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Spatial-SMOTE for handling imbalance in spatial regression tasks
Class imbalance in classification tasks leads to biased learning in the model training phase. In case of regression, the presence of rare cases over...
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Gradient-Boosted Tree Regression with Pandas, Scikit-Learn, and PySpark
In this chapter, we continue with supervised learning and tree-based regression. Specifically, we develop a gradient-boosted tree (GBT) regression...
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Scalable Smart Contracts for Linear Regression Algorithm
Linear regression algorithms capture information from previous experiences and build a cognitive model to forecast the future. The information and...
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White-Box Mutation Testing of Smart Contracts: A Quick Review
Once being deployed on the blockchain, smart contracts cannot be altered, requiring more testing. A fault-based testing technique called mutation...
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The Importance of Software Testing
Software testing is a crucial part of the software development process. It is the process of evaluating a software system or application to find...
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Genetic Programming with Synthetic Data for Interpretable Regression Modelling and Limited Data
A trained regression model can be used to create new synthetic training data by drawing from a distribution over independent variables and calling...
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Mathematical Modeling of Anaerobic Treatment of Urban Wastewater Using Multivariate Regression
This study is based on the proposal of a mathematical model that allows to know the amount of Biochemical Oxygen Demand (BOD) at the outlet of a...
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Obesity Level Prediction Using Multinomial Logistic Regression
With the increase in remote jobs and also due to the present ongoing pandemic for the past 1 year, most of the IT companies have taken a decision and...
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Memetic Semantic Genetic Programming for Symbolic Regression
This paper describes a new memetic semantic algorithm for symbolic regression (SR). While memetic computation offers a way to encode domain knowledge...
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Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model
AbstractAir pollution imposed by particle matter (PM) made it a public health concern and hazard to humans and the environment. Reduced vision,...
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YOLO-CORE: Contour Regression for Efficient Instance Segmentation
Instance segmentation has drawn mounting attention due to its significant utility. However, high computational costs have been widely acknowledged in...
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Security Assessment and Testing
Security Assessment and Testing represent a vital domain within the CISSP certification, emphasizing the intricate methodologies and practices...
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Simple Linear Regression
Imagine you are a business investor and want to invest in startup ventures that are likely to be highly profitable. What are all the factors you...
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Hybrid Differential Software Testing
Differential software testing is important for software quality assurance as it aims to automatically generate test inputs that reveal behavioral...
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Statistical Arbitrage with Hypothesis Testing
Statistical arbitrage is a market-neutral trading strategy leveraging statistical methods to identify and exploit significant relationships between...
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Unsupervised feature based algorithms for time series extrinsic regression
Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable...
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Chapter 2 Testing Throughout the Software Development Life Cycle
This chapter describes the different test levels and test types during the software development life cycle, including later maintenance testing.
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Multi-branch Residual Fusion Network for Imbalanced Visual Regression
Imbalance visual regression is an important and challenging task, and research on it is still in its early stages. Among the existing solutions,...
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A robust adaptive linear regression method for severe noise
Up to now, the inaccurate supervision problem caused by label noises poses a big challenge for regression modeling. Regularized noise-robust models...
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Multiple-output quantile regression neural network
Quantile regression neural network (QRNN) model has received increasing attention in various fields to provide conditional quantiles of responses....
