Ensemble Of Deep Neural Networks Research Articles

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks

Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the ‘segmentation-stacking’ method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image’s 90–99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99–1.00 [0.97–1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91–100%; middle cerebral arteries, 82–98%; anterior cerebral arteries, 88–100%; posterior cerebral arteries, 87–100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90–99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.

Application of deep convolutional networks for improved risk assessments of post-wildfire drinking water contamination

Climate change continues to increase the frequency of wildfires in the western United States, driven by land use change and prolonged and intensified droughts. Simultaneously, an ongoing extension of communities into wildland areas has been observed over the last decades, increasing the wildfire threat to populations in the wildland–urban interface. In recent years, the problem of increased levels of volatile organic compounds in drinking water distribution systems after wildfires has gained attention as it poses a significant health threat to communities in those wildfire-prone areas. No adequate deterministic process models to predict the risk of high levels of volatile organic compounds in drinking water distribution systems after wildfires are available at this point, leaving data-driven machine learning approaches as solutions for addressing this problem. Here we build on a preceding study and enhance the assessment of maximum contamination exceedance probability predictions by applying deep convolutional neural networks trained with water samples from communities affected by wildfires in California and Oregon. We used satellite surface reflectance imagery in combination with gridded information of topography, fuel load, meteorology, and infrastructure as model inputs. The results show that the ensemble of deep convolutional neural networks increases the accuracy of predictions of post-wildfire water contamination by 4.1% compared to preceding models based on Bayesian regularized shallow feedforward networks, reaching an overall accuracy of 92% for the test dataset. The method provides a practical approach for emergency planning and pre-fire allocation of resources to support the provision of safe drinking water for wildfire-prone communities.

Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs

AimsTo develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts.MethodsWe trained a deep convolutional ensemble (DCE), an...

Optimizing CNN‐LSTM hybrid classifier using HCA for biomedical image classification

AbstractIn medical science, imaging is the most effective diagnostic and therapeutic tool. Almost all modalities have transitioned to direct digital capture devices, which have emerged as a major future healthcare option. Three diseases such as Alzheimer's (AD), Haemorrhage (HD), and COVID‐19 have been used in this manuscript for binary classification purposes. Three datasets (AD, HD, and COVID‐19) were used in this research out of which the first two, that is, AD and HD belong to brain Magnetic Resonance Imaging (MRI) and the last one, that is, COVID‐19 belongs to Chest X‐Ray (CXR) All of the diseases listed above cannot be eliminated, but they can be slowed down with early detection and effective medical treatment. This paper proposes an intelligent method for classifying brain (MRI) and CXR images into normal and abnormal classes for the early detection of AD, HD, and COVID‐19 based on an ensemble deep neural network (DNN). In the proposed method, the convolutional neural network (CNN) is used for automatic feature extraction from images and long‐short term memory (LSTM) is used for final classification. Moreover, the Hill‐Climbing Algorithm (HCA) is implemented for finding the best possible value for hyper parameters of CNN and LSTM, such as the filter size of CNN and the number of units of LSTM while fixing the other parameters. The data‐set is pre‐processed (resized, cropped, and noise removed) before feeding the train images to the proposed models for accurate and fast learning. Forty‐five MR images of AD, Sixty MR images of HD, and 600 CXR images of COVID‐19 were used for testing the proposed model ‘CNN‐LSTM‐HCA’. The performance of the proposed model is evaluated using six types of statistical assessment metrics such as; Accuracy, Sensitivity, Specificity, F‐measure, ROC, and AUC. The proposed model compared with the other three types of hybrid models such as CNN‐LSTM‐PSO, CNN‐LSTM‐Jaya, and CNN‐LSTM‐GWO and also with state‐of‐art techniques. The overall accuracy of the proposed model received was 98.87%, 85.75%, and 99.1% for COVID‐19, Haemorrhage, and Alzheimer's data sets, respectively.

Learning ensembles of deep neural networks for extreme rainfall event detection

Accurate rainfall estimation is crucial to adequately assess the risk associated with extreme events capable of triggering floods and landslides. Data gathered from Rain Gauges (RGs), sensors devoted to measuring the intensity of the rain at individual points, are commonly used to feed interpolation methods (e.g., the Kriging geostatistical approach) and estimate the precipitation field over an area of interest. However, the information provided by RGs could be insufficient to model complex phenomena, and computationally expensive interpolation methods could not be used in real-time environments. Integrating additional data sources (e.g., radar and geostationary satellites) is an effective solution for improving the quality of the estimate, but it needs to cope with Big Data issues. To overcome all these issues, we propose a Rainfall Estimation Model (REM) based on an Ensemble of Deep Neural Networks (DeepEns-REM) that can automatically fuse heterogeneous data sources. The usage of Residual Blocks in the base models and the adoption of a Snapshot procedure to build the ensemble guarantees a fast convergence and scalability. Experimental results, conducted on a real dataset concerning a southern region in Italy, demonstrate the quality of the proposal in comparison with the Kriging interpolation technique and other machine learning techniques, especially in the case of exceptional rainfall events.

Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training

Objective. Steady-state visually evoked potentials (SSVEPs), measured with electroencephalogram (EEG), yield decent information transfer rates (ITRs) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training. Approach. We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants’ statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions. Main results. The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2–1.0] s on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility: https://github.com/osmanberke/Ensemble-of-DNNs. Significance. Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.

Cyber-Attacks in IoT-enabled Cyber-physical Systems

Cyber physical systems (CPS) that are Internet of Things (IoT) enabled might be difficult to secure since security measures designed for general data / value through the development (IT / OT) systems may not work as well in a CPS environment. Consequently, this research provides a two-level ensemble attack detection and attribution framework created for CPS, and more particularly in an industrial control system (ICS). For identifying assaults in unbalanced ICS environments, a decision tree integrated to an unique ensemble deep representation learning model is created at the first extent. An ensemble deep neural network is created for assault features at the second level. Applying actual data collections from the gas pipeline and water treatment system, Findings show that the suggested type is more effective than other competing methods with a similar level of computational complexity.

Deep Neural Network Ensembles Using Class-vs-Class Weighting

Deep Neural Network Ensembles Using Class-vs-Class Weighting

Accelerated and Adaptive Power Scheduling for More Electric Aircraft Via Hybrid Learning

In recent studies, the power scheduling in more electric aircraft (MEA) has been formulated as a mixed-integer quadratic programming problem. Many model-driven methods, such as branch-and-bound algorithms, are advocated to solve it. However, these methods are often prone to considerably high complexities, which makes real-time processing a problem. In this article, a two-stage hybrid learning-based optimization approach is proposed to address this challenging issue. In the first stage, the task of optimizing integer variables is considered as a multilabel classification problem, which is solved by a data-driven method, i.e., ensemble deep neural networks (EDNNs). In the second stage, with the obtained integer solutions, the problem is transformed into a quadratic programming problem that can be quickly solved by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model-driven</i> numerical optimization methods. Compared to the state-of-the-art power scheduling algorithms, the model-driven and data-driven methods can compensate each other so that this two-stage hybrid learning-based approach can achieve orders of magnitude speedup in computational time, while guaranteeing the optimal scheduling performance. The structures of EDNNs are well designed so that the proposed approach can adapt to varying operating conditions in MEA. An offline simulation test and an online hardware-in-the-loop test validate the above advantages of the proposed approach.

Skin cancer risk self-assessment using AI as a mass screening tool

BackgroundSkin cancer is one of the most common cancers on the planet. The lack of noticeable symptoms, inaccessibility of professional dermatology expertise, and low population awareness prevent early detection, which is vital for successful treatment. MethodsWe proposed the free AI-powered self-examination tool allowing everyone to perform regular skin checkups with a smartphone's photo coupled with the new set of risk factors suitable for self-assessment. While most of the existing AI solutions focus on dermoscopic images and assisting staff in medical facilities, we in contrast designed the ensemble of deep convolutional neural networks to be used by non-specialists for assessing self-taken skin clinical images via mobile app. We aimed to distinguish between malignant and benign neoplasms only, encouraging people to follow up with dermatologists for an in-depth physical examination in case of discovered skin cancer risk. Results400000+ clinical skin images coupled with risk factors and demographic data have been collected, processed by AI, and validated by dermatology experts, which made it one of the biggest mass-screening campaigns for skin cancer disease in the Russian Federation. 9321 malignant cases were discovered including 5230 melanomas.The comprehensive analysis of mass-screening results was conducted for the following regions in Russian Federation: Moscow and Moscow region, Nizhny Novgorod, St. Petersburg and Leningradsky region, and Krasnodar region. We discovered the significantly higher melanoma cancer prevalence rate (number of cases per 100 000 people is 5.43 cases for males and 11.88 cases for females while statistical bulletin reports 4.12 cases in 2021) and much younger average age of skin cancer development (melanoma: 46.78 years for males and 41.35 years for females vs official numbers of 61.5 years and 62.1 years correspondingly; BCC/SCC: 56.28 years for males and 55.98 years for females vs official numbers of 68.2 years and 70.2 years correspondingly) comparing to statistical bulletins. Our findings demonstrate that the female population is at risk almost 5 years earlier and reveal the most common locations for malignant tumors: face, upper body, and shoulders. Our new skin cancer risk self-assessment criteria (20+ spots on arm and solarium visits) associate with the earlier melanoma development in the female population and should be considered in further skin cancer studies as risk factors. ConclusionThe proposed approach proved to be a cost-efficient way of doing mass skin cancer screenings, which have not been considered a feasible option before. The presented self-assessment risk factors along with the revised average age of malignant tumors development and prevalence rates should be considered in skin cancer surveillance.

Ensemble of Deep Learning Models for Brain Tumor Detection

In the last two decades, improvement in artificial intelligence and medical imaging technology have made healthcare sector to achieve some remarkable achievements in diseases analysis and prediction. Due to advancement in medical imaging technology the brain images are taken in different modalities, that gives 3D view of different sections of brain for tumor diagnosis. The ability to extract relevant characteristics from magnetic resonance imaging (MRI) scans is a crucial step for brain tumor classifiers. As a result, several studies have proposed various strategies to extract relevant features from different modalities of MRI to predict the growth of abnormal tumor. Most of techniques used conventional techniques of image processing for feature extraction and machine learning for classification. More recently, the use deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumor. Since tumors are located at different regions of brain, the localizing the tumor and classifying it to particular category is challenging task. In this paper, we have solved this problem by designing deep ensemble model. In the proposed approach, first shallow convolutional neural network (SCNN) and VGG16 network were designed with T1C modality MRI image and subsequently loss and accuracy were examined. To improve the performance of model in terms accuracy and loss information, the extracted features from both the deep learning model were fused to improve the classification accuracy of three types of tumors. The obtained results from ensemble deep convolutional neural network model (EDCNN), proved that the fusion of deep learning model improves the accuracy of multiclass classification problem and also tries to address the problem of overfitting of model for imbalance dataset. The proposed model tries to give classification accuracy up to 97.77%. Furthermore, the proposed framework, achieves competitive results when compared with other state of art studies.

Differential Gene Expression Prediction by Ensemble Deep Networks on Histone Modification Data.

Predicting differential gene expression (DGE) from Histone modifications (HM) signal is crucial to understand how HM controls cell functional heterogeneity through influencing differential gene regulation. Most existing prediction methods use fixed-length bins to represent HM signals and transmit these bins into a single machine learning model to predict differential expression genes of single cell type or cell type pair. However, the inappropriate bin length may cause the splitting of the important HM segment and lead to information loss. Furthermore, the bias of single learning model may limit the prediction accuracy. Considering these problems, in this paper, we proposes an Ensemble deep neural networks framework for predicting Differential Gene Expression (EnDGE). EnDGE employs different feature extractors on input HM signal data with different bin lengths and fuses the feature vectors for DGE prediction. Ensemble multiple learning models with different HM signal cutting strategies helps to keep the integrity and consistency of genetic information in each signal segment, and offset the bias of individual models. Besides the popular feature extractors, we also propose a new Residual Network based model with higher prediction accuracy to increase the diversity of feature extractors. Experiments on the real datasets from the Roadmap Epigenome Project (REMC) show that for all cell type pairs, EnDGE significantly outperforms the state-of-the-art baselines for differential gene expression prediction.

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

Covid-19 Versus Lung Cancer: Analyzing Chest CT Images Using Deep Ensemble Neural Network

With a high rise in deaths caused due to novel coronavirus (nCoV), immunocompromised persons are at high risk. Lung cancer is no exception. Classifying lung cancer patients and Covid-19 is the primary aim of the paper. For this, we propose a deep ensemble neural network (VGG16, DenseNet121, ResNet50 and custom CNN) to detect Covid-19 and lung cancer using chest CT images. We validate our model using three different datasets, namely SPIE AAPM Lung CT Challenge (1503 images), Covid CT dataset (349 images), and SARS-CoV-2 CT-scan dataset (1252 images). We utilize a k(= 5) fold cross-validation approach on the individual deep neural networks (DNNs) and a custom designed CNN model architecture, and achieve a benchmark score of 96.30% (accuracy) with a sensitivity and precision value of 96.39% and 98.44%, respectively. The proposed model effectively utilizes diverse models. To the best of our knowledge, using ensemble DNN, this is the first time we analyze chest CT images to separate lung cancer from Covid-19 (and vice-versa). As our aim is to classify Covid-19 and lung cancer using chest CT images, it helps in prioritizing immunocompromised persons from Covid-19 for a better patient care. Also, mass screening is possible especially in resource-constrained regions since CT scans are cheaper. The long-term goal is to check whether AI-guided tool(s) is(are) able to prioritize patients that are at high risk (e.g., lung disease) from any possible future infectious disease outbreaks.

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Background and ObjectiveIn the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. MethodsTherefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DLmodels, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method.That is familiar with the idea of various DL perceptions on different classes. ResultsPulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To thebest of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXIthat we usedto assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. ConclusionThe empirical findings of our suggested approach PulDi-COVIDshow that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

Non-intrusive deep learning-based computational speech metrics with high-accuracy across a wide range of acoustic scenes.

Speech with high sound quality and little noise is central to many of our communication tools, including calls, video conferencing and hearing aids. While human ratings provide the best measure of sound quality, they are costly and time-intensive to gather, thus computational metrics are typically used instead. Here we present a non-intrusive, deep learning-based metric that takes only a sound sample as an input and returns ratings in three categories: overall quality, noise, and sound quality. This metric is available via a web API and is composed of a deep neural network ensemble with 5 networks that use either ResNet-26 architectures with STFT inputs or fully-connected networks with wav2vec features as inputs. The networks are trained and tested on over 1 million crowd-sourced human sound ratings across the three categories. Correlations of our metric with human ratings exceed or match other state-of-the-art metrics on 51 out of 56 benchmark scenes, while not requiring clean speech reference samples as opposed to metrics that are performing well on the other 5 scenes. The benchmark scenes represent a wide variety of acoustic environments and a large selection of post-processing methods that include classical methods (e.g. Wiener-filtering) and newer deep-learning methods.

CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and Colorectal cancer classification

Cancer is one of the most dangerous diseases globally, causing adverse effects on human life, with early detection and treatment planning being crucial for patients. Amongst different malignancies, Lung and Colorectal cancer cause the first and second most cancer deaths in the world, respectively. In this study, the authors aim to analyze LC25000 histopathological image dataset for lung and colon cancer detection. The fundamental goal of the proposed research is to leverage the ensemble learning approach to improve the classification performance of deep learning models. Many previous studies have proposed several ensemble methods and weighting schemes. However, none of them optimized the assigned weights using a meta-heuristic-based approach as per our best knowledge. The authors have applied Differential Evolution optimization to optimize and find the optimal assigned weights to the classifiers while training the ensemble model. In addition, a novel approach to ensemble base learners with majority voting based on Condorcet’s Jury Theorem has also been proposed. This proposed method has been shown to save a lot of computational efforts by eliminating the training procedure of meta-learners. Besides this, the authors also demonstrated that Condorcet’s Jury Theorem holds while ensembling the N number of classifiers in Neural Networks. Our proposed method and experimental results outperformed compared to the state-of-the-art with the optimized ensemble model showing an accuracy of 99.78% and Condorcet’s Jury Theorem-based ensemble model 99.88% on 5-class classification.

Improved prediction of drug-drug interactions using ensemble deep neural networks

Nowadays, combining multiple drugs is the optimal therapy to decelerate the pathologic process, which contains various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. Over the past years, many AI-based DDI prediction techniques, including both machine learning and deep learning, that harness available big data have been presented. Although significant progressives have been gained through previous methods, improvements are still crucial. In this work, we introduce an ensemble deep neural network that can help to improve the predictive performance of DDIs. As a result, our prediction model could predict 86 types of DDIs on a benchmark dataset with an average accuracy of 93.80%. Our ensemble classifier produces better performance when compared with the existing proposed methods on the same dataset. Our model's high performance places it among the top list of those well-built pharmacovigilance-assisted tools that facilitate the detection of DDIs to support medical decisions and drug development. We released our source codes and models at https://github.com/khanhlee/edn-ddi.

Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids

The Internet of Energy envisions the next generation of smart grids as a highly interconnected network, including advanced metering infrastructures, distributed energy resources, and bidirectional communication systems. The open architecture of IoE-based smart grid results in manifold security concerns, especially the risk of False Data Injection Attacks. The attack may target the technical aspects of a system since fabricating the network's data misleads power scheduling and routing strategies and interrupts the healthy operation of the power system. Additionally, monetary motivation for the intruder sometimes is the main motivation. The conventional cyber defense strategies are unable to detect well-developed False Data Injection Attacks, particularly once the intruder takes advantage of a Deep Reinforcement Learning-based attack development framework that analyzes the dynamic nature of the smart grids. This paper primarily outlines various possible passive attacks using statistical methods. Then, a reinforcement learning-based intruder as an active attack generator is developed, initialized by modeled passive attacks. The attack generator algorithm can simulate the network environment and subsequently creates unclassified attacks. After creating a dynamic attacker, a multilayer defense framework is developed using Snapshot Ensemble Deep Neural Network and an adoptable Deep Auto Encoder network to detect known and unknown threats. Performance evaluations and a real-world simulation prove that the proposed framework can successfully detect both passive and active, where the accuracy and false positive detection rate of the developed framework are 98.82% and 97.42%, respectively.

Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets

Early diagnosis plays a key role in prevention and treatment of skin cancer. Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. However, the classification accuracy of these models still tends to be severely limited by the scarcity of representative images from malignant tumours. We propose a novel ensemble-based convolutional neural network (CNN) architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with auxiliary data in the form of metadata associated with the input images, are combined using a meta-learner. The proposed approach improves the model’s ability to handle limited and imbalanced data. We demonstrate the benefits of the proposed technique using a dataset with 33,126 dermoscopic images from 2056 patients. We evaluate the performance of the proposed technique in terms of the F1-measure, area under the ROC curve (AUC-ROC), and area under the PR-curve (AUC-PR), and compare it with that of seven different benchmark methods, including two recent CNN-based techniques. The proposed technique compares favourably in terms of all the evaluation metrics.