M. Bakhouche

Papers

Total Citations

H-Index

About

M. Bakhouche is a researcher whose work bridges artificial intelligence and agricultural technology, with a growing focus on deep learning applications for customer service and crop analysis. Their most cited paper, "Empowering customer satisfaction chatbot using deep learning and sentiment analysis" (2024, 13 citations), introduces an innovative approach to enhancing virtual assistant interactions by integrating natural language processing (NLP) and sentiment analysis, demonstrating how intelligent systems can streamline reservation processes and improve user experience. This work highlights Bakhouche's contribution to making AI more responsive and human-centric. Earlier, Bakhouche explored agronomic challenges in "Texture analysis with statistical methods for wheat ear extraction" (2007, 2 citations), a foundational study aimed at automating crop counting for yield prediction—part of a larger project to develop a mobile robot for field image acquisition. Though less cited, this work underscores a commitment to practical, real-world applications in agriculture. Bakhouche’s research trajectory shows a shift from traditional statistical methods to cutting-edge deep learning, reflecting adaptability and a drive to solve pressing problems in both service and agricultural sectors. Their work offers valuable insights for students and researchers interested in applied AI, NLP, and precision agriculture.