scholarly journals Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Jiangnan Zhang ◽  
Kewen Xia ◽  
Ziping He ◽  
Shurui Fan
Keyword(s):  
Random Forest ◽  
Solution Space ◽  
Classification Model ◽  
Local Optimum ◽  
Random Forest Classification ◽  
Differential Learning ◽  
Forest Classification ◽  
Quantum Behavior ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm

Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

Intelligent identification of effective reservoirs based on the random forest classification model

2020 ◽  
Vol 591 ◽  
pp. 125324 ◽  
Author(s):  
Jieyu Li ◽  
Ping-an Zhong ◽  
Minzhi Yang ◽  
Feilin Zhu ◽  
Juan Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Classification Model ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Analysis of Frequency Bands of Uterine Electromyography Signals for the Detection of Preterm Birth

2021 ◽  
Author(s):  
Vinothini Selvaraju ◽  
P.A. Karthick ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Preterm Birth ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Peak Frequency ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Frequency Bands ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Uterine Electromyography

In this work, an attempt has been made to analyze the influence of the frequencies bands in uterine electromyography (uEMG) signals on the detection of preterm birth. The signals recorded from the women’s abdomen during pregnancy are considered in this study. The signals are subjected to preprocessing using digital bandpass Butterworth filter and decomposed into different frequency bands namely, 0.3-1.0 Hz (F1), 1.0-2.0 Hz (F2) and 2.0-3.0Hz (F3). Spectral features namely, peak magnitude, peak frequency, mean frequency and median frequency are extracted from the power spectrum. Classification models namely, k-nearest neighbor, support vector machine and random forest are employed to distinguish the term and preterm conditions. The results show that the features extracted from these frequency bands are able to differentiate term and preterm condition. Particularly, the frequency band F3 performs better than other frequency bands. The features associated with these frequencies along with random forest classification model achieves a maximum accuracy of 75.2%. Thus, these measures could be used to accurately detect the preterm birth well in advance.

scholarly journals A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 ◽  
Vol 10 (1) ◽  
pp. 256-261
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
General Public ◽  
Classification Model ◽  
Fake News ◽  
Random Forest Classification ◽  
Forest Classification ◽  
The World ◽  
The Impact

Today the world is gripped with fear of the most infectious disease which was caused by a newly discovered virus namely corona and thus termed as COVID-19. This is a large group of viruses which severely affects humans. The world bears testimony to its contagious nature and rapidity of spreading the illness. 50l people got infected and 30l people died due to this pandemic all around the world. This made a wide impact for people to fear the epidemic around them. The death rate of male is more compared to female. This Pandemic news has caught the attention of the world and gained its momentum in almost all the media platforms. There was an array of creating and spreading of true as well as fake news about COVID-19 in the social media, which has become popular and a major concern to the general public who access it. Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base. At the time it is undeniable that spreading of such fake news in and around creates lots of confusion and fear to the public. To stop all such rumors detection of fake news has become utmost important. To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model. In the context of the COVID-19 pandemic, we investigated and implemented by collecting the training data and trained a machine learning model by using various machine learning algorithms to automatically detect the fake news about the Corona Virus. The machine learning algorithm used in this investigation is Naïve Bayes classifier and Random forest classification algorithm for the best results. A separate model for each classifier is created after the data preparation and feature extraction Techniques. The results obtained are compared and examined accurately to evaluate the accurate model. Our experiments on a benchmark dataset with random forest classification model showed a promising results with an overall accuracy of 94.06%. This experimental evaluation will prevent the general public to keep themselves out of their fear and to know and understand the impact of fast-spreading as well as misleading fake news.

scholarly journals Comparison of Classification Methods used in Machine Learning for Dysgraphia Identification

2021 ◽  
Vol 12 (11) ◽  
pp. 1886-1891
Author(s):  
Sarthika Dutt, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Feature Selection Technique ◽  
Random Forest Classification ◽  
Visual Spatial ◽  
Forest Classification

Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task.  It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naïve Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.

Using Fitbit data to predict clinical deterioration and unplanned treatment events in cancer outpatients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13560-e13560
Author(s):  
Daniel France ◽  
Paromita Nath ◽  
Sankaran Mahadevan ◽  
Jason Slagle ◽  
Rajiv Agarwal ◽  
...  
Keyword(s):  
Random Forest ◽  
Cancer Patients ◽  
Pearson Correlation ◽  
Health Data ◽  
Clinical Deterioration ◽  
Classification Model ◽  
Model Accuracy ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Patient Reported

e13560 Background: A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in cancer patients, to mitigate the effects of adverse events, disease progression, and medical error. This problem requires effective clinical surveillance, early recognition, timely notification of the appropriate clinician, and effective intervention. Methods: Applying a user-centered systems engineering design approach, we designed and implemented a surveillance-and-response system to improve the detection and response to clinical deterioration in cancer outpatients. The surveillance system predicts 7-day risk of UTEs, defined as clinically meaningful changes in the patient’s treatment course or cancer care pathway (e.g., any unplanned/unexpected: clinic or ER visit, hospital admission, or major treatment change and/or delays, and/or death). Data inputs consist of: 1) patient activity and health data collected by a Fitbit monitor; 2) geolocation data to measure activity outside the home (i.e., locations preselected at study onset); 3) clinical data from the hospital’s electronic health record; and 4) patient-reported outcomes measures (i.e., PROMs; the NCCN Distress Thermometer, the Comprehensive OpeN-Ended Survey or CONES, Global Health Score, items from the Consumer Assessment of Healthcare Providers and Systems (CAHPS)). Herein, we measured the effectiveness of Fitbit data alone to UTEs in a pilot sample of patients. Dimension reduction of Fitbit variables was first carried out by using Pearson correlation analysis to eliminate redundant variables. As UTEs are rare events, they were oversampled using the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset. A random forest classification model was trained to predict 7-day UTE risk. Model accuracy was determined by calculating the mean of Stratified 5-Fold Cross-Validation with 10 repeats. Results: Fitbit data was collected over a 6-8-week period from 14 head and neck cancer patients receiving surgical resection, outpatient chemotherapy, and/or radiotherapy. We identified six UTEs in 5 patients. A random forest classification model was developed from 10 variables derived from 7 Fitbit measures. The following variables were averaged or summed daily: average heart rate (HR), resting HR, below 50% or zone 1 of maximum HR, zone 2 and zone 3 HR combined (i.e., 70-100% of max HR), total daily calories, steps, and sleep in minutes. We achieved a model accuracy of 94% (ROC AUC: 0.984, Precision-Recall AUC: 0.985). Conclusions: Activity and health data collected by a commercial activity monitor demonstrated effectiveness in predicting patient UTEs when an oversampling procedure was used to adjust for class imbalance (i.e., low UTE rate). Future studies are recommended to verify and validate this result in a larger patient sample.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification

A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

2012 ◽  
Vol 605-607 ◽  
pp. 2442-2446
Author(s):  
Xin Ran Li ◽  
Yan Xia Jin
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Convergence Problem ◽  
Early Maturity ◽  
Classical Function ◽  
Function Test ◽  
Quantum Behavior ◽  
Swarm Algorithm

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

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