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Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defects [article]

Steffen Herbold, Tobias Haar
2021 arXiv   pre-print
Concretely, we try to determine generic and simple smoke tests that can be used to assert that basic functions can be executed without crashing.  ...  severe bugs, even in mature machine learning libraries.  ...  Both surveys did not identify best practices for smoke testing of machine learning or similar generic best practices for the testing of machine learning.  ... 
arXiv:2009.01521v2 fatcat:cxkwgahwhjb47g7lu3hjveuwj4

Analysis of Supervised Machine Learning Algorithms

Priyank Bhardwaj
2020 International Journal for Research in Applied Science and Engineering Technology  
then these features are modelled for prediction and after the outcomes on the taken dataset we will be able to recognize that which supervised learning algorithm is best to predict the desirable result  ...  The cardiovascular diseases are also comes in this category and in this research paper we will use four different types of supervised machine learning algorithms on a single dataset that is collected by  ...  PROPOSED METHODOLOGY Machine Learning is a process to feed machine enough data to train and predict a possible outcome using the algorithms at bay.  ... 
doi:10.22214/ijraset.2020.5377 fatcat:xtajwpnbbfcvzn2n7xbnm5rx24

Heart Disease Prediction Using Machine Learning Algorithms

Abhay Agrahary
2020 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making  ...  In this paper commonly used data mining and machine learning techniques and their complexities are summarized.  ...  :The k-nearest neighbors (KNN) algorithm is a simple, easy-toimplement supervised machine learning algorithm that can be used to solve both classification and regression problems.The KNN algorithm assumes  ... 
doi:10.32628/cseit206421 fatcat:52ypcapbqjfhfp5squljhqkmrq

On Testing Machine Learning Programs [article]

Houssem Ben Braiek, Foutse Khomh
2018 arXiv   pre-print
Nowadays, we are witnessing a wide adoption of Machine learning (ML) models in many safety-critical systems, thanks to recent breakthroughs in deep learning and reinforcement learning.  ...  We also hope that the research community will act on our proposed research directions to advance the state of the art of testing for ML programs.  ...  Several mechanisms exist for the creation of adversarial examples, such as : making small modifications to the input pixels [10] , applying spatial transformations [11] , or simple guess-and-check to  ... 
arXiv:1812.02257v1 fatcat:yhomj6slnzeb5hp4xxww2ymaa4

Predication of cancer disease using machine learning approach

F.J. Shaikh, D.S. Rao
2021 Materials Today: Proceedings  
Many of these methods are widely used for the development of predictive models for predicating a cure for cancer, some of the methods are artificial neural networks (ANNs), support vector machine (SVMs  ...  Cancer has identified a diverse condition of several various subtypes.  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1016/j.matpr.2021.03.625 fatcat:meq7nl6gsfhufozyx62uam7woe

Disease Prediction by Machine Learning from Healthcare Communities

Sakshi Gupta
2019 International Journal for Research in Applied Science and Engineering Technology  
The machine studying calculations are proposed for a success expectation of ceaseless infection to beat the trouble of deficient facts.  ...  These days in biomedical field use of Mining and machine learning knowledge is expanding, genuine study of medicinal dataset advantages in early illness discovery, quiet care and organization administrations  ...  For Structured data, the system uses a traditional machine learning algorithm, i.e., NB algorithm to predict the disease. NB classification is a simple probabilistic classifier.  ... 
doi:10.22214/ijraset.2019.6114 fatcat:hmbwflxsyfcftojeeqrbhwtptq

Context-Aware Software Vulnerability Classification Using Machine Learning

Grzegorz Siewruk, Wojciech Mazurczyk
2021 IEEE Access  
DAST scans are executed during smoke tests [46] .  ...  Currently, several works exist in the literature that demonstrate how machine learning algorithms can be utilized to analyze software vulnerabilities.  ...  Since 2017 his research has concerned using machine learning algorithms in vulnerability classification to provide faster and more accurate results for CICD processes.  ... 
doi:10.1109/access.2021.3075385 fatcat:34xwkwkbszbzlcv3tzg3vhq2a4

Organization, learning and cooperation

Jason Barr, Francesco Saraceno
2009 Journal of Economic Behavior and Organization  
We study the prospects for cooperation given the need for the firm to learn the environment and its rival's output.  ...  The firm plays a repeated Prisoner's Dilemma type game, but must also learn to map environmental signals to demand parameters and to its rival's willingness to cooperate.  ...  the first to defect. (2) Be forgiving: be willing to return to cooperation even if your opponent defects. (3) Be simple: the easier it is for your rival to discover a pattern in your behavior, the easier  ... 
doi:10.1016/j.jebo.2008.03.014 fatcat:ij7c4fwisjd3zloitgrqtwaidy

Towards Causal Representation Learning [article]

Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio
2021 arXiv   pre-print
can contribute to modern machine learning research.  ...  Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.  ...  Thanks to Wouter van Amsterdam for pointing out typos in the first version. We also thank Thomas Kipf, Klaus Greff, and Alexander d'Amour for the useful discussions.  ... 
arXiv:2102.11107v1 fatcat:n25xwac72nfulgl3gvvs4kerca

Toward Causal Representation Learning

Bernhard Scholkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio
2021 Proceedings of the IEEE  
contribute to modern machine learning research.  ...  can contribute to modern machine learning research.  ...  This is related to causality in several ways. First, these attacks clearly constitute violations of the i.i.d. assumption that underlies statistical machine learning.  ... 
doi:10.1109/jproc.2021.3058954 fatcat:jqg6jm2f35aynlszy6w5nsyem4

Race and Genetics in Congenital Heart Disease: Application of iPSCs, Omics, and Machine Learning Technologies

McKay Mullen, Angela Zhang, George K. Lui, Anitra W. Romfh, June-Wha Rhee, Joseph C. Wu
2021 Frontiers in Cardiovascular Medicine  
Herein, we first review the prevalence, risk factors, and genetics of CHD and then discuss the use of iPSCs, omics, and machine learning technologies to investigate the etiology of CHD and its connection  ...  For instance, racial minorities are disproportionally affected by this disease and typically have worse prognosis, possibly due to environmental and genetic disparities.  ...  from the capability of machine learning models to learn from the datasets to identify inherent patterns and structure.  ... 
doi:10.3389/fcvm.2021.635280 pmid:33681306 pmcid:PMC7925393 fatcat:4grpiwvlkjf5dihjo33zogqekm

Big Data Analytics using Supervised Learning: A Comprehensive Review of Recent Techniques

Wedjdane Nahili
2020 International Journal for Research in Applied Science and Engineering Technology  
learning algorithms.  ...  Recently, there is a growing need for implementing various approaches and models for efficiently processing this type of data and extracting useful information.  ...  Various machine learning techniques were fitted with this problem. Their results were easily reproducible for they used IMDB dataset. A simple and powerful method was proposed for sentiment analysis.  ... 
doi:10.22214/ijraset.2020.1056 fatcat:hxl3jiphajh3jofmnou4sq5fzi

A process for predicting manhole events in Manhattan

Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac
2010 Machine Learning  
In the case of manhole event prediction, which is a new application for machine learning, the goal is to rank the electrical grid structures in Manhattan (manholes and service boxes) according to their  ...  The documents are linked to a set of instances to be ranked according to prediction criteria.  ...  From Con Edison, we would like to thank Serena Lee,  ... 
doi:10.1007/s10994-009-5166-y fatcat:fyvpz7v5j5esddcqm2bzwsj6iq

Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology

Nicholas Ekow Thomford, Christian Domilongo Bope, Francis Edem Agamah, Kevin Dzobo, Richmond Owusu Ateko, Emile Chimusa, Gaston Kuzamunu Mazandu, Simon Badibanga Ntumba, Collet Dandara, Ambroise Wonkam
2019 Omics  
A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics  ...  AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or  ...  Mzodidi Tutuka for assistance in illustrating Figure 1 . Author Disclosure Statement The authors declare they have no conflicting financial interests.  ... 
doi:10.1089/omi.2019.0142 pmid:31592719 fatcat:wxh26bv6crh4dc4ufkhmrsx3jq

Testing machine learning based systems: a systematic mapping

Vincenzo Riccio, Gunel Jahangirova, Andrea Stocco, Nargiz Humbatova, Michael Weiss, Paolo Tonella
2020 Empirical Software Engineering  
Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set.  ...  To report demographic information about the ongoing research. To identify open challenges for future research.  ...  To view a copy of this licence, visit http://creativecommonshorg/licenses/by/4.0/.  ... 
doi:10.1007/s10664-020-09881-0 fatcat:7w42stnm3nfafia7ycbmvhrjou
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