scholarly journals Learning Discriminative Recommendation Systems with Side Information

2017 ◽  
Author(s):  
Feipeng Zhao ◽  
Yuhong Guo
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Real World ◽  
Linear Regression Model ◽  
Side Information ◽  
Recommendation Systems ◽  
Joint Model ◽  
Partially Observed ◽  
Real World Applications

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.

scholarly journals Sampling the Porridge: A Comparison of Ordered Variable Regression with F and R2 and Multiple Linear Regression with Corrected F and R2 in the Presence of Multicollinearity

2020 ◽  
Vol 18 (1) ◽  
pp. 2-39
Author(s):  
Grayson L. Baird ◽  
Stephen L. Bieber
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Real World ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Real World Data ◽  
World Data

Differences between the multiple linear regression model with Corrected R2 and Corrected F and the ordered variable regression model with R2 and F when intercorrelation is present are illustrated with simulated and real-world data.

scholarly journals Effect of Ribavirin on Recovery Time in COVID-19 Patients: A Real-world Retrospective Cohort Study

2020 ◽  
Author(s):  
Yuanlong Hu ◽  
Xue Zhu ◽  
Ning Shen ◽  
Xinhua Jia ◽  
Xingcai Zhang ◽  
...  
Keyword(s):  
Cohort Study ◽  
Linear Regression ◽  
Regression Model ◽  
Real World ◽  
Linear Regression Model ◽  
Retrospective Cohort Study ◽  
Recovery Time ◽  
Retrospective Cohort ◽  
Specific Drug ◽  
Clinical Benefits

Abstract Background: Up to now, there is still no specific drug against COVID-19. However, Ribavirin may bring clinical benefits to COVID-19 patients.Methods: This study was designed as a real-world retrospective cohort study based on electronic medical record (EMR), and linear regression model was used to evaluate the effect of Ribavirin on recovery time.Results: 342 patients were enrolled in this study. Both unadjusted and unadjusted models showed that interferon or Lopinavir-Ritonavir combined with Ribavirin could shorten the recovery time of patients, which was evident in all subgroups considered except the severe subgroup and after fine adjustments.Conclusion: This study shows that interferon or Lopinavir-Ritonavir combined with Ribavirin can shorten the recovery time of patients with non-severe COVID-19.

Stock Forecasting Using an Improved Version of Adaptive Group Lasso

2020 ◽  
Vol 17 (8) ◽  
pp. 3370-3373
Author(s):  
S. Nandhini ◽  
Monojit Debnath ◽  
Siddarth Tyagi ◽  
Shashank Mishra ◽  
K. Ashok Kumar
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Linear Regression Model ◽  
Stock Price ◽  
Group Lasso ◽  
Stock Prediction ◽  
The World ◽  
The Future ◽  
Penalization Technique

Stock is one of the few things in the world that influence the economy of the society, hence its worth a shot and very desirable to predict a stock price in the future. Hence in this paper we propose a stock prediction model based on linear regression model. The database of the training is based on the Goldman Sachs database of stocks found from the Google. Here we choose Lasso penalization technique cause the, this performs well with the sparsity of the network, meaning when the network has less features and more observations. Here we have proposed an improved version of lasso function and have proposed an algorithm to improve the performance of the model.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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.

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