scholarly journals A semisupervised machine learning search for never-seen gravitational-wave sources

2020 ◽  
Vol 500 (4) ◽  
pp. 5408-5419
Author(s):  
Tom Marianer ◽  
Dovi Poznanski ◽  
J Xavier Prochaska
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Outlier Detection ◽  
Predictive Models ◽  
Detection Rate ◽  
Detection Algorithms ◽  
Compact Binary ◽  
Strain Data ◽  
Cent Detection

ABSTRACT By now, tens of gravitational-wave (GW) events have been detected by the LIGO and Virgo detectors. These GWs have all been emitted by compact binary coalescence, for which we have excellent predictive models. However, there might be other sources for which we do not have reliable models. Some are expected to exist but to be very rare (e.g. supernovae), while others may be totally unanticipated. So far, no unmodelled sources have been discovered, but the lack of models makes the search for such sources much more difficult and less sensitive. We present here a search for unmodelled GW signals using semisupervised machine learning. We apply deep learning and outlier detection algorithms to labelled spectrograms of GW strain data, and then search for spectrograms with anomalous patterns in public LIGO data. We searched ${\sim}13{{\ \rm per\ cent}}$ of the coincident data from the first two observing runs. No candidates of GW signals were detected in the data analyzed. We evaluate the sensitivity of the search using simulated signals, we show that this search can detect spectrograms containing unusual or unexpected GW patterns, and we report the waveforms and amplitudes for which a $50{{\ \rm per\ cent}}$ detection rate is achieved.

scholarly journals Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence

2021 ◽  
Vol 919 (2) ◽  
pp. 82
Author(s):  
Wei Wei ◽  
E. A. Huerta ◽  
Mengshen Yun ◽  
Nicholas Loutrel ◽  
Md Arif Shaikh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Compact Binary ◽  
Wave Forecasting

scholarly journals A Service Architecture Using Machine Learning to Contextualize Anomaly Detection

2020 ◽  
Vol 31 (1) ◽  
pp. 64-84
Author(s):  
Brandon Laughlin ◽  
Karthik Sankaranarayanan ◽  
Khalil El-Khatib
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Outlier Detection ◽  
Network Flow ◽  
Contextual Information ◽  
Nearest Neighbors ◽  
Service Architecture ◽  
Detection Algorithms ◽  
Unsupervised Outlier Detection

This article introduces a service that helps provide context and an explanation for the outlier score given to any network flow record selected by the analyst. The authors propose a service architecture for the delivery of contextual information related to network flow records. The service constructs a set of contexts for the record using features including the host addresses, the application in use and the time of the event. For each context the service will find the nearest neighbors of the record, analyze the feature distributions and run the set through an ensemble of unsupervised outlier detection algorithms. By viewing the records in shifting perspectives one can get a better understanding as to which ways the record can be considered an anomaly. To take advantage of the power of visualizations the authors demonstrate an example implementation of the proposed service architecture using a linked visualization dashboard that can be used to compare the outputs.

scholarly journals Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

2021 ◽  
Author(s):  
Himanshu Gupta ◽  
Hirdesh Varshney ◽  
Tarun Kumar Sharma ◽  
Nikhil Pachauri ◽  
Om Prakash Verma
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Detection Rate ◽  
Missing Values ◽  
Prediction Models ◽  
State Of The Art ◽  
Outlier Rejection ◽  
Comparative Performance ◽  
Quantum Machine Learning ◽  
Diabetes Prediction

Abstract Background Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results The performance measures such as precision, accuracy, recall, F1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

scholarly journals A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation

2021 ◽  
Vol 9 ◽  
Author(s):  
Chen Li ◽  
Gaoqi Liang ◽  
Huan Zhao ◽  
Guo Chen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Event Detection ◽  
Detection Algorithm ◽  
Demand Side ◽  
Rule Based ◽  
Detection Algorithms ◽  
Deep Model ◽  
Conventional Machine

Event detection is an important application in demand-side management. Precise event detection algorithms can improve the accuracy of non-intrusive load monitoring (NILM) and energy disaggregation models. Existing event detection algorithms can be divided into four categories: rule-based, statistics-based, conventional machine learning, and deep learning. The rule-based approach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and conventional machine learning methods are inferior to the deep learning algorithms due to their limited ability to extract complex features. Deep learning models require a long training time and are hard to interpret. This paper proposes a novel algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR). The deep model extracts the power time series patterns and the wide model utilizes the percentile information of the power time series. A randomized sparse backpropagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.

scholarly journals Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems

2020 ◽  
Vol 12 (10) ◽  
pp. 167
Author(s):  
Niraj Thapa ◽  
Zhipeng Liu ◽  
Dukka B. KC ◽  
Balakrishna Gokaraju ◽  
Kaushik Roy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
High Detection Rate ◽  
Detection Systems ◽  
Network Intrusion ◽  
High Detection

The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system.

scholarly journals Incorporating Machine Learning into Established Bioinformatics Frameworks

2021 ◽  
Vol 22 (6) ◽  
pp. 2903
Author(s):  
Noam Auslander ◽  
Ayal B. Gussow ◽  
Eugene V. Koonin
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Predictive Models ◽  
Biological Networks ◽  
Machine Learning Techniques ◽  
Biomedical Data ◽  
Protein Structure Analysis ◽  
Automatic Feature Extraction ◽  
Learning Techniques

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

scholarly journals Comparative Analysis of Predictive Models for Fine Particulate Matter in Daejeon, South Korea

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1295
Author(s):  
Tserenpurev Chuluunsaikhan ◽  
Menghok Heak ◽  
Aziz Nasridinov ◽  
Sanghyun Choi
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Deep Learning ◽  
Comparative Analysis ◽  
South Korea ◽  
Predictive Models ◽  
Fine Particulate Matter ◽  
Air Pollutant ◽  
Learning Models ◽  
Fine Particulate

Air pollution is a critical problem that is of major concern worldwide. South Korea is one of the countries most affected by air pollution. Rapid urbanization and industrialization in South Korea have induced air pollution in multiple forms, such as smoke from factories and exhaust from vehicles. In this paper, we perform a comparative analysis of predictive models for fine particulate matter in Daejeon, the fifth largest city in South Korea. This study is conducted for three purposes. The first purpose is to determine the factors that may cause air pollution. Two main factors are considered: meteorological and traffic. The second purpose is to find an optimal predictive model for air pollutant concentration. We apply machine learning and deep learning models to the collected dataset to predict hourly air pollutant concentrations. The accuracy of the deep learning models is better than that of the machine learning models. The third purpose is to analyze the influence of road conditions on predicting air pollutant concentration. Experimental results demonstrate that considering wind direction and wind speed could significantly decrease the error rate of the predictive models.

scholarly journals Intrusion Detection using Machine Learning and Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9704-9719
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Rate ◽  
False Positive Rate ◽  
Future Research ◽  
Different Types ◽  
Security Problem ◽  
Positive Rate ◽  
Networking Technology

With the increase in usage of networking technology and the Internet, Intrusion detection becomes important and challenging security problem. A number of techniques came into existence to detect the intrusions on the basis of machine learning and deep learning procedures. This paper will give inspiration to the use of ML and DL systems to IP traffic and gives a concise depiction of every one of the ML and DL strategies. This paper gives an audit of 40 noteworthy works that covers the period from 2015 to 2019. ML and DL methods are compared with regard to their accuracy and detection potential to detect different types of intrusions. Future Research includes ML and DL methods to find the intrusions so as to improve the detection rate, accuracy and to minimize the false positive rate.

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