<p>I have worked on a project titled <strong data-path-to-node="0" data-index-in-node="34">"Statistical analysis based on a two-staged SEM-SVM approach for environmental noise annoyance prediction and identification of influencing factors,"</strong> funded by the Deanship of Scientific Research at <strong data-path-to-node="0" data-index-in-node="233">Saudi Electronic University</strong> (Grant No. 8290-CAI-2023-1-202301-1). Serving as the <strong data-path-to-node="0" data-index-in-node="314">Co-Principal Investigator (CO PI)</strong>, I contributed to the development of a hybrid methodology that integrates Structural Equation Modeling with Support Vector Machines to enhance the accuracy of noise annoyance forecasting. This research, supported by a budget of <strong data-path-to-node="0" data-index-in-node="576">Rs. 10,00,000</strong>, successfully identified key environmental factors and was completed in August 2024. Furthermore, our findings have been formally <strong data-path-to-node="0" data-index-in-node="720">published as a research paper</strong>, contributing valuable insights into the intersection of machine learning and environmental acoustics.</p>