An efficient approach to state space management in model checking of complex software systems using machine learning techniques

2020 ◽  
Vol 38 (2) ◽  
pp. 1761-1773 ◽  
Author(s):  
Mohammad Yasrebi ◽  
Vahid Rafe ◽  
Hamid parvin ◽  
Samad Nejatian
Keyword(s):  
Machine Learning ◽  
Model Checking ◽  
State Space ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Space Management ◽  
Efficient Approach ◽  
Learning Techniques

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

An Efficient Approach For COVID-19 Using Machine Learning Techniques

2021 ◽  
Author(s):  
Anusha P Dongade ◽  
Bhoomika B S ◽  
Madhura B S ◽  
Prathiksha S Murthy ◽  
Gururaj H L ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Efficient Approach ◽  
Learning Techniques

scholarly journals EXPLORING THE STACKING STATE-SPACE

2002 ◽  
Vol 11 (02) ◽  
pp. 267-282 ◽  
Author(s):  
AGAPITO LEDEZMA ◽  
RICARDO ALER ◽  
DANIEL BORRAJO
Keyword(s):  
Machine Learning ◽  
State Space ◽  
Linear Response ◽  
Search Space ◽  
Learning Systems ◽  
Hill Climbing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Inductive Machine Learning ◽  
Simple Search

Nowadays, there is no doubt that machine learning techniques can be successfully applied to data mining tasks. Currently, the combination of several classifiers is one of the most active fields within inductive machine learning. Examples of such techniques are boosting, bagging and stacking. From these three techniques, stacking is perhaps the less used one. One of the main reasons for this relates to the difficulty to define and parameterize its components: selecting which combination of base classifiers to use, and which classifier to use as the meta-classifier. One could use for that purpose simple search methods (e.g. hill climbing), or more complex ones (e.g. genetic algorithms). But before search is attempted, it is important to know the properties of the search space itself. In this paper we study exhaustively the space of Stacking systems that can be built by using four base learning systems: C4.5, IB1, Naive Bayes, and PART. We have also used the Multiple Linear Response (MLR) as meta-classifier. The properties of this state-space obtained in this paper will be useful for designing new Stacking-based algorithms and tools.

Model checking and machine learning techniques for HummingBad mobile malware detection and mitigation

2020 ◽  
Vol 105 ◽  
pp. 102169 ◽  
Author(s):  
Fabio Martinelli ◽  
Francesco Mercaldo ◽  
Vittoria Nardone ◽  
Antonella Santone ◽  
Gigliola Vaglini
Keyword(s):  
Machine Learning ◽  
Model Checking ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Mobile Malware ◽  
Mobile Malware Detection

Learning Adaptive Behaviour

2011 ◽  
pp. 104-124 ◽  
Author(s):  
Martin E. Muller
Keyword(s):  
Machine Learning ◽  
Main Part ◽  
Machine Learning Techniques ◽  
Adaptive Behaviour ◽  
Software Systems ◽  
Interactive Software ◽  
Adaptive Hypermedia ◽  
Learning Techniques ◽  
Adaptive Hypermedia Systems ◽  
Definition Of

This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described. The most important learning methods are introduced by examples, and their applicability in adaptive hypermedia is discussed. The chapter concludes with a comparison of all approaches one might consider when applying machine learning in adaptive hypermedia systems.

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.

Sign in / Sign up

Export Citation Format

Share Document