aspg-18-4056-1792025573 20261015005253 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 2 Inderjeet Kaur Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College Ghaziabad, India P. Udayakumar Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Kinathukadavu, Coimbatore, India B. Arundhati Department of EEE, Vignan's Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India M. V. Rajesh Department of Information Technology, Aditya University, Surampalem, India Naif Almakayeel Department of Industrial Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia Elvir Akhmetshin Department of Science, Innovations and Technology, Mamun University, 220900, Khiva, Uzbekistan 16.00 Normal false false false TR X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> 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Colorful Accent 4"/> Name="Grid Table 1 Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Sign language (SL) detection and classification for deaf persons is an essential application of machine learning (ML) and computer vision (CV) techniques. It covers emerging forms, which acquire SL implemented by entities and convert them into auditory or textual output. It is highly significant to understand that determining a correct and robust SL detection approach is a very challenging due to many tasks such as alterations in occlusions, and lighting states in hand actions and forms. Consequently, the CV and ML models is must for testing and training. A Hand gesture detection method discovers beneficial for hearing and speaking-impaired individuals by creating usage of convolutional neural network (CNN) and human-computer interface (HCI) for classifying the constant signals of SL. In this article, an Improved Fennec Fox Algorithm for Deep Learning-Based Sign Language Recognition in Hearing and Speaking Impaired People (IFFADL-SLRHSIP) technique is proposed. The presented IFFADL-SLRHSIP technique main intention is to provide effectual communication between deaf and dumb persons and normal persons utilizing CV and artificial intelligence techniques. In the IFFADL-SLRHSIP model, the enhanced SqueezeNet model is used to capture the intricate patterns and nuances of SL gestures. For detection of the SL classification process, the recurrent neural network (RNN) method is used. To optimize model performance, the improved fennec fox algorithm (IFFA) is applied for parameter tuning, enhancing the model's precision and efficiency. The experimental outputs of the IFFADL-SLRHSIP algorithm are legalized on the SL dataset. The simulation outcomes demonstrate the greater outcomes of the IFFADL-SLRHSIP approach in terms of diverse measures. 2026 304 314 4056 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180221 https://americaspg.com/journal/18/article/4056 https://americaspg.com/storage/51756740828.pdf