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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 IEEE.

关键词： data quality

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Semi-supervised Fingerprint Construction and Localization System for Large Indoor Area

Semi-supervised Fingerprint Construction and Localization Sy...

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2022 International Conference on Automation, Robotics and Computer Engineering, ICARCE 2022

作者： Gao, Min Fei, Yuanyuan Wang, Zhouhong Ma, Chunming Luo, Li Guangzhou HKUST Fok Ying Tung Research Institute Digital Life Research Center Guangzhou China Data Science and Analytics Thrust Guangzhou China Jiangsu Mashi Information Technology Co. Ltd Research and Development Center Jiangsu China

ISBN: (纸本)9781665475488

Many applications of location-based indoor navigation services require precise location information of a user. While Global Positioning System (GPS) loses reliability indoors, fingerprints-based localization technology (FBLT) embodies superiority regarding accuracy and robustness. In a Bluetooth-based fingerprint localization system, a radio map is constructed offline and used as a reference for subsequent real-time localization tasks. However, the quality of the fingerprint radio map could be problematic when it comes to a large, broad space with low beacon density. data collection in such a space could be exhausting as well. Another main issue is that different mobile devices receive heterogeneous signal strength at the same location. In this article, we propose a highly practical localization system with a semi-supervised learning fingerprints construction method that provides an efficient solution for a large-scale localization system in a complex indoor environment. We also conducted a series of experiments to evaluate the performance of this system. © 2022 IEEE.

关键词： Bluetooth

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Opinion mining based crime classification model for emergency data 3

Opinion mining based crime classification model for emergenc...

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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Selvan, A. Kalai Janet, B. Bhattacharjee, Rahul Sivakumaran, N. C-DAC Trivandrum India National Institute of Technology Department of Computer Applications Tiruchirappalli India National Institute of Technology Data Analytics Tiruchirappalli India National Institute of Technology Dept. of Instrumentation and Communication Engineering Tiruchirappalli India

ISBN: (纸本)9780738111834

In India, there is a problem of reaction time in case of emergencies. Due to the problem of delayed reaction time during emergencies, it is important to discuss how we can use NLP to speed up the deployment of emergency services. This paper aims to classify a statement made by a caller to a Police Station in case of an emergency. We have four categories: Police, Ambulance, Fire Brigade, and Non-Crime. We will be analyzing the statements made by the caller during the phone call, which we will use to try to extract information and opinions regarding events that are happening, or have already occurred. This paper focuses on analyzing the statement made by the person and classify the statement, which will help to predict if the caller needs Police Assistance, Medical Assistance, or Fire Brigade Assistance, which will help in better decision making and will speed up the process of dispatching the required emergency service(s). © 2021 IEEE.

关键词： Sentiment analysis

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TabLog: test-time adaptation for tabular data using logic rules 24

TabLog: test-time adaptation for tabular data using logic ru...

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Proceedings of the 41st International Conference on Machine Learning

作者： Weijieying Ren Xiaoting Li Huiyuan Chen Vineeth Rakesh Zhuoyi Wang Mahashweta Das Vasant Honavar Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology The Pennsylvania State University PA Visa Research CA Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology and College of Information Sciences and Technology The Pennsylvania State University PA

We consider the problem of test-time adaptation of predictive models trained on tabular data. Effective solution of this problem requires adaptation of predictive models trained on the source domain to a target domain, using only unlabeled target domain data, without access to source domain data. Existing test-time adaptation methods for tabular data have difficulty coping with the heterogeneous features and their complex dependencies inherent in tabular data. To overcome these limitations, we consider test-time adaptation in the setting wherein the logical structure of the rules is assumed to remain invariant despite distribution shift between source and target domains whereas the numerical parameters associated with the rules and the weights assigned to them can vary to accommodate distribution shift. TabLog discretizes numerical features, models dependencies between heterogeneous features, introduces a novel contrastive loss for coping with distribution shift, and presents an end-to-end framework for efficient training and test-time adaptation by taking advantage of a logical neural network representation of a rule ensemble. We present results of experiments using several benchmark data sets that demonstrate TabLog is competitive with or improves upon the state-of-the-art methods for testtime adaptation of predictive models trained on tabular data. Our code is available at https://***/WeijieyingRen/TabLog.

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Architectural Design for Enhancing Remote Patient Monitoring in Heart Failure: A Case Study of the RETENTION Project

Architectural Design for Enhancing Remote Patient Monitoring...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Manta, Ourania Vasileiou, Nikolaos Giannakopoulou, Olympia Bromis, Konstantinos Kouris, Ioannis Haritou, Maria Koumakis, Lefteris Spanoudakis, George Nicolae, Irina E. Septimiu Nechifor, C. Kokkonidis, Miltiadis Vakalelis, Michalis Goletsis, Yorgos Roumpi, Maria Fotiadis, Dimitrios I. Galanis, Heraklis Dimitrakopoulos, Panagiotis Matsopoulos, George K. Koutsouris, Dimitrios D. Biomedical Engineering Laboratory Institute of Communication and Computer Systems National Technical University of Athens Athens15773 Greece Sphynx Technology Solutions AG Zug6300 Switzerland Configuration Technologies Data Analytics and Artificial Intelligence Siemens Technology Brașov500097 Romania AEGIS IT Research GmbH Braunschweig38106 Germany Biomedical Research Institute FORTH University of Ioannina Ioannina Greece Datamed SA Athens15124 Greece

This paper introduces the RETENTION Platform, an integrated healthcare data management system meticulously crafted to support personalised interventions, thereby enhancing outcomes for heart failure (HF) patients. Comprising three fundamental components—the Global Insights Cloud (GIC), the Clinical Site Backend (CSB), and Patient Edge (PE)—the platform coordinates a sophisticated array of functions. The GIC facilitates data analysis and machine learning model training, while the CSB enables daily patient check-ups, data gathering, and intervention application. The Patient Edge enables continuous monitoring and feedback collection from patients. The system is deployed using virtual machines (VMs) and Docker containers on a cloud-based infrastructure. Integration and testing procedures are outlined to safeguard system functionality. This paper provides a comprehensive overview of the RETENTION Platform's architecture and highlights its potential for improving healthcare delivery through personalised interventions. © 2024 by SCITEPRESS–Science and technology Publications, Lda.

关键词： Electronic health record

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Perception datasets for Anomaly Detection in Autonomous Driving: A Survey

Perception Datasets for Anomaly Detection in Autonomous Driv...

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IEEE Symposium on Intelligent Vehicle

作者： Daniel Bogdoll Svenja Uhlemeyer Kamil Kowol J. Marius Zöllner FZI Research Center for Information Technology Germany Karlsruhe Institute of Technology Germany University of Wuppertal Germany Interdisciplinary Center for Machine Learning and Data Analytics Germany

Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations. However, these DNN are usually restricted to a closed set of semantic classes available in their training data, and are therefore unreliable when confronted with previously unseen instances. Thus, multiple perception datasets have been created for the evaluation of anomaly detection methods, which can be categorized into three groups: real anomalies in real-world, synthetic anomalies augmented into real-world and completely synthetic scenes. This survey provides a structured and, to the best of our knowledge, complete overview and comparison of perception datasets for anomaly detection in autonomous driving. Each chapter provides information about tasks and ground truth, context information, and licenses. Additionally, we discuss current weaknesses and gaps in existing datasets to underline the importance of developing further data.

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Perception datasets for Anomaly Detection in Autonomous Driving: A Survey

arXiv

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arXiv 2023年

作者： Bogdoll, Daniel Uhlemeyer, Svenja Kowol, Kamil Zöllner, J. Marius FZI Research Center for Information Technology Germany Karlsruhe Institute of Technology Germany University of Wuppertal Germany Interdisciplinary Center for Machine Learning and Data Analytics Germany

关键词： Autonomous vehicles

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Hierarchical Classification of Adverse Events Based on Consumer’s Comments 23rd

Hierarchical Classification of Adverse Events Based on Consu...

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23rd International Conference on Computational Science, ICCS 2023

作者： Kaczorowska, Monika Szymczak, Piotr Tkachuk, Sergiy Reckitt Benckiser Group Global Data and Analytics Zajęcza 15 Warsaw00-351 Poland Polish-Japanese Academy of Information Technology Koszykowa 86 Warsaw02-008 Poland Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9783031360206

This paper focuses on autonomously classifying adverse events based on consumers’ comments regarding health and hygiene products. The data, comprising over 152,000 comments, were collected from e-commerce sources and social media. In the present research, we propose a language-independent approach using machine translation, allowing for unified analysis of data from various countries. Furthermore, this study presents a real-life application, making it potentially beneficial for subsequent scientific research and other business applications. A distinguishing feature of our approach is the efficient modeling of colloquial language instead of medical jargon, which is often the focus of adverse event research. Both hierarchical and non-hierarchical classification approaches were tested using Random Forest and XGBoost classifiers. The proposed feature extraction and selection process enabled us to include tokens important to minority classes in the dictionary. The F1 score was utilized to quantitatively assess the quality of classification. Hierarchical classification allowed for faster classification processes than the non-hierarchical approach for the XGBoost classifier. We obtained promising results for XGBoost;however, further research on a wider range of categories is required. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Modeling languages

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Quantifying the Contribution of Track Changes to Interannual Variations of North Atlantic Intense Hurricanes

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Advances in Atmospheric Sciences 2022年第2期39卷 260-271页

作者： Jun LU Liguang WU Shunwu ZHOU Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Joint Center for Data Assimilation Research and ApplicationsNanjing University of Information Science and TechnologyNanjing 210031China Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200433China Innovation Center of Ocean and Atmosphere System Zhuhai Fudan Innovation Research InstituteZhuhai 518057China

Previous studies have linked interannual variability of tropical cyclone(TC)intensity in the North Atlantic basin(NA)to Sahelian rainfall,vertical shear of the environmental flow,and relative sea surface temperature(SST).In this study,the contribution of TC track changes to the interannual variations of intense hurricane activity in the North Atlantic basin is evaluated through numerical *** is found that that observed interannual variations of the frequency of intense hurricanes during the period 1958–2017 are dynamically consistent with changes in the large-scale ocean/atmosphere *** changes can account for~50%of the interannual variability of intense hurricanes,while no significant difference is found for individual environmental parameters between active and inactive *** only significant difference between active and inactive years is in the duration of TC intensification in the region east of 60°*** duration increase is not due to the slow-down of TC *** active years,a southeastward shift of the formation location in the region east of 60°W causes TCs to take a westward prevailing track,which allows TCs to have a longer opportunity for *** the other hand,most TCs in inactive years take a recurving track,leading to a shorter duration of *** study suggests that the influence of track changes should be considered to understand the basin-wide intensity changes in the North Atlantic basin on the interannual time scale.

关键词： interannual variations intense hurricanes track changes vertical shear

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