scholarly journals Model for Evaluation of Stock Values by Ensemble Model Using Deep Learning

2018 ◽  
Vol 33 (1) ◽  
pp. A-H51_1-11 ◽  
Author(s):  
Koichiro Tamura ◽  
Katsuya Uenoyama ◽  
Shuhei Iitsuka ◽  
Yutaka Matsuo
Keyword(s):  
Deep Learning ◽  
Ensemble Model ◽  
Stock Values

Diagnosing breast cancer tumors using stacked ensemble model

2021 ◽  
pp. 1-9
Author(s):  
Ahmet Haşim Yurttakal ◽  
Hasan Erbay ◽  
Türkan İkizceli ◽  
Seyhan Karaçavuş ◽  
Cenker Biçer
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Stage ◽  
False Negative ◽  
Gradient Boosting ◽  
Physical Sign ◽  
Ensemble Model ◽  
Learning Methods ◽  
Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

scholarly journals An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3020
Author(s):  
Anam-Nawaz Khan ◽  
Naeem Iqbal ◽  
Atif Rizwan ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Cluster Analysis ◽  
Deep Learning ◽  
Residential Buildings ◽  
Ensemble Forecasting ◽  
Forecasting Model ◽  
Ensemble Model ◽  
Learning Models ◽  
Short Term ◽  
Floor Level ◽  
Consumption Data

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-24
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Final Diagnosis ◽  
Learning Rate ◽  
Ensemble Model ◽  
Fundus Images ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

scholarly journals Efficient Deep Learning Models for DGA Domain Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Juhong Namgung ◽  
Siwoon Son ◽  
Yang-Sae Moon
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Ensemble Model ◽  
Learning Models ◽  
Short Term ◽  
Domain Names ◽  
Additional Information ◽  
Domain Sequence ◽  
Long Short Term Memory ◽  
And Control

In recent years, cyberattacks using command and control (C&C) servers have significantly increased. To hide their C&C servers, attackers often use a domain generation algorithm (DGA), which automatically generates domain names for the C&C servers. Accordingly, extensive research on DGA domain detection has been conducted. However, existing methods cannot accurately detect continuously generated DGA domains and can easily be evaded by an attacker. Recently, long short-term memory- (LSTM-) based deep learning models have been introduced to detect DGA domains in real time using only domain names without feature extraction or additional information. In this paper, we propose an efficient DGA domain detection method based on bidirectional LSTM (BiLSTM), which learns bidirectional information as opposed to unidirectional information learned by LSTM. We further maximize the detection performance with a convolutional neural network (CNN) + BiLSTM ensemble model using Attention mechanism, which allows the model to learn both local and global information in a domain sequence. Experimental results show that existing CNN and LSTM models achieved F1-scores of 0.9384 and 0.9597, respectively, while the proposed BiLSTM and ensemble models achieved higher F1-scores of 0.9618 and 0.9666, respectively. In addition, the ensemble model achieved the best performance for most DGA domain classes, enabling more accurate DGA domain detection than existing models.

scholarly journals DELPHI: accurate deep ensemble model for protein interaction sites prediction

2020 ◽  
Author(s):  
Yiwei Li ◽  
G Brian Golding ◽  
Lucian Ilie
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Binding Sites ◽  
Web Server ◽  
Fine Tuning ◽  
Supplementary Information ◽  
Ensemble Model ◽  
Position Information ◽  
Interaction Sites ◽  
Protein Interaction Sites

Abstract Motivation Proteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein–protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods. Results We propose DEep Learning Prediction of Highly probable protein Interaction sites (DELPHI), a new sequence-based deep learning suite for PPI-binding sites prediction. DELPHI has an ensemble structure which combines a CNN and a RNN component with fine tuning technique. Three novel features, HSP, position information and ProtVec are used in addition to nine existing ones. We comprehensively compare DELPHI to nine state-of-the-art programmes on five datasets, and DELPHI outperforms the competing methods in all metrics even though its training dataset shares the least similarities with the testing datasets. In the most important metrics, AUPRC and MCC, it surpasses the second best programmes by as much as 18.5% and 27.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model and, especially, the three new features. Using DELPHI it is shown that there is a strong correlation with protein-binding residues (PBRs) and sites with strong evolutionary conservation. In addition, DELPHI’s predicted PBR sites closely match known data from Pfam. DELPHI is available as open-sourced standalone software and web server. Availability and implementation The DELPHI web server can be found at delphi.csd.uwo.ca/, with all datasets and results in this study. The trained models, the DELPHI standalone source code, and the feature computation pipeline are freely available at github.com/lucian-ilie/DELPHI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1942
Author(s):  
Pyae Pyae Phyo ◽  
Yung-Cheol Byun
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Absolute Error ◽  
Percentage Error ◽  
Ensemble Model ◽  
Load Prediction ◽  
Energy Prediction ◽  
The Times ◽  
One Year

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

scholarly journals A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization

2021 ◽  
Vol 33 (2) ◽  
pp. 93-114
Author(s):  
Mallika G.C. ◽  
Abeer Alsadoon ◽  
Duong Thu Hang Pham ◽  
Salma Hameedi Abdullah ◽  
Ha Thi Mai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Deep Learning ◽  
Intelligent System ◽  
Model Solution ◽  
Majority Voting ◽  
Ensemble Model ◽  
Pima Indians ◽  
Learning Models

Type 2 Diabetes (T2DM) makes up about 90% of diabetes cases, as well as tough restriction on continuous monitoring and detecting become one of key aspects in T2DM. This research aims to develop an ensemble of several machine learning and deep learning models for early detection of T2DM with high accuracy. With high diversity of models, the ensemble will provide more excessive performance than single models. Methodology: The proposed system is modified enhanced ensemble of machine learning models for T2DM prediction. It is composed of Logistic Regression, Random Forest, SVM and Deep Neural Network models to generate a modified ensemble model. Results: The output of each model in the modified ensemble is used to figure out the final output of the system. The datasets being used for these models include Practice Fusion HER, Pima Indians diabetic's data, UCI AIM94 Dataset and CA Diabetes Prevalence 2014. In comparison to the previous solutions, the proposed ensemble model solution exposes the effectiveness of accuracy, sensitivity, and specificity. It provides an accuracy of 87.5% from 83.51% in average, sensitivity of 35.8% from 29.59% as well as specificity of 98.9% from 96.27%. The processing time of the proposed model solution with 96.6ms is faster than the state-of-the-art with 97.5ms. Conclusion: The proposed modified enhanced system in this work improves the overall prediction capability of T2DM using an ensemble of several machine learning and deep learning models. A majority voting scheme utilizes the output from several models to make the final accurate prediction. Regularization function in this work is modified in order to include the regularization of all the models in ensemble, that helps prevent the overfitting and encourages the generalization capacity of the proposed system.

scholarly journals Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks

2021 ◽  
pp. 60-96
Author(s):  
Gabriel Zaid ◽  
Lilian Bossuet ◽  
Amaury Habrard ◽  
Alexandre Venelli
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Public Key ◽  
Side Channel ◽  
Ensemble Model ◽  
Secret Key ◽  
Side Channel Attacks ◽  
Ensemble Models ◽  
Remaining Time ◽  
Multiple Scenarios

Deep Learning based Side-Channel Attacks (DL-SCA) are considered as fundamental threats against secure cryptographic implementations. Side-channel attacks aim to recover a secret key using the least number of leakage traces. In DL-SCA, this often translates in having a model with the highest possible accuracy. Increasing an attack’s accuracy is particularly important when an attacker targets public-key cryptographic implementations where the recovery of each secret key bits is directly related to the model’s accuracy. Commonly used in the deep learning field, ensemble models are a well suited method that combine the predictions of multiple models to increase the ensemble accuracy by reducing the correlation between their errors. Linked to this correlation, the diversity is considered as an indicator of the ensemble model performance. In this paper, we propose a new loss, namely Ensembling Loss (EL), that generates an ensemble model which increases the diversity between the members. Based on the mutual information between the ensemble model and its related label, we theoretically demonstrate how the ensemble members interact during the training process. We also study how an attack’s accuracy gain translates to a drastic reduction of the remaining time complexity of a side-channel attacks through multiple scenarios on public-key implementations. Finally, we experimentally evaluate the benefits of our new learning metric on RSA and ECC secure implementations. The Ensembling Loss increases by up to 6.8% the performance of the ensemble model while the remaining brute-force is reduced by up to 222 operations depending on the attack scenario.

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