and Communication Coventry University School of Computer Science Beijing Universitiy of Posts and Telecommunications China

ISBN: (纸本)9781665476881

Traffic speed prediction is a significant branch of the intelligent transportation system (ITS). A good prediction could alleviate the non-recurring congestion on the road and provide a strong decision-making basis for traffic management and control. However, it is always a challenging research problem due to the complexity of the road network and the dynamics of traffic conditions. Many deep learning-based methods have been applied to the traffic prediction problem, which could extract both spatial and temporal information efficiently. However, for some dataset that suffers from data paucity problem, the generalization ability of the model is not good and the performance degrades. To tackle this problem, we proposed a novel graph-based generative adversarial network for the traffic speed prediction problem. We design a generative network to generate some fake traffic data and use a discriminative network to distinguish between real and fake targets. The generator consists of a GraphSAGE and LSTM model to learn the representation of spatial-temporal traffic data. Several experiments have been conducted on several real-world traffic datasets, demonstrating that our proposed model outperforms other baseline models. The experiment results illustrate the importance of utilizing GAN in the training process, which improves the generalization ability of the prediction model.

关键词： Training Roads Predictive models Generative adversarial networks Feature extraction Data models Generators

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Role of Wireless Communications in Digital Economy in the Present Context 6

Role of Wireless Communications in Digital Economy in the Pr...

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6th International Conference on Trends in Electronics and Informatics, ICOEI 2022

作者： Revathi, S. Shrivastava, Arpan Yussupova, Ardak Hidayatulloh, A. Nururrochman Saltanat, Daurbekova Mishra, Arpana Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Management Devi Ahilya Vishva Vidyalaya Madhya Pradesh India Kazakh National University of Arts Department of Art Studies Nur-Sultan Kazakhstan Universitas Gadjah Mada Leadership and Policy Innovation Program Indonesia International Education Corporation Department of Construction Almaty050042 Kazakhstan Sharda University Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9781665483285

Wireless communication has connected millions of people across the world to internet to enable them to reap benefits of digital economy in present scenario. Almost all the sectors of economy including healthcare, agriculture and transportation nowadays rely largely upon wireless communications. In the present context, the sudden outbreak of Covid-19 has caused governments to impose restrictions on various activities and employees have been directed to work from their homes to ensure safety. In such a situation, wireless communication has made it possible to connect with each other via the internet. Apart from this, Smart healthcare-system has been developed through utilisation of wireless communication systems. During this pandemic situation, delivery of healthcare services in remote areas has gained huge momentum and wireless communication plays a vital role in this regard. Aim of current critical assessment is to provide an in-depth analysis of the role played by wireless communication in establishment of a well-structured digital economy in present scenario. In literature-review, different applications of wireless technology via ICT in various sectors and aspects of life have been discussed through a conceptual and a theoretical framework. The Method section of current study has provided an in-depth insight into the ways required information have been collected in this study. As found in the results and discussion section, a positive relation exists between wireless-communication and establishment of digital economy in current situation. This study has concluded that there are certain challenges associated with wireless communication;however, it has significant influence in making processes smarter and faster as compared to earlier. © 2022 IEEE.

关键词： Internet of things

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Evaluating the Transferability of a Bistatic Radiative Transfer Model to Monostatic Scattering for Wheat Crop with Sentinel-1 SAR Dual Polarimetric Data

Evaluating the Transferability of a Bistatic Radiative Trans...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Sumana Khamrai Rajendra Prasad Suraj A Yadav Shubham Kumar Singh Gulab Singh Prashant K. Srivastava Muskan Dua Dept. of Physics Indian Institute of Technology (BHU) Varanasi India Electrical and Computer Engineering Department Mississippi State University USA Dept. of Urban and Environmental Policy and Planning Tufts University USA Centre of Studies in Resources Engineering Indian Institute of Technology Bombay India Dept. of Earth and Atmospheric Sciences IESD Banaras Hindu University India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The study presents a first-order Radiative Transfer model for monostatic scattering by integrating a modified Oh model for soil surface scattering and radiative transfer model with higher order interaction terms for volumetric scattering. The approach aims to improve crop signature analysis for monitoring and retrieval purposes of biophysical parameters, particularly soil moisture (SM) and leaf area index (LAI) in our case, with the use of Sentinel-1 satellite dual-polarized SAR data. The SAR data (GRD and SLC), along with the in situ LAI and SM data from February to April 2024, has been used for training and testing purposes of the model. The Oh model shows the surface scattering part, and it has been modified by the incorporation of attenuation factor and one soil-scaling factor fsoil. Similarly, the volumetric scattering part has been described for the vegetation components, along with the 1st order interaction terms from the soil-veg, veg-soil double bounce scattering has been used in this model. Least-square optimization technique has been used to calibrate the scattering albedo and the loss factor present in the attenuation term. Finally, the inversion algorithm to retrieve the soil moisture data, has been done again with the Look-up Table (LUT) approach and the least square technique again for the sake of simplicity. The backscattering coefficient retrieval performance metric for VV polarization ($\mathrm{R}=0.8922$; $\mathrm{RMSE}=$ 0.6126) shows a better correlation with RMSE slightly higher when compared with VH polarization ($\mathrm{R}=0.8745$; RMSE = 0.4150). However, for soil moisture, the VH ($\mathrm{R}=\mathbf{0. 6 0 7 4}$; RMSE =0.1065) polarization shows better results than $\mathrm{VV}(\mathrm{R}=0.4169$; RMSE = 0.1576).

关键词： Biological system modeling Soil moisture Scattering Sentinel-1 Vegetation mapping Crops Data models Table lookup Optimization Backscatter

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Combining Context-Awareness and Data Analytics in Support of Drone Technology 1

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11th International Conference on Telecommunications and Remote Sensing, ICTRS 2022

作者： Shishkov, Boris Ivanova, Krassimira Verbraeck, Alexander van Sinderen, Marten Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria Faculty of Information Sciences University of Library Studies and Information Technologies Sofia Bulgaria Institute IICREST Sofia Bulgaria Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (数字)9783031232268

ISBN: (纸本)9783031232251

Drones performing an autonomous mission need to adapt to frequent changes in their environment. In other words, they have to be context-aware. Most current context-aware systems are designed to distinguish between situations that have been pre-defined in terms of anticipated situation types and corresponding desired behavior types. This only partially benefits drone technology because many types of drone missions can be characterized by situations that are hard to predict at design time. We suggest combining context-awareness and data analytics for a better situation coverage. This could be achieved by using performance data (generated at real-time) as training data for supervised machine learning – it would allow relating situations to appropriate behaviors that a drone could follow. The conceptual ideas are presented in this position paper while validation is left for future work. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data Analytics

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Unlocking the Power of EHRs: Harnessing Unstructured Data for Machine Learning-based Outcome Predictions

Unlocking the Power of EHRs: Harnessing Unstructured Data fo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Noaeen Somayeh Amini Shveta Bhasker Zohreh Ghezelsefli Aisha Ahmed Omid Jafarinezhad Zahra Shakeri Hossein Abad Harvard Business School USA Dalla Lana School of Public Health University of Toronto Canada Department of Psychology University of Toronto Canada Department of Computer Engineering Sharif University of Technology Iran Institute of Health Policy Management and Evaluation Dalla Lana School of Public Health University of Toronto Canada

The integration of Electronic Health Records (EHRs) with Machine Learning (ML) models has become imperative in examining patient outcomes due to the vast amounts of clinical data they provide. However, critical information regarding social and behavioral factors that affect health, such as social isolation, stress, and mental health complexities, is often recorded in unstructured clinical notes, hindering its accessibility. This has resulted in an over-reliance on clinical data in current EHR-based research, potentially leading to disparities in health outcomes. This study aims to evaluate the impact of incorporating patient-specific context from unstructured EHR data on the accuracy and stability of ML algorithms for predicting mortality, using the MIMIC III database. Results from the study confirmed the significance of incorporating patient-specific information into prediction models, leading to a notable improvement in the discriminatory power and robustness of the ML algorithms. Furthermore, the findings underline the importance of considering non-clinical factors related to a patient’s daily life, in addition to clinical factors, when making predictions about patient outcomes. The advent of advanced generative models, such as GPT-4, presents new opportunities for effectively extracting social and behavioral factors from unstructured clinical notes, further enhancing the accuracy and stability of ML algorithms in predicting patient outcomes. The results of our study have significant ramifications for improving ML in clinical decision support and patient outcome predictions, specifically highlighting the potential role of generative models like GPT-4 in advancing ML-based outcome predictions.

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Use of smart thermostat data to investigate harmful exposure to heat in Calgary, Canada

Use of smart thermostat data to investigate harmful exposure...

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International Workshop on Secure Internet of Things (SIoT)

作者： Júlia Perassolli de Lázari Arlene Oetomo Paula Dornhofer Paro Costa Plinio Pelegrini Morita School of Electrical and Computer Engineering (FEEC) State University of Campinas (UNICAMP) Campinas Brazil School of Public Health and Health Systems University of Waterloo Waterloo Canada

With the increase in temperatures and extreme heat events, developing a better strategy to assess and quantify heat exposure indoors is crucial. Although people spend most of their time indoors, most studies and alert systems focus on outdoor temperatures. The growing adoption of smart thermostats in home heating, ventilation, and air conditioning (HVAC) systems offers an opportunity to study the relationship between indoor and outdoor temperatures. The present study uses indoor smart thermostats and weather station data to investigate heat exposure metrics for indoor and outdoor temperatures. We also analyzed the percentage of time indoor temperatures are within the ASHARE acceptability limit. We found that houses spend around 5% of the time above the ASHRAE limit and that indoor heat exposure metrics are higher than outdoor metrics, meaning greater harmful exposure to human health during extreme events like heatwaves.

关键词： Heating systems Measurement Air conditioning Temperature distribution HVAC Ventilation Social Internet of Things

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MABSplit: faster forest training using multi-armed bandits 22

MABSplit: faster forest training using multi-armed bandits

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Mo Tiwari Ryan Kang Je-Yong Lee Sebastian Thrun Chris Piech Ilan Shomorony Martin Jinye Zhang Department of Computer Science Stanford University Oxford University Electrical and Computer Engineering University of Illinois at Urbana-Champaign Department of Epidemiology Harvard T.H. Chan School of Public Health

ISBN: (纸本)9781713871088

Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget.

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The impact of different ethical frameworks underlying a robot’s advice on charitable donations

The impact of different ethical frameworks underlying a robo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Boyoung Kim Ruchen Wen Qin Zhu Tom Williams Elizabeth Phillips Center for Security Policy Studies-Korea George Mason University Korea Incheon South Korea Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Department of Engineering Education Virginia Tech Virginia USA Department of Computer Science Colorado School of Mines Colorado USA Department of Psychology George Mason University Virginia USA

The current work explored to what extent a robot could persuade people to participate in charitable giving by offering moral advice grounded in different ethical theories. In a laboratory, participants, who are students at a university, first performed a task to acquire lottery tickets and then received from a robot information about a charity event organized for students at their university. The robot also offered them moral advice of which the underlying framework was grounded in either deontological or Confucian role ethics to encourage donating their lottery tickets to the event. We found advice grounded in Confucian role ethics to be more effective in inducing donations than advice grounded in deontological ethics. We also found that the more strongly participants felt close to other students at their university, the less donations they would make after receiving advice grounded in deontological ethics. These findings suggest the benefits of framing moral messages of robots based upon theories of Confucian role ethics in promoting prosocial behavior. We discuss potential explanations for the negative relationship between participants’ sense of closeness with other students and their donation behavior when the robot’s advice focuses on theories of deontological ethics.

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Post COVID-19 Intelligent public Healthcare Management

Post COVID-19 Intelligent Public Healthcare Management

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Bristy, Anika Tabassum Ferdousi, Rahatara Hossain, Arman Islam, M M Faniqul Hossain, M. Anwar El Saddik, Abdulmotaleb North South University Pharmaceutical Sciences Bangladesh Canada North South University Public Health Bangladesh Queen Mary University of London Global Public Health Policy United Kingdom King Saud University College of Computer Information Sciences Department of Software Engineering Saudi Arabia

ISBN: (纸本)9781665423908

Epidemic outbreaks are collective effects of ongoing globalization, urbanisation, population mobility, climate change, demographic change and evolution of newer strains of infectious agents that result in high morbidity, mortality and huge financial loss, such as COVID-19. Thus, the early prediction of the emergence of a disease can play a pivotal role to prevent a disease to become epidemic. The Edge AI based solution has been proposed for healthcare prediction using machine learning (ML). In this paper, our focus is to propose ML based advanced model for public healthcare to reduce and control epidemic outbreaks. Collective knowledge from interconnected disciplines, shared data repository, and diverse roles have been embedded into the proposed framework. An evaluation based on actual COVID-19 related data demonstrates that ML can be used for COVID risk prediction for public health data as well as to take preventive steps to combat epidemics in early-stage. © 2021 IEEE.

关键词： COVID-19

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Vessel's trim optimization using IoT data and machine learning models 13

Vessel's trim optimization using IoT data and machine learni...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Panagiotakopoulos, Theodor Filippopoulos, Ioannis Filippopoulos, Christos Filippopoulos, Evangelos Lajic, Zoran Violaris, Antonis Chytas, Sotirios Panagiotis Kiouvrekis, Yiannis Hellenic Open University School of Science and Technology Greece University of Nicosia Business School Cyprus Cyprus International Institute of Management Nicosia Cyprus Infralabs Ltd Nicosia Cyprus Angelicoussis Group Athens Greece University of Thessaly Department of Electrical and Computer Engineering Greece University of Thessaly Department of Public and One Health Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

The shipping industry is an important source of greenhouse gas emissions, such as carbon dioxide, methane and nitrogen oxides. In the past few years, environmental and policy reasons dictate the immense reduction of greenhouse gas emissions in industries worldwide. Towards this direction, the shipping industry has focused on ship trim optimization in the last few years as an operational measure for better energy efficiency and thus a way to reduce consumption and energy-related emissions. In this paper, we present a machine learning solution to the problem of trim optimization. Specifically, we use Internet of Things (IoT) data for speed, draft, and trim in order to accurately predict shaft power. After our machine learning model is trained, we use its predicting capabilities to create the shaft power surface as part of the trim monitoring user interface of the maritime company infrastructure. © 2022 IEEE.

关键词： Machine learning

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