چکیده مقاله
This review investigates the most effective AI and web mining techniques for identifying technological innovation patterns in electrical engineering patent databases from ۲۰۱۰ to ۲۰۲۴ Analyzing, we find that integrated frameworks combining hierarchical topic modeling e g , Latent Dirichlet Allocation with cosine similarity , word embedding e g , Word Vec, Glove , and deep learning e g , LSTM, deep neural networks excel in mapping multi dimensional topic evolution, trend declines, and sub technology clusters These methods, applied to datasets ranging from ۲۰۱۰ to ۲۰۲۴, reveal shifts like sensor integrated architectures and transitions to hybrid AI models Validation metrics, such as precision ~۰% , recall ~۰% , and cross validation, highlight their reliability, though some studies lack explicit performance reporting Multi method approaches offer nuanced insights but increase implementation complexity Findings inform strategic technology development in fields like renewable energy, robotics, and electronics, with future potential in trend forecasting and competitive intelligence
کلیدواژهها
نویسندگان
Mohammad Bahrami
Msc student of business administration MBA , Department of Management, science and Technology, Amirkabir University of Technology, Tehran, Iran
شیوه ارجاع
Bahrami، Mohammad،1404،Most effective web mining and AI techniques in identifying technological innovation patterns in electrical engineering patent databases from ۲۰۱۰-۲۰۲۴،هفتمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات،مشهد
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات21 مرداد 1404 · مشهد