scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

scholarly journals Productive Housing: Spatial Structuring and Social Division in Urban Centers

2018 ◽  
Author(s):  
Daniel Esguevillas ◽  
◽  
Luz Carruthers ◽  
Keyword(s):  
New York ◽  
Rental Housing ◽  
Sharing Economy ◽  
Accelerated Expansion ◽  
Short Term ◽  
Urban Centers ◽  
Social Division ◽  
The Public ◽  
The Impact ◽  
The Welfare State

This paper examines the way in which Airbnb dynamics are changing spatial and social conditions in urban centers. A comparative study of the situation in three important global metropolis—New York, London and Barcelona—provides an approach to analyzing how policymakers struggle to control the accelerated expansion of the short-term rental housing platform, under the scrutiny of the public. It aims to foster a broader understanding of the impact of the sharing economy in the realm of housing, in a context of economic globalization and decline of the welfare state, where advances in technology meet with sociological and generational shifts in behavior.

scholarly journals A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

2017 ◽  
Author(s):  
Aymen A. Elfiky ◽  
Maximilian J. Pany ◽  
Ravi B. Parikh ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Palliative Chemotherapy ◽  
Learning Algorithm ◽  
Cancer Center ◽  
Short Term ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Short Term Mortality ◽  
And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

scholarly journals District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2122 ◽  
Author(s):  
Guixiang Xue ◽  
Yu Pan ◽  
Tao Lin ◽  
Jiancai Song ◽  
Chengying Qi ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Feature Fusion ◽  
District Heating ◽  
Accurate Prediction ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Short Term ◽  
District Heating System ◽  
Heating Load ◽  
The Impact

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

scholarly journals Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4368 ◽  
Author(s):  
Chun-Wei Chen ◽  
Chun-Chang Li ◽  
Chen-Yu Lin
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Important Method ◽  
Gap Statistic ◽  
Machine Learning Model ◽  
Key Variables

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

scholarly journals Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Nature Energy ◽  
2020 ◽  
Vol 5 (12) ◽  
pp. 1051-1052
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Gasoline Demand ◽  
Motor Gasoline ◽  
Machine Learning Model ◽  
The Impact

scholarly journals Unscented Kalman Filter-Aided Long Short-Term Memory Approach for Wind Nowcasting

Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 236
Author(s):  
Junghyun Kim ◽  
Kyuman Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Unscented Kalman Filter ◽  
Aviation Industry ◽  
Learning Models ◽  
Short Term ◽  
Machine Learning Model ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Machine Learning Models

Obtaining reliable wind information is critical for efficiently managing air traffic and airport operations. Wind forecasting has been considered one of the most challenging tasks in the aviation industry. Recently, with the advent of artificial intelligence, many machine learning techniques have been widely used to address a variety of complex phenomena in wind predictions. In this paper, we propose a hybrid framework that combines a machine learning model with Kalman filtering for a wind nowcasting problem in the aviation industry. More specifically, this study has three objectives as follows: (1) compare the performance of the machine learning models (i.e., Gaussian process, multi-layer perceptron, and long short-term memory (LSTM) network) to identify the most appropriate model for wind predictions, (2) combine the machine learning model selected in step (1) with an unscented Kalman filter (UKF) to improve the fidelity of the model, and (3) perform Monte Carlo simulations to quantify uncertainties arising from the modeling process. Results show that short-term time-series wind datasets are best predicted by the LSTM network compared to the other machine learning models and the UKF-aided LSTM (UKF-LSTM) approach outperforms the LSTM network only, especially when long-term wind forecasting needs to be considered.

scholarly journals Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

2020 ◽  
Author(s):  
Ahmed Tageldin ◽  
Dalia Adly ◽  
Hassan Mostafa ◽  
Haitham S Mohammed
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Prediction Accuracy ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Total Period ◽  
Cotton Leafworm ◽  
Main Challenge ◽  
The Impact

AbstractThe use of technology in agriculture has grown in recent years with the era of data analytics affecting every industry. The main challenge in using technology in agriculture is identification of effectiveness of big data analytics algorithms and their application methods. Pest management is one of the most important problems facing farmers. The cotton leafworm, Spodoptera littoralis (Boisd.) (CLW) is one of the major polyphagous key pests attacking plants includes 73 species recorded at Egypt. In the present study, several machine learning algorithms have been implemented to predict plant infestation with CLW. The moth of CLW data was weekly collected for two years in a commercial hydroponic greenhouse. Furthermore, among other features temperature and relative humidity were recorded over the total period of the study. It was proven that the XGBoost algorithm is the most effective algorithm applied in this study. Prediction accuracy of 84 % has been achieved using this algorithm. The impact of environmental features on the prediction accuracy was compared with each other to ensure a complete dataset for future results. In conclusion, the present study provided a framework for applying machine learning in the prediction of plant infestation with the CLW in the greenhouses. Based on this framework, further studies with continuous measurements are warranted to achieve greater accuracy.

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