A Comparison of State-of-the-Art Machine Learning Models for OpCode-Based IoT Malware Detection

2020 ◽  
pp. 109-120
Author(s):  
William Peters ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Gautam Srivastava
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Malware Detection ◽  
Learning Models ◽  
Machine Learning Models

Malware Detection Using Machine Learning Models

2020 ◽  
pp. 238-246
Author(s):  
Glaucio H. S. Carvalho ◽  
Isaac Woungang ◽  
Alagan Anpalagan ◽  
Issa Traore ◽  
Leonard Barolli
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Learning Models ◽  
Machine Learning Models

Analysis of machine learning models for malware detection

2020 ◽  
Vol 23 (2) ◽  
pp. 395-407 ◽  
Author(s):  
Rahul ◽  
Priyansh Kedia ◽  
Subrat Sarangi ◽  
Monika
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

2021 ◽  
Author(s):  
Bruno Barbosa Miranda de Paiva ◽  
Polianna Delfino Pereira ◽  
Claudio Moises Valiense de Andrade ◽  
Virginia Mara Reis Gomes ◽  
Maria Clara Pontello Barbosa Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
State Of The Art ◽  
Laboratory Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Learning Models ◽  
Learning Methods ◽  
Meta Learning ◽  
Machine Learning Models

Objective: To provide a thorough comparative study among state ofthe art machine learning methods and statistical methods for determining in-hospital mortality in COVID 19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods: De-identified data were obtained from COVID 19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID 19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross validation procedure, from which we assessed performance and interpretability metrics. Results: The Stacking of machine learning models improved over the previous state of the art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macroF1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. Conclusion: The best results were obtained using the meta learning ensemble model Stacking. State of the art explainability techniques such as SHAP values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions. Key words: COVID-19; prognosis; prediction model; machine learning

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

2020 ◽  
Vol 39 (3) ◽  
pp. 713-756 ◽  
Author(s):  
A. Chatzimparmpas ◽  
R. M. Martins ◽  
I. Jusufi ◽  
K. Kucher ◽  
F. Rossi ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
The State ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Decoding defect statistics from diffractograms via machine learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Cody Kunka ◽  
Apaar Shanker ◽  
Elton Y. Chen ◽  
Surya R. Kalidindi ◽  
Rémi Dingreville
Keyword(s):  
Machine Learning ◽  
Spatial Distribution ◽  
State Of The Art ◽  
Structural Information ◽  
Atomistic Simulations ◽  
Learning Models ◽  
Machine Learning Model ◽  
Electron Diffractograms ◽  
Material Information ◽  
Machine Learning Models

AbstractDiffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

scholarly journals A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection

2020 ◽  
Vol 34 (03) ◽  
pp. 2451-2458
Author(s):  
Akansha Bhardwaj ◽  
Jie Yang ◽  
Philippe Cudré-Mauroux
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Event Detection ◽  
State Of The Art ◽  
Regularization Parameter ◽  
Learning Models ◽  
Training Process ◽  
Model Training ◽  
Real World Datasets ◽  
Machine Learning Models

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation of the distribution – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.

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