scholarly journals Insights on Video Compression Strategies using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2724-2733
Keyword(s):  
Machine Learning ◽  
Video Compression ◽  
Research Problem ◽  
Learning Approach ◽  
Unsolved Research Problem ◽  
Compression Technique ◽  
Compression Performance ◽  
Machine Learning Approach ◽  
Research Problems ◽  
Research Gap

With the rising advancement of the multimedia technology, video compression is becoming a challenging problem. Although, there is availability of various standard compression algorithms, yet robust compression performance is yet to be seen in existing compression techniques. This paper also highlights that machine learning plays a significant contributory role in improving the performance of the video compression. Therefore, this manuscript offers a technical insight about the performance of existing video compression technique using machine learning approach. The contribution of this paper is its findings which states that machine learning approach do have significant advantage but the advantageous features are limited by the inherent and unsolved research problem. The core findings of this paper are basically to highlight the strength and limitations of existing methods as well as to highlight the research gap in terms of open-end research problems which requires immediate attention.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

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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