{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:22:18Z","timestamp":1780586538509,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is a stacked encoded model, combining artificial intelligence (AI) with the Internet of Things (IoT), which refines feature extraction and classification to overcome these challenges. We leverage a lightweight backbone model for preliminary feature extraction and use stacked autoencoders to further refine these features. Our approach harnesses the scalability of big data, showing notable improvement in accuracy, precision, recall, F1-score, and complexity analysis. Our model\u2019s effectiveness is demonstrated through testing on the ArSL2018 benchmark dataset, showcasing superior performance compared to state-of-the-art approaches. Additional validation through an ablation study with pre-trained convolutional neural network (CNN) models affirms our model\u2019s efficacy across all evaluation metrics. Our work paves the way for the sustainable development of high-performing, IoT-based sign-language-recognition applications.<\/jats:p>","DOI":"10.3390\/s23229068","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T10:19:27Z","timestamp":1699525167000},"page":"9068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-4451","authenticated-orcid":false,"given":"Muhammad","family":"Islam","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1655-8098","authenticated-orcid":false,"given":"Mohammed","family":"Aloraini","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4349-8710","authenticated-orcid":false,"given":"Suliman","family":"Aladhadh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-2520","authenticated-orcid":false,"given":"Shabana","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asma","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Islamia College, Peshawar 25120, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0646-5872","authenticated-orcid":false,"given":"Abduatif","family":"Alabdulatif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1314-7146","authenticated-orcid":false,"given":"Turki M.","family":"Alanazi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shukla, P., Garg, A., Sharma, K., and Mittal, A. (2015, January 21\u201324). A DTW and fourier descriptor based approach for Indian sign language recognition. Proceedings of the 2015 Third International Conference on Image Information Processing (ICIIP), Waknaghat, India.","DOI":"10.1109\/ICIIP.2015.7414750"},{"key":"ref_2","unstructured":"Kushalnagar, R. (2019). Web Accessibility, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e03554","DOI":"10.1016\/j.heliyon.2020.e03554","article-title":"A comparison of Arabic sign language dynamic gesture recognition models","volume":"6","author":"Almasre","year":"2020","journal-title":"Heliyon"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1016\/j.asoc.2012.11.036","article-title":"A proposed PCNN features quality optimization technique for pose-invariant 3D Arabic sign language recognition","volume":"13","author":"Elons","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_5","unstructured":"Tharwat, A., Gaber, T., Hassanien, A.E., Shahin, M.K., and Refaat, B. Sift-based arabic sign language recognition system. Proceedings of the Afro-European Conference for Industrial Advancement."},{"key":"ref_6","first-page":"144","article-title":"Automated Arabic sign language recognition system based on deep transfer learning","volume":"19","author":"Shahin","year":"2019","journal-title":"IJCSNS Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59612","DOI":"10.1109\/ACCESS.2021.3069714","article-title":"Arabic sign language recognition system using 2D hands and body skeleton data","volume":"9","author":"Bencherif","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4101","DOI":"10.1007\/s12652-020-01790-w","article-title":"A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers","volume":"12","author":"Mustafa","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-019-0771-2","article-title":"Supervised learning classifiers for Arabic gestures recognition using Kinect V2","volume":"1","author":"Hisham","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_10","first-page":"41","article-title":"Recognition of Arabic sign language (ArSL) using recurrent neural networks","volume":"4","author":"Maraqa","year":"2012","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_11","first-page":"185","article-title":"Image based Arabic sign language recognition system","volume":"9","author":"Alzohairi","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_12","first-page":"2996","article-title":"Automatic recognition of Arabic alphabets sign language using deep learning","volume":"12","author":"Duwairi","year":"2022","journal-title":"Int. J. Electr. Comput. Eng. (2088-8708)"},{"key":"ref_13","first-page":"57","article-title":"Toward human-centered automated driving: A novel spatial-temporal vision transformer-enabled head tracker","volume":"17","author":"Hu","year":"2022","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_14","first-page":"45","article-title":"Arabic sign language (arsl) recognition system using hmm","volume":"2","author":"Youssif","year":"2011","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_15","first-page":"209","article-title":"Arabic alphabet and numbers sign language recognition","volume":"6","author":"Abdo","year":"2015","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"El-Bendary, N., Zawbaa, H.M., Daoud, M.S., Hassanien, A.E., and Nakamatsu, K. (2010, January 8\u201310). Arslat: Arabic sign language alphabets translator. Proceedings of the 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), Krakow, Poland.","DOI":"10.1109\/CISIM.2010.5643519"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"ElBadawy, M., Elons, A., Shedeed, H.A., and Tolba, M. (2017, January 5\u20137). Arabic sign language recognition with 3d convolutional neural networks. Proceedings of the 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/INTELCIS.2017.8260028"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hayani, S., Benaddy, M., El Meslouhi, O., and Kardouchi, M. (2019, January 22\u201324). Arab sign language recognition with convolutional neural networks. Proceedings of the 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), Agadir, Morocco.","DOI":"10.1109\/ICCSRE.2019.8807586"},{"key":"ref_19","unstructured":"Kayalibay, B., Jensen, G., and van der Smagt, P. (2017). CNN-based segmentation of medical imaging data. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.ins.2019.07.040","article-title":"Emotion recognition using secure edge and cloud computing","volume":"504","author":"Hossain","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_21","unstructured":"Kamruzzaman, M. (2020). Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3941","DOI":"10.1007\/s00521-016-2294-8","article-title":"Deep learning in vision-based static hand gesture recognition","volume":"28","author":"Oyedotun","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pigou, L., Dieleman, S., Kindermans, P.-J., and Schrauwen, B. (2015). Sign Language Recognition Using Convolutional Neural Networks, Springer International Publishing.","DOI":"10.1007\/978-3-319-16178-5_40"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.neucom.2018.12.065","article-title":"A CRNN module for hand pose estimation","volume":"333","author":"Hu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Islam, M., Hassan, J., Ahmed, M.U., Ferdosi, B.J., Saha, S., and Shopon, M. (2019). Hand sign to Bangla speech: A deep learning in vision based system for recognizing hand sign digits and generating Bangla speech. arXiv.","DOI":"10.2139\/ssrn.3358187"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TNSRE.2019.2896269","article-title":"Deep learning for electromyographic hand gesture signal classification using transfer learning","volume":"27","author":"Fall","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.neucom.2021.01.048","article-title":"Deep convolutional neural network-based Bernoulli heatmap for head pose estimation","volume":"436","author":"Hu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2100046","DOI":"10.1002\/aisy.202100046","article-title":"Flexible strain sensors for wearable hand gesture recognition: From devices to systems","volume":"4","author":"Si","year":"2022","journal-title":"Adv. Intell. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1007\/s13042-021-01482-7","article-title":"sEMG based hand gesture recognition with deformable convolutional network","volume":"13","author":"Wang","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108200","DOI":"10.1016\/j.patcog.2021.108200","article-title":"Unified learning approach for egocentric hand gesture recognition and fingertip detection","volume":"121","author":"Alam","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_31","first-page":"031023","article-title":"Lightweight neural network hand gesture recognition method for embedded platforms","volume":"34","author":"Chenyi","year":"2022","journal-title":"High Power Laser Particle Beams"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJCVR.2022.119239","article-title":"Dynamic hand gesture recognition of sign language using geometric features learning","volume":"12","author":"Joudaki","year":"2022","journal-title":"Int. J. Comput. Vis. Robot."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/THMS.2015.2406692","article-title":"Glove-based continuous Arabic sign language recognition in user-dependent mode","volume":"45","author":"Tubaiz","year":"2015","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_34","unstructured":"Al-Buraiky, S.M. (2004). Arabic Sign Language Recognition Using an Instrumented Glove, King Fahd University of Petroleum and Minerals."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2501","DOI":"10.1007\/s10489-017-1092-z","article-title":"Hand pose estimation with multi-scale network","volume":"48","author":"Hu","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_36","first-page":"251","article-title":"Arabic sign language translation system on mobile devices","volume":"8","author":"Halawani","year":"2008","journal-title":"IJCSNS Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1109\/THMS.2014.2318280","article-title":"Image-based and sensor-based approaches to Arabic sign language recognition","volume":"44","author":"Mohandes","year":"2014","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Almasre, M.A., and Al-Nuaim, H. (2017). Comparison of four SVM classifiers used with depth sensors to recognize Arabic sign language words. Computers, 6.","DOI":"10.3390\/computers6020020"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1109\/TIE.2021.3057033","article-title":"Data-driven estimation of driver attention using calibration-free eye gaze and scene features","volume":"69","author":"Hu","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_40","first-page":"692","article-title":"Arabic Sign Language Recognition using Faster R-CNN","volume":"12","author":"Alawwad","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_41","first-page":"124","article-title":"ASLR: Arabic sign language recognition using convolutional neural networks","volume":"20","author":"Althagafi","year":"2020","journal-title":"IJCSNS Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4567989","DOI":"10.1155\/2022\/4567989","article-title":"Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique","volume":"2022","author":"Zakariah","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"715","DOI":"10.12785\/ijcds\/090418","article-title":"An automatic Arabic sign language recognition system based on deep CNN: An assistive system for the deaf and hard of hearing","volume":"9","author":"Latif","year":"2020","journal-title":"Int. J. Comput. Digit. Syst."},{"key":"ref_44","first-page":"141","article-title":"Sign language semantic translation system using ontology and deep learning","volume":"11","author":"Elsayed","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_45","first-page":"1096","article-title":"ArSL-CNN: A convolutional neural network for Arabic sign language gesture recognition","volume":"22","author":"Alani","year":"2021","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Khan, Z.A., Hussain, T., Ullah, A., Rho, S., Lee, M., and Baik, S.W. (2020). Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework. Sensors, 20.","DOI":"10.3390\/s20051399"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104695","DOI":"10.1016\/j.engappai.2022.104695","article-title":"Graft: A graph based time series data mining framework","volume":"110","author":"Mishra","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6331","DOI":"10.1109\/TIP.2022.3207006","article-title":"Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark","volume":"31","author":"Yar","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_49","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"120465","DOI":"10.1016\/j.eswa.2023.120465","article-title":"A modified YOLOv5 architecture for efficient fire detection in smart cities","volume":"231","author":"Yar","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JSTSP.2023.3260627","article-title":"Visual Appearance and Soft Biometrics Fusion for Person Re-identification using Deep Learning","volume":"17","author":"Khan","year":"2023","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Khan, S.U., Haq, I.U., Khan, N., Ullah, A., Muhammad, K., Chen, H., Baik, S.W., and de Albuquerque, V.H.C. (2023). Efficient Person Re-identification for IoT-Assisted Cyber-Physical Systems. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2023.3259343"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1109\/TSMC.2018.2830099","article-title":"Efficient deep CNN-based fire detection and localization in video surveillance applications","volume":"49","author":"Muhammad","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Avula, S.B., Badri, S.J., and Reddy, G. (2020, January 7\u201311). A Novel forest fire detection system using fuzzy entropy optimized thresholding and STN-based CNN. Proceedings of the 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS48256.2020.9027347"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bari, A., Saini, T., and Kumar, A. (2021, January 4\u20136). Fire detection using deep transfer learning on surveillance videos. Proceedings of the 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India.","DOI":"10.1109\/ICICV50876.2021.9388485"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"101815","DOI":"10.1016\/j.jksus.2021.101815","article-title":"Boosting energy harvesting via deep learning-based renewable power generation prediction","volume":"34","author":"Khan","year":"2022","journal-title":"J. King Saud Univ.-Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/2.144401","article-title":"Functional-link net computing: Theory, system architecture, and functionalities","volume":"25","author":"Pao","year":"1992","journal-title":"Computer"},{"key":"ref_58","unstructured":"Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), Budapest, Hungary."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/72.471375","article-title":"Stochastic choice of basis functions in adaptive function approximation and the functional-link net","volume":"6","author":"Igelnik","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TEVC.2019.2916183","article-title":"Evolving deep convolutional neural networks for image classification","volume":"24","author":"Sun","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.neucom.2017.08.040","article-title":"A review on neural networks with random weights","volume":"275","author":"Cao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_62","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2020, January 14\u201319). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"8704","DOI":"10.1109\/TPAMI.2019.2918284","article-title":"Convolutional networks with dense connectivity","volume":"44","author":"Huang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5195508","DOI":"10.1155\/2021\/5195508","article-title":"Vision sensor-based real-time fire detection in resource-constrained IoT environments","volume":"2021","author":"Yar","year":"2021","journal-title":"Comput. Intell. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9068\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:20:07Z","timestamp":1760131207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,9]]},"references-count":64,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229068"],"URL":"https:\/\/doi.org\/10.3390\/s23229068","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,9]]}}}