Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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180 The role of social constructs in placental insufficiency outcomes: an unsupervised machine learning approach

American Journal of Obstetrics and Gynecology ◽

10.1016/j.ajog.2020.12.202 ◽

2021 ◽

Vol 224 (2) ◽

pp. S121-S122

Author(s):

Ramamurthy Siripuram ◽

Nathan R. Blue ◽

Robert M. Silver ◽

William A. Grobman ◽

Uma M. Reddy ◽

...

Keyword(s):

Machine Learning ◽

Placental Insufficiency ◽

Learning Approach ◽

Social Constructs ◽

Unsupervised Machine Learning ◽

Machine Learning Approach

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Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma

BJS Open ◽

10.1093/bjsopen/zraa061 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

F Torresan ◽

F Crimì ◽

F Ceccato ◽

F Zavan ◽

M Barbot ◽

...

Keyword(s):

Machine Learning ◽

Imaging Techniques ◽

Second Order ◽

Learning Approach ◽

Adrenocortical Adenoma ◽

Unsupervised Machine Learning ◽

First Order ◽

Main Challenge ◽

Machine Learning Approach ◽

Adrenocortical Tumours

Abstract Background The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. Methods CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Results Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Conclusion Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

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Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

EAI Endorsed Transactions on Energy Web ◽

10.4108/eai.13-7-2018.160834 ◽

2019 ◽

Vol 6 (24) ◽

pp. 160834

Author(s):

Maitreyi Ponguwala ◽

DR.Sreenivasa Rao

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Unsupervised Machine Learning ◽

Iot Applications ◽

Machine Learning Approach

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On Extraction of Parameters for Pyrolysis Reaction Kinetics of Organic Ablative Materials with Molecular Dynamics and Unsupervised Machine Learning Approach

AIAA AVIATION 2021 FORUM ◽

10.2514/6.2021-3127 ◽

2021 ◽

Author(s):

Abhishek Bhesania ◽

Parvesh Kamboj ◽

Sai Abhishek Peddakotla ◽

Rakesh K. Mathpal ◽

Vaibhav Arghode

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Reaction Kinetics ◽

Learning Approach ◽

Unsupervised Machine Learning ◽

Pyrolysis Reaction ◽

Machine Learning Approach ◽

Kinetics Of

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A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

10.5194/egusphere-egu21-15637 ◽

2021 ◽

Author(s):

Julia Kaltenborn ◽

Viviane Clay ◽

Amy R. Macfarlane ◽

Joshua Michael Lloyd King ◽

Martin Schneebeli

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Snow Layer ◽

Arctic Sea ◽

Execution Speed

Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.We will test and compare the different algorithms and evaluate the algorithms&#8217; effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms&#8217; comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation.&#160;&#160;King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. The Cryosphere, 14(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.

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