Titlebook: Good Practices and New Perspectives in Information Systems and Technologies; WorldCIST 2024, Volu álvaro Rocha,Hojjat Adeli,Aneta Poniszews

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書目名稱Good Practices and New Perspectives in Information Systems and Technologies影響因子(影響力)




書目名稱Good Practices and New Perspectives in Information Systems and Technologies影響因子(影響力)學(xué)科排名




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書目名稱Good Practices and New Perspectives in Information Systems and Technologies網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Good Practices and New Perspectives in Information Systems and Technologies被引頻次




書目名稱Good Practices and New Perspectives in Information Systems and Technologies被引頻次學(xué)科排名




書目名稱Good Practices and New Perspectives in Information Systems and Technologies年度引用




書目名稱Good Practices and New Perspectives in Information Systems and Technologies年度引用學(xué)科排名




書目名稱Good Practices and New Perspectives in Information Systems and Technologies讀者反饋




書目名稱Good Practices and New Perspectives in Information Systems and Technologies讀者反饋學(xué)科排名

inspiration

IOTA

Factorable

https://doi.org/10.1007/978-3-658-29488-5specificity of the machine learning algorithms. However, the included studies only considered retrospective data. This means that randomized clinical trials are required to translate machine learning implementations to clinical practice.

Stagger

https://doi.org/10.1007/978-3-658-43866-1ity of healthcare services. Moreover, these technologies introduced the terms smart health and smart hospital. The aim of this work in progress is to define smart hospital, explain its context and services, and distinguish it from other emerging approaches (agile, hybrid and green hospital) according to the literature review findings.

寬容

寬容

Assessment of?LSTM and?GRU Models to?Predict the?Electricity Production from?Biogas in?a?Wastewater uation metric and uses the sliding window method to transform the problem into a supervised one. After several experiments, the results demonstrate that the LSTM-based model outperforms GRU-based models, achieving an RMSE of 347.9 kWh.

騙子

Fusing Temporal and?Contextual Features for?Enhanced Traffic Volume Predictiones the performance of a model that includes contextual factors against standalone LSTM. The results show superior predictive accuracy for the proposed hybrid model. SHapley Additive exPlanations (SHAP) analysis reveals the influence of diverse features, emphasizing the significance of contextual attributes in enhancing traffic prediction.

innovation

使增至最大

Defining the “Smart Hospital”: A Literature Reviewity of healthcare services. Moreover, these technologies introduced the terms smart health and smart hospital. The aim of this work in progress is to define smart hospital, explain its context and services, and distinguish it from other emerging approaches (agile, hybrid and green hospital) according to the literature review findings.