scholarly journals Machine-learning approach to crop yield prediction with the spatial extent of drought

2021 ◽  
Author(s):  
Vitali Diaz ◽  
Ahmed A. A. Osman ◽  
Gerald A. Corzo Perez ◽  
Henny A. J. Van Lanen ◽  
Shreedhar Maskey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Input Data ◽  
Polynomial Regression ◽  
Growth Models ◽  
Data Retrieval ◽  
Spatial Extent ◽  
Ann Models ◽  
The Impact ◽  
Drought Effects

Abstract. Crop yield is one of the variables used to assess the impact of droughts on agriculture. Crop growth models calculate yield and variables related to plant development and become more suitable for crop yield estimation. However, these models are limited in that specific data are needed for computation. Given this limitation, machine learning (ML) models are often widely utilised instead, but their use with the spatial characteristics of droughts as input data is limited. This research explored the spatial extent of drought (area) as input data for building an approach to predict seasonal crop yield. This ML approach is made up of two components. The first includes polynomial regression (PR) models, and the second considers artificial neural network (ANN) models. In this approach, the purpose was to evaluate both types of ML models (PR and ANN) and integrate them into one operational tool. The logic is as follows: ANN models determine the most accurate predictions, but in practice, issues regarding data retrieval and processing can make the use of equations, i.e. PR, preferable. The proposed approach provides these PR equations to perform such calculations with early and preliminary input. The estimates can be further improved when the ANN models are run with the final input data. The results indicated that the empirical equations (PR) produced good predictions when using drought area as the input. ANN provides better estimates, in general. This research will improve drought monitoring systems for assessing drought effects. Since it is currently possible to calculate drought areas within these systems, the direct application of the prediction of drought effects is possible to integrate by following approaches such as the one presented or similar.

scholarly journals Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods

2021 ◽  
Vol 12 (1) ◽  
pp. 1-20
Author(s):  
Mamata Garanayak ◽  
Goutam Sahu ◽  
Sachi Nandan Mohanty ◽  
Alok Kumar Jagadev
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Polynomial Regression ◽  
Support Vector ◽  
Regression Methods ◽  
Percentage Improvement ◽  
Information Sets ◽  
Accurate Comparison ◽  
The Republic ◽  
The Impact

Agriculture is a foremost field within the world, and it's the backbone in the Republic of India. Agriculture has been in poor condition. The impact of temperature variations and its uncertainty has engendered the bulk of the agricultural crops to be overripe in terms of their manufacturing. A correct forecast of crop expansion is a vital character in crop forecast management. Such forecasts will hold up the federated industries for accomplishing the provision of their occupation. ML is the method of finding new models from giant information sets. Numerous regressive ways like random forest, linear regression, decision tree regression, polynomial regression, and support vector regression will be used for the aim. Area and production are among the meteorological information that's made by necessary data. This paper figures out the yield recommendation of the crop by the accurate comparison of numerous machine learning ML regressions where the overall percentage improvement over several existing methods is 3.6%.

scholarly journals FEA and Machine Learning Techniques for Hidden Structure Analysis

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5159
Author(s):  
Xijin Shi ◽  
Sheng-Jen Hsieh ◽  
Roseli Aparecida Francelin Romero
Keyword(s):  
Machine Learning ◽  
Root System ◽  
Infrared Imaging ◽  
Polynomial Regression ◽  
Statistical Tests ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Root Structure ◽  
Fea Model ◽  
Ann Models

This study focuses on investigating and predicting two hidden structures: plant root system architecture and non-visible bubbles in plexiglass. Current approaches are damaging, expensive, or time-consuming. Infrared imaging was used to study the root structure and depth of small plants and to detect the diameter and depth of bubbles in plexiglass. A finite element analysis (FEA) model was built to simulate the infrared imaging process to realize the detection and prediction given the amount of heat flux required to obtain thermal images and data. For the root system, based on a tree structure thermal profile over time derived from the FEA model, a line scan method was developed to predict root structure. The main root branches can be viewed from the detection results. Polynomial regression, support vector machine (SVM), and artificial neural network (ANN) models were designed to predict root depth. For bubble defects, three ANN models were developed to predict bubble size using temperature data generated by the FEA model. Results indicated that these models provide valid predictions. Statistical tests were applied to evaluate and compare the above predictive models. Results suggest that infrared imaging and machine learning models can be used to provide information on both hidden structures.

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.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

scholarly journals The Impact of Time Interval between Hepatic Resection and Liver Transplantation on Clinical Outcome in Patients with Hepatocellular Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2398
Author(s):  
Matteo Serenari ◽  
Enrico Prosperi ◽  
Marc-Antoine Allard ◽  
Michele Paterno ◽  
Nicolas Golse ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Hepatic Resection ◽  
Primary Outcome ◽  
Polynomial Regression ◽  
Secondary Outcome ◽  
Time Interval ◽  
Comprehensive Complication Index ◽  
Fractional Polynomial ◽  
The Impact

Hepatic resection (HR) for hepatocellular carcinoma (HCC) may require secondary liver transplantation (SLT). However, a previous HR is supposed to worsen post-SLT outcomes. Data of patients treated by SLT between 2000 and 2018 at two tertiary referral centers were analyzed. The primary outcome of the study was to analyze the impact of HR on post-LT complications. A Comprehensive Complication Index ≥ 29.6 was chosen as cutoff. The secondary outcome was HCC-related death by means of competing-risk regression analysis. In the study period, 140 patients were included. Patients were transplanted in a median of 23 months after HR (IQR 14–41). Among all the features analyzed regarding the prior HR, only time interval between HR and SLT (time HR-SLT) was an independent predictor of severe complications after LT (OR = 0.98, p < 0.001). According to fractional polynomial regression, the probability of severe complications increased up to 15 months after HR (43%), then slowly decreased over time (OR = 0.88, p < 0.001). There was no significant association between HCC-related death and time HR-SLT at the multivariable competing risks regression model (SHR, 1.06; 95% CI: 0.69–1.62, p = 0.796). This study showed that time HR-SLT was key in predicting complications after LT, without affecting HCC-related death.

PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Author(s):  
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Keynote Speaker ◽  
Lively Discussion ◽  
Theoretical Understanding ◽  
New Directions ◽  
New Ideas ◽  
New Challenges ◽  
The Impact ◽  
Distributed Machine Learning

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

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