Multi-model ensemble with rich spatial information for object detection
اثر گروهی چند مدلی با اطلاعات مکانی غنی برای ردیابی شی-2020
Due to the development of deep learning networks and big data dimensionality, research on ensemble deep learning is receiving an increasing amount of attention. This paper takes the object detection task as the research domain and proposes an object detection framework based on ensemble deep learning. To guarantee the accuracy as well as real-time detection, the detector uses a Single Shot MultiBox Detector (SSD) as the backbone and combines ensemble learning with context modeling and multi-scale feature representation. Two modes were designed in order to achieve ensemble learning: NMS Ensembling and Feature Ensembling. In addition, to obtain contextual information, we used dilated convolution to ex- pand the receptive field of the network. Compared with state-of-the-art detectors, our detector achieves superior performance on the PASCAL VOC set and the MS COCO set.
Keywords: Ensemble learning | Object detection | Dilated convolution | Feature fusion
A non-canonical hybrid metaheuristic approach to adaptive data stream classification
یک روش متاوریستی ترکیبی غیر متعارف برای طبقه بندی جریان داده تطبیقی-2020
Data stream classification techniques have been playing an important role in big data analytics recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices and sensors, websites and social network feeds). Streaming data is often prone to evolution over time. In this context, the main challenge for computational models is to adapt to changes, known as concept drifts, using data mining and optimisation techniques. We present a novel ensemble technique called RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size, each created by randomly selecting a certain percentage of features from a pool of features of the target data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt to different concept drifts; it allows good performing types to grow and poor performing ones to shrink in size. In addition, the selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. PSO allows the types in each layer to go towards local (within the same type) and global (in all types) optimums with a specified velocity. A set of experiments are conducted to compare the performance of the proposed method to state-of-the-art algorithms using real-world and synthetic data streams in immediate and delayed prequential evaluation settings. The results show a favourable performance of our method in different environments.
Keywords: Ensemble learning | Data stream mining | Concept drifts | Bio-inspired algorithms | Non-stationary environments | Particle swarm optimisation | Replicator dynamics
HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
HealthFog: یک سیستم هوشمند درمانی هوشمند مبتنی بر یادگیری عمیق برای تشخیص خودکار بیماری های قلبی در محیط های IoT و محاسبات مه-2020
Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.
Keywords: Fog computing | Internet of things | Healthcare | Deep learning | Ensemble learning | Heart patient analysis
Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations
کشف جهانی پروسکوتیتهای آلی غیر آلی ترکیبی پایدار و غیر سمی برای سیستم های فتوولتائیک با ترکیب روش یادگیری ماشین با محاسبات اصلی-2019
Traditional trial-and-error methods seriously restrict and hinder the searching of high-performance functional materials, especially when the search space is large. Rapid searching for advanced functional materials has always been a hot research topic, and attracted a lot of experimental and theoretical research attention. Here, by combining machine learning method with density functional theory (DFT) calculations, a target-driven method is proposed here to speed up the discovery of hidden hybrid organic-inorganic perovskites (HOIPs) for photovoltaic applications from 230808 HOIPs candidates which is almost two orders larger than previous studied. After imposing two criterions, i.e., charge neutrality condition and stability condition, on potential HOIPs candidates, followed by a machine learning (ML) screening, 686 orthorhombic-like HOIPs with proper bandgap are selected. In machine learning screening, ensemble learning using three ML models, including gradient boosting regression (GBR), supporting vector regression (SVR) and kernel ridge regression (KRR), are applied to predict the bandgap of 38086 HOIPs candidates. 132 stable and non-toxic (Cd-, Pb- and Hg-free) orthorhombiclike HOIPs are finally verified by DFT calculations with appropriate band gap for solar cells. In the present study, not only a series of unexplored stable and non-toxic HOIPs are discovered for further experimental synthesis, a new HOIPs database is constructed as well, thus beneficial to future functional material design.
Keywords: Machine learning | Hybrid organic-inorganic perovskites | First principle calculations | Photovoltaics
Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals
تشخیص خودکار آریتمی با استفاده از الگوی جدید موضعی hexadecimal و تبدیل موجک چند سطحی با سیگنالهای ECG-2019
Electrocardiography (ECG) is widely used for arrhythmia detection nowadays. The machine learning methods with signal processing algorithms have been used for automated diagnosis of cardiac health using ECG signals. In this article, discrete wavelet transform (DWT) coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection. The ECG signals of 10 s duration are subjected to DWT to decompose up to five levels. The 1D-HLP extracts 512 dimensional features from each level of the five levels of low pass filter. Then, these extracted features are concatenated to obtain 512 × 6 = 3072 dimensional feature set. These fused features are subjected to neighborhood component analysis (NCA) feature reduction technique to obtain 64, 128 and 256 features. Finally, these features are subjected to 1 nearest neighborhood (1NN) classifier for classification with 4 distance metrics namely city block, Euclidean, spearman and cosine. We have obtained a classification accuracy of 95.0% in classifying 17 arrhythmia classes using MIT-BIH Arrhythmia ECG dataset. Our results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmia detection using ECG signals.
Keywords: Hexadecimal local pattern | Multilevel DWT | ECG classification | Pattern recognition | Biomedical engineering
An ensemble learning approach to lip-based biometric verification, with a dynamic selection of classifiers
یک رویکرد یادگیری گروه برای تأیید بیومتریک مبتنی بر لب ، با انتخاب پویای طبقه بندی کننده ها-2019
Machine learning approaches are largely focused on pattern or object classification, where a combination of several classifier systems can be integrated to help generate an optimal or suboptimal classification decision. Multiple classification systems have been extensively developed because a committee of clas- sifiers, also known as an ensemble, can outperform the ensemble’s individual members. In this paper, a classification method based on an ensemble of binary classifiers is proposed. Our strategy consists of two phases: (1) the competence of the base heterogeneous classifiers in a pool is determined, and (2) an ensemble is formed by combining those base classifiers with the greatest competences for the given input data. We have shown that the competence of the base classifiers can be successfully calculated even if the number of their learning examples was limited. Such a situation is particularly observed with biomet- ric data. In this paper, we propose a new biometric data structure, the Sim coefficients, along with an efficient data processing technique involving a pool of competent classifiers chosen by dynamic selection.
Keywords: Lip-based biometrics | Dynamic classifiers selection | Pattern recognition | Ensemble classification | Person verification
Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining
پیش بینی خواص مکانیکی متعدد بتن هیدرولیک در قالب سدسازی ترکیبی توسط داده کاوی آزمایشی-2019
The application of combined damming with large volume conventional concrete and roller compacted concrete indicates that the development of hydraulic concrete materials embarking on a new stage. However, the large particle size of coarse aggregate and high fly ash content in hydraulic concrete lead to a highly nonlinear relationship between mixture proportion and mechanical properties. This phenomenon will increase the difficulty, workload, and costs of mechanical experiments. To solve these problems, data mining techniques are mostly applied to the prediction of single concrete performance but not multiple mechanical properties. This research compares four data mining models in predicting the mechanical properties of hydraulic concrete in the form of combined damming. These models involve a linear regression model (Bayesian Ridge), an advanced predictive model (Gaussian Processes), a regression tree model (Decision Trees) and an ensemble learning regression model (Gradient Boosting). The performance measures of these techniques are evaluated on the basis of the concrete data from the combined damming engineering. The results show that ensemble regression model (Gradient Boosting) performs higher accuracy, better measures, and stronger robustness than the other three types of prediction models used to predict the mechanical properties of hydraulic concrete. The feature importance of concrete components derived from Gradient Boosting is analyzed and validated by conclusions of previous studies. Therefore, efficient prediction of mechanical properties and rapid mixture proportion designs of hydraulic concrete can be realized and the construction technology of combined damming will be applied widely.
Keywords: Hydraulic concrete | Combined damming | Multiple mechanical properties | Data mining | Prediction models | Feature importance analysis
Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database
تشخیص خودکار بیماری گوش با استفاده از یادگیری عمیق گروه با یک پایگاه داده بزرگ تصویر otoendoscopy-2019
Background: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively lowdiagnostic accuracy calls for a newway of diagnostic strategy, inwhich deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. Methods: Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performingmodelswere selected to compose an ensemble classifier, by combining classification scores of each classifier. Findings: According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. Interpretation: The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trainedwith data in a various acquisition condition,which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. Fund: This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(NRF-2017M3C7A1049051).
Keywords: Convolutional neural network | Deep learning | Otoendoscopy | Tympanic membrane | Ear disease | Ensemble learning
Applications of machine learning in addiction studies: A systematic review
کاربردهای یادگیری ماشین در مطالعات اعتیاد: یک مرور سیستماتیک-2019
This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review. The selected studies covered mainly substance addiction (N=14, 82.4%), including smoking (N=4), alcohol drinking (N=3), as well as uses of cocaine (N=4), opioids (N=1), and multiple substances (N=2). Other studies were non-substance addiction (N=3, 17.6%), including gambling (N=2) and internet gaming (N=1). There were eight cross-sectional, seven cohort, one non-randomized controlled, and one crossover trial studies. Majority of the studies employed supervised learning (N=13), and others employed unsupervised learning (N=2) and reinforcement learning (N=2). Among the supervised learning studies, five studies used ensemble learning methods or multiple algorithm comparisons, six used regression, and two used classification. The two included reinforcement learning studies used the direct methods. These results suggest that machine learning methods, particularly supervised learning are increasingly used in addiction psychiatry for informing medical decisions.
Keywords: Machine learning | Supervised learning | Unsupervised learning | Reinforcement learning | Addiction
CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer
طبقه بندی یادگیری گروه CWV-BANN-SVM برای تشخیص دقیق سرطان پستان-2019
This paper presents a new data mining technique for an accurate prediction of breast cancer (BC), which is one of the major mortality causes among women around the globe. The main objective of our study is to expand an automatic expert system (ES) to provide an accurate diagnosis of BC. Both, Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) were applied to analyze BC data. The wellknown Wisconsin Breast Cancer Dataset (WBCD), available in the UCI repository, was examined in our study. We first tested the SVM algorithm using various values of the C, e and c parameters. As a result of the first experiment, we were able to observe that the adjustment of these regularization parameters can greatly improve the performance of the traditional SVM algorithm applied for BC detection. The highest obtained accuracy at the first step was 99.71%. Then, we performed a new BC detection approach based on two ensemble learning techniques: the confidence-weighted voting method and the boosting ensemble technique. Our model, called CWV-BANNSVM, combines boosting ANNs (BANN) and two SVMs, using optimal parameters selected during the first experiment. The performance of the applied methods was evaluated using several popular metrics, such as specificity, sensitivity, precision, FPR, FNR, F1 score, AUC, Gini and accuracy. The proposed CWV-BANNSVM model was able to improve the performance of the traditional machine learning algorithms applied to BC detection, reaching the accuracy of 100%. To overcome the overfitting issue, we determined and used some appropriate parameter values of polynomial SVM. Our comparison with the existing studies dedicated to BC prediction suggests that the proposed CWV-BANN-SVM model provides one of the best prediction performances overall.
Keywords: Data mining | Machine learning | Ensemble technique | Breast cancer | Support vector machine | Artificial neural network