Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

Hydrological regime classification of Russian Plain rivers was always done by hand and by using subjective analysis of various characteristics of a seasonal runoff. Last update to this classification was made in the early 1990s.&#160;In this work we make an attempt at using different machine learning methods for objective classification. Both clustering (DBSCAN, K-Means) and classification (XGBoost) methods were used to establish 1) if an established runoff types can be inferred from the data using supervised approach 2) similar clusters can be inferred from data (unsupervised approach). Monthly runoff data for 237 rivers of Russian Plain since 1945 and until 2016 were used as a dataset.&#160;In a first attempt dataset was divided into periods of 1945-1977 and 1978-2016 in attempt to detect changes in river water regimes due to climate change. Monthly data were transformed into following features: annual and seasonal runoff, runoff levels for different seasons, minimum and maximum values of monthly runoff, ratios of the minimum and maximum runoff compared to yearly average and others. Supervised classification using XGBoost method resulted in 90% accuracy in water regime type identification for 1945-1977 period. Shifts in water regime types for southern rivers of Russian Plain rivers in a Don region were identified by this classifier.DBSCAN algorithm for clustering was able to identify 6 major clusters corresponding to existing water regime types: Kola peninsula, North-East part of Russian Plain and polar Urals, Central Russia, Southern Russia, arid South-East, foothills and separately higher altitudes of the Caucasus. Nonetheless a better approach was sought due to intersections of a clusters because of the continuous nature of data. Cosine similarity metric was used as an alternative way to separate river runoff types, this time for each year. Yearly cutoff also allows us to make a timeline of water regime changes over the course of 70 years. By using it as an objective ground truth we plan to remake classification and clusterization made earlier and establish an automated way to classify changes in water regime over time.As a result, the following conclusions can be made<ol><li>It&#8217;s possible to train an accurate classifier based on established water regime type and apply it to detect changes in water regime types over the course of time</li> <li>By applying the classifier to different periods of time we can detect a shift to &#8220;southern&#8221; type of water regime in the central area of Russian Plain</li> <li>Despite the highly continuous nature of data it seems possible to use cosine similarity metric to separate water regime types into zones corresponding to established ones</li> </ol>The study was supported by the Russian Science Foundation (grant No.19-77-10032) in methods&#160;and Russian Foundation for Basic Research (grant No.18-05-60021)&#160;for analyses in Arctic region&#160;

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Clasificación espacial del suelo urbano por el valor especulativo del suelo e imágenes MSI satelitales usando K-MEANS, Huancayo, Perú

Revista Urbano ◽

10.22320/07183607.2021.24.44.06 ◽

2021 ◽

Vol 24 (44) ◽

pp. 70-83

Author(s):

Gonzalo Rodolfo Peña-Zamalloa

Keyword(s):

Machine Learning ◽

Urban Areas ◽

Land Use Changes ◽

Land Market ◽

Objective Classification ◽

Urban Land Market ◽

The City ◽

Sentinel 2 ◽

Urban Territory

The city of Huancayo, like other intermediate cities in Latin America, faces problems of poorly planned land-use changes and a rapid dynamic of the urban land market. The scarce and outdated information on the urban territory impedes the adequate classification of urban areas, limiting the form of its intervention. The purpose of this research was the adoption of unassisted and mixed methods for the spatial classification of urban areas, considering the speculative land value, the proportion of urbanized land, and other geospatial variables. Among the data collection media, Multi-Spectral Imagery (MSI) from the Sentinel-2 satellite, the primary road system, and a sample of direct observation points, were used. The processed data were incorporated into georeferenced maps, to which urban limits and official slopes were added. During data processing, the K-Means algorithm was used, together with other machine learning and assisted judgment methods. As a result, an objective classification of urban areas was obtained, which differs from the existing planning.

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Machine Learning Classification of Spinal Lesions: Compared Accuracy of Texture Parameters Extracted by Different Software

10.1055/s-0039-1692578 ◽

2019 ◽

Author(s):

V. Chianca ◽

D. Albano ◽

R. Cuocolo ◽

C. Messina ◽

S. Gitto ◽

...

Keyword(s):

Machine Learning ◽

Machine Learning Classification ◽

Spinal Lesions ◽

Texture Parameters

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Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

10.1055/s-0039-1692575 ◽

2019 ◽

Author(s):

S. Gitto ◽

D. Albano ◽

V. Chianca ◽

R. Cuocolo ◽

L. Ugga ◽

...

Keyword(s):

Machine Learning ◽

Texture Analysis ◽

Low Grade ◽

High Grade ◽

Machine Learning Classification

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A Brief Survey on Text Classification Using Various Machine Learning Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i1.521 ◽

2018 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Padmavathi .S ◽

M. Chidambaram

Keyword(s):

Machine Learning ◽

Text Classification ◽

Fixed Number ◽

Machine Learning Techniques ◽

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207318666150525094503 ◽

2015 ◽

Vol 18 (5) ◽

pp. 476-485 ◽

Cited By ~ 7

Author(s):

Vilas Belekar ◽

Karthik Lingineni ◽

Prabha Garg

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Learning Approaches ◽

Breast Cancer Resistant Protein

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OBJECTIVE CLASSIFICATION OF FRACTIONAL CRYSTALLIZATION, ASSIMILATION AND RECHARGE SIGNATURES IN MULTIDIMENSIONAL WHOLE ROCK GEOCHEMICAL DATA

10.1130/abs/2020am-355086 ◽

2020 ◽

Author(s):

Rachel Lynn Hampton ◽

◽

Leif Karlstrom

Keyword(s):

Fractional Crystallization ◽

Geochemical Data ◽

Objective Classification

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Learning and control

10.1093/oso/9780199674923.003.0026 ◽

2018 ◽

Author(s):

Ivan Herreros

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Brain Function ◽

Control Strategies ◽

Learning Problems ◽

Animal Learning ◽

Feed Forward Control ◽

Machine Learning Applications ◽

And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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