{"id":12616,"date":"2025-06-19T09:23:57","date_gmt":"2025-06-19T06:23:57","guid":{"rendered":"https:\/\/coie-nahrain.edu.iq\/en\/?p=12616"},"modified":"2025-06-19T09:23:57","modified_gmt":"2025-06-19T06:23:57","slug":"a-research-project-from-the-college-of-information-engineering-wins-best-scientific-paper-award-at-brandenburg-university-conference-in-germany","status":"publish","type":"post","link":"https:\/\/coie-nahrain.edu.iq\/en\/a-research-project-from-the-college-of-information-engineering-wins-best-scientific-paper-award-at-brandenburg-university-conference-in-germany\/","title":{"rendered":"A Research Project from the College of Information Engineering Wins Best Scientific Paper Award at Brandenburg University Conference in Germany"},"content":{"rendered":"

A scientific research project from the College of Information Engineering has won the Best Scientific Paper Award<\/strong> at a conference held at Brandenburg University in Germany. The research was conducted by researchers: Asst. Lecturer Elaf Ahmed Saeed, Asst. Prof. Ammar Dawood Jasim, and Prof. Munthir Ali Abdul Malik.<\/strong><\/p>\n

The study focuses on the detection of ancient scripts using artificial intelligence techniques in computer vision.<\/p>\n

The research presents an innovative approach to recognizing and classifying ancient cuneiform texts using advanced computer vision techniques, specifically through the deep learning model YOLOv8. The study aims to accurately detect and pronounce modern Assyrian cuneiform signs by utilizing a dataset of over 2,000 images sourced from the Iraqi Museum.<\/p>\n

The training process yielded significant results, achieving a mean Average Precision (mAP50) of 82.7%<\/strong>, with precision<\/strong> and recall<\/strong> rates of \u00a071.3%<\/strong>\u00a0and 85.6%<\/strong>, respectively. The study also focuses on the classification of cuneiform tablets, using a total of 1,321 images<\/strong>\u00a0collected for this purpose, facilitating the logical representation of sign categories.<\/p>\n

For classification purposes, cuneiform tablets were gathered from the Iraqi Museum Initiative<\/strong> website and the Cuneiform Digital Library Initiative (CDLI)<\/strong>, with each language’s dataset comprising approximately 1,000 images<\/strong>, achieving 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 study highlights the importance of high-quality data processing and the potential of automated systems to assist in understanding historical texts\u2014contributing to the preservation and interpretation of cultural heritage<\/strong>\u00a0through modern technology.<\/p>\n

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

A scientific research project from the College of Information Engineering has won the Best Scientific Paper Award at a conference held at Brandenburg University in Germany. The research was conducted by researchers: Asst. Lecturer Elaf Ahmed Saeed, Asst. Prof. Ammar Dawood Jasim, and Prof. Munthir Ali Abdul Malik. The study focuses on the detection of ancient scripts using artificial intelligence techniques in computer vision. The research presents an innovative approach to recognizing and classifying ancient cuneiform texts using advanced computer vision techniques, specifically through the deep learning model YOLOv8. The study aims to accurately detect and pronounce modern Assyrian cuneiform signs by utilizing a dataset of over 2,000 images sourced from the Iraqi Museum. The training process yielded significant results, achieving a mean Average Precision (mAP50) of 82.7%, with precision and recall rates of \u00a071.3%\u00a0and 85.6%, respectively. The study also focuses on the classification of cuneiform tablets, using a total of 1,321 images\u00a0collected for this purpose, facilitating the logical representation of sign categories. For classification purposes, cuneiform tablets were gathered from the Iraqi Museum Initiative website and the Cuneiform Digital Library Initiative (CDLI), with each language’s dataset comprising approximately 1,000 images, achieving 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 study highlights the importance of high-quality data processing and the potential of automated systems to assist in understanding historical texts\u2014contributing to the preservation and interpretation of cultural heritage\u00a0through modern technology.<\/p>\n","protected":false},"author":4,"featured_media":12620,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25,26],"tags":[],"class_list":["post-12616","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","category-spotlight"],"views":11,"_links":{"self":[{"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts\/12616","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=12616"}],"version-history":[{"count":1,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts\/12616\/revisions"}],"predecessor-version":[{"id":12621,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/posts\/12616\/revisions\/12621"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/media\/12620"}],"wp:attachment":[{"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/media?parent=12616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/categories?post=12616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coie-nahrain.edu.iq\/en\/wp-json\/wp\/v2\/tags?post=12616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}