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

scholarly journals Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Sapna Juneja ◽  
Abhinav Juneja ◽  
Gaurav Dhiman ◽  
Shashank Jain ◽  
Anu Dhankhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Sign Language ◽  
Character Recognition ◽  
Recommendation System ◽  
Machine Learning Techniques ◽  
Deaf People ◽  
Layer By Layer ◽  
Learning Techniques ◽  
Reliable Solution

Hand gesture recognition is one of the most sought technologies in the field of machine learning and computer vision. There has been an unprecedented demand for applications through which one can detect the hand signs for deaf people and people who use sign language to communicate, thereby detecting hand signs and correspondingly predicting the next word or recommending the word that may be most appropriate, followed by producing the word that the deaf people and people who use sign language to communicate want to say. This article presents an approach to develop such a system by that we can determine the most appropriate character from the sign that is being shown by the user or the person to the system. To enable pattern recognition, various machine learning techniques have been explored and we have used the CNN networks as a reliable solution in our context. The creation of such a system involves several convolution layers through which features have been captured layer by layer. The gathered features from the image are further used for training the model. The trained model efficiently predicts the most appropriate character in response to the sign exposed to the model. Thereafter, the predicted character is used to predict further words from it according to the recommendation system used in this case. The proposed system attains a prediction accuracy of 91.07%.

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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