scholarly journals Application of Machine learning in Crop Yield Prediction of Finger Millet using Multiple Linear Regression

2020 ◽  
pp. 280-284
Author(s):  
Tarun Mourya. S S ◽  
S. Nagini ◽  
Anne Slagha
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Crop Yield ◽  
Crop Production ◽  
Finger Millet ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Climatic Conditions ◽  
The Given

Agriculture is the mainstay of the Indian economy and it is important to enhance the production with the help of technology. Crop production is a complex phenomenon that is influenced by various parameters like climatic conditions, fertilizers, production, rainfall, etc. In the present study Machine learning is used to predict the crop yield of finger millet using multiple linear regression analysis. Multiple linear regression is considered as a model for prediction and the accuracy of the model for the given data is significantly high when compared to other models. Object oriented Python is used and packages like NumPy and Pandas are utilized for performing operations and data analysis respectively. The main advantage of the research is the prediction of the approximate crop yield well ahead of its harvest, which would help the farmers in taking appropriate measures in crop cultivation, marketing and storage. Such predictions will also help the associated industries for planning the logistics of their business.

The application of machine learning for predicting methane uptake and working capacity of MOFs

2021 ◽  
Author(s):  
Mikhail Suyetin
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Metal Organic Frameworks ◽  
Multiple Linear Regression Analysis ◽  
Working Capacity ◽  
Methane Uptake ◽  
Metal Organic

Multiple Linear Regression Analysis as a part of machine learning is employed to develop equations for the quick and accurate prediction of methane uptake and working capacity of metal-organic frameworks...

scholarly journals HR Analytics using R-Machine Learning Algorithm: Multiple Linear Regression Analysis

2020 ◽  
Vol 9 (5) ◽  
pp. 1179-1183
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Machine Learning Algorithm ◽  
Relevant Topic ◽  
The Impact

The purpose of empirical research study to know the impact of various HRD practices and its impact on predictor (job satisfaction). The structured survey research instrument was used to gather the data from 500 sample respondents. The questionnaire was validated with pilot study and data was with crone Bach’s alpha reliability test. The results of the outcome validated with R-Machine Learning Algorithm, multiple linear regression analysis with the help of train data and test data (30:70) ratio. Furthermore results reveals corrgram plot, matrix correlation plot and validation of data with validation match test among various HRD practices and it’s inter relationship. The analysis supported with various reviews which include both western and Indian reviews. The study can be generalized to any sector wherever HRD practices can be implemented. The study feasible/applicable to social implications and employee concern problems and related productivity. The study provides new insights to the readers and analysis which was not published by any other in the relevant topic related machine learning algorithm in analytics world.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

scholarly journals Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Andres Mauricio Munar ◽  
José Rafael Cavalcanti ◽  
Juan Martin Bravo ◽  
David Manuel Lelinho Da Motta Marques ◽  
Carlos Ruberto Fragoso Júnior
Keyword(s):  
Linear Regression ◽  
Spatial Heterogeneity ◽  
Multiple Linear Regression ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Accurate Estimation ◽  
Shallow Waters ◽  
Chl A ◽  
Input Variables

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

scholarly journals The Association of Circulating MiR-29b and Interleukin-6 with Subclinical Atherosclerosis

2017 ◽  
Vol 44 (4) ◽  
pp. 1537-1544 ◽  
Author(s):  
Yu-qing Huang ◽  
Jie Li ◽  
Ji-yan Chen ◽  
Ying-ling Zhou ◽  
An-ping Cai ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Interleukin 6 ◽  
Subclinical Atherosclerosis ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Control Group ◽  
Study Group ◽  
Circulating Levels

Background/Aims: Although it is widely acknowledged that atherosclerosis is mainly a chronic inflammatory process, in which both miR-29b and interleukin-6 (IL-6) play multifaceted roles, the association between miR-29b and IL-6 remains unknown. The aim of the present study was to explore the relationship between miR-29b and IL-6 and to test whether circulating levels of miR-29b and IL-6 could predict atherosclerosis. Methods: A total of 170 participants were divided into two groups according to carotid intima-media thickness (CIMT): study group (CIMT ≥ 0.9mm) and control group (CIMT < 0.9mm). Levels of circulating miR-29b and IL-6 were measured by quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The association of miR-29b and IL-6 levels with CIMT was assessed using Spearman correlation analysis and multiple linear regression analysis. Results: The study group showed higher miR-29b levels (31.61 ± 3.05 vs. 27.91 ± 1.71 Ct, p < 0.001) and IL-6 levels (3.40 ± 0.67 vs. 2.99 ± 0.37 pg/ml, p < 0.001), compared with the control group. CIMT was positively correlated with miR-29b (r = 0.587, p < 0.001) and IL-6 (r = 0.410, p < 0.001), and miR-29b levels were also correlated with IL-6 (r = 0.242, p = 0.001). Multiple linear regression analysis also showed that CIMT was positively correlated with miR-29b and IL-6. After adjustment for age, body mass index, systolic blood pressure, total cholesterol and C-reactive protein, CIMT was still closely correlated with miR-29b and IL-6. The combination of miR-29b and IL-6 (AUC = 0.901, p < 0.001) offered a better predictive index for atherosclerosis than either miR-29b (AUC = 0.867, p < 0.001) or IL-6 (AUC = 0.747, p < 0.001) alone. Conclusion: Circulating levels of miR-29b and IL-6 may be independently correlated with subclinical atherosclerosis, and may serve as novel biomarkers for the identification of atherosclerosis.

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