FMCW Radar-Based Anomaly Detection in Toilet by Supervised Machine Learning Classifier

2019 ◽  
Author(s):  
Wataru Takabatake ◽  
Kohei Yamamoto ◽  
Kentaroh Toyoda ◽  
Tomoaki Ohtsuki ◽  
Yohei Shibata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Supervised Machine Learning ◽  
Fmcw Radar ◽  
Learning Classifier

scholarly journals Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

2020 ◽  
Author(s):  
John T. Halloran ◽  
Gregor Urban ◽  
David Rocke ◽  
Pierre Baldi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Support Vector ◽  
Proteomics Data ◽  
Learning Classifier

AbstractSemi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

scholarly journals Effective and efficient network anomaly detection system using machine learning algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Mukrimah Nawir ◽  
Amiza Amir ◽  
Naimah Yaakob ◽  
Ong Bi Lynn
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Processing Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

scholarly journals Semi-supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

2020 ◽  
Author(s):  
Yutao Lu ◽  
Juan Wang ◽  
Miao Liu ◽  
Kaixuan Zhang ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Expert Knowledge ◽  
Learning Algorithm ◽  
Positive Sample ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Soft Decision ◽  
Decision Methods

The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

scholarly journals Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

2020 ◽  
Vol 69 (8) ◽  
pp. 8459-8467 ◽  
Author(s):  
Yutao Lu ◽  
Juan Wang ◽  
Miao Liu ◽  
Kaixuan Zhang ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Cellular Networks ◽  
Detection Method ◽  
Supervised Machine Learning

Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud

2021 ◽  
Vol 11 (1) ◽  
pp. 52-72
Author(s):  
Rajendra Kumar Dwivedi ◽  
Rakesh Kumar ◽  
Rajkumar Buyya
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Anomaly Detection ◽  
Gaussian Distribution ◽  
Supervised Machine Learning ◽  
Sensor Data ◽  
Support Vector ◽  
Learning Scheme ◽  
Healthcare Monitoring ◽  
Sensor Cloud

Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Rna Seq ◽  
Learning Classifier ◽  
Single Nucleus

scholarly journals Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

2021 ◽  
Vol 13 (2) ◽  
pp. 971
Author(s):  
Papiya Debnath ◽  
Pankaj Chittora ◽  
Tulika Chakrabarti ◽  
Prasun Chakrabarti ◽  
Zbigniew Leonowicz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vital Role ◽  
Earthquake Forecasting ◽  
Supervised Machine Learning ◽  
Learning Technology ◽  
Learning Classifier ◽  
Moderate Earthquake ◽  
Supervised Machine Learning Classifiers ◽  
Simple Logistic ◽  
Classifier Algorithms

Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case.

scholarly journals Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing

2020 ◽  
Author(s):  
Ahmed M. Moustafa ◽  
Paul J. Planet
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Whole Genome Sequence ◽  
Viral Diversity ◽  
Whole Genome ◽  
Learning Classifier ◽  
Travel Restrictions ◽  
Multiple Introduction ◽  
Classification Tool

AbstractBackgroundDiscrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events.MethodsWe developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states.ResultsGNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed.ConclusionsOur classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.

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