aspg-18-4055-1792025547 20261015005227 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 Reem Alshenaifi Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia 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 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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-ansi-language:TR;} Blind and visually challenged people face the range of practical issues by undertaking outside travels as pedestrians. In the last decade, various beneficial devices is investigated and established to assist people with disabilities move independently and safely. Anomaly detection in pedestrian paths for visually impaired individuals, using remote sensing (RS), is crucial for improving pedestrian traffic flow and safety. Engineers and investigators can create efficient methods and tools with the effect of computer vision (CV) and machine learning (ML) to recognize anomalies and alleviate possible security hazards in pedestrian walkways. With recent progress in deep learning (DL) and ML fields, researchers have realised that the image recognition problem is supposed to be developed as classification problems. This paper proposes a Coati Optimization Algorithm-Based Parameter Tuning for Pedestrian Walkways with Transfer Learning Model (COAPT-PWTLM) technique. The main goal of COAPT-PWTLM technique is to provide automatic detection of pedestrian walkways for disability using advanced models. Initially, the median filtering (MF) is employed in the image pre-processing stage to eliminate the noise from an input image data. Furthermore, the SquezeNet1.1 model is utilized for feature extraction. For the classification process, the multi-layer autoencoder (MLAE) model is implemented. Finally, the modified update coati optimization algorithm (MUCOA) model adjusts the hyperparameter range of MLAE method optimally and results in improved classification performance. The experimental validation of the COAPT-PWTLM is verified on a benchmark image dataset and the outcomes are evaluated under dissimilar measures. The experimental outcome underlined the progress of the COAPT-PWTLM model over the existing models. 2026 290 303 4055 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180220 https://americaspg.com/journal/18/article/4055 https://americaspg.com/storage/31756740381.pdf