Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization
Abstract Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with “curse of dimensionality” due to excessive features resulting in prolonged tra...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82785-x |
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| author | Rajeev Goel Sandhya Bansal Kavita Gupta |
| author_facet | Rajeev Goel Sandhya Bansal Kavita Gupta |
| author_sort | Rajeev Goel |
| collection | DOAJ |
| description | Abstract Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with “curse of dimensionality” due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue. The proposed technique, initially, extracts features using histrogram of gradient (HOG) approach and then reduces dimensions of extracted features using unsupervised autoencoder and subsequently refining the feature set with an improved GWO algorithm. A handcrafted artificial neural network serves as the classifier within this integrated framework, denoted as AEGWO-Net. Exhaustive experimentations were conducted on six different datasets namely ASL, ASL MNIST, ISL, ArSL, MNIST Digits, and IEEE-ISL containing gestures of different languages to demonstrate the performance of AEGWO-Net. The AEGWO-Net demonstrates superior performance improving accuracy and F1 score by 6% and 4% respectively compared to PCA-IGWO and KPCA-IGWO algorithms. Achieving high accuracy (98.40%), F1-score (96.59%), MCC (97.14%), and AUC (96.21%) indicates the robustness and generalizability of the AEGWO-Net method even with reduced dimensionality. Furthermore, a comparison between AEGWO-Net with other existing swarm intelligence techniques is also made to demonstrate its superiority. |
| format | Article |
| id | doaj-art-2d6ce82a4f1c4c098207f98548c7ba1b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-2d6ce82a4f1c4c098207f98548c7ba1b2025-01-19T12:23:42ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-82785-xImproved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf OptimizationRajeev Goel0Sandhya Bansal1Kavita Gupta2Government CollegeCSE Department, Maharishi Markandeshwar (Deemed to be) UniversityUniversity Institute of Computing, Chandigarh UniversityAbstract Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with “curse of dimensionality” due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue. The proposed technique, initially, extracts features using histrogram of gradient (HOG) approach and then reduces dimensions of extracted features using unsupervised autoencoder and subsequently refining the feature set with an improved GWO algorithm. A handcrafted artificial neural network serves as the classifier within this integrated framework, denoted as AEGWO-Net. Exhaustive experimentations were conducted on six different datasets namely ASL, ASL MNIST, ISL, ArSL, MNIST Digits, and IEEE-ISL containing gestures of different languages to demonstrate the performance of AEGWO-Net. The AEGWO-Net demonstrates superior performance improving accuracy and F1 score by 6% and 4% respectively compared to PCA-IGWO and KPCA-IGWO algorithms. Achieving high accuracy (98.40%), F1-score (96.59%), MCC (97.14%), and AUC (96.21%) indicates the robustness and generalizability of the AEGWO-Net method even with reduced dimensionality. Furthermore, a comparison between AEGWO-Net with other existing swarm intelligence techniques is also made to demonstrate its superiority.https://doi.org/10.1038/s41598-024-82785-xSign language recognitionFeature selectionGrey Wolf OptimizationAutoencoder |
| spellingShingle | Rajeev Goel Sandhya Bansal Kavita Gupta Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization Scientific Reports Sign language recognition Feature selection Grey Wolf Optimization Autoencoder |
| title | Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization |
| title_full | Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization |
| title_fullStr | Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization |
| title_full_unstemmed | Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization |
| title_short | Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization |
| title_sort | improved feature reduction framework for sign language recognition using autoencoders and adaptive grey wolf optimization |
| topic | Sign language recognition Feature selection Grey Wolf Optimization Autoencoder |
| url | https://doi.org/10.1038/s41598-024-82785-x |
| work_keys_str_mv | AT rajeevgoel improvedfeaturereductionframeworkforsignlanguagerecognitionusingautoencodersandadaptivegreywolfoptimization AT sandhyabansal improvedfeaturereductionframeworkforsignlanguagerecognitionusingautoencodersandadaptivegreywolfoptimization AT kavitagupta improvedfeaturereductionframeworkforsignlanguagerecognitionusingautoencodersandadaptivegreywolfoptimization |