389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Meta-analysis of voice disorders databases and applied machine learning techniques

2020 ◽  
Vol 17 (6) ◽  
pp. 7958-7979
Author(s):  
Sidra Abid Syed ◽  
◽  
Munaf Rashid ◽  
Samreen Hussain ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Voice Disorders ◽  
Machine Learning Techniques ◽  
Applied Machine Learning ◽  
Learning Techniques

scholarly journals A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

2018 ◽  
Vol 22 ◽  
Author(s):  
Manojit Chattopadhyay ◽  
Rinku Sen ◽  
Sumeet Gupta
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Meta Analysis ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Business Organizations ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Network Technologies

Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

scholarly journals Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis

2020 ◽  
Vol 62 ◽  
pp. 116-123
Author(s):  
Khaled Rjoob ◽  
Raymond Bond ◽  
Dewar Finlay ◽  
Victoria McGilligan ◽  
Stephen J. Leslie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

Buildings ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Maria Anastasiadou ◽  
Vítor Santos ◽  
Miguel Sales Dias
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Building Energy ◽  
Energy Performance ◽  
Machine Learning Techniques ◽  
Energy Performance Of Buildings ◽  
Learning Techniques ◽  
Specific Factors

The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.

scholarly journals Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis

PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0220242 ◽  
Author(s):  
Ana Luiza Dallora ◽  
Peter Anderberg ◽  
Ola Kvist ◽  
Emilia Mendes ◽  
Sandra Diaz Ruiz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Bone Age ◽  
Machine Learning Techniques ◽  
Age Assessment ◽  
Learning Techniques ◽  
Bone Age Assessment

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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