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Temperature change between neighboring days and hospital admissions in China

Advances in Climate Change Research 2023年第6期14卷 847-855页

作者： Yu-Shu HUANG He-Jia SONG Yi-Bin CHENG Shilu TONG Rui ZHANG Xiao-Yuan YAO Yong-Hong LI China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China Deparment of Big Data in Health Science School of Public Health and Centerof Clinical Big Data and Analytics of the Second Affiliated HospialZhejiang University School of MedicineHangzhou 310058China Jiaxing Center for Disease Control and Prevention Jiaxing 314050China Instinurte of Environmenr and Population Health School of Public HealrhAnhui Medical UniversityHefei 230032China School of Public Health and Social Work Queensland University of TechnologyBrisbane 4006Australia Information Center Chinese Center for Disease Control and PreventionBeiing 102206China

The short-term temperature fluctuation caused by global climate change is one of the risk factors affecting public *** the association between temperature fluctuation and diseases,which has received relatively limited research attention,can contribute to enhancing awareness of health risks and taking timely measures for health risk alert and ***,this study aims to investigate the relationship between temperature change between neighboring days(TCN)and hospitalizations,identify diseases sensitive to extreme TCN,and evaluate the related disease *** collected meteorological and hospitalization data from 2014 to 2019 in 23 sites of China to explore the impact of TCN on *** first quantified site-specific associations between TCN and hospitalizations and then conducted meta-analysis to pool the results,to assess the relative risk of extreme TCN for susceptible diseases,and to estimate the related disease burden attributed to *** analyses were undertaken by age,sex,and disease *** showed that all-cause hospital admission was significantly linked to TCN.A negative TCN(below-1.9℃)in the cool season and a positive TCN(above 1.0℃)in the warm season increased the risk of *** aged 15-64 years,men,and patients with musculoskeletal system or connective tissue diseases were more sensitive to extremely negative TCN during the cool *** aged over 65 years,men,and patients with respiratory diseases were more sensitive to extremely positive TCN during the warm *** attributable fraction to all-cause hospitalization from negative TCN in the cool season was 2.05%(95%CI:-0.90%,4.53%)and from positive TCN in the warm season was 5.79%(95%CI:2.98%,8.31%).Circulatory diseases in the cool season and respiratory diseases in the warm season had the highest disease *** findings indicate that awareness of TCN and its health risks should be promoted and evidence-informed policies are nee

关键词： Temperature change between neighboring days Morbidity Hospital admissions Sensitive diseases Vulnerable populations Disease burden

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Implementing Parallel Computing to Enhance the Performance of K-mean Algorithm

Implementing Parallel Computing to Enhance the Performance o...

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Taha, Ranyah Alshakrani, Sara Alqaddoumi, Abdallah University of Bahrain Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology College of Science Bahrain

ISBN: (纸本)9781665416566

Clustering is a common tool in data science, and it is used in many areas, such as statistics and bioinformatics. Clustering has a number of algorithms. The most well-known is k-means, which is popular due to its simplicity and efficiency. Similarly, as a programming model, the Message Passing interface (MPI4py) improves performance by reducing the execution time and increasing the speedup. By applying the K-means clustering algorithm to the MPI4py library, the performance of the algorithm in a parallel setting improved. The efficiency of clustering data using K-means is examined in this study in terms of overhead expenditure and execution between running the K-means algorithm sequentially and running the K-means algorithms using a Message Passing Interface (MPI) parallel architecture. © 2021 IEEE.

关键词： Message passing

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Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction

arXiv

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arXiv 2024年

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an China National University of Singapore Singapore

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024, CC BY.

关键词： Extraction

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Parameter Based Literature Survey of COVID-19 Mortality Dynamics Using Machine Learning Techniques 6

Parameter Based Literature Survey of COVID-19 Mortality Dyna...

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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Sewariya, Manish Katarya, Rahul Delhi Technical University Big Data Analytics and Web Intelligence Laboratory Department of Computer Science New Delhi India

ISBN: (纸本)9781665435598

The pandemic COVID-19 (SARS-CoV-2) known as the coronavirus is spreading at a very alarming rate in the world which is quite a threatening situation. In terms of disease presentation, the global epidemic COVID-19 can be traced to the ancestral influenza viruses. COVID-19 outbreak is a very perturbing situation for the high rate of mortality as reported in recent studies. The mortality and morbidity rates have always been a concerning issue in the determination of the scale of epidemics. These mortality rate predictions subsume an interesting factor within them i.e. variance of mortality rates across regions. The advancement in machine learning-based tools is a vital component corresponding to addressing the 'heterogeneity in fatality rates'. This review envelopes the recent developments in mortality rate predictions for COVID-19. The review helps in gaining an insight into Spatio-temporal mortality dynamics which will be fruitful with reference to future control-strategy implementation. By observing the Spatio-temporal pattern of mortality dynamics, the inter-dependence of various factors can be explored, and hence, mortality rates can be reduced in the upcoming time. © 2021 IEEE.

关键词： COVID-19

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Imprints of dark matter-massive neutrino interaction in upcoming post-reionization and galaxy surveys

arXiv

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arXiv 2023年

作者： Dey, Antara Paul, Arnab Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute Kolkata700108 India School of Physical Sciences Indian Association for the Cultivation of Science Kolkata700032 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute Kolkata700108 India

We explore possible signatures of the interaction between dark matter (DM) and massive neutrinos during the post-reionization epoch. Using both Fisher matrix forecast analysis and Markov Chain Monte-Carlo (MCMC) simulation, we conduct a thorough investigation of the constraints and imprints of the scenario on the upcoming post-reionization and galaxy surveys. Our investigation focuses on two key parameters: the strength of the DM-massive neutrino interaction (u) and the total neutrino mass (Mtot), on top of the usual 6 cosmological parameters. We utilize future 21-cm intensity mapping, galaxy clustering as well as cosmic shear observations in order to investigate the possible constraints of these parameters in the future observations: Square Kilometre Array (SKA1 and SKA2) and Euclid, taking both conservative and realistic approaches. All these missions show promise in constraining both the parameters u and Mtot by few orders compared to the current constraints from Planck18 (SKA2 performing the best among them). Although we do not find much improvement in H0 and σ8 tensions from our forecast analysis, SKA2 constrains them better in conservative approach. We further perform a brief investigation of the prospects of some of the next generation Cosmic Microwave Background (CMB) missions in combinations with LSS experiments in improving the constraints. Our analysis reveals that both SKA2 and CMB-S4 + Euclid + SKA1 IM2 combination will put the strongest bounds on the model parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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Review previous studies of machine learning techniques to analyze road traffic accidents in different countries

Review previous studies of machine learning techniques to an...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Salman Alhaddad Ahmed M. Zeki MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Information System College of Information Technology University of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

RTAs are considered public health issues. For that, many countries and organizations, such as the Gulf Cooperation Council and united Nations, are trying to reduce the number of accidents caused by road traffic. This study reviews several previous studies that used machine learning algorithms to find the best models that can deal with the dataset related to RTAs, it is found the results differentiated by changing the dataset, some high results achieved with the NB algorithm, and the others achieved by using MLP and DT. This study includes more than 30 studies with more than 15 machine learning algorithms. There are several factors that affect the RTAs such as gender driver, age, type of vehicle, cause of accident, and lighting condition. The high number of algorithms used in the previous studies discussed during this study are DT, followed by RF, SVM, and NB respectively. This study provides a review to help researchers to understand the techniques used to analyze the dataset in the RTA sector.

关键词： Support vector machines Radio frequency Machine learning algorithms Sensitivity Reviews Prediction algorithms Road traffic

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FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation

arXiv

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arXiv 2023年

作者： Dhar, Mrinal Kanti Zhang, Taiyu Patel, Yash Gopalakrishnan, Sandeep Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53201 United States

This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes patients, which uses the pretrained EfficientNet-b7 as a backbone to address the issue of limited training samples. A modified spatial and channel squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed that combines additive and max-out scSE. A new arrangement is introduced for the module by fusing it in the middle of each decoder stage. As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE. A set of augmentations, comprising geometric, morphological, and intensity-based augmentations, is applied before feeding the data into the network. The proposed model is first evaluated on a publicly available chronic wound dataset where it achieves a data-based dice score of 92.70%, which is the highest score among the reported approaches. The model outperforms other scSE-based UNet models in terms of Pratt's figure of merits (PFOM) scores in most categories, which evaluates the accuracy of edge localization. The model is then tested in the MICCAI 2021 FUSeg challenge, where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet model, which takes the average of outputs obtained by FUSegNet using 5-fold cross-validation, achieves a dice score of 89.23%, placing it at the top of the FUSeg Challenge leaderboard. The source code for the model is available on GitHub. © 2023, CC BY-NC-SA.

关键词： Image segmentation

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Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

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