An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

Elakkiya, R.; Selvamani, K.

doi:10.1016/j.procs.2015.04.142

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…where more details are provided in [35]. A quite important research orientation of the related community is the proposal of a new QS, either introducing new metrics which may measure a behavior that seems more favorable for specific tasks [36,37] or trying to capture better the reasoning of some choices made by similar methods [38]. One representative work related to this last category is the work of Vu-Linh Nguyen et al [39], exploring further Uncertainty Sampling (UncS) QS, discriminating this into epistemic and aleatoric sampling strategies, highlighting their differences and proposing the first variant as more promising.…”

Section: Methodsmentioning

confidence: 99%

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

2020

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Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.

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Section: Methodsmentioning

confidence: 99%

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

2020

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“…Furthermore, due to the high cost of sensors, real deployments of sensor-based SLR devices are impractical. On the other side, vision-based SLR systems have profoundly affected researchers due to their weight and capacity to handle crowded, dynamic heterogeneous situations and fluctuations under varied illuminations and occlusions in the feature extraction stage ( Elakkiya & Selvamani, 2015 ; Elakkiya & Selvamani, 2018 ). The population sampling methods are critical in categorizing various SLR works’ essential aspects.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

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Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. Results This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. Conclusion Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.

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Automatic recognition of the American sign language fingerspelling alphabet to assist people living with speech or hearing impairments

Quesada

López

Guerrero

2017

J Ambient Intell Human Comput

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An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

Cited by 7 publications

References 16 publications

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks

Recognition of Urdu sign language: a systematic review of the machine learning classification

Automatic recognition of the American sign language fingerspelling alphabet to assist people living with speech or hearing impairments

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