{"id":12654,"date":"2025-09-29T10:16:34","date_gmt":"2025-09-29T07:16:34","guid":{"rendered":"https:\/\/coie-nahrain.edu.iq\/en\/?p=12654"},"modified":"2025-09-29T10:16:34","modified_gmt":"2025-09-29T07:16:34","slug":"college-of-information-engineering-researchers-present-ai-study-at-potsdam-conference","status":"publish","type":"post","link":"https:\/\/coie-nahrain.edu.iq\/en\/college-of-information-engineering-researchers-present-ai-study-at-potsdam-conference\/","title":{"rendered":"College of Information Engineering Researchers Present AI Study at Potsdam Conference"},"content":{"rendered":"

The College of Information Engineering at Al-Nahrain University participated in a scientific research project through a funded invitation to attend a conference at the University of Potsdam, Germany. The researchers involved were Asst. Lecturer Ilaf Ahmed Saeed<\/strong>, in collaboration with Asst. Prof. Ammar Dawood Jasim<\/strong> and Prof. Munther Ali Abdulmalik<\/strong>. The title of the research is:<\/p>\n

\"\" \"\" \"\"<\/p>\n

\u201cComputer Vision in Archaeology: Automatic Recognition and Pronunciation of Cuneiform Writing.\u201d<\/strong><\/p>\n

The research focuses on detecting ancient inscriptions using artificial intelligence and computer vision techniques.<\/p>\n

The study presents an innovative approach to recognizing and classifying ancient cuneiform texts using advanced computer vision techniques, specifically employing a deep learning model (YOLOv8<\/strong>). The aim of the study is to accurately detect Neo-Assyrian cuneiform signs and pronounce them, relying on a dataset of more than 2,000 images sourced from the Iraq Museum.<\/p>\n

The training process produced significant results, achieving a mean average precision (mAP50) of 82.7%<\/strong>, along with precision and recall rates of 71.3%<\/strong> and 85.6%<\/strong>, respectively. The research also focuses on classifying cuneiform tablets, with a total of 1,321 images collected for this purpose, facilitating the logical representation of sign categories.<\/p>\n

For classification purposes, the cuneiform tablets were collected from the Iraq Museum Initiative<\/strong> and the Cuneiform Digital Library Initiative (CDLI)<\/strong>, with each language dataset containing around 2,000 images, reaching an accuracy rate of up to 100%<\/strong>. This methodology not only enhances the efficiency of analyzing cuneiform texts but also provides a valuable tool for researchers in the field of ancient languages.<\/p>\n

The research emphasizes the importance of high-quality data processing and the potential of automated systems to aid in understanding historical texts, thereby contributing to the preservation and interpretation of cultural heritage through modern technology.<\/p>\n

\"\" \"\" \"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

The College of Information Engineering at Al-Nahrain University participated in a scientific research project through a funded invitation to attend a conference at the University of Potsdam, Germany. The researchers involved were Asst. Lecturer Ilaf Ahmed Saeed, in collaboration with Asst. Prof. Ammar Dawood Jasim and Prof. Munther Ali Abdulmalik. The title of the research is: \u201cComputer Vision in Archaeology: Automatic Recognition and Pronunciation of Cuneiform Writing.\u201d The research focuses on detecting ancient inscriptions using artificial intelligence and computer vision techniques. The study presents an innovative approach to recognizing and classifying ancient cuneiform texts using advanced computer vision techniques, specifically employing a deep learning model (YOLOv8). The aim of the study is to accurately detect Neo-Assyrian cuneiform signs and pronounce them, relying on a dataset of more than 2,000 images sourced from the Iraq Museum. The training process produced significant results, achieving a mean average precision (mAP50) of 82.7%, along with precision and recall rates of 71.3% and 85.6%, respectively. The research also focuses on classifying cuneiform tablets, with a total of 1,321 images collected for this purpose, facilitating the logical representation of sign categories. For classification purposes, the cuneiform tablets were collected from the Iraq Museum Initiative and the Cuneiform Digital Library Initiative (CDLI), with each language dataset containing around 2,000 images, reaching an accuracy rate of up to 100%. This methodology not only enhances the efficiency of analyzing cuneiform texts but also provides a valuable tool for researchers in the field of ancient languages. The research emphasizes the importance of high-quality data processing and the potential of automated systems to aid in understanding historical texts, thereby contributing to the preservation and interpretation of cultural heritage through modern technology.<\/p>\n","protected":false},"author":4,"featured_media":12661,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[92,25,26],"tags":[],"class_list":["post-12654","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-coie-news","category-research","category-spotlight"],"views":5,"_links":{"self":[{"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts\/12654","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/comments?post=12654"}],"version-history":[{"count":1,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts\/12654\/revisions"}],"predecessor-version":[{"id":12662,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts\/12654\/revisions\/12662"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/media\/12661"}],"wp:attachment":[{"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/media?parent=12654"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/categories?post=12654"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/tags?post=12654"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}