Application of Machine Learning Algorithms in the Mitigation Phase of Disaster Management - A review

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Disaster Management ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Key Factors ◽  
Property A ◽  
Solid Foundation

The Mitigation phase in disaster management (DM) is a widely researched subject, and rightly so due to its invaluable role in dampening the consequences of disasters on life and property. A successful mitigation phase serves to be a solid foundation for the smooth execution of the subsequent phases in DM. This paper looks at some of the recent studies and developments pertinent to the mitigation phase in DM, in an attempt to deduce the prevalent Machine Learning (ML) Techniques that are employed across various disaster scenarios. The paper also looks into some of the key factors that have to be considered to ensure a sustainable plan for mitigation.

Strengthening Post-Disaster Management Activities by Rating Social Media Corpus

2020 ◽  
Vol 10 (1) ◽  
pp. 34-50
Author(s):  
Banujan Kuhaneswaran ◽  
Banage T. G. S. Kumara ◽  
Incheon Paik
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Disasters ◽  
Disaster Management ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Relief Operations ◽  
Post Disaster

In times of natural disasters such as floods, tsunamis, earthquakes, landslides, etc., people need information so that relief operations such as help can save many lives. The implications of using social media in post-disaster management are explored in the article. The approach has three main parts: (1) extraction, (2) classification, and (3) validation. The results prove that machine learning algorithms are highly reliable in elimination disaster non-related tweets and news posts. The authors strongly believe that their model is more reliable as they are validating the tweets using news posts by providing various ratings according to the trueness.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset

The new approach to improvement oil reservoir proxy model predictions using machine learning algorithms

2019 ◽  
Vol 12 ◽  
pp. 60-63
Author(s):  
O.V. Zotkin ◽  
◽  
M.V. Simonov ◽  
A.E. Osokina ◽  
A.M. Andrianova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Oil Reservoir ◽  
New Approach ◽  
Proxy Model ◽  
Model Predictions
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