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Bayesian Causal Mediation Analysis with Longitudinal data 7th

Bayesian Causal Mediation Analysis with Longitudinal Data

引用

7th international conference on smart computing and Communication, smartCom 2022

作者： Zhang, Yu Yang, Lintao Liu, Fuhao Zhang, Lei Zheng, Jingjing Zhao, Chongxi Central China Normal University Wuhan430079 China Wuhan Fiberhome Technical Services Co. Ltd. Wuhan430073 China

ISBN: (纸本)9783031281235

Mediation analysis was concerned with the decomposition of the total effect of exposure on the outcome into the indirect effects and the remaining indirect effects, through a given mediation. However, when longitudinal data including time varying exposure and mediator variables, the estimated causal effects are affected by time varying confounders. Standard generalized linear equations did not give unbiased estimates. In this paper, we introduced inverse probability weighting technique to adjust such time varying confounders. Considering that the amount of data may be small and the distribution is not uniform, we decide to use Bayesian Inference to estimate the Structural Equation Model (SEM) parameters, and finally estimates the causal effect through counterfactual thought. This paper summarized the relevant theoretical knowledge of this method, verified the feasibility of this method by using the simulated data, and compared the performance of different methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Inference engines

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smart Signaling: A smart Internet of Things Assisted Traffic Light Controlling and Monitoring System using intelligent Sensors

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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international conference on Science Technology Engineering and Management (ICONSTEM)

作者： G. Saranya R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability.

关键词： System performance Urban areas Control systems Real-time systems Sensor systems Safety Internet of Things

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Machine Learning Platform for Extreme Scale computing on Compressed IoT data

Machine Learning Platform for Extreme Scale Computing on Com...

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2022 IEEE international conference on Big data, Big data 2022

作者： Tirupathi, Seshu Salwala, Dhaval Zizzo, Giulio Rawat, Ambrish Purcell, Mark Kejser Jensen, Soren Thomsen, Christian Ho, Nguyen Muniz Cuza, Carlos E. Brusokas, Jonas Bach Pedersen, Torben Alexiou, Giorgos Giannopoulos, Giorgos Gidarakos, Panagiotis Kalimeris, Alexandros Maroulis, Stavros Papastefanatos, George Psarros, Ioannis Stamatopoulos, Vassilis Terrovitis, Manolis Ibm Research Europe Dublin Ireland Aalborg University Denmark Athena Research Center Greece

ISBN: (纸本)9781665480451

With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated and analyzed, especially in IoT domains like energy and smart homes. Consequently, applications that require accurate short-term forecasts and predictions are also steadily increasing. In this paper, we provide an overview of a novel end-to-end platform that provides efficient ingestion, compression, transfer, query processing, and machine learning-based analytics for high-frequency and high-volume time series from IoT. The performance of the platform is evaluated using real-world dataset from RES installations. The results show the importance of high-frequency analytics and the surprisingly positive impact of error bounded lossy compression on machine learning in the form of AutoML. For example, when detecting yaw misalignments in wind turbines, an improvement of 9% in accuracy was observed for AutoML models on lossy compressed data compared to the current industry standard of 10-minute aggregated data. Thus, these small-scale experiments show the potential of the platform, and larger pilots are planned. © 2022 IEEE.

关键词： Big data

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Query-driven Edge Node Selection in Distributed Learning Environments

Query-driven Edge Node Selection in Distributed Learning Env...

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international conference on data Engineering Workshops

作者： Tahani Aladwani Christos Anagnostopoulos Kostas Kolomvatsos Ibrahim Alghamdi Fani Deligianni School of Computing Science University of Glasgow UK Dept. of Informatics and Telecommunications University of Thessaly GR Dept. of Computer Science Faculty of Science and Arts in Baljurashi Al-Baha University SA

computing nodes in Edge computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

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Framework of smart Regulation System for Large-Scale Power Grid Based on Big data and Artificial Intelligence

Framework of Smart Regulation System for Large-Scale Power G...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Cai, Xinlei Cui, Yanlin Dong, Kai Meng, Zijie Pan, Yuan Electric Power Dispatching and Control Center of Guangdong Power Grid Guangzhou510600 China

ISBN: (纸本)9783030627423

In order to effectively improve the operating environment of large-scale power grid and effectively monitor the safety of power grid, this paper analyzes the concept of smart regulation system for large-scale power grid based on big data and artificial intelligence (AI) in the new era environment of smart operation, then analyzes problems existing in the current dispatching system of large-scale power grid in China, as well as the specific needs of smart dispatch system in the design process. And the corresponding smart control system is designed to effectively optimize and improve the operation environment of power grid, and upgrade and improve the core technology of smart dispatching system of power grid. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Real-Time Traffic Flow Optimization using Adaptive IoT and data analytics: A Novel DeepStreamNet Model

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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Sustainable Expert Systems (ICSES), international conference on

作者： V.S. Saranya Garimidi Subbarao Dega Balakotaiah Maloth Bhavsingh K. Suresh Babu Srinivasa Rao Dhanikonda Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Department o Computer Science and Engineering (AIDS) Pace Institute of Technology and Sciences Department of Computer Science and Engineering Vignan's Founadation for Science Technology & Research (Deemed to be University) Department of Computer Science and Engineering Ashok: Womens Engineering College Kurnool Andhra Pradesh Department of Computer Science and Engineering Narasaraopeta Engineering College(Autonomous) Narasaraopet A.P Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education (Deemed to Be University) Hyderabad Telangana India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts

关键词： Adaptation models Analytical models Accuracy smart cities Urban planning data models Real-time systems Internet of Things Optimization Context modeling

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Ovarian Cancer Prediction using Machine Learning

Ovarian Cancer Prediction using Machine Learning

引用

smart Generation computing, Communication and Networking (smart GENCON), international conference on

作者： Yashika Agnihotri Tarun Jain Vijay Shankar Sharma Department of Computer Science and Engineering Manipal University Jaipur Department of IOT & Intelligent Systems Manipal University Jaipur

Ovarian cancer is the type of cancer that has the highest recurrence rate in women and poses a serious threat to women. Due to the lack of observable signs, this quiet invader often goes undetected in the beginning, leaving women susceptible to its sneaky development. Once it has advanced and its symptoms are obvious, it becomes arduous to treat. Machine learning is emerging as a ray of hope in the healthcare scene in the current era of rapid technological innovation. Machine learning provides a promising path for the early diagnosis and prediction of ovarian cancer by harnessing the power of algorithms and data analytics, perhaps turning the tide against this tough enemy. The dataset [17] comprises of 349 instances and 49 features. The goal here is to derive a model that helps in classification of instances into two classes: Benign Ovarian Tumors (BOT) and Ovarian Cancer (OC). Therefore, methodical machine learning algorithms are employed to detect ovarian cancer. Our research findings revealed that out of all the classifiers, the Random Forest Model has the highest accuracy (91.43 %). This approach is capable of accurately classifying BOT and OC and could have immense effect on diagnosis of patients, leading to better treatments and higher survival rate.

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TL-AGRO: Transfer Learning-based Perilous Insect Detection Framework for smart Agriculture Underlying Edge computing

TL-AGRO: Transfer Learning-based Perilous Insect Detection F...

引用

intelligent Technologies (CONIT), international conference on

作者： Fenil Ramoliya Krisha Darji Riya Kakkar Rajesh Gupta Sudeep Tanwar Smita Agrawal Department of Comp. Sci. & Engg Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Remote sensing (RS) technology has revolutionized agriculture by harnessing advanced satellites with exceptional temporal and spatial resolution capabilities. These satellites capture Earth’s emitted energy and employ specialized sensors for data collection. Pre-processing techniques refine this data, which is seamlessly integrated into the Edge Device TL-AGRO AI system, transforming precision agriculture. Simultaneously, Unmanned Aerial Vehicles (UAVs) have become vital tools, capturing high-resolution data for plant ecology and Perilous farm insect detection. Our proposed framework TL-AGRO is an advanced agricultural analytics method that leverages meticulously preprocessed data to make precise predictions. It detects insects on agricultural properties and provides accurate classification using CNN Architectures, such as Visual Geometry Group 16 (VGG16), Xception Version 2 (XceptionV2), Residual Network 50 Version 2 (ResNet50V2), and Mobile Network Version 2 (MobileNetV2). Integration of coordinated alert system offers timely recommendations and warnings, empowering farmers to make informed decisions and mitigate insect-related risks. TL-AGRO can even identify individual insect infestations and recommend suitable pesticides, revolutionizing farming with an integrated framework that enhances productivity and sustainability. Performance evaluation of TL-AGRO includes various metrics such as accuracy and loss curves, confusion matrix, F1-score, recall and precision, demonstrating its effectiveness in advancing smart agriculture and ecological studies.

关键词： smart agriculture Visualization Accuracy Satellites Insects Transfer learning Computer architecture

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Enhancing Road Safety with I2V Communication with LoRa & IoT Technology

Enhancing Road Safety with I2V Communication with LoRa & IoT...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： RajeshKumar C Monisha P Priyadharshini M Siva Dhayanithi T Information Technology Sri Krishna College of Technology

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The rise in road accident numbers demonstrates a pressing demand for novel approaches that will bring improvements to road safety systems. Infrastructure-to-Vehicle (I2V) communication presents one solution which supports data exchanges between road systems and vehicles to deliver needed information. The research evaluates the feasibility of creating a dependable Infrastructures-to-Vehicle communication network for immediate safety messages using LoRa (Long Range) technology together with Internet of Things architecture. Because of its ability to connect over long distances with minimal power usage LoRa stands out as a perfect option for systems that transmit vehicle communications. This research introduces an IoT structure based on LoRa for delivering road conditions and traffic reports to vehicles which supports driver actions to avoid potential hazards. The system proposal leads to more prompt and dependable safety alerts which contributes to decreased accident rates according to simulation results. The study shows that implementing LoRa and IoT together creates highly scalable smart transportation solutions which promise improved road safety. The next phase of research needs to advance system optimization for broad deployment and analyze its practical performance in live traffic environments.

关键词： Temperature sensors LoRa Road safety Sensor systems Hazards smart transportation Predictive analytics intelligent sensors Optimization Vehicles

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A practical adversarial attack on Graph Neural Networks by attacking single node structure 24

A practical adversarial attack on Graph Neural Networks by a...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/smartCity/DependSys 2022

作者： Chen, Yang Ye, Zhonglin Zhao, Haixing Meng, Lei Wang, Zhaoyang Yang, Yanlin School of Computer Science Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai810008 China

ISBN: (纸本)9798350319934

In recent years, Graph Neural Networks (GNNs) have performed significantly in node classification tasks. Re-search on GNNs adversarial attacks has recently attracted attention. Many works have demonstrated that single node attacks can fool GNNs in extreme scenarios. Existing single node attacks can be divided into two main types: 1) attacking the node features;2) injecting a single fake node. However, they have several major drawbacks: 1) they set the attack node features, which makes them not efficient;2) they require higher privileges and don't have strict budget constraints, which makes them less concealable and easier to notice. In this paper, we propose an attack with lower privileges and strict budget constraints -Single Node Structure Attack (SNSA). Specifically, SNSA is set more efficient structure attacks and does not require higher privileges to change links. Compared to other attack models, the attack space of SNSA is small and not easily detected by GNNs. Furthermore, we study the influence of the attack node selection on SNSA and observe that it is optimal to use a gradient to select an attack node. Experiments on three common datasets demonstrate the effectiveness of our model. © 2022 IEEE.

关键词： Graph neural networks

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