Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

Kaur, Amandeep; Kaur, Inderpreet

doi:10.7763/lnse.2014.v2.118

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…Absorption and synthesis of images (A) Five distinct groups of authors look at how statistics training influences picture creation in this first part. The publishers of an article titled "Computational Modeling in Ultrasound Scans," written by R. van Sloun, R. Cohen and Y. Eldar in [8], look at how deep, information training can be applied to all elements of ultrasound scans, from concepts at the intersection of raw audio obtaining (such as module facilitates forming) and portrait structure to studying flexural guidelines for color Doppler acquirement to studying clutter inhibition techniques. They provide an picture of ultrasound's future, based on ultra-portable and sophisticated scanning that enables intelligent, cordless devices for a variety of purposes.…”

Section: Surveymentioning

confidence: 99%

Evaluation of Biomedical Imaging in Deep Neural Networks

Mr¹

2021

JBSHA

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Whereas the historical background of medical field started in 1895 with Roentgen Wilhelm participating in the first x-rays photograph and proceeded through 1913 with the discovery of mammogram and 1927 with first cerebellar echocardiogram, advanced medicine tomography came into focus in the 1950s with the discovery of PET and ultrasonic image processing. The first computed tomography (CT) scanners was created by Hounsfield Godfrey and CoreMark Allanin 1972, while the first commercialized Magnet Resonance Imaging (MRI) scanners were produced by Raymond Dalmatian in 1977. The creation of general methods and terminology of digitized signal and image processing occurred in tandem with the growth of medical imaging technology in the 1970s and beyond, as well as the advent of digital processors. In an examination of biological applications and analysis in the era of big data and deep learning, this article analyzes background and phraseology: Pattern Classification, Artificial Intelligence, Machine Learning, Big Data.

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Section: Surveymentioning

confidence: 99%

Evaluation of Biomedical Imaging in Deep Neural Networks

Mr¹

2021

JBSHA

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“…By relying on the Weka tool 6 , three different ML algorithms-namely the Naive Bayes, J48, and Random Forest-were trained to predict if the vulnerabilityproneness level of an app should be marked as either low or high. The selection of these specific algorithms is not random, as they have been successfully adopted in previous work concerning defect prediction tasks [34,47].…”

Section: Richness Of Functionalities Description Lengthmentioning

confidence: 99%

Exposed! A case study on the vulnerability-proneness of Google Play Apps

Sorbo

Panichella

2021

Empir Software Eng

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Mobile applications are used for accomplishing everyday life activities, such as shopping, banking, and social communications. To leverage the features of mobile apps, users often need to share sensitive information. However, recent research demonstrated that most of such apps present critical security and privacy defects. In this context, we define as vulnerabilityproneness the risk level(s) that users meet in downloading specific apps, to better understand whether (1) users select apps with lower risk levels and if (2) vulnerability-proneness of an app might affect its success. We use as proxy to measure such risk level the "number of different types of potential security issues exhibited by the app". We conjecture that the vulnerabilityproneness levels may vary based on (i) the types of data handled by the app, and (ii) the operations for which the app is supposed to be used. Hence, we investigate how the vulnerability-proneness of apps varies when observing (i) different app categories, and (ii) apps with different success levels. Finally, to increase the awareness of both users and developers on the vulnerabilityproneness of apps, we evaluate the extent to which contextual information provided by the app market can be exploited to estimate the vulnerabilityproneness levels of mobile apps. Results of our study show that apps in the Medical category exhibit the lowest levels of vulnerability-proneness. Besides, while no strong relations between vulnerability-proneness and average rating are observed, apps with a higher number of downloads tend to have higher vulnerability-proneness levels, but lower vulnerability-proneness density. Fi-* Corresponding author.

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“…Machine Learning is used to make exact decisions based on observations and predictions. Machine Learning examines the areas of algorithms that can make high-end predictions on data [7]. The learning process in Machine Learning is classified into Training and Testing.…”

Section: A Machine Learningmentioning

confidence: 99%

Logistic Regression for Employability Prediction

Celine*¹,

Devi²

2020

IJITEE

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Prediction is a conjecture about something which may happen. Prediction need not be based upon the previous knowledge or experience on the unknown event of interest in the future. But it is a necessity for mankind to foresee and make the right decisions to live better. Every person does predictions but the quality of the predictions differs and that differentiates successful persons and unsuccessful persons. In order to automate the prediction process and to make quality predictions available to every person, machines are trained to make predictions and such field comes under machine learning and later on deep learning algorithms. Various fields such as health care, weather forecasting, natural calamities, and crime prediction are some of the applications of prediction. The researchers have applied the field of prediction to see whether a model can predict the employability of a candidate in a recruitment process. Organizations use human expertise to identify a skilled candidate for employment based on various factors and now these organizations are trying to migrate to automated systems by harnessing the benefits of the exponential growth in the area of machine learning and deep learning. This investigation presents the development of a model to predict the employability by using Logistic Regression. A set of candidates was tested in the proposed model and results are discussed in this paper.

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Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

Cited by 15 publications

References 33 publications

Evaluation of Biomedical Imaging in Deep Neural Networks

Evaluation of Biomedical Imaging in Deep Neural Networks

Exposed! A case study on the vulnerability-proneness of Google Play Apps

Logistic Regression for Employability Prediction

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