Search Results - (( java application sensor algorithm ) OR ( variables reduction machine algorithm ))
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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management
Published 2024“…Particle Swarm Optimization (PSO) has demonstrated its efficacy in addressing the issue of construction waste reduction and enhancing the accuracy of cost estimation through the identification of optimal combinations of variables. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The process begins through the monitoring of plants using sensors connected to the Arduino device. Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. …”
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Variable step size least mean square optimization for motion artifact reduction: A review
Published 2019“…Therefore, we propose a research to formulate an improved motion artifact reduction approach using variable step-size least mean square (VSSLMS). …”
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Design Of Robot Motion Planning Algorithm For Wall Following Robot
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Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA
Published 2025“…To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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Automated detection and evaluation of ischemic stroke on ct brain imaging using machine learning techniques
Published 2025“…This study investigates the application of machine learning algorithms for the detection of ischemic stroke using CT brain images. …”
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11
Human activity recognition via accelerometer and gyro sensors
Published 2023“…To implement the data engineering system proposed, two mobile applications, SensorData and SensorDataLogger with user-friendly interfaces and intuitive functionalities are developed using Java programming language and Android Studio. …”
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Reduced torque ripple and switching frequency using optimal DTC switching strategy for open-end winding induction machines
Published 2017“…Direct Torque Control (DTC) of induction machine has received wide acceptance in many Variable Speed Drive (VSD) applications due to its simple control structure and excellent torque dynamic control performances. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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15
Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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16
Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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17
Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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18
Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting
Published 2024“…Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. …”
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An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method
Published 2015“…In experiment, three independent variables: spindle speed, feed rate and depth of cut were manipulated in collecting data. …”
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