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Discriminating pure Tamarix species and their putative hybrids using field spectrometer

GEOCARTO INTERNATIONAL 2022年第25期37卷 7733-7752页

作者： Tesfamichael, Solomon G. Newete, Solomon W. Adam, Elhadi Byrne, Marcus J. Univ Johannesburg Dept Geog Environm Management & Energy Studies Johannesburg South Africa Agr Res Council GeoInformat Div Inst Soil Climate & Water Pretoria South Africa Univ Witwatersrand Sch Anim Plant & Environm Sci Johannesburg South Africa Univ Witwatersrand Sch Geog Archaeol & Environm Studies Johannesburg South Africa Univ Witwatersrand DST NRF Ctr Excellence Invas Biol Sch Anim Plant & Environm Sci Johannesburg South Africa

South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, non-native and hybrid Tamarix species recorded in South Africa. Leaf- and canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species.

关键词： Tamarix spp machine learning classification field spectrometer leaf-level classification Canopy-level classification multispectral simulation

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Applying recurrence plots to classify time series 5

Applying recurrence plots to classify time series

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5th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Kirichenko, Lyudmyla Radivilova, Tamara Stepanenko, Juliia Kharkiv Natl Univ Radio Elect 14 Nauky Ave UA-61166 Kharkiv Ukraine

The article describes a new approach to the classification of time series based on the construction of their recurrence plots. After transforming the time series into recurrence plots, two approaches are applied for classification. On the first approach, numerical recurrence characteristics are used for classification as features. In the second case, the time series is interpreted as image of its recurrence plot. A convolutional neural network is chosen for image classification. The data for the classification are the electrocardiograms realizations of 100 values, which contained records of healthy people and patients with a diagnosis of ischemia. Research results showed the advantages of classifying images of recurrence plots, indicate a good classification accuracy in comparison with other methods and the potential capabilities of this approach.

关键词： Time series classification machine learning classification recurrence plot ECG time series numerical recurrence characteristic

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Modern Standard Arabic Readability Prediction 6th

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6th International Conference on Arabic Language Processing (ICALP)

作者： Nassiri, Naoual Lakhouaja, Abdelhak Cavalli-Sforza, Violetta Univ Mohamed First Fac Sci Dept Comp Sci B-P 717 Oujda 60000 Morocco Al Akhawayn Univ Sch Sci & Engn Ifrane Morocco

ISBN: (纸本)9783319735009;9783319734996

Reading is the most critical skill for satisfactory progress in school, as well as being highly important for access to information throughout one's life. For this reason, readability is one of the main challenges when choosing academic texts for learners or for readers in general, and especially with materials containing important information, such as newspapers and medical or legal articles. Readability refers to the ability of a text to be understood by the reader. Readability level prediction is an important measure in several domains, but primarily in education. In the current paper we present our approach to readability prediction for Modern Standard Arabic. This method is based on 170 features of measuring different types of text characteristics. We have used a corpus of 230 Arabic texts, annotated with the Interagency Language Roundtable (ILR) scale, and a frequency dictionary obtained using Tashkeela corpora. The results obtained are very encouraging and better than for previously presented work.

关键词： Readability Modern Standard Arabic machine learning classification Arabic readability

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Impact of Harmonization on MRI Radiomics Feature Variability Across Preprocessing Methods for Parkinson's Disease Motor Subtype classification

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年 1-14页

作者： Panahi, Mehdi Hosseini, Mahboube Sadat Payame Noor Univ Dept Comp Engn Erbil Branch Erbil Iraq Shahid Beheshti Univ Dept Med Radiat Engn Tehran Iran

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple preprocessing methods for classifying Parkinson's disease (PD) motor subtypes and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant and 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's Progression Markers Initiative (PPMI) database, acquired using different scanner models. Radiomic features were extracted from 16 brain regions using various preprocessing pipelines. ComBat harmonization was applied using a combined batch variable incorporating both scanner models and preprocessing methods. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Feature selection was performed using Linear Support Vector Classifier with L1 regularization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC >= 0.90) increased from 40.2 to 56.3% after harmonization. First-order statistic features showed the highest robustness, with 71.11% demonstrating excellent ICC after harmonization. The proportion of features significantly affected by preprocessing methods was reduced following harmonization. classification accuracy improved dramatically, from a range of 34-75% before harmonization to 89-96% after harmonization across all preprocessing methods. AUC values similarly increased from 0.28-0.87 to 0.95-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across preprocessing methods and improved PD motor subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD a

关键词： Parkinson's disease MRI radiomics ComBat harmonization Preprocessing methods machine learning classification Motor subtypes

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USING machine learning CLASSIFIERS AND REGRESSION MODELS FOR ESTIMATING THE STAND AGES OF FALCATA PLANTATIONS FROM SENTINEL DATA

USING MACHINE LEARNING CLASSIFIERS AND REGRESSION MODELS FOR...

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ISPRS WG IV/7 Geoinformation Week on Broadening Geospatial Science and Technology

作者： Escasio, J. C. L. Santillan, J. R. Makinano-Santillan, M. Caraga State Univ Coll Engn & Geosci Dept Geodet Engn Butuan City 8600 Philippines Caraga State Univ Caraga Ctr Geoinformat Butuan City 8600 Philippines

Falcata is a widely planted Industrial Tree Plantation (ITP) species in the Caraga Region, Mindanao, Philippines, significantly contributing to more than 80% of the country's Falcata log production in recent years. Currently, Falcata plantations face several challenges, especially regarding plantation monitoring and management. The information on stand age is essential for the efficient monitoring and sustainable management of ITPs. With advances in technology and freely available satellite data, primarily from the Sentinel satellites, remote sensing has provided an alternative approach to determining stand age information. In this context, this study used multivariate regression models and machine learning classifiers, namely Support Vector machine (SVM) and Random Forest (RF) to estimate Falcata stand ages from single-date and multitemporal Sentinel-1 and Sentinel-2 images and vegetation indices (VIs). The eleven multivariate regression models have R-2 values ranging from 0.23 and 0.81 and with estimation errors of 1.72 to 3.58 years. The best multivariate regression model developed is an exponential model that relates Falcata stand age with the year 2021 Sentinel-2 surface reflectance bands and year 2021 Sentinel-1 VV and VH polarization bands. The model has training and validation data R-2 of 0.61 and 0.55, which are the most consistent among the 11 regression models developed. When used for stand age estimation, this model may underestimate, or overestimate stand age by 1.72 years. When a machine learning approach is to be employed, the RF classifier performed better than SVM, particularly when estimating stand ages using multitemporal Sentinel-1 and Sentinel-2 data and VIs. The classifier has an overall accuracy of 84.69%, the highest among the eight classification results generated by the study.

关键词： Multivariate Regression machine learning classification Sentinel Falcata Stand Age

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The relationship between visual enclosure for neighbourhood street walkability and elders' mental health in China: Using street view images

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JOURNAL OF TRANSPORT & HEALTH 2019年 13卷 90-102页

作者： Wang, Ruoyu Lu, Yi Zhang, Jinbao Liu, Penghua Yao, Yao Liu, Ye China Univ Geosci Sch Informat Engn Wuhan 430074 Hubei Peoples R China Univ Edinburgh Inst Geog Sch GeoSci Edinburgh Midlothian Scotland Sun Yat Sen Univ Sch Geog & Planning Xingang Xi Rd Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Guangdong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

Background: Neighbourhood walkability has been consistently associated with more physical activities in adults. Nevertheless, evidence of a beneficial association between walkability and older adults' mental health is still scarce in developing countries. Furthermore, walkability was often measured through Geographic Information System (GIS) methods. Recently, the methodological development in street view images, such as using Google or Tencent Street View, has offered an attractive alternative. Methods: Using Tencent street view images and machine learning classification methods, we quantify visual enclosure for neighbourhood street walkability with the proportion of visible sky in those images. Depression and anxiety data were extracted from mental health surveys for 1231 older adults in Beijing, China in 2011. Multilevel linear regression models were used to assess the associations. Results: Street walkability was negatively associated with Geriatric Depression Scale scores (GDS 15-item) and Geriatric Anxiety Inventory scores (GAI 20-item), and the results of different robustness checks also support this relationship. The associations were stronger for disadvantaged older adults than others. Conclusion: The present study indicates a potential beneficial role of street walkability on mental health (depression and anxiety) in older residents, especially for disadvantaged older adults. Additional longitudinal studies are required to address some limitations of this study, such as residential self-selection bias.

关键词： Street walkability machine learning classification Anxiety Depression Older adults

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Applying Computer Vision to Track Tool Movement in an Automotive Assembly Plant 19

Applying Computer Vision to Track Tool Movement in an Automo...

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Annual ACM Southeast Conference (ACMSE)

作者： Turner, Emily Newberry, Landon Santinga, Sheridan Gray, Jeff Gopu, Sandeep Peoples, Jeffery Hobbs, Jon White, Sanford Univ Alabama Tuscaloosa AL 35487 USA Mercedes Benz US Int Vance AL USA

ISBN: (纸本)9781450362511

In the context of automobile assembly, torque tools are used by technicians to mount bolts to parts of a vehicle. Different bolts require varying torque levels to be fastened correctly. An intelligent tool can be programmed to deliver torque levels in a specified order that must be strictly followed by the technician as they secure the bolts. Because of the pre-specified order, it is imperative that the technician follows the correct order precisely. This paper describes our investigation into the use of computer vision to identify, correct, and document the human error involved in the bolt securing process (i.e., incorrect ordering) on an automotive factory assembly line. We built a computer vision application to select a desired order of bolts, detect visitation of a torque tool to each bolt location, and report errors made in the sequence of actions in a vehicle assembly line. The application is used to determine the accuracy of error detection for varying degrees of distance between bolt locations and sizes in order to determine which assembly line stations are appropriate for this type of monitoring. The context for application of our project is a large automotive manufacturing facility in the Southeastern United States - the Mercedes-Benz US International (MBUSI) factory in Vance, Alabama. The limitations of computer vision for determining errors in assembly order using the torque tool are reported with a discussion of lessons learned.

关键词： Computer Vision Automobile Assembly machine learning classification Object Detection

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Integral Homeostasis Index in a Decision Support System for the Assessment and Prediction of the Clinical Condition of Children with Severe Mechanical Trauma

Integral Homeostasis Index in a Decision Support System for ...

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Special Topic Conference of the European-Fedeeration-for-Medical-Informatics (EFMI STC)

作者： Arseniev, Sergei B. Amcheslavsky, Valeri G. Lukianov, Valeri I. Rappelsberger, Andrea Adlassnig, Klaus-Peter Clin & Res Inst Emergency Pediat Surg & Trauma Bolshaya Polyanka 22 Moscow 119180 Russia Med Univ Vienna Ctr Med Stat Informat & Intelligent Syst Sect Artificial Intelligence & Decis Support Spitalgasse 23 A-1090 Vienna Austria Medexter Healthcare GmbH Borschkegasse 7-5 A-1090 Vienna Austria

ISBN: (纸本)9781614999218;9781614999201

Objective: Daily assessment of the acid-base balance (ABB) in blood is one of the important elements of multi-parameter patient monitoring at intensive care units (ICUs). The present work aims to determine the effectiveness and validity of the integral homeostasis index IHx calculated from ABB blood test data for the assessment and prognosis of children with critical traumatic conditions. Methods: 345 patients were studied. IHx was calculated and the data were subjected to statistical evaluation. An Arden-Syntax-based clinical decision support (CDS) platform was used. One purpose of the study was to incorporate the platform into the ICU IT landscape of the hospital, and the second purpose was to develop a CDS module for the calculation of IHx and present the results in real time to the attending physician. Results: Integral homeostasis index IHx calculations as well as their prompt assessment permit better and more rapid treatment of children with severe traumatic injury.

关键词： Severe mechanical trauma in children Acid-Base Balance (ABB) Clinical Decision Support (CDS) machine learning classification Arden Syntax CDS software

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Twitter Sentiment Analysis of Covid Vaccines 2021

Twitter Sentiment Analysis of Covid Vaccines

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5th International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Zhu, Wenbo Hu, Tiechuan NYU Courant Inst Math Sci New York NY 10012 USA

ISBN: (纸本)9781450384148

In this paper, we look at a database of tweets sorted by various keywords that could indicate the users' sentiment towards covid vaccines. With social media becoming such a prevalent source of opinion, sorting and ranking tweets that hold important information such as opinions on covid vaccines is of utmost importance. Two different ranking scales were used, and ranking a tweet in this way could represent the difference between an opinion being lost and an opinion being featured on the site, which affects the decisions and behavior of people, and why researchers were interested in it. Using natural language processing techniques, our aim is to determine and categorize opinions about covid vaccines with the highest accuracy possible.

关键词： Sentiment Analysis Ranking algorithm machine learning classification

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machine learning Behavioral Recognition to Support Neuropsychological Diagnosis of Cognitive Decline 18

Machine Learning Behavioral Recognition to Support Neuropsyc...

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23rd International Conference on Intelligent User Interfaces (IUI)

作者： Lara-Garduno, Raniero A. Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9781450349451

With nearly 93,500 deaths were attributed to Alzheimer's Disease in 2014 and numbers projected to climbs, medical experts have recently placed increasing emphasis in the early detection of Alzheimer's disease and other types of dementia. An open challenge in early detection is developing standardized testing that helps clinicians detect subtle signs of Mild Cognitive Impairment that occur prior to the development of said dementias. In this regard we present our preliminary work on leveraging machine-learning behavioral classification to detect subtle behavioral abnormalities. Our framework consists of detecting behavioral abnormalities through the computerization of an existing paper-based clinical neuropsychological tests as well as the development of novel tests that could only be realized with modern touch tablet technology.

关键词： Human-Computer Interaction Clinical Neuropsychology machine learning classification Alzheimer's Disease Mild Cognitive Impairment

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