Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽

10.3390/electronics10101195 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1195

Author(s):

Priya Varshini A G ◽

Anitha Kumari K ◽

Vijayakumar Varadarajan

Keyword(s):

Deep Learning ◽

Random Forest ◽

Software Development ◽

Weighted Averaging ◽

Software Project ◽

Effort Estimation ◽

Software Effort Estimation ◽

Single Model ◽

Ensemble Techniques ◽

Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic Adenocarcinoma: A Pilot Study

The American Surgeon ◽

10.1177/0003134820982557 ◽

2020 ◽

pp. 000313482098255

Author(s):

Michael D. Watson ◽

Maria R. Baimas-George ◽

Keith J. Murphy ◽

Ryan C. Pickens ◽

David A. Iannitti ◽

...

Keyword(s):

Deep Learning ◽

Neoadjuvant Therapy ◽

Pancreatic Adenocarcinoma ◽

Hybrid Model ◽

Tumor Response ◽

Tumor Regression ◽

Area Under The Curve ◽

Learning Model ◽

Response To Therapy ◽

Deep Learning Model

Background Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC. Methods Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve. Results A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 ( P < .001), whereas the hybrid model had an AUC of .785 ( P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 ( P = .096). Conclusions A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.

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An automatic recognition of glaucoma in fundus images using deep learning and random forest classifier

Applied Soft Computing ◽

10.1016/j.asoc.2021.107512 ◽

2021 ◽

pp. 107512

Author(s):

Shanmugam P. ◽

Raja J. ◽

Pitchai R.

Keyword(s):

Deep Learning ◽

Random Forest ◽

Automatic Recognition ◽

Random Forest Classifier ◽

Fundus Images

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An Improved Hybrid Model base on SVM and Random Forest for the Prediction of Corporate Taxation

10.1109/iccasit53235.2021.9633659 ◽

2021 ◽

Author(s):

Hui Xu ◽

Mingyang Ma

Keyword(s):

Random Forest ◽

Hybrid Model ◽

Corporate Taxation ◽

Model Base

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Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE

Journal of Applied Biomedicine ◽

10.1016/j.bbe.2020.06.001 ◽

2020 ◽

Vol 40 (3) ◽

pp. 1225-1232 ◽

Cited By ~ 1

Author(s):

Raheleh Hashemzehi ◽

Seyyed Javad Seyyed Mahdavi ◽

Maryam Kheirabadi ◽

Seyed Reza Kamel

Keyword(s):

Deep Learning ◽

Brain Tumors ◽

Hybrid Model

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Short-Term Streamflow Forecasting for Paraíba do Sul River Using Deep Learning

Progress in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-030-30241-2_43 ◽

2019 ◽

pp. 507-518

Author(s):

Luciana Conceição Dias Campos ◽

Leonardo Goliatt da Fonseca ◽

Tales Lima Fonseca ◽

Gabriel Dias de Abreu ◽

Letícia Florentino Pires ◽

...

Keyword(s):

Deep Learning ◽

Streamflow Forecasting ◽

Short Term ◽

Paraíba Do Sul River

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Forecasting stock price movement: new evidence from a novel hybrid deep learning model

Journal of Asian Business and Economic Studies ◽

10.1108/jabes-05-2021-0061 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yang Zhao ◽

Zhonglu Chen

Keyword(s):

Time Series ◽

Deep Learning ◽

Hybrid Model ◽

Stock Price ◽

Linear Time ◽

Price Movement ◽

Content Type ◽

Non Linear ◽

New Evidence ◽

Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

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