An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition
The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020036 |
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| author | Seetharam Khetavath Navalpur Chinnappan Sendhilkumar Pandurangan Mukunthan Selvaganesan Jana Lakshmanan Malliga Subburayalu Gopalakrishnan Sankuru Ravi Chand Yousef Farhaoui |
| author_facet | Seetharam Khetavath Navalpur Chinnappan Sendhilkumar Pandurangan Mukunthan Selvaganesan Jana Lakshmanan Malliga Subburayalu Gopalakrishnan Sankuru Ravi Chand Yousef Farhaoui |
| author_sort | Seetharam Khetavath |
| collection | DOAJ |
| description | The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model’s performance with other classification approaches. |
| format | Article |
| id | doaj-art-2e387370e65c4f45af145fbc88033f1e |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-2e387370e65c4f45af145fbc88033f1e2025-02-03T02:14:29ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016332133510.26599/BDMA.2022.9020036An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image RecognitionSeetharam Khetavath0Navalpur Chinnappan Sendhilkumar1Pandurangan Mukunthan2Selvaganesan Jana3Lakshmanan Malliga4Subburayalu Gopalakrishnan5Sankuru Ravi Chand6Yousef Farhaoui7Department of Electronics and Communication Engineering, Chaitanya (Deemed to be University), Warangal 506001, India.Department of Electronics and Communication Engineering, Sri Indu College of Engineering & Technology, Sheriguda, Hyderabad 501510, India.Department of Electronics and Communication Engineering, Sri Indu College of Engineering & Technology, Sheriguda, Hyderabad 501510, India.Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.Department of Electronics and Communication Engineering, Malla Reddy Engineering College for Women (Autonomous), Telangana 500100, India.Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.Department of Electronics and Communication Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions-Integrated Campus, Hyderabad 500088, India.STI Laboratory, the IDMS Team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 52000, Morocco.The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.https://www.sciopen.com/article/10.26599/BDMA.2022.9020036hand gesture recognitionskin color detectionmorphological operationsmultifaceted feature extraction (mfe) modelheuristic manta-ray foraging optimization (hmfo)adaptive extreme learning machine (aelm) |
| spellingShingle | Seetharam Khetavath Navalpur Chinnappan Sendhilkumar Pandurangan Mukunthan Selvaganesan Jana Lakshmanan Malliga Subburayalu Gopalakrishnan Sankuru Ravi Chand Yousef Farhaoui An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition Big Data Mining and Analytics hand gesture recognition skin color detection morphological operations multifaceted feature extraction (mfe) model heuristic manta-ray foraging optimization (hmfo) adaptive extreme learning machine (aelm) |
| title | An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition |
| title_full | An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition |
| title_fullStr | An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition |
| title_full_unstemmed | An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition |
| title_short | An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition |
| title_sort | intelligent heuristic manta ray foraging optimization and adaptive extreme learning machine for hand gesture image recognition |
| topic | hand gesture recognition skin color detection morphological operations multifaceted feature extraction (mfe) model heuristic manta-ray foraging optimization (hmfo) adaptive extreme learning machine (aelm) |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020036 |
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