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作者： Kaur, Pavneet University of Guelph

学位级别：硕士

Record Linkage is the process of identifying the same entities in one or more data sources in the absence of unique identifiers. Longitudinal data constructed by linking two or more historical sources can provide us with valuable information about the characteristics of population change over time. However, the construction of such longitudinal data is challenged by the unavailability of personal identifiers. In this thesis, we link the Canadian censuses 1871 and 1881 using different methods and conditions. The support vector machine and the Random Forest classifiers are used to establish the record linkage system. The performance of these different methods is compared to investigate the upper bound achieved in the linkage rate and the conditions which provided us with that rate are inspected. Experiments show that the Random Forest classification explored in this thesis improves upon the linkage rate by 3.6% while maintaining a false positive rate no greater than 5%.

关键词： Record Linkage machine Learning classifiers The Random Forest classifier Historical census data support vector machine classifier

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Preliminary machine Learning-Based Classification of Ink Disease in Chestnut Orchards Using High-Resolution Multispectral Imagery from Unmanned Aerial Vehicles: A Comparison of Vegetation Indices and classifiers

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Forests 2025年第5期16卷 754-754页

作者： Lorenzo Arcidiaco Roberto Danti Manuela Corongiu Giovanni Emiliani Arcangela Frascella Antonietta Mello Laura Bonora Sara Barberini David Pellegrini Nicola Sabatini Gianni Della Rocca Institute of BioEconomy National Research Council (CNR-IBE) Via Madonna Del Piano 10 50019 Sesto Fiorentino Italy Institute for Sustainable Plant Protection (IPSP) Via Madonna Del Piano 10 50019 Sesto Fiorentino Italy Environmental Monitoring and Modeling Laboratory for the Sustainable Development (Lamma Consortium) c/o CNR Research Area—Building D Via Madonna Del Piano 10 50019 Sesto Fiorentino Italy Institute for Sustainable Plant Protection (IPSP) Viale Mattioli 25 10125 Turin Italy

Ink disease, primarily caused by the pathogen Phytophthora xcambivora, significantly threatens the health and productivity of sweet chestnut (Castanea sativa Mill.) orchards, highlighting the need for accurate detection methods. This study investigates the efficacy of machine learning (ML) classifiers combined with high-resolution multispectral imagery acquired via unmanned aerial vehicles (UAVs) to assess chestnut tree health at a site in Tuscany, Italy. Three machine learning algorithms—support vector machines (SVMs), Gaussian Naive Bayes (GNB), and logistic regression (Log)—were evaluated against eight vegetation indices (VIs), including NDVI, GnDVI, and RdNDVI, to classify chestnut tree crowns as either symptomatic or asymptomatic. High-resolution multispectral images were processed to derive vegetation indices that effectively captured subtle spectral variations indicative of disease presence. Ground-truthing involved visual tree health assessments performed by expert forest pathologists, subsequently validated through leaf area index (LAI) measurements. Correlation analysis confirmed significant associations between LAI and most VIs, supporting LAI as a robust physiological metric for validating visual health assessments. GnDVI and RdNDVI combined with SVM and GNB classifiers achieved the highest classification accuracy (95.2%), demonstrating their superior sensitivity in discriminating symptomatic from asymptomatic trees. Indices such as MCARI and SAVI showed limited discriminative power, underscoring the importance of selecting appropriate VIs that are tailored to specific disease symptoms. This study highlights the potential of integrating UAV-derived multispectral imagery and machine learning techniques, validated by LAI, as an effective approach for the detection of ink disease, enabling precision forestry practices and informed orchard management strategies.

关键词： chestnut decline vegetation index machine learning support vector machine classifier logistic classifier Gaussian Naive Bayes classifier

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A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 112卷 460-475页

作者： Ahmadipour, Masoud Hizam, Hashim Othman, Mohammad Lutfi Radzi, Mohd Amran Mohd Chireh, Nikta Univ Putra Malaysia Dept Elect & Elect Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia ALPER Serdang 43400 Malaysia Alaodoleh Semnani Inst Higher Educ Dept Architecture Engn Semnan Iran

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn.

关键词： Islanding detection Slantlet transform Ridgelet Probabilistic Neural Network support vector machine classifier Harmony search method

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Evaluation of Radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation

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International Journal of Digital Earth 2019年第10期12卷 1177-1197页

作者： Amit Kumar Sharma Laurence Hubert-Moy Buvaneshwari Sriramulu M.Sekhar Laurent Ruiz S.Bandyopadhyay Shiv Mohan Samuel Corgne Univ Rennes UMR LETG CNRSRennesFrance Indo-French Cell for Water Science Indian Institute of ScienceBangaloreIndia Department of Civil Engineering Indian Institute of ScienceBangaloreIndia UMR 1069 SAS INRAAgro-campus OuestRennesFrance Géosciences Environnement Toulouse Universitéde ToulouseCNRSIRDToulouseFrance EOS Indian Space Research OrganizationBangaloreIndia PLANEX Physical Research LaboratoryAhmedabadIndia

Groundwater assists farmers to irrigate crops for fulfilling the crop-water *** agriculture system is characterized by three cropping seasons known as Kharif(monsoon),Rabi(post-monsoon)and summer(pre-monsoon).In tropical countries like India,monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover,which can be well addressed by microwave remote *** the proposed research,the strength of C-band polarimetric Synthetic Aperture Radar(SAR)time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year *** present study was performed in the Berambadi experimental watershed of Kabini river basin,southern peninsular India.A total of fifteen polarimetric variables were estimated includes four backscattering coefficients(HH,HV,VH,VV)and eleven polarimetric indices for all Radarsat-2 SAR *** cumulative temporal sum(seasonal and dual-season)of these parameters was supervised classified using support vector machine(SVM)classifier with intensive ground observation *** results using the best equation(highest accuracy and kappa)shows that the Kharif,Rabi and irrigated double croplands are respectively 9.58 km2(20.6%),16.14 km2(34.7%)and 6.22 km2(13.4%)with a kappa coefficient respectively 0.84,0.74 and 0.94.

关键词： Radarsat-2 Synthetic Aperture Radar Polarimetric Indices Irrigated Cropland support vector machine classifier Kabini Critical Zone Observatory

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HRRP multi-target recognition in a beam using prior-independent DBSCAN clustering algorithm

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IET RADAR SONAR AND NAVIGATION 2019年第8期13卷 1366-1372页

作者： Guo, Peng-cheng Liu, Zheng Wang, Jing-jing Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China Xian Elect Engn Res Inst Xian Shaanxi Peoples R China

During the operation of monopulse radar, the high-resolution range profiles (HRRPs) of multiple targets may be overlapped, which will reduce the target recognition performance. In this study, a novel multi-target recognition method is proposed based on prior-independent density-based spatial clustering of applications with noise (PI-DBSCAN) algorithm. In the training phase, various features of training samples are extracted, among which the distribution of strong range cells is utilised to obtain the parameters of PI-DBSCAN algorithm while the others are used to train the support vector machine (SVM) classifier. In the test phase, PI-DBSCAN algorithm is exploited at the radial distance-azimuth plane to segment the test multi-target HRRP. Afterward, the features of the segmented HRRPs are extracted and fed to the SVM classifier to be recognised. The proposed method has no constraint on the target motion and does not need to set any parameters manually, which benefits its application. The experiment results of real measured data show that the proposed method is robust against noise and the increasing target HRRP overlap ratio comparing with traditional methods.

关键词： radar resolution support vector machines radar target recognition pattern clustering radar signal processing radar computing HRRP multitarget recognition prior-independent DBSCAN clustering algorithm monopulse radar high-resolution range profiles multiple targets target recognition performance noise algorithm training phase training samples PI-DBSCAN algorithm support vector machine classifier test phase radial distance-azimuth plane test multitarget HRRP SVM classifier target motion

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New approach to transient stability prediction of power systems in wide area measurement systems based on multiple-criteria decision making theory

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第21期13卷 4960-4967页

作者： Hosseini, Hossein Naderi, Soheil Afsharnia, Saeed Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran

To prevent power system blackouts following a large disturbance such as a fault, the speed and precision of the stability status prediction are two important factors for the remedial action schemes. A new approach for the transient stability prediction is presented employing the multi-criteria decision making (MCD) theory to consider both factors simultaneously in this study. It is shown that they depend on the prediction time, therefore solving the MCD problem results in finding the best time for the stability prediction. As the criteria quantifying is necessary in the MCD methods, in this study a new index is proposed to quantify the prediction speed. Furthermore, the support vector machine (SVM) classifier is employed as a transient stability predictor. In addition, the best features are selected and incorporated as the inputs of the SVM classifier according to the features selection methods. Finally, all proposed schemes are implemented on the IEEE 39-Bus test system. The obtained results show the prediction of stability at the optimum time causing the instability prevention with the less required remedial action.

关键词： power engineering computing power system transient stability pattern classification power system measurement decision making support vector machines transient stability prediction power systems wide area measurement systems multiple-criteria decision making theory power system blackouts stability status prediction remedial action schemes multicriteria decision making theory prediction time MCD problem results criteria quantifying MCD methods prediction speed support vector machine classifier transient stability predictor IEEE 39-Bus test system

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Higher order statistics for modulation and STBC recognition in MIMO systems

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IET COMMUNICATIONS 2019年第16期13卷 2436-2446页

作者： Khosraviyani, Mokhtar Kalbkhani, Hashem Shayesteh, Mahrokh G. Urmia Univ Dept Elect Engn Orumiyeh Iran Urmia Univ Technol Fac Elect Engn Orumiyeh Iran Sharif Univ Technol Elec Engn Dept ACRI Wireless Res Lab Tehran Iran

Identification of modulation and space-time block code (STBC) is an important task of receivers in applications such as military, civilian, and commercial communications. Here, we consider multiple-input multiple-output (MIMO) systems. We propose two methods for STBC identification when the modulation is known. We also introduce a method for joint identification of code and modulation. Additionally, we present an enhanced zero-forcing (ZF) equaliser to improve the separation between the features of different classes. Higher order cumulants are used as the statistical features. In the first method of STBC identification, after the proposed equalisation, received data samples are segmented, and then using the mean and Frobenius norm of the covariance matrix of extracted cumulants as threshold values, the STBCs are detected. This method requires the knowledge of the threshold values for each modulation type and noise power. In the second method, the knowledge of noise power and the type of modulation are not needed. After the proposed ZF equalisation, feature vector based on the cumulants is calculated, and then support vector machine (SVM) classifier is used to classify different STBCs. In the third method, joint detection of STBC and modulation type is performed using the proposed ZF equaliser and mapping the received samples. This method deals with the theoretical values calculated from the cumulants of four STBCs and four modulations, where there is no need to know the noise power. The results indicate that the proposed methods perform well even at low signal-to-noise ratios (SNRs).

关键词： equalisers higher order statistics support vector machines covariance matrices MIMO communication space-time block codes pattern classification telecommunication computing higher order statistics STBC recognition MIMO systems space-time block code multiple-input multiple-output systems STBC identification joint identification higher order cumulants statistical features threshold values modulation type noise power feature vector joint detection ZF equaliser Frobenius norm support vector machine classifier zero-forcing equaliser covariance matrix SVM classifier signal-to-noise ratios

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EEG entropies as estimators for the diagnosis of encephalopathy

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2019年第3期101卷 463-474页

作者： Jacob, Jisu Elsa Nair, Gopakumar Kuttappan LBS Ctr Sci & Technol Fac Engn & Technol Thiruvananthapuram Kerala India TKM Coll Engn Dept ECE Kollam Kerala India

Brain consists of a network of millions of neurons and the neural activities of the brain are clearly pictured in its signal, electroencephalogram (EEG). Many pathological conditions of brain can be studied in detail by inspecting the EEG signal in detail rather than just visual inspection. Non linear analysis has proved to be an efficient method for exploring the subtle information embedded in EEG. Approximate entropy and sample entropy are utilized in this study for comparing EEGs of patients with a neurological disease called encephalopathy, with normal EEGs. Both entropies were found to be significantly less (p < 0.01;independent sample t test) for encephalopathy group than normal healthy controls. support vector machine, multilayer perceptron and random forest classifiers have been employed for identifying disease groups based on the EEG entropies and their performance were evaluated. Random forest classifier gave the maximum accuracy of 90% while multilayer perceptron and SVM classifier gave an accuracy of 87% and 84% respectively. The optimum performance was obtained by combining both approximate entropies and sample entropies as features to the classifiers, than using individual set of features. Thus, this work emphasizes that entropies of EEG are good bio-markers for the diagnosis of encephalopathy and that non linear analysis techniques should be employed for analyzing EEG signals.

关键词： EEG Encephalopathy Approximate entropy Sample entropy Random forest classifier Multi-layer perceptron classifier support vector machine classifier

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Detection of focal electroencephalogram signals using higher-order moments in EMD-TKEO domain

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HEALTHCARE TECHNOLOGY LETTERS 2019年第3期6卷 64-69页

作者： Chatterjee, Soumya Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Detection of epileptogenic focus based on electroencephalogram (EEG) signal screening is an important pre-surgical step to remove affected regions inside the human brain. Considering the fact above, in this work, a novel technique for detection of focal EEG signals is proposed using a combination of empirical mode decomposition (EMD) and Teager-Kaiser energy operator (TKEO). EEG signals belonging to focal (Fo) and non-focal (NFo) groups were at first decomposed into a set of intrinsic mode functions (IMFs) using EMD. Next, TKEO was applied on each IMF and two higher-order statistical moments namely skewness and kurtosis were extracted as features from TKEO of each IMF. The statistical significance of the selected features was evaluated using student's t-test and based on the statistical test, features from first three IMFs which show very high discriminative capability were selected as inputs to a support vector machine classifier for discrimination of Fo and NFo signals. It was observed that the classification accuracy of 92.65% is obtained in classifying EEG signals using a radial basis kernel function, which demonstrates the efficacy of proposed EMD-TKEO based feature extraction method for computer-based treatment of patients suffering from focal seizures.

关键词： brain support vector machines electroencephalography feature extraction medical signal processing medical disorders medical signal detection signal classification focal electroencephalogram signals higher-order moments EMD-TKEO domain epileptogenic focus electroencephalogram signal screening important pre-surgical step human brain empirical mode decomposition Teager-Kaiser energy operator nonfocal groups intrinsic mode functions IMF higher-order statistical moments focal seizures proposed EMD-TKEO based feature extraction method radial basis kernel function support vector machine classifier high discriminative capability statistical test statistical significance kurtosis skewness

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An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks

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COMPUTER NETWORKS 2020年 180卷 107390-107390页

作者： Kasim, Omer Kutahya Dumlupinar Univ Simav Technol Fac Dept Elect & Elect Engn TR-43500 Kutahya Turkey

The number of devices connected to the Internet is increasing day by day. This increase causes cyber-attacks to be larger and more complex. It is important to sdetect the anomalies rapidly when there is a cyber-attack. In detecting anomalies, high false positive rate is obtained by using feature extraction based on statistical calculations and machine learning algorithms. In proposed approach, the measured values obtained from the network are normalized between 0 and 1. These values applied to autoencoder model trained with optimum hyper parameters. This model contributes to feature learning and dimensional reduction. support vector machines effectively differentiate between normal and DDOS attack traffic by using these features. The CICIDS dataset and virtually generated DDOS traffic are used to validate the proposed approach and measure its performance. The results show that the proposed approach speeds up training and testing times and performs better classification performance metrics than most previous approaches. The novelty of the study is that AE-SVM trained with CICIDS successfully captures virtually generated DDOS traffic data. Despite the unbalanced data set, 99.1% test success was achieved in detection of DDOS traffic which is produced with Kali Linux. This success contributed to the solution of the high false-positive problem compared to other models.

关键词： DOS attack detection Normalization Autoencoder deep learning model support vector machine classifier

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