Estimation of wheat planting date using machine learning algorithms based on available climate data

2020 ◽  
Vol 28 ◽  
pp. 100308 ◽  
Author(s):  
Abdülkadir Gümüşçü ◽  
Mehmet Emin Tenekeci ◽  
Ali Volkan Bilgili
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Planting Date ◽  
Machine Learning Algorithms ◽  
Climate Data

scholarly journals Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7416
Author(s):  
Mohd Anul Haq ◽  
Prashant Baral ◽  
Shivaprakash Yaragal ◽  
Biswajeet Pradhan
Keyword(s):  
Machine Learning ◽  
Global Climate ◽  
Learning Algorithms ◽  
Remotely Sensed ◽  
Machine Learning Algorithms ◽  
Himalayan Region ◽  
Lapse Rate ◽  
Climate Data ◽  
Modis Data ◽  
Bulk Processing

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

scholarly journals ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS

2021 ◽  
Vol 13 (2) ◽  
pp. 211
Author(s):  
Rahmat Azul Mizan ◽  
Prima Widayani ◽  
Nur Mohammad Farda
Keyword(s):  
Machine Learning ◽  
Dengue Fever ◽  
Spatial Modeling ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Climate Data ◽  
Major Health Problem

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image

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
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