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What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

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SENSORS 2021年第20期21卷 6816页

作者： Ikurior, Seer J. Marquetoux, Nelly Leu, Stephan T. Corner-Thomas, Rene A. Scott, Ian Pomroy, William E. Massey Univ Sch Vet Sci Palmerston North 4410 New Zealand Univ Agr Coll Vet Med Makurdi 970231 Nigeria Massey Univ Sch Vet Sci EpiCtr Palmerston North 4410 New Zealand Univ Adelaide Sch Anim & Vet Sci Roseworthy 5371 Australia Massey Univ Sch Agr & Environm Palmerston North 4410 New Zealand

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

关键词： sheep diel activity classification algorithm tri-axial accelerometers health monitoring

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Detecting the impact of software vulnerability on attacks: A case study of network telescope scans

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2021年 195卷 103230-103230页

作者： Houmz, Abdellah Mezzour, Ghita Zkik, Karim Ghogho, Mounir Benbrahim, Houda Int Univ Rabat TICLab Rabat Morocco Mohammed V Univ Rabat ENSIAS Rabat Morocco DASEC Rabat Morocco

Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 2014 to 2019. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans. The resulting classifier achieves over 85% accuracy in predicting the impact. In addition, we performed an analysis of the key characteristics of vulnerabilities that directly affect scanning activities. Our findings show that this approach is able to classify vulnerabilities that have an impact on network scans. The implementation of our model and validation tests proved the efficiency of the selected features, as well as the robustness of our model to classify vulnerabilities' impact on scans.

关键词： Network scans classification algorithm Time series NVD CVE Machine learning

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Study on Earthquake Prediction Model Based on Traffic Disaster Data 11

Study on Earthquake Prediction Model Based on Traffic Disast...

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11th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Han, Wanjiang Gan, Yuanlin Chen, Shuwen Wang, Xiaoxiang Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781728165790

This paper collects data on the damage to the traffic system caused by earthquakes in China in the past two decades, and uses KNN algorithm, SVM algorithm, logistic regression algorithm, naive Bayes algorithm and decision tree algorithm to train the data, then establish earthquake prediction models. The paper introduces the process of preprocessing, modelling, evaluation, and visualization of disaster data. An earthquake disaster inversion model based on traffic data has been established, which can predict the earthquake intensity based on the relevant data provided by the traffic department. The prediction accuracy is relatively accurate, which is very helpful for earthquake prediction and rescue operations.

关键词： Traffic loss earthquake intensity earthquake prediction model classification algorithm machine learning

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Polyadic Quantum Classifier

Polyadic Quantum Classifier

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IEEE International Conference on Quantum Computing and Engineering (QCE)

作者： Cappelletti, William Erbanni, Rebecca Keller, Joaquin Entrop Labs Singapore Singapore

ISBN: (纸本)9781728189697

We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy -compared to a classical machine learning model- for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.

关键词： quantum machine learning variational quantum algorithm NISQ architecture classification algorithm supervised learning

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A clustering method of asset cybersecurity classification

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IFAC-PapersOnLine 2019年第13期52卷 928-933页

作者： Promyslov, Vitaly G. Semenkov, Kirill V. Shumov, Alexander S. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Science 65 Profsoyuznaya Moscow117997 Russia

A formal method for asset classifying by cyber security is provided. The problem is solved in the frame of a clustering problem. The data structure and content are specified. A classification algorithm for systems with arbitrary number of subsystems is provided. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Formal methods Hierarchical clustering Hierarchical systems Security of data Arbitrary number classification algorithm Clustering Clustering methods Clustering problems Cyber security

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Heart rate variability feature selection method for automated prediction of sudden cardiac death

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年 65卷 102310-102310页

作者： Parsi, Ashkan Byrne, Dallan Glavin, Martin Jones, Edward Natl Univ Ireland NUI Galway Coll Engn & Informat Galway H91 TK33 Ireland

Sudden cardiac death is often caused by a major heart dysfunction with approximately 80% of occurrences attributed to ventricular arrhythmias. This paper proposes a novel ventricular arrhythmia prediction method to differentiate ventricular fibrillation and ventricular tachycardia from non-life-threatening cardiac activities, which may result in lower rates of unnecessary therapeutic intervention. Time series, frequency, bispectrum and nonlinear cardiac data can be analyzed on implantable devices such as implantable cardioverter defibrillators, to detect a life-threatening event. In order to reduce the computational overhead of the detection method, a feature selection technique is applied to reduce the number of features. Signal buffer lengths are examined, where one-minute and five-minute signal durations are processed. The features are then applied to classifiers and results from a selection of classification algorithms are compared. The proposed method achieves 88.8% sensitivity and 94.2% specificity using only six HRV features with a k-nearest neighbor classifier while processing a 5-minute window of R-R interval signals. Using a 1-minute window of R-R interval signals results in 86.9% sensitivity and 93.5% specificity, with a support vector machine classifier. The results show that the proposed method, using between four and eleven features, outperforms related work in the literature.

关键词： Ventricular arrhythmia Implantable cardioverter defibrillator classification algorithm Heart rate variability Cardiac signal analysis

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An Ensemble Feature Selection Methodology That Incorporates Domain Knowledge for Cardiovascular Disease Diagnosis 28

An Ensemble Feature Selection Methodology That Incorporates ...

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Kolukisa, Burak Gungor, V. Cagri Gungor, Burcu Bakir Abdullah Gul Univ Bilgisayar Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781728172064

Coronary Artery Disease (CAD) is the condition where, the heart is not fed enough as a result of the accumulation of fatty matter called atheroma in the walls of the arteries. In 2016, CAD accounts for 31% (17.9 million) of the world's total deaths and its diagnosis is difficult. It is estimated that approximately 23.6 million people will die from this disease in 2030. With the development of machine learning and data mining techniques, it might be possible to diagnose CAD inexpensively and easily via examining some physical and biochemical values. In this study, for the CAD classification problem, a novel ensemble feature selection methodology that incorporates domain knowledge is proposed. Via applying the proposed methodology on the UCI Cleveland CAD dataset and using different classification algorithms, performance metrics are compared. It is shown that in our experiments, when Multilayer Perceptron classifier is used with 9 selected features, our proposed solution reached 85.47% accuracy, 82.96% accuracy and 0.839 F-Measure. As a future work, we aim to generate a machine learning model that can quickly diagnose CAD on real-time data in hospitals.

关键词： Data mining Machine Learning classification algorithm Coronary Artery Disease

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Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

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ENERGIES 2021年第20期14卷 6677页

作者： Koteleva, N. I. Korolev, N. A. Zhukovskiy, Y. L. St Petersburg Min Univ Educ Res Ctr Digital Technol St Petersburg 191106 Russia

The paper discusses the method of identifying the technical condition of induction motors by classifying the energy data coming from the main common power bus. The work shows the simulation results of induction motor operation. The correlation between occurring defects and current diagrams is presented. The developed simulation model is demonstrated. The general algorithm for conducting experiments is described. Five different experiments to develop an algorithm for the classification are conducted: determination of the motors number in operation with different power;determination of the motors number in operation with equal power;determination of the mode and load of induction electric motor;determination of the fault and its magnitude with regard to operation and load of induction motor;determination of the fault and its magnitude with regard to operation and load of induction motor with regard to non-linear load in the flow. The article also presents an algorithm for preprocessing data to solve the classification problem. In addition, the classification results are shown and recommendations for testing and using the classification algorithm on a real object are made.

关键词： classification algorithm induction electric motor simulation model the coefficient of electromagnetic momentum ripple current harmonic distortion factor

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Designing a Model to Study Data Mining in Distributed Environment

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Journal of Data Analysis and Information Processing 2021年第1期9卷 23-29页

作者： Md. Abadur Rahman Masud Karim School of Business & Tourism Southern Cross University Sydney Australia Department of Computer Science & Engineering Daffodil Institute of Science & Technology Dhaka Bangladesh

To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using data mining to perform such tasks. Data mining techniques are used to find hidden information from large data source. Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach. Data is becoming larger and spreading geographically. So it is difficult to find better result from only a central data source. For knowledge discovery, we have to work with distributed database. On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data. For this reason, distributed database is essential for future processing. In this paper, we have proposed a framework to study data mining in distributed environment. The paper presents a framework to bring out actionable knowledge. We have shown some level by which we can generate actionable knowledge. Possible tools and technique for these levels are discussed.

关键词： Data Mining Distributed Database Knowledge Discovery classification algorithm

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Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 179卷 105857-105857页

作者： Hu, Shuwen Ingham, Aaron Schmoelzl, Sabine McNally, Jody Little, Bryce Smith, Daniel Bishop-Hurley, Greg Wang, You-Gan Li, Yutao Queensland Univ Technol Sch Math Sci Gardens Point Qld 4001 Australia CSIRO Agr & Food 306 Carmody Rd St Lucia Qld 4067 Australia CSIRO Agr & Food Armidale NSW 2350 Australia CSIRO DATA61 Private Bag 12 Hobart Tas 7001 Australia

Inertial motion sensors located on the animal have been used to study the behaviour of ruminant livestock. The time window size of segmented signal data can significantly affect the classification accuracy of animal behaviours. To date, there have been no studies evaluating the impact of a mixture of time window size features on the accuracy of animal behaviour classification. In this study, data was collected from accelerometers attached to the neck of 17 Merino sheep over a period of two days. We also recorded a ground truth dataset of behaviour recordings (grazing, ruminating, walking, and standing) over the same time period, We then investigated the ability of three machine learning (ML) approaches, Random Forest (RF), Support Vector Machine (SVM) and linear discriminant analysis (LDA), to accurately classify sheep behaviour. Our results clearly show that simultaneous inclusion of features derived from time windows of mixed sizes, ranging from 2 to 15 s, significantly improved the behaviour classification accuracy, in comparison to those determined from a single unique time window size. Of the three ML methods applied here, the RF approach yielded the best results. Together our results show that including features obtained from mixed window sizes improved the classification accuracy of sheep behaviours.

关键词： Mixture of time window sizes Features ranking classification algorithm Machine learning Sheep behaviour Accelerometer

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