scholarly journals A Survey on Extracting Hidden Patterns within Road Accident Data using Machine Learning Techniques

2016 ◽  
Vol 6 (4) ◽  
pp. 1-6 ◽  
Author(s):  
S. Vasavi
Keyword(s):  
Machine Learning ◽  
Road Accident ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Accident Data ◽  
Hidden Patterns

Study of Machine Learning Techniques on Accident Data

2020 ◽  
pp. 25-37
Author(s):  
Zakaria Shams Siam ◽  
Rubyat Tasnuva Hasan ◽  
Soumik Sarker Anik ◽  
Ankit Dev ◽  
Sumaia Islam Alita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Accident Data

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

A Survey on Machine Learning Techniques for Supply Chain Management

2021 ◽  
pp. 24-38
Author(s):  
Amal F.Abd El .. ◽  
◽  
◽  
◽  
Shereen Zaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Management Processes ◽  
Learning Techniques ◽  
Hidden Patterns ◽  
Learning Types ◽  
Discontinuous Data

Machine learning arose from the increasing ability of machines to handle large amounts of data over the last two decades, and some machines could also identify hidden patterns and complicated associations that humans couldn't, allowing them to make rational and precise decisions, especially for disruptive and discontinuous data. In several areas of decision-making, machines could produce more reliable outcomes than humans and have already begun to replace them. Machine learning, which is widely recognized as a breakthrough technology, has recently made significant progress in improving supply chain management processes and efficiency. From planning to delivery, machine learning may be applied at different stages of the supply chain management process. Machine learning types are supervised, unsupervised, reinforcement. Each type has many tools which are discussed below in detail. This paper presents a detailed survey on machine learning techniques for supply chain management including supply chain and supply chain management interpretation, machine learning definition, its types, and some algorithms that belong to it.

Road Accident Data Analysis: Data Preprocessing for Better Model Building

2019 ◽  
Vol 16 (9) ◽  
pp. 4019-4027 ◽  
Author(s):  
Salahadin Seid ◽  
Pooja
Keyword(s):  
Model Building ◽  
Analysis Data ◽  
Road Accident ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Model Accuracy ◽  
Data Set ◽  
Learning Techniques ◽  
Accident Data

In this study we focused on the relationship between preprocessing and model accuracy. The performance of the Machine learning techniques depends on the quality of the data set. Preprocessing is not only advantageous but it is very necessary and a preliminary work in predicting model. As a result, experiments discovered that preprocessed techniques increased performance for model building. To see the performance preprocessing support vector machine is applied before preprocessing and after preprocessing. Its model accuracy increased from 68.7% to 88.5%.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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