Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 49-66
Author(s):  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Pandit Byomakesha Dash ◽  
Danilo Pelusi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Processing ◽  
Biomedical Informatics ◽  
Learning Model ◽  
Biomedical Data ◽  
Fuzzy C Means ◽  
Biomedical Data Analysis ◽  
Machine Learning Model ◽  
Efficient Machine

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

2020 ◽  
Vol 223 (3) ◽  
pp. 437.e1-437.e15
Author(s):  
Joshua Guedalia ◽  
Michal Lipschuetz ◽  
Michal Novoselsky-Persky ◽  
Sarah M. Cohen ◽  
Amihai Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Learning Model ◽  
Time Data ◽  
Machine Learning Model ◽  
Vaginal Deliveries ◽  
Real Time Data

scholarly journals Text and Data Formatting for Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2756-2760
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Preprocessing ◽  
Learning Model ◽  
Poor Quality ◽  
Big Data Analysis ◽  
Model Quality ◽  
Machine Learning Model ◽  
Learning Analysis

Machine learning is a prominent tool for getting data from large amounts of information. Whereas a good amount of machine learning analysis has targeted on increasing the accuracy and potency of coaching and reasoning algorithms, there is less attention within the equally vital issues of observing the standard of information fed into the machine learning model. The standard of huge information is far away from good. Recent studies have shown that poor quality will bring serious errors to the result of big data analysis and this could have an effect on in making additional precise results from the information. Advantages of data preprocessing within the context of ML are advanced detection of errors, model-quality improves by the usage of better data, savings in engineering hours to debug issues

scholarly journals Constructing an Efficient Machine Learning Model for Tornado Prediction

2020 ◽  
Vol 19 (05) ◽  
pp. 1177-1187
Author(s):  
Fuad Aleskerov ◽  
Sergey Demin ◽  
Michael B. Richman ◽  
Sergey Shvydun ◽  
Theodore B. Trafalis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Superposition Principle ◽  
Learning Models ◽  
Analysis Techniques ◽  
Machine Learning Model ◽  
Choice Procedures ◽  
Efficient Machine ◽  
Machine Learning Models

Tornado prediction variables are analyzed using machine learning and decision analysis techniques. A model based on several choice procedures and the superposition principle is applied for different methods of data analysis. The constructed model has been tested on a database of tornadic events. It is shown that the tornado prediction model developed herein is more efficient than a previous set of machine learning models, opening the way to more accurate decisions.

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