scholarly journals Editorial: Applications of Soft Computing and Machine Learning Techniques for Biomedical Signals and Images

2021 ◽  
Vol 15 (1) ◽  
pp. 57-57
Author(s):  
Rahul K. Kher ◽  
Chirag Paunwala ◽  
Falgun Thakkar ◽  
Heena Kher ◽  
Mita Paunwala
Keyword(s):  
Machine Learning ◽  
Soft Computing ◽  
Machine Learning Techniques ◽  
Biomedical Signals ◽  
Learning Techniques

scholarly journals Applications of Soft Computing and Machine Learning Techniques for Biomedical Signals and Images

2021 ◽  
Vol 15 (1) ◽  
pp. 131-131
Author(s):  
Rahul K. Kher ◽  
Chirag Paunwala ◽  
Falgun Thakkar ◽  
Heena Kher ◽  
Mita Paunwala
Keyword(s):  
Machine Learning ◽  
Soft Computing ◽  
Machine Learning Techniques ◽  
Biomedical Signals ◽  
Learning Techniques

scholarly journals Network Malware Detection using Soft Computing and Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 879-885
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Anomaly Detection ◽  
Soft Computing ◽  
Naive Bayes ◽  
Malware Detection ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Network Anomaly Detection

In today’s world there is rapid increase in the information which makes addressing of security issues more important. Malware detection is an important area for research in effective and secure functioning of computer networks. Research efforts are required to protect the systems from various security attacks. In this paper, we analyze usefulness of Soft Computing and Machine Learning Techniques for network malware detection. Hamamoto et al. [1] used combination of Genetic Algorithm and Fuzzy logic for implementation of network anomaly detection. The research work proposed in this paper extends the concepts discussed in [1]. The proposed work explores use of various Machine Learning algorithms such as K-Nearest Neighbor, Naïve Bayes and Decision Tree for network anomaly detection. The experimental observations are conducted on CIDDS (Coburg Intrusion Detection Data Set) dataset [14]. It is observed that Decision Tree approach gave better results as compared to KNN and Naïve Bayes techniques. Decision Tree technique gives 99% of accuracy and precision of 1 and recall of 1.

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.

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

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.

Sign in / Sign up

Export Citation Format

Share Document