LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers
IEEE Access, 2022 In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android
Durmus Ozkan Sahin +2 more
doaj +1 more source
Predicting protein function by machine learning on amino acid sequences – a critical evaluation [PDF]
, 2007 Copyright @ 2007 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use ...
Al-Shahib, A, Breitling, R, Gilbert, D
core +7 more sources
Application of ensemble learning techniques to model the atmospheric concentration of SO2 [PDF]
Global Journal of Environmental Science and Management, 2019 In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide.
A. Masih
doaj +1 more source
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
, 2018 In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the ...
G Tandon +4 more
core +1 more source
Learning sentiment from students’ feedback for real-time interventions in classrooms [PDF]
, 2014 Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose,
Altrabsheh, Nabeela +2 more
core +2 more sources
Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers
Sustainability, 2021 Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy.
P. Debnath +7 more
semanticscholar +1 more source
Parameterized Machine Learning for High-Energy Physics [PDF]
, 2016 We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.
Baldi, Pierre +4 more
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Using Machine Learning to Classify Test Outcomes [PDF]
2019 IEEE International Conference On Artificial Intelligence Testing (AITest), 2019 When testing software it has been shown that there are substantial benefits to be gained from approaches which exercise unusual or unexplored interactions with a system - techniques such as random testing, fuzzing, and exploratory testing. However, such approaches have a drawback in that the outputs of the tests need to be manually checked for ...
openaire +2 more sources
Enhancing internet of things security: evaluating machine learning classifiers for attack prediction
International Journal of Electrical and Computer Engineering (IJECE)The internet of things (IoT) has contributed to improving the quality of service and operational efficiency in many areas, such as smart cities, but this technology has faced a major dilemma: the problem of cyber-attacks of various types.
Areen Arabiat, M. Altayeb
semanticscholar +1 more source
Ensemble Learning for Free with Evolutionary Algorithms ? [PDF]
, 2007 Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result.
Gagné, Christian +3 more
core +4 more sources