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31 articles for “multiple linear regression”
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Effect of Slope on Estimating Tree Biomass using Satellite Data in Belasitsa Mountain Forest, Bulgaria
Abstract: 800x600 Remote sensing of above ground tree biomass (AGTB) in rugged and mountainous forest sites incurs uncertainties in accuracy due to the influences of various bio-physical and topographic factors. Particularly slope is an important factor affecting the accuracy of remote sensing AGTB. This study investigates how slope affects the estimation of above ground tree biomass using vegetation indices (VIs) calculated from an ASTER image in the Belasitsa Mountain forest in …
Published in Journal of Remote Sensing & GIS · Vol. 5, Issue 3, 2014 · pp. 17–30 Read article
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Regression Analysis of Research Data using R Programming
Abstract: ABSTRACTThe goal of the review paper is discussing the relationship between various types of regression analysis whose output was sufficiently analyzed using R programming. The primary purpose of this paper is to explain the relationship of linear, multiple, quantile and polynomial regression models to achieve final conclusion with different data sets. Therefore, this paper presents the easiest way of regression analysis commands and R programming strengths for of data analysis. …
Published in Recent Trends in Programming languages · Vol. 6, Issue 2, 2019 · pp. 42–50 Read article
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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 21–29 Read article
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Prenatal estimation of the birth weight above 4,000 g (fetal macrosomia) using fetal abdominal circumference(AC)
Abstract: Purpose: This study is to determine if the birth weight above 4,000g can be correctly estimated by prenatal ultrasonographic measurements of abdominal circumference.Background: The delivery of macrosomatic fetus can increase the perinatal morbidity and mortality of both parturients and fetuses/neonates; the risk for shoulder dystocia, fetal asphyxia and birth trauma as well as vacuum or forceps delivery, hysterotomy and postpartum bleeding due to uterine atony. Thus, the accurate estimation of …
Published in Research and Reviews: A Journal of Medicine · Vol. 12, Issue 3, 2022 · pp. 27–33 Read article
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An Innovative Approach to Find the Optimum Lubricant for Diverse Applications Based on Scikit-Learn Library Using Python
Abstract: This paper presents an innovative approach for finding the optimum lubricant using the Scikit-learn library in Python. The proposed approach uses a linear regression model to analyze a dataset of lubricant properties and performance, specifically the viscosity, wear, and friction. The model is trained on the dataset to predict the wear and friction for a given viscosity, which can be used to identify the optimum lubricant. By analyzing a dataset …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 25–35 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Utilizing Machine Learning to Evaluate the Connection between Poisson's Ratio and the Petrophysical Properties of Reservoir Rocks
Abstract: The Poisson's ratio is a crucial cornerstone, illuminating our understanding of geomechanical behaviour in wells during the dynamic drilling process and the inspiring recovery journey. This research rigorously employs machine learning methods to analyse the significant impact of geophysical parameters on the Poisson ratio in hydrocarbon reservoirs found in oil fields. The analysis utilized data from multiple oil and gas fields, highlighting the crucial relationships between the Poisson ratio, the …
Published in Journal of Petroleum Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 33–43 Read article
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Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications
Abstract: This study aims to develop an integrated machine learning and optimization framework for the intelligent design of lightweight polymer composites suited for IoT-enabled automotive applications. The goal is to enhance material performance while satisfying multiple design constraints such as mechanical strength, thermal stability, and process compatibility. A curated dataset of polymer composite formulations was used to train a Random Forest Regression (RFR) model capable of predicting tensile strength, thermal conductivity, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 12–27 Read article
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Application of TOPSIS Method for Selection of Best Natural Fiber for Retrofitting of Structure
Abstract: Selection of best natural fiber is multi-criteria decision problem including both quantitative and qualitative criteria. Decision-making problem is the process of finding the best option from all of the feasible alternatives. In this paper, from among multi-criteria models in making complex decision and multiple attribute models for the most preferable choice, technique for order preferences by similarity to ideal solution (TOPSIS) approach has been dealt with. Technique for TOPSIS is …
Published in Journal of Structural Engineering and Management · Vol. 2, Issue 3, 2015 · pp. 34–40 Read article
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Detecting Electronic Circuitboard Thermal Abnormalities with Dynamic Hypernumbers
Abstract: This paper introduces the algebra of data acquisition using dynamic hypernumbers used in predicting thermal abnormalities with a rapid forecasting algorithm. Algebraic rules are defined for monitoring data from multiple sources. Each data source is considered a dynamic receptive synchronized hypernumber for building the resulting dynamic hypernumber. The algebraic dynamic hypernumberdefinition is used to present a low-complexity algorithm for identifying the thermal state of an electronic circuit board. The algorithm …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 10, Issue 3, 2023 · pp. 10–24 Read article