{"id":5291,"date":"2026-04-10T12:44:34","date_gmt":"2026-04-10T12:44:34","guid":{"rendered":"https:\/\/cognitionconferences.com\/artificialintelligence\/?post_type=speaker&#038;p=5291"},"modified":"2026-04-10T12:44:34","modified_gmt":"2026-04-10T12:44:34","slug":"ahmad-ali","status":"publish","type":"speaker","link":"https:\/\/cognitionconferences.com\/artificialintelligence\/speaker\/ahmad-ali\/","title":{"rendered":"Ahmad Ali"},"content":{"rendered":"<p><strong>Abstract:<\/strong><\/p>\n<p>The prediction of crowd flows is an important urban computing issue whose purpose is to predict the future number of incoming and outgoing people in regions. Measuring the complicated spatial\u2013temporal dependencies with external factors, such as weather conditions and surrounding point-of-interest (POI) distribution is the most difficult aspect of predicting crowd flows movement. To overcome the above issue, this paper advises a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city. The DHSTNet model is made up of four separate components: a recent, daily, weekly, and an external branch component. Our proposed approach simultaneously assigns various weights to different branches and integrates the four properties\u2019 outputs to generate final predictions. Moreover, to verify the generalization and scalability of the proposed model, we apply a Graph Convolutional Network (GCN) based on Long Short Term Memory (LSTM) with the previously published model, termed as GCN-DHSTNet; to capture the spatial patterns and short-term temporal features; and to illustrate its exceptional accomplishment in predicting the traffic crowd flows. The GCN-DHSTNet model not only depicts the spatio-temporal dependencies but also reveals the influence of different time granularity, which are recent, daily, weekly periodicity and external properties, respectively. Finally, a fully connected neural network is utilized to fuse the spatio-temporal features and external properties together. Using two different real-world traffic datasets, our evaluation suggests that the proposed GCN-DHSTNet method is approximately 7.9%\u201327.2% and 11.2%\u201311.9% better than the AAtt-DHSTNet method in terms of RMSE and MAPE metrics, respectively. Furthermore, AAtt-DHSTNet outperforms other state-of-the-art methods.<\/p>\n","protected":false},"featured_media":5292,"template":"","meta":{"_acf_changed":false},"schedule":[5],"speaker-category":[6],"class_list":["post-5291","speaker","type-speaker","status-publish","has-post-thumbnail","hentry","schedule-day-1","speaker-category-speakers"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Ahmad Ali | Hainan Bielefeld University of Applied Sciences | China<\/title>\n<meta name=\"description\" content=\"Dr. AHmad Ali has completed his PhD at the age of 32 years from Shanghai Jiao Tong Univeristy, China and postdoctoral studies from Shenzhen University. He is currently working as a Doctor Lecturer at Hainan Bielefeld University of Applied Sciences, Germany. He has published more than 25 papers in reputed journals and has been serving as an editorial board member of repute.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cognitionconferences.com\/artificialintelligence\/speaker\/ahmad-ali\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ahmad Ali | Hainan Bielefeld University of Applied Sciences | China\" \/>\n<meta property=\"og:description\" content=\"Dr. AHmad Ali has completed his PhD at the age of 32 years from Shanghai Jiao Tong Univeristy, China and postdoctoral studies from Shenzhen University. He is currently working as a Doctor Lecturer at Hainan Bielefeld University of Applied Sciences, Germany. 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