scholarly journals Forecasting energy consumption in Tamil Nadu using hybrid heuristic based regression model

2019 ◽  
Vol 23 (5 Part B) ◽  
pp. 2885-2894 ◽  
Author(s):  
Karuppusamy Sakunthala ◽  
Salvarasan Iniyan ◽  
Selvaraj Mahalingam
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Tamil Nadu ◽  
Multiple Linear Regression Model ◽  
Percentage Error

Energy consumption forecasting is vitally important for the deregulated electricity industry in the world. A large variety of mathematical models have been developed in the literature for energy forecasting. However, researchers are involved in developing novel methods to estimate closer values. In this paper, authors attempted to develop new models in minimizing the forecasting errors. In the present study, the economic indicators of the state including population, gross state domestic product, yearly peak demand, and per capita income were considered for forecasting the electricity consumption of a state in a developing country. Initially, a multiple linear regression model has been developed. Then, the coefficients of the regression model were optimized using two heuristic approaches namely genetic algorithm and simulated annealing. The mean absolute percentage error obtained for the three models were 2.00 for multiple linear regression model, 1.94 for genetic algorithm based linear regression and 1.86 for simulated annealing based linear regression.

Canine Parvoviral Enteritis and Its Determinants-An Epidemiological Analysis

2021 ◽  
Author(s):  
M. Geetha ◽  
G. Selvaraju
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Tamil Nadu ◽  
Multiple Linear Regression Model ◽  
Age Group ◽  
Environmental Determinants ◽  
Disease Occurrence ◽  
The Relationship

Background: Canine parvoviral enteritis (CPVE) is a highly contagious disease of dogs of less than two years age group characterized by vomiting, haemorrhagic foul smelling diarrhoea, high grade pyrexia, dehydration and followed by death. The disease is caused by Canine parvovirus type-2 (CPV-2) and its variants, CPV-2a, 2b and 2c. Environmental and host determinants are playing an important role in the occurrence of CPVE in dogs. Limited numbers of research studies have been were conducted on the role of the determinants associated with the disease occurrence. Hence, the present study was aimed to assess the influence of host and environmental determinants associated with the incidence of CPVE in dogs. Methods: Retrospective data on the incidence of CPVE in Namakkal region, Tamil Nadu was collected (2017-2019) from Veterinary Clinical Complex (VCC), Veterinary College and Research Institute (VC and RI), Namakkal, Tamil Nadu and had been subjected to temporal and spatial clustering and regression analysis. One hundred and twenty three faecal samples were collected from dogs with clinical signs of CPVE and subjected to PCR using H primer of CPV. Cross-sectional study was used to investigate the relationship between the disease and hypothesized causal factors. Relative risk, odds ratio were used to determine the causal association. Weather data was collected for the period from 2017-2019 from Animal Feed Analytical and Quality Control Laboratory (AFAQAL), VC and RI, Namakkal to assess the relationship of disease occurrence with the environmental determinants. Multiple linear regression model was developed for prediction of CPVE by correlation of environmental determinants with the occurrence of CPVE. Result: Temporal analysis revealed endemic pattern of CPVE started last week of April, peaks in June and ends in August and second peak was noticed at November month. Higher incidences ( greater than 70%) were noticed in males and less than 6 months age group dogs. Polymerase chain reaction for confirmation of CPV infection in dogs revealed the positivity of 70.73%. Analysis of risk factors associated with CPVE revealed that vaccination, roaming of dogs, maternal vaccination and early weaning having positive statistical association with the incidence of CPVE. Multiple linear regression model revealed that relative humidity is positively associated with the occurrence of CPVE in dogs. Vaccination of dogs against CPV and administration of boosters at regular intervals, weaning of dogs after 45 days of age are used as primary strategies for prevention of CPVE.

Multiple Linear Regression Model Based Research on Relationship between Energy Consumption and Economic Growth

2011 ◽  
Vol 361-363 ◽  
pp. 1296-1299
Author(s):  
Ke Liu ◽  
Xiao Liu Shen ◽  
Yi Mo Ji
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Research Method ◽  
Multiple Linear Regression Model ◽  
Effect Factor ◽  
Policy Optimization

This paper selects energy consumption and annual GDP data of Beijing from the year of 1990 to 2009 as a sample, and adopted the research method of combining the quality and quantity, theory and empirical research, and we also employed the multiple linear regression model to analyze the effect of energy consumption to economic growth and the sensitivity of each effect factor. We wish this paper could provide a support to the future economic growth and policy optimization of energy and industry development of Beijing from theory to data.

scholarly journals Factors Affecting the Productivity of the Big Onion in Hambantota District during the Off Season

2019 ◽  
pp. 1-10
Author(s):  
M. K. M. Sulochana ◽  
L. S. Nawarathna
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Factors Affecting ◽  
Absolute Percentage Error ◽  
Average Productivity

Aim: The main aim of this study is to identify the factors affecting the big onion productivity of Hambantota district during the off-season. Moreover, we identify the average productivity per acre from Hambantota district and compare it with the other areas that cultivated the big onion. Further, identify the main issues encountered in big onion cultivation in Hambantota and identify the critical contributing factors for the big onion cultivation in this area. Place and Duration of Study: During the off seasons in 2015 to 2016 in Hambantota District. Methodology: Sample data was collected from 201 farmers in Hambantota district. Multiple linear regression model was used to identify the factors affecting the big onion productivity in Hambantota district during the off-season. The normality assumption of the regression model was checked using Kolmogorov–Smirnov test, Shapiro Wilk normality test and Skewness and Kurtosis test. Pearson, Spearman’s Rank and Partial correlation tests were used to check the correlations between variables. Mean absolute percentage error (MAPE) and Symmetrical Mean absolute percentage error (SMAPE) values were used to validate the fitted model. Results: By the multiple linear regression model main factors affecting the productivity of big onion in Hambantota area were Seasonal Months, Monthly Income, Subsidies Fertilizer and Cultivated Quantity. And the R-squared value was most like to 80% and this means these independent variables were described 80% of the dependent variable.  Model accuracies were reported as 98.48% and 98.49% from MAPE and SMAPE respectively. Therefore, this multiple linear regression model was suitable for this study. Further, the model determined the affected factors for the big onion cultivation in Hambantota district during the off-season. Conclusion: Hambantota district average productivity was less than other areas. Big onion productivity of Matale is more than 2 times greater than big onion productivity of Hambantota. Off season big onion cultivation in Hambantota district is not very effective because of the average productivity is less than other areas in Sri Lanka.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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