966 research outputs found
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Support Vector Machines in High Energy Physics
This lecture will introduce the Support Vector algorithms for classification
and regression. They are an application of the so called kernel trick, which
allows the extension of a certain class of linear algorithms to the non linear
case. The kernel trick will be introduced and in the context of structural risk
minimization, large margin algorithms for classification and regression will be
presented. Current applications in high energy physics will be discussed.Comment: 11 pages, 12 figures. Part of the proceedings of the Track
'Computational Intelligence for HEP Data Analysis' at iCSC 200
Optimization of tau identification in ATLAS experiment using multivariate tools
In elementary particle physics the efficient analysis of huge amount of collected data require the use of sophisticated selection and analysis algorithms. We have implemented a Support Vector Machine (SVM) integrated with the CERN TMVA/ROOT package. SVM approach to signal and background separation is based on building a separating hyperplane defined by the support vectors. The margin between them and the hyperplane is maximized. The extensions to a non-linear separation is performed by mapping the input vectors into a high dimensional space, in which data can be linearly separated. The use of kernel functions allows to perform computations in a high dimension feature space without explicitly knowing a mapping function. Our SVM implementation is based on Platt's Sequential Minimal Optimization (SMO) algorithm and includes various kernel functions like a linear function, polynomial and Gaussian. The identification of hadronic decays of tau leptons in the ATLAS experiment using a tau1P3P package is performed using, beside the baseline cut analysis, also multivariate analysis tools: neural network, PDE_RS and our implementation of the SVM algorithm. The use and the comparison of the three algorithms is presented
Heart Diseases Diagnosis Using Artificial Neural Networks
Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions.
Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis.
The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets.
The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases
Penerapan Algoritma Genetika untuk Optimasi Parameter pada Support Vector Machine untuk Meningkatkan Prediksi Pemasaran Langsung
Pemasaran langsung adalah proses mengidentifikasi potensi pembeli produk tertentu dan mempromosikan produk dengan sesuai. pelaksanaan pemasaran langsung dari waktu ke waktu menghasilkan data dan informasi dalam bentuk laporan yang perlu di analisis oleh manajer dalam rangka mendukung keputusan. Namun itu adalah tugas yang sulit bagi manusia untuk menganalisis data yang kompleks yang luas. Kesulitan ini menyebabkan perkembangan teknik intelejen bisnis, yang bertujuan mengklasifikasi pengetahuan yang berguna untuk mendukung pengambilan keputusan. Metode support vector machine mampu mengatasi masalah yang berdimensi tinggi, mengatasi masalah klasifikasi dan regresi dengan linier ataupun nonlinier kernel yang dapat menjadi satu kemampuan algoritma pembelajaran untuk klasifikasi serta regresi, namun support vector machine memiliki masalah dalam pemilihan parameter yang sesuai. Untuk mengatasi masalah tersebut di perlukan metode algoritma genetika untuk pemilihan parameter yang sesuai pada metode support vector machine. Beberapa eksperimen dilakukan untuk mendapatkan akurasi yang optimal. Hasil penelitian menunjukan, eksperimen dengan menggunakan metode support vector machine dan algoritma genetika yang digunakan untuk melakukan optimasi parameter C, γ dan ε dengan tiga jenis kernel. Kernel pertama tipe kernel dot dengan akurasi sebesar 85,59%, AUC sebesar 0,911 yang kedua tipe kernel radial dengan akurasi sebesar 98.89%, AUC sebesar 0,981 dan yang ketiga dengan tipe kernel Polynomial dengan akurasi sebesar 98.67% dan AUC sebesar 0.938. Hasil eksperimen tersebut menunjukan pengujian data set menggunakan penerapan algoritma genetika pada support vector machine menunjukan hasil yang lebih akurat untuk prediksi pemasaran langsung.
Global ECG Classification by Self-Operational Neural Networks with Feature Injection
Objective: Global (inter-patient) ECG classification for arrhythmia detection
over Electrocardiogram (ECG) signal is a challenging task for both humans and
machines. The main reason is the significant variations of both normal and
arrhythmic ECG patterns among patients. Automating this process with utmost
accuracy is, therefore, highly desirable due to the advent of wearable ECG
sensors. However, even with numerous deep learning approaches proposed
recently, there is still a notable gap in the performance of global and
patient-specific ECG classification performances. This study proposes a novel
approach to narrow this gap and propose a real-time solution with shallow and
compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In
this study, we propose a novel approach for inter-patient ECG classification
using a compact 1D Self-ONN by exploiting morphological and timing information
in heart cycles. We used 1D Self-ONN layers to automatically learn
morphological representations from ECG data, enabling us to capture the shape
of the ECG waveform around the R peaks. We further inject temporal features
based on RR interval for timing characterization. The classification layers can
thus benefit from both temporal and learned features for the final arrhythmia
classification. Results: Using the MIT-BIH arrhythmia benchmark database, the
proposed method achieves the highest classification performance ever achieved,
i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N)
segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the
supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10%
recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs)
Computational models and approaches for lung cancer diagnosis
The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results
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