scholarly journals Multiple Linear Regression of Multi-class Images in Devices of Internet of Things

2020 ◽  
Vol 37 (6) ◽  
pp. 965-973
Author(s):  
Dan Chen
Keyword(s):  
Linear Regression ◽  
Internet Of Things ◽  
Multiple Linear Regression ◽  
Important Application ◽  
Data Sets ◽  
Benchmark Data ◽  
Theoretical Evidence ◽  
Classification Of Images ◽  
Expert Network

The correct classification of images is an important application in the monitoring of Internet of things (IoT). In the research of IoT images, a key issue is to recognize multi-class images at a high accuracy. As a result, this paper puts forward a classification method for multi-class images based on multiple linear regression (MLR). Firstly, the convolutional neural network (CNN) was improved to automatically generate a network from the IoT terminals, and used to classify images into disjoint class sets (clusters), which were processed by the subsequently constructed expert network. After that, the MLR was introduced to evaluate the accuracy and robustness of the classification of multi-class images. Finally, the proposed method has been verified on CIFAR-10, CIfar-100 and MNIST, etc. benchmark data sets. Our method was found to outperform other methods in classification, and improve the accuracy of the classic AlexNet by 2%. The research results provide theoretical evidence and lay practical basis for the classification of multi-class IoT images.

scholarly journals Multiple Linear Regression: Bayesian Inference for Distributed and Big Data in the Medical Informatics Platform of the Human Brain Project

2018 ◽  
Author(s):  
Lester Melie-Garcia ◽  
Bogdan Draganski ◽  
John Ashburner ◽  
Ferath Kherif
Keyword(s):  
Big Data ◽  
Linear Regression ◽  
Human Brain ◽  
Multiple Linear Regression ◽  
Medical Informatics ◽  
Data Privacy ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Explanatory Variables ◽  
Human Brain Project

ABSTRACTWe propose a Multiple Linear Regression (MLR) methodology for the analysis of distributed and Big Data in the framework of the Medical Informatics Platform (MIP) of the Human Brain Project (HBP). MLR is a very versatile model, and is considered one of the workhorses for estimating dependences between clinical, neuropsychological and neurophysiological variables in the field of neuroimaging. One of the main concepts behind MIP is to federate data, which is stored locally in geographically distributed sites (hospitals, customized databases, etc.) around the world. We restrain from using a unique federation node for two main reasons: first the maintenance of data privacy, and second the efficiency in management of big volumes of data in terms of latency and storage resources needed in the federation node. Considering these conditions and the distributed nature of data, MLR cannot be estimated in the classical way, which raises the necessity of modifications of the standard algorithms. We use the Bayesian formalism that provides the armamentarium necessary to implement the MLR methodology for distributed Big Data. It allows us to account for the heterogeneity of the possible mechanisms that explain data sets across sites expressed through different models of explanatory variables. This approach enables the integration of highly heterogeneous data coming from different subjects and hospitals across the globe. Additionally, it offers general and sophisticated ways, which are extendable to other statistical models, to suit high-dimensional and distributed multimodal data. This work forms part of a series of papers related to the methodological developments embedded in the MIP.

scholarly journals Penerapan Data Mining Untuk Memprediksi Pemesanan Bibit Pohon Dengan Regresi Linear Berganda

2020 ◽  
Vol 7 (1) ◽  
pp. 56
Author(s):  
Devi Sari Oktavia Panggabean ◽  
Efori Buulolo ◽  
Natalia Silalahi
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Large Data ◽  
Soil Conditions ◽  
Tree Seedlings ◽  
Data Sets ◽  
Estimation Problems ◽  
Knowledge Discovery In Database ◽  
Spss Software

Data mining, often also called knowledge discovery in database (KDD), is an activity that includes collecting, using historical data to find order, patterns or relationships in large data sets. Outputs from data mining can be used to improve future decision making. Problems that often occur in BPDASHL are estimation problems such as weather, difficulties in planting, lack of labor, lack of experience in tree nurseries, and different soil conditions. Another problem that is found in agencies is that they do not have a system to predict estimated tree seedlings orders every year so that a method is needed, namely the Multiple Linear Regression Algorithm. So with this was made the Application of Data Mining To Predict Ordering Tree Seeds With Multiple Linear Regression. Multiple Linear Regression Algorithms which are methods that support estimating or predicting order targets for the coming period. Algorithm testing is done using SPSS software. From the results of the research that has been done, it can help BPDASHL to make it easier to predict the ordering of seeds using SPSS Software 

Variable Selection in Multiple Linear Regression Using a Genetic Algorithm

2016 ◽  
pp. 133-159 ◽  
Author(s):  
Javier Trejos ◽  
Mario A. Villalobos-Arias ◽  
Jose Luis Espinoza
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Fitness Function ◽  
Data Sets ◽  
Genetic Operators ◽  
Wheel Model ◽  
Crossover And Mutation

In this article it is studied the application of a genetic algorithm in the problem of variable selection for multiple linear regression, minimizing the least squares criterion. The algorithm is based on a chromosomic representation of variables that are considered in the least squares model. A binary chromosome indicates the presence (1) or absence (0) of a variable in the model. The fitness function is based on the adjusted square R, proportional to the fitness for chromosome selection in a roulette wheel model selection. Usual genetic operators, such as crossover and mutation are implemented. Comparisons are performed with benchmark data sets, obtaining satisfying and promising results.

scholarly journals ASSESSMENT OF PCA AND MNF INFLUENCE IN THE VHR SATELLITE IMAGE CLASSIFICATIONS

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 55-60
Author(s):  
P. C. Molina ◽  
M. P. Castro ◽  
C. S. Anjos
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Urban Areas ◽  
Satellite Image ◽  
Data Sets ◽  
Detailed Characterization ◽  
Noise Interference ◽  
Spectral Bands ◽  
Classification Of Images

Abstract. Orbital images have been increasingly refined spatially as spectrally as that is the case with those provided by satellite Earth observation WorldView-3 used in this paper. However, the images are very susceptible to noise interference, so it is difficult to identify and characterize objects. Therefore, it is essential to use techniques to minimize them. Thus, through increasingly innovative processing, it is possible to carry out detailed characterization mainly of urban areas. This work aims to perform the classification of images Worldview-3 using the advanced methods of classification Random Forest and Deep Learning for the region of Botafogo in the municipality of Rio de Janeiro, Brazil. Such classifications were performed for four different data sets, including the spectral bands and transformations (MNF and PCA) resulting from the original images. The results demonstrate that the use of transformations resulting from the original images as input data for the extraction of attributes in conjunction with the spectral bands improves the accuracy of the classifications generated by the Random Forest and Deep Learning method.

scholarly journals So2Sat LCZ42: A Benchmark Data Set for the Classification of Global Local Climate Zones [Software and Data Sets]

2020 ◽  
Vol 8 (3) ◽  
pp. 76-89 ◽  
Author(s):  
Xiao Xiang Zhu ◽  
Jingliang Hu ◽  
Chunping Qiu ◽  
Yilei Shi ◽  
Jian Kang ◽  
...  
Keyword(s):  
Data Sets ◽  
Data Set ◽  
Climate Zones ◽  
Local Climate ◽  
Benchmark Data ◽  
Local Climate Zones

An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model

2019 ◽  
Vol 14 (4) ◽  
pp. 64-78
Author(s):  
Ravichandran M ◽  
Subramanian K M ◽  
Jothikumar R
Keyword(s):  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Data Sets ◽  
Pearson Correlation Coefficient ◽  
Trust Evaluation ◽  
New Approach ◽  
Benchmark Data ◽  
Implicit Trust ◽  
Trust Inference

Multi-view affinity propagation (MAP) methods are widely accepted techniques, measure the within-view clustering and clustering consistency. These suffer from similarity and correlation between clusters. The trust and similarity measured was introduced as a new approach to overcome the problem. But these approaches suffer from low accuracy and coverage due to avoidance of implicit trust. So, a framework called multi-view clustering based on gray affinity (MVC-GA) created by integrating both similarity and implicit trust. Similarity between two clusters is obtained by applying the Pearson Correlation Coefficient-based similarity. It utilizes the collaborative filter-based trust evaluation for each clustered view in terms of the similarity based on the gray affinity nn algorithm. Classification of incomplete occurrences is addressed based on GA Function. Experiments on the benchmark data sets have been performed to validate the proposed framework. It is shown that MVC-GA can improve the multi-view clustering accuracy and coverage.

Sign in / Sign up

Export Citation Format

Share Document