scholarly journals LFO2: An Enhanced Version of Learning-From-OPT Caching Algorithm

2021 ◽  
Author(s):  
Yipkei Kwok ◽  
David L. Sullivan
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
State Of The Art ◽  
Window Size ◽  
Learning Model ◽  
The State ◽  
Small Window ◽  
Machine Learning Model ◽  
New Machine

Recent machine learning-based caching algorithm have shown promise. Among them, Learning-FromOPT (LFO) is the state-of-the-art supervised learning caching algorithm. LFO has a parameter named Window Size, which defines how often the algorithm generates a new machine-learning model. While using a small window size allows the algorithm to be more adaptive to changes in request behaviors, experimenting with LFO revealed that the performance of LFO suffers dramatically with small window sizes. This paper proposes LFO2, an improved LFO algorithm, which achieves high object hit ratios (OHR) with small window sizes. This results show a 9% OHR increase with LFO2. As the next step, the machine-learning parameters will be investigated for tuning opportunities to further enhance performance.

Development of new machine learning model for streamflow prediction: case studies in Pakistan

2021 ◽  
Author(s):  
Rana Muhammad Adnan ◽  
Reham R. Mostafa ◽  
Ahmed Elbeltagi ◽  
Zaher Mundher Yaseen ◽  
Shamsuddin Shahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Learning Model ◽  
Streamflow Prediction ◽  
Machine Learning Model ◽  
New Machine

scholarly journals Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 59
Author(s):  
Alexander Chowdhury ◽  
Jacob Rosenthal ◽  
Jonathan Waring ◽  
Renato Umeton
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Unlabeled Data ◽  
Data Repositories ◽  
Public And Private ◽  
Complex Models ◽  
Definition Of

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that can take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state of the art published in each of those subsets between the years of 2014 and 2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

Next Generation Cloud Security: State of the Art Machine Learning Model

2021 ◽  
pp. 125-144
Author(s):  
Sachi Nandan Mohanty ◽  
Gouse Baig Mohammad ◽  
Sirisha Potluri ◽  
P. Ramya ◽  
P. Lavanya
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Cloud Security ◽  
Learning Model ◽  
Next Generation ◽  
Machine Learning Model

scholarly journals Applied Self-Supervised Learning: Review of the State-of-the-Art and Implementations in Medicine

2021 ◽  
Author(s):  
Alexander Chowdhury ◽  
Jacob Rosenthal ◽  
Jonathan Waring ◽  
Renato Umeton
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Unlabeled Data ◽  
Data Repositories ◽  
Public And Private ◽  
Complex Models ◽  
Definition Of

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.

scholarly journals A Graphical Model to Diagnose Product Defects with Partially Shuffled Equipment Data

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 934
Author(s):  
Gilseung Ahn ◽  
Sun Hur ◽  
Dongmin Shin ◽  
You-Jin Park
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Supervised Learning ◽  
Latent Variables ◽  
Manufacturing Industry ◽  
Graphical Model ◽  
Learning Model ◽  
High Quality ◽  
Machine Learning Model ◽  
Product Order

The diagnosis of product defects is an important task in manufacturing, and machine learning-based approaches have attracted interest from both the industry and academia. A high-quality dataset is necessary to develop a machine learning model, but the manufacturing industry faces several data-collection issues including partially shuffled data, which arises when a product ID is not perfectly inferred and yields an unstable machine learning model. This paper introduces latent variables to formulate a supervised learning model that addresses the problem of partially shuffled data. The experimental results show that our graphical model deals with the shuffling of product order and can detect a defective product far more effectively than a model that ignores shuffling.

Sign in / Sign up

Export Citation Format

Share Document