Search
Search Results
-
A New Random Forest and Support Vector Machine-based Intrusion Detection Model in Networks
There exist many intrusion detection systems (IDSs) to provide privacy and security to user data in networks. However, these models are prone to...
-
Interpreting random forest analysis of ecological models to move from prediction to explanation
As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to...
-
Alzheimer’s disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm
Alzheimer’s disease (AD) is a physical illness, which damages a person’s brain; it is the most common cause of dementia. AD can be characterized by...
-
A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models
The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of...
-
Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers...
-
High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in...
-
Analyzing the scale dependent effect of urban building morphology on land surface temperature using random forest algorithm
With continuous urban densification, revealing impacts of urban structures on thermal environment is necessary for climate adaptive design. In this...
-
Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor
Small-scale distributed water treatment equipment such as sequencing batch reactor (SBR) is widely used in the field of rural domestic sewage...
-
Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram
Cuffless blood pressure measurement enables unobtrusive and continuous monitoring that can be integrated with wearable devices to extend healthcare...
-
Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by Shapley value analysis
The random forest (RF) and support vector machine (SVM) methods are mainstays in molecular machine learning (ML) and compound property prediction. We...
-
Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes
Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been...
-
Development of alkali activated paver blocks for medium traffic conditions using industrial wastes and prediction of compressive strength using random forest algorithm
Geopolymer is an environment friendly construction material that could be synthesized using either the natural source or the industrial byproducts...
-
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms
Evaporation is the primary aspect causing water loss in the hydrological cycle; therefore, water loss must be precisely measured. Evaporation is an...
-
RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard...
-
Remote sensing inversion on heavy metal content in salinized soil of Yellow River Delta based on Random Forest Regression—a case study of Gudao Town
To explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal...
-
How to improve SME performance using iterative random forest in the empirical analysis of institutional complementaritty
Empirically investigating the workings of institutional complementarity in organisations has been a challenge in the social sciences domain for a...
-
Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest
Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common...
-
Using random forest to identify longitudinal predictors of health in a 30-year cohort study
Due to the wealth of exposome data from longitudinal cohort studies that is currently available, the need for methods to adequately analyze these...
-
Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method
Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While...
-
40 years of forest dynamics and tree demography in an intact tropical forest at M’Baïki in central Africa
A vast silvicultural experiment was set up in 1982 nearby the town of M’Baïki in the Central African Republic to monitor the recovery of tropical...
