The proceedings contain 41 papers. The topics discussed include: prediction and mapping of air pollution in Bandung using generalized space time autoregressive and simple kriging;neural network modeling on wave dissip...
ISBN:
(纸本)9781728182353
The proceedings contain 41 papers. The topics discussed include: prediction and mapping of air pollution in Bandung using generalized space time autoregressive and simple kriging;neural network modeling on wave dissipation due to mangrove forest;modeling traffic flow on Buah Batu exit toll gate using cellular automata;exploration-exploitation balanced krill herd algorithm for thesis examination timetabling;principal component analysis to determine main factors stock price of consumer goods industry;analysis of adversarial attacks on skin cancer recognition;twitter sentiment analysis: case study on the revision of the Indonesia’s corruption eradication commission (KPK) law 2019;instantaneous height of sea surface: a comparison between local field observation and the simulated level from global models;and big data analytics for processing real-time unstructured data from CCTV in traffic management.
With the global health crisis of breast cancer, which is expected to affect about 2.3 million people by 2020, it is clear that there is increasing need for developing early detection tools. This study has achieved 98....
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This proceedings contains 14 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. The conference topics include: on graph process...
This proceedings contains 14 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. The conference topics include: on graph processing, road networks, and database systems on new hardware. It also features on knowledge basis and data integration and proposes new probabilistic models over databases that can capture important database characteristics while the other studies impact of GDPR on database systems. It also advances the state-of-the-art in time series processing and another studies queries over probabilistic preference databases, etc. The key terms of this proceedings include structured datasets, web-scale graphs, demand-aware route planning, data migration, graph storage system, KBPearl, compressed time series, traversing large graphs, entity and relation linking, probabilistic preferences.
The smart cities paradigm covers multiple domains which span from citizens' accessibility and mobility to general infrastructures and services. Hence, smart cities can be seen as an excellent showcase of heterogen...
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ISBN:
(纸本)9781665493130
The smart cities paradigm covers multiple domains which span from citizens' accessibility and mobility to general infrastructures and services. Hence, smart cities can be seen as an excellent showcase of heterogeneity, namely at the data level. For this reason, they are a perfect candidate for linked data and semantic web concept applications. This powerful combination leads to interoperability at the data level which is one of the ultimate goals of the Internet of Things (IoT). In this reference frame, NGSI-LD is an open framework for context information processing consisting of both a semantic information model and a RESTful Application Programming Interface (API). This paper proposes a methodology for creating semantic datamodels in the context of IoT, namely to represent and describe data associated with digital twins. The methodology is presented in a practical way, through the process of creating an NGSI-LD semantic data model for the VALLPASS project, inserted in the traffic domain, which is one of the most popular in smart cities.
In this paper, the algorithm of late factor model is used to calculate the satisfaction degree of the target user to the product to be tested by using the known conditions obtained from big data, so as to design the m...
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New trends in digitalization in construction have created opportunities for research and informed decision-making. Concepts like digital twins and sensorization have successfully enabled the direct collection of data ...
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New trends in digitalization in construction have created opportunities for research and informed decision-making. Concepts like digital twins and sensorization have successfully enabled the direct collection of data from construction processes and equipment. For instance, integrating sensors into trucks transporting construction materials facilitates gathering valuable information about the equipment and the surrounding environment. This previously unattainable data can now be utilized to provide pertinent insights into the decision-making process. On one hand, accurate fuel consumption estimations are required to help optimization in construction and transportation infrastructure projects as they represent a major expense. On the other hand, despite the numerous studies conducted to detect cracks and potholes in road pavements, the classification of road types is frequently overlooked. This study aims to bridge this gap by developing a methodological framework that utilizes vibration data from sensors installed in construction trucks to predict the fuel consumption of heavy vehicles and the road category based on the pavement surface quality through which it is circulated. Given their promising results in prior research, the models Random Forest, Neural Network, and Support Vector Machine were applied to the database. The results demonstrate that vibration-based data acquisition methods combined with machine learning algorithms can accurately predict fuel consumption, identify different road categories, and can be successfully applied on a larger scale.
The proceedings contain 103 papers. The topics discussed include: artificial intelligence cyber security strategy;legal regulation of cyberbullying — from a Chinese perspective;cyber-based intelligent route planning ...
ISBN:
(纸本)9781728166094
The proceedings contain 103 papers. The topics discussed include: artificial intelligence cyber security strategy;legal regulation of cyberbullying — from a Chinese perspective;cyber-based intelligent route planning with multi-constraints;a neural embedding for source code: security analysis and CWE lists;feature extraction approach to unearth domain generating algorithms (DGAs);a survey of key issues in UAV data collection in the Internet of things;a new dynamic conditional proxy broadcast re-encryption scheme for cloud storage and sharing;efficiency optimization in supply chain using RFID technology;and executing complex calculations in the cloud to enable real-time dataprocessing.
The paper addresses the use of an adaptive, recursive fuzzy modeling based on the notion of level set to forecast monthly streamflows of a major hydroelectric power plant reservoir at the northeast of Brazil. Streamfl...
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ISBN:
(纸本)9783031739965;9783031739972
The paper addresses the use of an adaptive, recursive fuzzy modeling based on the notion of level set to forecast monthly streamflows of a major hydroelectric power plant reservoir at the northeast of Brazil. Streamflows are highly complex nonstationary time series with high variability during the year, a feature that turns modeling and forecasting very hard and challenging. The adaptive level set method is evaluated against periodic autoregressive moving average models, currently adopted by many power industries, and against granular, neural, neural fuzzy, recurrent neural, and data driven level set models. The results show that adaptive level set modeling achieves the best root mean square error performance, surpassing all the models considered herein.
The COVID-19 pandemic has significantly impacted mental health, with economic instability being a major factor. This study examines the effects of economic income on mental health in Argentina post-quarantine, focusin...
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ISBN:
(纸本)9798331541859;9798331541842
The COVID-19 pandemic has significantly impacted mental health, with economic instability being a major factor. This study examines the effects of economic income on mental health in Argentina post-quarantine, focusing on depression, suicide risk, anxiety state, and anxiety trait. Using a detailed dataset, we analyze these relationships and consider the influence of education, age, and province. Our analysis employs descriptive statistics and regression models, revealing that economic income is associated with better mental health outcomes. Specifically, individuals with economic income exhibit significantly lower levels of depression, suicide risk, anxiety state, and anxiety trait. These findings highlight the importance of economic stability for mental health. Furthermore, the interaction between economic income and education suggests that higher education enhances the positive impact of economic income. This underscores the need for economic support policies that consider educational backgrounds to effectively address mental health issues exacerbated by the pandemic.
This paper proposes an original approach for the automatic detection of AI-generated images, using features derived from noise residuals artefacts. Contrary to most current research that leverages sophisticated deep l...
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