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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 2 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 3 |
Chess AI: Competing Paradigms for Machine Intelligence
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In: Entropy; Volume 24; Issue 4; Pages: 550 (2022)
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| 4 |
Singing voice separation using waveform-level deep neural networks ... : Διαχωρισμός Φωνητικών χρησιμοποιώντας Βαθιά Νευρωνικά Δίκτυα σε Επίπεδο Κυματομορφών ...
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| 5 |
Καταστολή ηχητικού θορύβου μέσω τεχνικών μηχανικής μάθησης ...
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| 6 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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| 7 |
Implementing a Statistical Parametric Speech Synthesis System for a Patient with Laryngeal Cancer
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In: Sensors; Volume 22; Issue 9; Pages: 3188 (2022)
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| 8 |
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2298 (2022)
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| 9 |
Ultrasonic Doppler Based Silent Speech Interface Using Perceptual Distance
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In: Applied Sciences; Volume 12; Issue 2; Pages: 827 (2022)
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| 10 |
Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database
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In: Applied Sciences; Volume 12; Issue 4; Pages: 1889 (2022)
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| 11 |
Advancements in Oncology with Artificial Intelligence—A Review Article
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In: Cancers; Volume 14; Issue 5; Pages: 1349 (2022)
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| 12 |
Deep Learning-Based End-to-End Language Development Screening for Children Using Linguistic Knowledge
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4651 (2022)
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| 13 |
A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter
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In: Processes; Volume 10; Issue 3; Pages: 439 (2022)
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| 14 |
An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition
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In: Electronics; Volume 11; Issue 6; Pages: 968 (2022)
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Abstract:
Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.
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Keyword:
convolutional neural networks; hand gesture recognition; machine learning; sign MNIST
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URL: https://doi.org/10.3390/electronics11060968
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| 15 |
Artificial Neural Networks Combined with the Principal Component Analysis for Non-Fluent Speech Recognition
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In: Sensors; Volume 22; Issue 1; Pages: 321 (2022)
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| 16 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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| 17 |
Performance Study on Extractive Text Summarization Using BERT Models
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In: Information; Volume 13; Issue 2; Pages: 67 (2022)
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| 18 |
A Bottleneck Auto-Encoder for F0 Transformations on Speech and Singing Voice
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In: Information; Volume 13; Issue 3; Pages: 102 (2022)
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| 19 |
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators
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