Combining machine learning techniques with Kappa–Kendall indexes for robust hard-cluster assessment in substation pattern recognition

2022 ◽  
Vol 206 ◽  
pp. 107778
Author(s):  
Fabricio Alves de Almeida ◽  
Estevão Luiz Romão ◽  
Guilherme Ferreira Gomes ◽  
José Henrique de Freitas Gomes ◽  
Anderson Paulo de Paiva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Hard Cluster

A pattern recognition model for static gestures in malaysian sign language based on machine learning techniques

2021 ◽  
Vol 95 ◽  
pp. 107383
Author(s):  
Ali.H. Alrubayi ◽  
M.A. Ahmed ◽  
A.A. Zaidan ◽  
A.S. Albahri ◽  
B.B. Zaidan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Sign Language ◽  
Machine Learning Techniques ◽  
Recognition Model ◽  
Learning Techniques

The Reverse of Engineering

2020 ◽  
Vol 119 (1) ◽  
pp. 75-93
Author(s):  
Brett Neilson
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Reverse Engineering ◽  
Computer Science ◽  
Science Studies ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Financial Speculation ◽  
Planning Processes

Insofar as planning mediates between the order of what is and the question of what might be, it is not only a matter of philosophy but also one of engineering. Particularly at a time when routines of financial speculation and pattern recognition have colonized the making of futures, planning has become a process of creating architectural opportunities from scattered corpuses of extracted data. Mindful of the importance of machine learning in such processes, this article critically grapples with the proposition that techniques of reverse engineering offer a means of cracking these future making routines and turning them toward projects of social and political ameli oration. I argue that technical practices of reverse engineering need to articulate to radical political projects and modes of organization. Drawing on computer science studies of adversarial machine learning, I also consider the question of whether reverse engineering of machine learning techniques is technically possible. Ultimately, the article contrasts political claims for reverse engineering with what I call the reverse of engineering, or a program that entails the subordination of data to futures rather than planning processes that work from the merely evidential and measurable.

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