Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy

2021 ◽  
Author(s):  
Chao Li ◽  
Li Chen ◽  
Chiahung Chou ◽  
Surachat Ngorsuraches ◽  
Jingjing Qian
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Learning Approaches ◽  
Short Term

On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis

2020 ◽  
Vol 98 ◽  
pp. 91-104 ◽  
Author(s):  
Makoto Chikaraishi ◽  
Prateek Garg ◽  
Varun Varghese ◽  
Kazuki Yoshizoe ◽  
Junji Urata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Exploratory Analysis ◽  
Traffic Prediction ◽  
Learning Approaches ◽  
Short Term

scholarly journals Detection of Cyberattacks Traces in IoT Data

2020 ◽  
Vol 26 (11) ◽  
pp. 1422-1434
Author(s):  
Vibekananda Dutta ◽  
Michał Choraś ◽  
Marek Pawlicki ◽  
Rafał Kozik
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Short Term Memory ◽  
Research Network ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Short Term ◽  
Term Memory ◽  
Network Intrusion ◽  
Long Short Term Memory

Artificial Intelligence plays a significant role in building effective cybersecurity tools. Security has a crucial role in the modern digital world and has become an essential area of research. Network Intrusion Detection Systems (NIDS) are among the first security systems that encounter network attacks and facilitate attack detection to protect a network. Contemporary machine learning approaches, like novel neural network architectures, are succeeding in network intrusion detection. This paper tests modern machine learning approaches on a novel cybersecurity benchmark IoT dataset. Among other algorithms, Deep AutoEncoder (DAE) and modified Long Short Term Memory (mLSTM) are employed to detect network anomalies in the IoT-23 dataset. The DAE is employed for dimensionality reduction and a host of ML methods, including Deep Neural Networks and Long Short-Term Memory to classify the outputs of into normal/malicious. The applied method is validated on the IoT-23 dataset. Furthermore, the results of the analysis in terms of evaluation matrices are discussed.

scholarly journals Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 174530-174541
Author(s):  
Xiaofei Ye ◽  
Jinfen Wang ◽  
Tao Wang ◽  
Xingchen Yan ◽  
Qiming Ye ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Parking Space ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaoxue Yang ◽  
Yajie Zou ◽  
Jinjun Tang ◽  
Jian Liang ◽  
Muhammad Ijaz
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Prediction Performance ◽  
Periodic Component ◽  
Learning Approaches ◽  
Learning Models ◽  
Short Term ◽  
Speed Prediction ◽  
The Impact ◽  
Machine Learning Models

Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

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