Ensemble Deep Learning Research Articles

Efficient brain tumor grade classification using ensemble deep learning models

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

An Improving Lung Disease Detection by Combining Ensemble Deep Learning and Maximum Mean Discrepancy Transfer Learning

An Improving Lung Disease Detection by Combining Ensemble Deep Learning and Maximum Mean Discrepancy Transfer Learning

Unveiling Agricultural Soil Runoff: Remote Sensing and Ensemble Deep Learning Models to Evaluate Impact of Climate on Water Quality and Human Health

Unveiling Agricultural Soil Runoff: Remote Sensing and Ensemble Deep Learning Models to Evaluate Impact of Climate on Water Quality and Human Health

Deep Learning Ensemble for Flood Probability Analysis

Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, and challenges in spatial and temporal scaling. This research innovatively uses only three remotely sensed and computed factors: rainfall, runoff and temperature. We also employ three deep learning models—Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—along with a deep neural network ensemble (DNNE) using synthetic data to predict future flood probabilities, utilizing the Savitzky–Golay filter for smoothing. Using a hydrometeorological dataset from 1993–2022 for the Nile River basin, six flood predictors were derived. The FNN and LSTM models exhibited high accuracy and stable loss, indicating minimal overfitting, while the CNN showed slight overfitting. Performance metrics revealed that FNN achieved 99.63% accuracy and 0.999886 ROC AUC, CNN had 95.42% accuracy and 0.893218 ROC AUC, and LSTM excelled with 99.82% accuracy and 0.999967 ROC AUC. The DNNE outperformed individual models in reliability and consistency. Runoff and rainfall were the most influential predictors, while temperature had minimal impact.

Automated System for Credibility Diagnosis of Forged Facial Images Using Ensemble Deep Learning and Explainable AI

Automated System for Credibility Diagnosis of Forged Facial Images Using Ensemble Deep Learning and Explainable AI

Cough2COVID-19 detection using an enhanced multi layer ensemble deep learning framework and CoughFeatureRanker

In response to the pressing requirement for precise and easily accessible COVID-19 detection methods, we present the Cough2COVID-19 framework, which is cost-effective, non-intrusive, and widely accessible. The conventional diagnostic methods, notably the PCR test, are encumbered by limitations such as cost and invasiveness. Consequently, the exploration of alternative solutions has gained momentum. Our innovative approach employs a multi-layer ensemble deep learning (MLEDL) framework that capitalizes on cough audio signals to achieve heightened efficiency in COVID-19 detection. This study introduces the Cough2COVID-19 framework, effectively addressing these challenges through AI-driven analysis. Additionally, this study proposed the CoughFeatureRanker algorithm, which delves into the robustness of pivotal features embedded within cough audios. The CoughFeatureRanker algorithm selects the most prominent features based on their optimal discriminatory performance from 15 features to detect COVID-19. The effectiveness of the CoughFeatureRanker algorithm within the ensemble framework is scrutinized, confirming its favorable influence on the accuracy of COVID-19 detection. The Cough2COVID-19 (MLEDL) framework achieves remarkable outcomes in COVID-19 detection through cough audio signals, boasting a specificity of 98%, sensitivity of 97%, accuracy of 98%, and an AUC score of 0.981. Our framework asserts its supremacy in precise non-invasive screening through an exhaustive comparison with cutting-edge methodologies. This groundbreaking innovation holds the potential to enhance urban resilience by transforming disease diagnosis, offering a significant approach to curtailing transmission risks and facilitating timely interventions in the ongoing battle against the pandemic.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19.

Multimodal medical image fusion methods, which combine complementary information from many multi-modality medical images, are among the most important and practical approaches in numerous clinical applications. Various conventional image fusion techniques have been developed for multimodality image fusion. Complex procedures for weight map computing, fixed fusion strategy and lack of contextual understanding remain difficult in conventional and machine learning approaches, usually resulting in artefacts that degrade the image quality. This work proposes an efficient hybrid learning model for medical image fusion using pre-trained and non-pre-trained networks i.e. VGG-19 and SNN with stacking ensemble method. The model leveraging the unique capabilities of each architecture, can effectively preserve the detailed information with high visual quality, for numerous combinations of image modalities in image fusion challenges, notably improved contrast, increased resolution, and lower artefacts. Additionally, this ensemble model can be more robust in the fusion of various combinations of source images that are publicly available from Havard-Medical-Image-Fusion Datasets, GitHub. and Kaggle. Our proposed model performance is superior in terms of visual quality and performance metrics to that of the existing fusion methods in literature like PCA+DTCWT, NSCT, DWT, DTCWT+NSCT, GADCT, CNN and VGG-19.

Skin lesion classification by weighted ensemble deep learning

Skin lesion classification by weighted ensemble deep learning

Early detection of serious but struggling learners in MOOCs using ensemble deep learning

ABSTRACT This study works on the early identification of serious but struggling students in Massive Open Online Courses (MOOCs), which facilitates a transition to a hybrid learning format. Firstly, we exclusively utilize basic behavior log features that are universally available across all MOOC platforms. Secondly, we encode each learning activity with a distinct identifier, which helps us utilize the current advances in NLP to address our problem. Thirdly, we seamlessly integrate our LSTM model with a Logistic Regression model, configured as a single dense layer with a sigmoid activation function, within the broader framework of deep learning.

Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT

In Industry 4.0, information and communication technology (ICT) was employed in numerous significant infrastructures, like financial networks, smart factories, and power plants, to automate and certify industrial systems. In power control systems, ICT technologies such as IIoT have improved automated monitoring, but legacy methods, originally autonomous, now connect with external networks. This progress has presented safety vulnerabilities from legacy ICT systems. Hence, various cybersecurity approaches are developed and examined to deal with cyberattacks and vulnerabilities. Utilizing new cybersecurity models in power control systems poses risks due to their uncertified safety. Ensuring their stability and efficiency is significant for maintaining reliable power delivery and incorporating these technologies into power control systems. Therefore, this study designs a Next–Generation Cybersecurity Attack Detection using an ensemble deep learning model (NGCAD-EDLM) technique in the IIoT environment. The main cause of the NGCAD-EDLM technique is the automatic recognition of cyber-attacks. In the NGCAD-EDLM approach, the primary data normalization phase utilizing min-max normalization is performed. Next, the honey-badger algorithm (HBA) approach selects the feature subsets. Furthermore, an ensemble deep learning (DL) of two methods, namely convolutional neural networks (CNNs) and deep belief networks (DBNs) methods, are employed for classification. In addition, the DL techniques' hyperparameter selection is accomplished using the lotus effect optimization algorithm (LEOA) method. A complete set of simulation validation is performed to establish the experimental analysis of the NGCAD-EDLM method. The performance validation of the NGCAD-EDLM method exhibited a superior accuracy value of 99.21 % over other existing techniques.

Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture.

Dry age-related macular degeneration (AMD) is a retinal disease, which has been the third leading cause of vision loss. But current AMD classification technologies did not focus on the classification of early stage. This study aimed to develop a deep learning architecture to improve the classification accuracy of dry AMD, through the analysis of optical coherence tomography (OCT) images. We put forward an ensemble deep learning architecture which integrated four different convolution neural networks including ResNet50, EfficientNetB4, MobileNetV3 and Xception. All networks were pre-trained and fine-tuned. Then diverse convolution neural networks were combined. To classify OCT images, the proposed architecture was trained on the dataset from Shenyang Aier Excellence Hospital. The number of original images was 4,096 from 1,310 patients. After rotation and flipping operations, the dataset consisting of 16,384 retinal OCT images could be established. Evaluation and comparison obtained from three-fold cross-validation were used to show the advantage of the proposed architecture. Four metrics were applied to compare the performance of each base model. Moreover, different combination strategies were also compared to validate the merit of the proposed architecture. The results demonstrated that the proposed architecture could categorize various stages of AMD. Moreover, the proposed network could improve the classification performance of nascent geographic atrophy (nGA). In this article, an ensemble deep learning was proposed to classify dry AMD progression stages. The performance of the proposed architecture produced promising classification results which showed its advantage to provide global diagnosis for early AMD screening. The classification performance demonstrated its potential for individualized treatment plans for patients with AMD.

Real-Time Liver Tumor Detection with a Multi-Class Ensemble Deep Learning Framework

Real-Time Liver Tumor Detection with a Multi-Class Ensemble Deep Learning Framework

Reduction of Misclassifications in Wildfire Detection: A Weighted Ensemble Deep Learning Approach

Governments worldwide are increasingly prioritizing early wildfire detection to safeguard lives, property, and the environment. Although CNN-based models have demonstrated exceptional performance in various computer vision applications, the evolving nature of wildfire images poses significant challenges for a single CNN-based model in wildfire detection. In this study, we addressed this issue by integrating and weighting the differential learning capabilities of three individual transfer learning models: InceptionV3, ResNet50, and VGG16. Experimental results show that the ensemble deep learning models significantly outperformed all single classifiers across all performance metrics. Both the ensemble and weighted ensemble deep learning models achieved 99.7% accuracy, 99.5% precision, 100% recall, 99.8% F1-score, 0.5%false positive rate, 0.0% false negative rate and 0.3% error rate. Additionally, these models reduced the error rate by 98%, 91%, and 40% compared to the error rates of ResNet50, InceptionV3, and VGG16 respectively. A false negative rate of 0% indicates that our proposed ensemble deep learning models identified and predicted all the wildfire instances present in the test set correctly without a single misclassification. This positions our proposed ensemble deep learning models as superior choices for reducing misclassifications in wildfire detection.

Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms.

AI-enhanced 12-lead ECG can detect a range of structural heart diseases (SHDs) but has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHD and predict the risk of their development using wearable/portable devices. Using 266,740 ECGs from 99,205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed ADAPT-HEART, a noise-resilient, deep-learning algorithm, to detect SHD using lead I ECG. SHD was defined as a composite of LVEF<40%, moderate or severe left-sided valvular disease, and severe LVH. ADAPT-HEART was validated in four community hospitals in the US, and the population-based cohort of ELSA-Brasil. We assessed the model's performance as a predictive biomarker among those without baseline SHD across hospital-based sites and the UK Biobank. The development population had a median age of 66 [IQR, 54-77] years and included 49,947 (50.3%) women, with 18,896 (19.0%) having any SHD. ADAPT-HEART had an AUROC of 0.879 (95% CI, 0.870-0.888) with good calibration for detecting SHD in the test set, and consistent performance in hospital-based external sites (AUROC: 0.852-0.891) and ELSA-Brasil (AUROC: 0.859). Among those without baseline SHD, high vs. low ADAPT-HEART probability conferred a 2.8- to 5.7-fold increase in the risk of future SHD across data sources (all P<0.05). We propose a novel model that detects and predicts a range of SHDs from noisy single-lead ECGs obtainable on portable/wearable devices, providing a scalable strategy for community-based screening and risk stratification for SHD.

DELM: Deep Ensemble Learning Model for Anomaly Detection in Malicious Network Traffic-based Adaptive Feature Aggregation and Network Optimization

With the rapid advancements in internet technology, the complexity and sophistication of network traffic attacks are increasing, making it challenging for traditional anomaly detection systems to analyze and detect malicious network attacks. The increasing advancedness of cyber threats calls for innovative approaches to identify malicious patterns within network traffic precisely. The primary issue lies in the fact that these approaches do not focus on the essential adaptive features of network traffic. We proposed an effective anomaly detection system for malicious network traffic attacks called the Deep Ensemble Learning Model (DELM). We leverage the structure of the Feedforward Deep Neural Network (FDNN), and Deep Belief Network (DBN), incorporating multiple hidden layers with non-linear activation functions. Integrating Adaptive Feature Aggregation (AFA) with the FDNN algorithm dynamically adjusts the feature aggregation process based on incoming traffic characteristics to improve adaptability. The Conditional Generative Network was employed to enhance DELM for generating data for minority classes. To improve the model’s accuracy, we applied batch normalization and data augmentation techniques for preprocessing, utilized n-gram, one-hot encoding, and feature aggregation methods for effective feature extraction. This study significantly contributes to network security by enhancing systems for detecting malicious network traffic. With its interpretability and adaptability, our proposed model shows promise in addressing the evolving cyber threat and fortifying critical network infrastructure. The experimental results demonstrate that our model performs with higher stability than the existing state-of-the-art detection approaches, as reflected by its higher accuracy, precision, recall, F1-score, and AUC-ROC.

An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.

Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility. To leverage 12-lead ECG images for automated detection and prediction of multiple SHDs using a novel deep learning model. We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH). SHDs were defined based on TTEs with LV ejection fraction <40%, moderate-to-severe left-sided valvular disease (aortic/mitral stenosis or regurgitation), or severe left ventricular hypertrophy (IVSd > 1.5cm and diastolic dysfunction). We developed an ensemble XGBoost model, PRESENT-SHD, as a composite screen across all SHDs. We validated PRESENT-SHD at 4 US hospitals and a prospective population-based cohort study, the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), with concurrent protocolized ECGs and TTEs. We also used PRESENT-SHD for risk stratification of new-onset SHD or heart failure (HF) in clinical cohorts and the population-based UK Biobank (UKB). The models were developed using 261,228 ECGs from 93,693 YNHH patients and evaluated on a single ECG from 11,023 individuals at YNHH (19% with SHD), 44,591 across external hospitals (20-27% with SHD), and 3,014 in the ELSA-Brasil (3% with SHD). In the held-out test set, PRESENT-SHD demonstrated an AUROC of 0.886 (0.877-894), sensitivity of 89%, and specificity of 66%. At hospital-based sites, PRESENT-SHD had AUROCs ranging from 0.854-0.900, with sensitivities and specificities of 93-96% and 51-56%, respectively. The model generalized well to ELSA-Brasil (AUROC, 0.853 [0.811-0.897], sensitivity 88%, specificity 62%). PRESENT-SHD performance was consistent across demographic subgroups. A positive PRESENT-SHD screen portended a 2- to 4-fold higher risk of new-onset SHD/HF, independent of demographics, comorbidities, and the competing risk of death across clinical sites and UKB, with high predictive discrimination. We developed and validated PRESENT-SHD, an AI-ECG tool identifying a range of SHD using images of 12-lead ECGs, representing a robust, scalable, and accessible modality for automated SHD screening and risk stratification. Screening for structural heart disorders (SHDs) requires cardiac imaging, which has limited accessibility. To leverage 12-lead ECG images for automated detection and prediction of multiple SHDs, we developed PRESENT-SHD, an ensemble deep learning-based model. PRESENT-SHD demonstrated excellent performance for detecting SHDs across 5 US hospitals and a population-based cohort in Brazil. The model successfully predicted the risk of new-onset SHD or heart failure in both US clinical cohorts and the community-based UK Biobank. By using ubiquitous ECG images to predict a composite outcome of multiple SHDs, PRESENT-SHD establishes a scalable paradigm for cardiovascular screening and risk stratification.

Towards Improving E-Commerce Customer Review Analysis for Arabic Language Opinion Mining

In recent years, the rapid development of Internet-related technologies has facilitated the widespread adoption of online purchasing as a convenient means of satisfying consumer needs. Conducting sentiment analysis on user reviews on e-commerce platforms can substantially improve customer satisfaction. In order to resolve this issue, we propose a novel model for sentiment analysis that employs hybrid deep learning ensembles, combining RNN and TreeLSTM with AraBERT as the word embedding. Our research concentrates on creating a hybrid deep-learning model to predict Arabic sentiment accurately. We employ deep learning models with various word embeddings, such as RNN, LSTM, BiLSTM, TreeLSTM, RNN-LSTM, RNN-BiLSTM, and RNN-TreeLSTM. Multiple open-access datasets are used to evaluate the efficacy of the proposed model, including the BRAD dataset, the ARD dataset, and merged datasets containing 610,600 items. The experimental findings indicate that our proposed model is well-suited for evaluating the sentiments expressed in Arabic texts. Our strategy starts with extracting features using the Arabert model, followed by developing and training five hybrid deep-learning models. We attained a significant accuracy improvement of 0.9409 when comparing our method to traditional and hybrid deep learning techniques. This demonstrates that our proposed model precisely analyzes sentiment. These findings are important for enhancing the comprehension of emotions conveyed in Arabic text and have practical implications for various applications, especially e-commerce. By accurately assessing sentiment, businesses can better comprehend customer preferences and enhance consumer satisfaction by enhancing their offerings.

Groundnut (ARACHIS HYPOGAEA L.) seed defect classification using ensemble deep learning techniques

Groundnut is an oil seed cash crop, that can be consumed directly by humans and animals. It is also used as an intelligence in the industrial production of butter and other products. However, Groundnut seeds can become infected with fungi, viruses, pests, physical damage, and high heat, which can negatively impact crop yield, quality, and economic value, and pose health hazards. To address these challenges, computer vision technology detects and classifies defects. This study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images. To enhance model performance, collected images are preprocessed using novel techniques tailored to different image types. Preprocessing techniques are chosen based on image-quality evaluation metrics. Watershed segmentation is applied followed by detecting the region of interest in the image using YOLOv3. The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. The ensemble model achieves an accuracy of 96.25 % with a split ratio of 10 % for testing, 10 % for validation, and 80 % for training set. This research benefits farmers looking to improve yield, consumers of groundnut products, and researchers studying seed defects. The work provides valid insights for future researchers regarding dataset creation, augmentation, methods, and feature selection for modeling. However, the model experienced misclassifications due to the similar appearance of sample images in different classes. Future researchers should address these challenges and consider factors such as oil content and defect grading.

Prediction of delivery truck arrivals at container terminals: an ensemble deep learning model

Prediction of delivery truck arrivals at container terminals: an ensemble deep learning model