Socialized Teaching of English Learning in Mobile Internet Technology

Distractions and multitasking can hinder the effectiveness of language learning, requiring innovative strategies to engage and maintain learners focus. Equitable access to mobile internet technology and ensuring accurate and culturally appropriate language learning materials are challenges in the socialized teaching of English learning in mobile internet technology. To overcome this issue, present for Socialized Teaching of English Learning in Mobile Internet Technology (STEL-MIT) is proposed. Initially, the data is collected from English education database. In pre-processing segment; it removes the noise and enhances the input data's utilizing Sub Aperture Keystone Transform Matched Filtering (SAKTMF).Afterward, the data's are fed to pre-processing. In preprocessing segment; it eliminates the missing data and enhances the input data's utilizing Sub Aperture Keystone Transform Matched Filtering (SAKTMF).The outcome from the pre-processing data is transferred to the ITFCZNN. The listening, speaking, reading, writing are successfully classified by using ITFCZNN. The MCOA is used to optimize the weight parameter of ITFCZNN. The proposed method is implemented using MATLAB, and several performance metrics, including accuracy, precision, recall, sensitivity, F1 score, and calculation time, are used to determine the proposed STEL-MIT method's efficiency. Proposed STEL-MIT method attains higher accuracy 16.65%, 18.85% and 16.45%; higher sensitivity 16.34%, 12.23%, and 19.12%; higher precision 14.89%, 16.89% and 20.67%,higher recall 16.34%, 12.23%, and 18.54% analysed to the existing methods, such as Access to Online Learning: Machine Learning Analysis from a Social Justice Perspective (AOL-ML-SJP), Design of Online Intelligent English Teaching Platform Based on Artificial Intelligence Techniques (OIET-AI), and Novel Attack Finding System for Investigating Pedagogical Challenges of Mobile Technology to English Teaching (IPC-MT-ET), respectively.