{"id":35524,"date":"2026-04-05T18:18:44","date_gmt":"2026-04-05T15:18:44","guid":{"rendered":"https:\/\/bti.stu.edu.iq\/?p=35524"},"modified":"2026-04-08T21:30:43","modified_gmt":"2026-04-08T18:30:43","slug":"%d8%aa%d8%af%d8%b1%d9%8a%d8%b3%d9%8a-%d9%81%d9%8a-%d8%a7%d9%84%d9%85%d8%b9%d9%87%d8%af-%d8%a7%d9%84%d8%aa%d9%82%d9%86%d9%8a-%d8%a7%d9%84%d8%aa%d9%83%d9%86%d9%88%d9%84%d9%88%d8%ac%d9%8a-%d9%81%d9%8a","status":"publish","type":"post","link":"https:\/\/bti.stu.edu.iq\/en\/35524\/","title":{"rendered":"Dr. Mortada Mohammed Sahib, a faculty member at the Technological Technical Institute in Basra, has successfully published two scientific research papers in internationally recognized journals indexed in Scopus."},"content":{"rendered":"
As part of its ongoing commitment to distinguished scientific achievement, <\/span>Technological Technical Institute<\/em> proudly announces that <\/span>Dr. Mortada Mohammed Sahib, a faculty member in the <\/span>Production Mechanics Technologies Division<\/em>, has successfully published two high-quality research papers in internationally recognized journals and conferences indexed in <\/span>Scopus. These publications were conducted in collaboration with a German research team from <\/span>Otto von Guericke University Magdeburg, within the framework of advanced research projects in the fields of <\/span>Artificial Intelligence and <\/span>Deep Learning.<\/span><\/div>\n
\n

First Research Paper<\/strong><\/h3>\n

Condition Monitoring Model Development for Belt Systems Using Hybrid CNN\u2013BiLSTM Deep-Learning Techniques<\/p>\n

This study presents the development of a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks to analyze belt drive systems and predict their operational condition. The proposed model demonstrated exceptional predictive performance, achieving an accuracy of approximately 99% in detecting faults and imbalance conditions. This represents a significant advancement toward the implementation of intelligent predictive maintenance systems, contributing to the reduction of unexpected failures and production losses.<\/p>\n

Second Research Paper<\/h3>\n

Surface Roughness Prediction in Turning Stainless Steel Applying Deep Learning and LSTM Networks<\/strong><\/p>\n

This research was presented at the:
7th International Conference on Industry of the Future and Smart Manufacturing \u2013 Malta<\/strong><\/p>\n

The study focuses on the application of Long Short-Term Memory (LSTM) networks to predict surface quality in stainless steel turning processes. The model relies on the analysis of vibration signals alongside operational parameters. The results demonstrated a high level of prediction accuracy, highlighting the model\u2019s effectiveness in improving product quality across industrial applications.<\/p>\n

Notably, the deep learning models were trained using high-performance GPU computing servers at Otto von Guericke University Magdeburg, Germany, enabling accelerated training processes and efficient data analysis. This significantly enhanced the accuracy and reliability of the obtained results.<\/p>\n

This achievement represents a valuable scientific contribution that supports the integration of Artificial Intelligence technologies in the development of industrial monitoring systems and the optimization of production efficiency.<\/p>\n<\/div>\n

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

As part of its ongoing commitment to distinguished scientific achievement, Technological Technical Institute proudly announces that Dr. Mortada Mohammed Sahib, a faculty member in the Production Mechanics Technologies Division, has successfully published two high-quality research papers in internationally recognized journals and conferences indexed in Scopus. […]<\/p>\n","protected":false},"author":14,"featured_media":35304,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[116,101],"tags":[],"table_tags":[],"class_list":["post-35524","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-scientific-activities"],"_links":{"self":[{"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/posts\/35524","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/comments?post=35524"}],"version-history":[{"count":4,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/posts\/35524\/revisions"}],"predecessor-version":[{"id":35535,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/posts\/35524\/revisions\/35535"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/media\/35304"}],"wp:attachment":[{"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/media?parent=35524"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/categories?post=35524"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/tags?post=35524"},{"taxonomy":"table_tags","embeddable":true,"href":"https:\/\/bti.stu.edu.iq\/en\/wp-json\/wp\/v2\/table_tags?post=35524"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}