On the relationship between spectroscopic constants of diatomic molecules: a machine learning approach

Through a machine learning approach, we show that the equilibrium distance, harmonic vibrational frequency and the binding energy of diatomic molecules are universally related, independently of the nature of the bond of a molecule.

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A Machine Learning Approach to Analyzing the Relationship Between Temperatures and Multi-Proxy Tree-Ring Records

Tree-Ring Research ◽

10.3959/1536-1098-74.2.210 ◽

2018 ◽

Vol 74 (2) ◽

pp. 210-224 ◽

Cited By ~ 4

Author(s):

Jernej Jevšenak ◽

Sašo Džeroski ◽

Saša Zavadlav ◽

Tom Levanič

Keyword(s):

Machine Learning ◽

Tree Ring ◽

Learning Approach ◽

Machine Learning Approach ◽

The Relationship

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On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach

Annals of Operations Research ◽

10.1007/s10479-020-03652-2 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zied Ftiti ◽

Kais Tissaoui ◽

Sahbi Boubaker

Keyword(s):

Machine Learning ◽

Oil And Gas ◽

Learning Approach ◽

Machine Learning Approach ◽

Gas Markets ◽

The Relationship

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PhGC: A Machine Learning Based Workﬂow for Phenotype-Genotype Co-analysis on Autism

10.29007/ctfl ◽

2020 ◽

Author(s):

Safa Shubbar ◽

Chen Fu ◽

Zhi Liu ◽

Anthony Wynshaw-Boris ◽

Qiang Guan

Keyword(s):

Machine Learning ◽

Clustering Algorithms ◽

Autism Spectrum ◽

Diagnostic Tools ◽

Learning Approach ◽

The Core ◽

Core Member ◽

Machine Learning Approach ◽

Genetic Mechanisms ◽

The Relationship

Autism spectrum disorder (ASD) is a heterogeneous disorder, diagnostic tools attempt to identify homogeneous subtypes within ASD. Previous studies found many behavioral/- physiological commodities for ASD, but the clear association between commodities and underlying genetic mechanisms remains unknown. In this paper, we want to leverage ma- chine learning to figure out the relationship between genotype and phenotype in ASD. To this purpose, we propose PhGC pipeline to leverage machine learning approach to to identify behavioral phenotypes of ASD based on their corresponding genomics data. We utilize unsupervised clustering algorithms to extract the core members of each clusters and profile the core member subsets to explore the characteristics using genotype data from the same dataset. Our genome annotation results showed that most of the alleles with different frequency among clusters were represented by the core members.

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A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm

Biomechanics and Modeling in Mechanobiology ◽

10.1007/s10237-017-0903-9 ◽

2017 ◽

Vol 16 (5) ◽

pp. 1519-1533 ◽

Cited By ~ 30

Author(s):

Liang Liang ◽

Minliang Liu ◽

Caitlin Martin ◽

John A. Elefteriades ◽

Wei Sun

Keyword(s):

Machine Learning ◽

Aortic Aneurysm ◽

Learning Approach ◽

Shape Features ◽

Ascending Aortic Aneurysm ◽

Machine Learning Approach ◽

The Relationship ◽

Predicted Risk

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The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach

Computational Economics ◽

10.1007/s10614-021-10097-7 ◽

2021 ◽

Author(s):

Cheng-Feng Wu ◽

Shian-Chang Huang ◽

Chei-Chang Chiou ◽

Tsangyao Chang ◽

Yung-Chih Chen

Keyword(s):

Machine Learning ◽

Economic Growth ◽

Electricity Consumption ◽

Learning Approach ◽

Machine Learning Approach ◽

Fourier Function ◽

The Relationship

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Language Relationship Model for Automatic Generation of Tamil Stories from Hints

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2017040102 ◽

2017 ◽

Vol 13 (2) ◽

pp. 21-40

Author(s):

Rajeswari Sridhar ◽

V. Janani ◽

Rasiga Gowrisankar ◽

G. Monica

Keyword(s):

Machine Learning ◽

Automatic Generation ◽

Learning Phase ◽

Its Sequences ◽

Learning Approach ◽

Machine Learning Approach ◽

Relationship Model ◽

Conceptual Graph ◽

The Relationship

In this paper, we propose to develop a Story Generator from hints using a machine learning approach. During the learning phase, the system is fed with stories which are POS tagged and are converted into a Language Relationship model that is represented as a conceptual graph. During the synthesis phase, the input hints which are delimited using hyphen and converted to a conceptual graph. This graph is matched with the conceptual graph of the corpus and probable words, its sequences along with the relationship are determined using three proposed methods namely Randomized selection, Weighted Selection using Bigram Probability of hint phrases and Weighted Selection using product of Bigram Probability of Conceptual Graph and Bigram Probability of hint phrases. Using the words, sequences and relationships, a sentence assembler algorithm is designed to position the words to form a sentence. To make the story complete and readable, suffixes are added using Tamil grammar to the assembled words and a story is generated which is syntactically and semantically correct.

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