scholarly journals Comparative Analysis of Regression based and Supervised Learning Algorithms for Predicting Traffic Noise Levels in Indian Scenario

2013 ◽  
Vol 74 (15) ◽  
pp. 45-50
Author(s):  
Prashant Ruwali ◽  
Vikas Tripathi
Keyword(s):  
Comparative Analysis ◽  
Supervised Learning ◽  
Traffic Noise ◽  
Learning Algorithms ◽  
Noise Levels ◽  
Supervised Learning Algorithms ◽  
Indian Scenario

Supervised learning algorithms in the classification of plant populations with different degrees of kinship

2021 ◽  
Author(s):  
Leandro Skowronski ◽  
Paula Martin de Moraes ◽  
Mario Luiz Teixeira de Moraes ◽  
Wesley Nunes Gonçalves ◽  
Michel Constantino ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Plant Populations ◽  
Supervised Learning Algorithms

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 139 ◽  
Author(s):  
Ioannis Livieris ◽  
Andreas Kanavos ◽  
Vassilis Tampakas ◽  
Panagiotis Pintelas
Keyword(s):  
Supervised Learning ◽  
Predictive Models ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Classification Problem ◽  
Classification Methods ◽  
Training Algorithm ◽  
Traditional Classification ◽  
Supervised Learning Algorithms ◽  
Significant Research

Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

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