scholarly journals Study on Evaluation of Machine Learning Approaches in Brain Tumour MR Images

2021 ◽  
Vol 12 (5) ◽  
pp. 1361-1371
Author(s):  
Nisha Joseph, Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumour ◽  
Performance Metrics ◽  
Dice Similarity Coefficient ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Supervised Segmentation ◽  
Segmentation Methods ◽  
Segmentation Approach

The principal intention of this work is to compare the performance of the supervised brain tumour segmentation methods. These segmentation methods are based on machine learning. First, the input MR brain image is denoised by employing the adaptive bilateral filter, and the image contrast is enhanced employing the histogram equalization. Then we retrieve the features from the pre-processed image. Among several feature extraction methods, this work uses the shape, intensity, and texture feature extractors. Subsequent to removing these three types of features, fragment the tumor dependent on these recovered segments. The supervised segmentation approach is used for this. Among several supervised segmentation methods, this work uses three machine learning methods, namely Probabilistic Neural Network (PNN), Artificial Neural Network (ANN), and Convolution Neural Network (CNN). Finally, the retrieved features are feed into these machine learning methods to segment the brain tumour regions. To find out the best machine learning approach, the performance of these three supervised machines learning methods is evaluated by four performance metrics. Based on these evaluations, the best segmentation approach is discovered. Four execution boundaries are utilized, in particular, Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), Jaccard list (JI), and Sensitivity (SEN) to analyze the presentation of the AI strategy. The experimental outputs exposed that the CNN makes greater than other methods.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

scholarly journals Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 771
Author(s):  
Toshiya Arakawa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Markov Models ◽  
Tracking System ◽  
Video Tracking ◽  
Training Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

Landslide susceptibility mapping based on convolutional neural network and conventional machine learning methods

2021 ◽  
Author(s):  
Rui Liu ◽  
Xin Yang ◽  
Chong Xu ◽  
Luyao Li ◽  
Xiangqiang Zeng
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine

Abstract Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced Convolutional Neural Network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected the Jiuzhaigou region in Sichuan Province, China as the study area. A total number of 710 landslides and 12 predisposing factors were stacked to form spatial datasets for LSM. The ROC analysis and several statistical metrics, such as accuracy, root mean square error (RMSE), Kappa coefficient, sensitivity, and specificity were used to evaluate the performance of the models in the training and validation datasets. Finally, the trained models were calculated and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine-learning based models have a satisfactory performance (AUC: 85.72% − 90.17%). The CNN based model exhibits excellent good-of-fit and prediction capability, and achieves the highest performance (AUC: 90.17%) but also significantly reduces the salt-of-pepper effect, which indicates its great potential of application to LSM.

Detecting Items with the Biggest Weight Based on Neural Network and Machine Learning Methods

2020 ◽  
pp. 383-396
Author(s):  
Vitaliy Danylyk ◽  
Victoria Vysotska ◽  
Vasyl Lytvyn ◽  
Svitlana Vyshemyrska ◽  
Iryna Lurie ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Multi-label pathway prediction based on active dataset subsampling

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J. Hallam
Keyword(s):  
Machine Learning ◽  
Microbial Communities ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Training Data ◽  
Great Promise ◽  
Biological Organization ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pathway Prediction

AbstractMachine learning methods show great promise in predicting metabolic pathways at different levels of biological organization. However, several complications remain that can degrade prediction performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of enzymes and pathways within a dataset. This class imbalance problem is commonly encountered by the machine learning community when the proportion of instances over class labels within a dataset are uneven, resulting in poor predictive performance for underrepresented classes. Here, we present leADS, multi-label learning based on active dataset subsampling, that leverages the idea of subsampling points from a pool of data to reduce the negative impact of training loss due to class imbalance. Specifically, leADS performs an iterative process to: (i)-construct an acquisition model in an ensemble framework; (ii) select informative points using an appropriate acquisition function; and (iii) train on selected samples. Multiple base learners are implemented in parallel where each is assigned a portion of labeled training data to learn pathways. We benchmark leADS using a corpora of 10 experimental datasets manifesting diverse multi-label properties used in previous pathway prediction studies, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previously reported machine learning methods for both organismal and multi-organismal genomes while establishing an extensible framework for navigating class imbalances across diverse real world datasets.Availability and implementationThe software package, and installation instructions are published on github.com/[email protected]

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

scholarly journals Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

2019 ◽  
Vol 11 (12) ◽  
pp. 1440 ◽  
Author(s):  
Qiangqiang Yuan ◽  
Shuwen Li ◽  
Linwei Yue ◽  
Tongwen Li ◽  
Huanfeng Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Content ◽  
Linear Regression Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vegetation Water Content ◽  
Drought Prediction ◽  
Vegetation Water ◽  
Surface Fusion

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

scholarly journals Application of Machine Learning Algorithms for Solving Problems in the Oil and Gas Sector

2021 ◽  
Vol 19 (3) ◽  
pp. 55-64
Author(s):  
K. N. Maiorov ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Machine Learning Algorithms ◽  
Design Stage ◽  
Learning Methods ◽  
Field Development ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
The Neural Network

The paper examines the life cycle of field development, analyzes the processes of the field development design stage for the application of machine learning methods. For each process, relevant problems are highlighted, existing solutions based on machine learning methods, ideas and problems are proposed that could be effectively solved by machine learning methods. For the main part of the processes, examples of solutions are briefly described; the advantages and disadvantages of the approaches are identified. The most common solution method is feed-forward neural networks. Subject to preliminary normalization of the input data, this is the most versatile algorithm for regression and classification problems. However, in the problem of selecting wells for hydraulic fracturing, a whole ensemble of machine learning models was used, where, in addition to a neural network, there was a random forest, gradient boosting and linear regression. For the problem of optimizing the placement of a grid of oil wells, the disadvantages of existing solutions based on a neural network and a simple reinforcement learning approach based on Markov decision-making process are identified. A deep reinforcement learning algorithm called Alpha Zero is proposed, which has previously shown significant results in the role of artificial intelligence for games. This algorithm is a decision tree search that directs the neural network: only those branches that have received the best estimates from the neural network are considered more thoroughly. The paper highlights the similarities between the tasks for which Alpha Zero was previously used, and the task of optimizing the placement of a grid of oil producing wells. Conclusions are made about the possibility of using and modifying the algorithm of the optimization problem being solved. Аn approach is proposed to take into account symmetric states in a Monte Carlo tree to reduce the number of required simulations.

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