Data Mining by Soft Computing Methods for The Coronary Heart Disease Database

For improvement of data mining technology, the advantages and disadvantages on respective data mining methods should be discussed by comparison under the same condition. For this purpose, the Coronary Heart Disease database (CHD DB) was developed in 2004, and the data mining competition was held in the International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). In the competition, two methods based on soft computing were presented. In this paper, we report the overview of the CHD DB and the soft computing methods, and discuss the features of respective methods by comparison of the experimental results

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs

COVID-19 Outbreak Prediction with Machine Learning

Abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high Level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis ofmachine learning and soft computingmodels to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine Learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.publishedVersio

COVID-19 Outbreak Prediction with Machine Learning

Abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high Level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis ofmachine learning and soft computingmodels to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine Learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.publishedVersio

Inverse design of core-shell particles with discrete material classes using neural networks

The design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this paper, we propose a machine learning-assisted optimization framework to design multi-layered core-shell particles that provide a scattering response on demand. Artificial neural networks can learn to predict the scattering spectrum of core-shell particles with high accuracy and can act as fully differentiable surrogate models for a gradient-based design approach. To enable the fabrication of the particles, we consider existing materials and introduce a novel two-step optimization to treat continuous geometric parameters and discrete feasible materials simultaneously. Moreover, we overcome the non-uniqueness of the problem and expand the design space to particles of varying numbers of shells, i.e., different number of optimization parameters, with a classification network. Our method is 1–2 orders of magnitudes faster than conventional approaches in both forward prediction and inverse design and is potentially scalable to even larger and more complex scatterers

Expert Systems and Artificial Neural Networks for Spatial Analysis and Modelling: Essential Components for Knowledge-Based Geographical Information Systems

Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Black shale lithofacies prediction and distribution Pattern analysis of middle Devonian Marcellus Shale in the Appalachian Basin, northeastern U.S.A.

The Marcellus Shale, marine organic-rich mudrock deposited during Middle Devonian in the Appalachian basin, is considered the largest unconventional shale-gas resource in United State. Although homogeneous in the appearance, the mudstone shows heterogeneity in mineral composition, organic matter richness, gas content, and fracture density. Two critical factors for unconventional mudstone reservoirs are units amenable to hydraulic fracture stimulation and rich of organic matter. The effectiveness of hydraulic fracture stimulation is influenced by rock geomechanical properties, which are related to rock mineralogy. The natural gas content in mudrock reservoirs has a strong relationship with organic matter, which is measured by total organic carbon (TOC). In place of using petrographic information and sedimentary structures, Marcellus Shale lithofacies were based on mineral composition and organic matter richness and were predicted by conventional logs to make the lithofacies \u27meaningful’, ‘predictable’ and ‘mappable’ at multiple scales from the well bore to basin. Core X-ray diffraction (XRD) and TOC data was used to classify Marcellus Shale into seven lithofacies according to three criteria: clay volume, the ratio of quartz to carbonate, and TOC. Pulsed neutron spectroscopy (PNS) logs provide similar mineral concentration and TOC content, and were used to classify shale lithofacies by the same three criteria. Artificial neural network (ANN) with improvements (i.e., learning algorithms, performance function and topology design) was utilized to predict Marcellus Shale lithofacies in 707 wells with conventional logs. To improve the effectiveness of wireline logs to predict lithofacies, the effects of barite and pyrite were partly removed and eight petrophysical parameters commonly used for a conventional reservoir analysis were derived from conventional logs by petrophysical analysis. These parameters were used as input to the ANN analysis. Geostatistical analysis was used to develop the experimental variogram models and vertical proportion of each lithofacies. Indictor kriging, truncated Gaussian simulation (TGS), and sequential indicator simulation (SIS) were compared, and SIS algorithm performed well for modeling Marcellus Shale lithofacies in three-dimensions. Controlled primarily by sediment dilution, organic matter productivity, and organic matter preservation/decomposition, Marcellus Shale lithofacies distribution was dominantly affected by the water depth and the distance to shoreline. The Marcellus Shale lithofacies with the greatest organic content and highest measure of brittleness is concentrated along a crescent shape region paralleling the inferred shelf and shoreline, showing shape of crescent paralleling with shoreline. The normalized average gas production rate from horizontal wells supported the proposed approach to modeling Marcellus Shale lithofacies. The proposed 3-D modeling approach may be helpful for (1) investigating the distribution of each lithofacies at a basin-scale; (2) developing a better understanding of the factors controlling the deposition and preservation of organic matter and the depositional model of marine organic-rich mudrock; (3) identifying organic-rich units and areas and brittle units and areas in shale-gas reservoirs; (4) assisting in the design of horizontal drilling trajectories and location of stimulation activity; and (5) providing input parameters for the simulation of gas flow and production in mudrock (e.g., porosity, permeability and fractures)

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.Comment: 18 pages, 8 figures, published in Electronics MDPI journa