Selection of Machine Learning Algorithm and Input Variables for Detecting Driver’s Lane Change Intention

2020 ◽  
Vol 25 (1) ◽  
pp. 24-35
Author(s):  
Sang Hyung Lee ◽  
Sang Hun Lee
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Lane Change ◽  
Input Variables ◽  
Selection Of

scholarly journals Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2860 ◽  
Author(s):  
Jee-Heon Kim ◽  
Nam-Chul Seong ◽  
Wonchang Choi
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Air Conditioning System ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Input Variables

This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

Identification of power quality events: selection of optimum base wavelet and machine learning algorithm

2019 ◽  
Vol 13 (2) ◽  
pp. 260-271 ◽  
Author(s):  
Faizal Hafiz ◽  
Akshya Swain ◽  
Chirag Naik ◽  
Scott Abecrombie ◽  
Andrew Eaton
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Selection Of

scholarly journals Clinical presentation of COVID-19 – a model derived by a machine learning algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Malik Yousef ◽  
Louise C. Showe ◽  
Izhar Ben Shlomo
Keyword(s):  
Machine Learning ◽  
Clinical Presentation ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Verbal Description ◽  
Machine Learning Algorithm ◽  
Proper Selection ◽  
Selection Of

Abstract COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.

Software-Based Testing Kit Using Machine Learning for Diagnosis and Predictive Analytics of COVID-19 Patients

2021 ◽  
Vol 12 (2) ◽  
pp. 39-50
Author(s):  
Vishal Kumar Goar ◽  
Jyoti Prabha
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Prime Factor ◽  
Research Work ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm ◽  
Selection Of

Nowadays, the global community is being affected with COVID-19 disease and integrated infections, which are becoming a menace to the whole world. Research is going on to find out the solution, and still, no particular vaccination or solution has been achieved. This research work is focusing on the analytics of dataset extracted, which has assorted attributes, and these attributes are processed in the machine learning algorithm so that the prime factor can be recognized. In this research manuscript, the usage of COVID-19 dataset is done and trained using supervised learning approach of artificial neural network (ANN) on Levenberg-Marquardt (LM) algorithm so that the predictions of the test patients can be done on the key attributes of age, gender, location, and related parameters. The selection of LM-based implementation with ANN is done as it is the faster approach compared to other functions in neural networks.

scholarly journals Photometric selection of quasars in large astronomical data sets with a fast and accurate machine learning algorithm

2014 ◽  
Vol 439 (1) ◽  
pp. 644-650 ◽  
Author(s):  
Pramod Gupta ◽  
Andrew J. Connolly ◽  
Jeffrey P. Gardner
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Astronomical Data ◽  
Selection Of

Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city

2020 ◽  
Vol 107 ◽  
pp. 433-442 ◽  
Author(s):  
Muhammad Shafiq ◽  
Zhihong Tian ◽  
Yanbin Sun ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Smart City ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Traffic Identification ◽  
Selection Of

scholarly journals Unsupervised functional neurocartography of the Aplysia buccal ganglion

2021 ◽  
Author(s):  
Renan M Costa ◽  
Vijay A Dharmaraj ◽  
Ryota Homma ◽  
Curtis L Neveu ◽  
William B Kristan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Motor Pattern ◽  
Pattern Generation ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Buccal Ganglion ◽  
Multiple Features ◽  
Selection Of

A major limitation of large-scale neuronal recordings is the difficulty in locating the same neuron in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semi-supervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. A matching method was developed based on a cyclic matching algorithm that allows for unsupervised extraction of groups of neurons, thereby enhancing scalability of the analysis. Cyclic matching was also used to automate the selection of high-quality matches, which allowed for unsupervised implementation of the machine learning algorithm. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, as well as helps to adapt this framework to other systems.

Sign in / Sign up

Export Citation Format

Share Document