Cihan University-Erbil Scientific Journal <p>Cihan University-Erbil Scientific journal (CUESJ) is a biannual academic journal published by the Cihan University-Erbil. CUESJ a periodical journal publishes original researches in all areas of Science, Engineering and Technology. CUESJ is a Peer-Reviewed Open Access journal with Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (CC BY-NC-ND 4.0). CUESJ provides immediate, worldwide, barrier-free access to the full text of research articles without requiring a subscription to the journal, and has neither article processing charge (APC) nor article submission charge (ASC). CUESJ applies the highest standards to everything it does and adopts IEEE citation/referencing style. CUESJ Section Policy includes three types of publications; Articles, Review Articles, and Letters. CUESJ has a print-ISSN: <a href="">2519-6979</a>. It is a member of the ROAD with e-ISSN: <a href="">2707-6377</a> and a member of the Crossref with a doi: <a href="">10.24086/issn.2519-6979</a>.</p> Cihan University-Erbil en-US Cihan University-Erbil Scientific Journal 2519-6979 <p>Authors who publish with this journal agree to the following terms:<br>1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.<br>2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.<br>3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).</p> Real-Time Vehicles Detection Using a Tello Drone with YOLOv5 Algorithm <p>Incorporating Unmanned Aerial Vehicles (UAVs) within Artificial Intelligence systems has given rise to an essential and academically significant approach in the domain of vehicle detection. This study introduces a real-time vehicle detection framework leveraging the you only look once (YOLO) algorithm for precise identification of vehicles, employing the camera on the DJI Tello drone. The research is underpinned by a rich dataset encompassing approximately 2000 images, meticulously annotated with the respective vehicle angles. The framework’s innovation lies in its comprehensive training regimen, encompassing all angles of vehicles: Vehicle-front, vehicle-rear, vehicle-above, and vehicle-sides. This holistic approach aims to yield a model capable of accurately identifying and tracking vehicles from a multitude of viewing angles. The choice of the YOLO algorithm, further enhanced by Ultralytics HUB, ensures the robustness and accuracy required for the detection of moving objects. The model’s capability to effectively track objects is a testament to the algorithm’s efficacy. In assessing the framework’s performance, we employed a comprehensive set of evaluation parameters, including mean average precision, precision, recall, and F1 score. This research not only underscores the practicality of UAVs in the field of artificial intelligence but also highlights the excellence achieved in real-time vehicle detection.</p> Shalaw M. Abdallah ##submission.copyrightStatement## 2024-01-20 2024-01-20 8 1 1 7 10.24086/cuesj.v8n1y2024.pp1-7 An Overview of Signs and Symptoms to Determine Coronavirus and Omicron Patients in Primary Care and Hospitals <p>Coronaviruses, a type of virus family, cause severe respiratory diseases in people. The coronavirus is most frequently linked to the common cold, but in persons with SARS (severe acute respiratory syndrome) virus infection, it can also lead to serious respiratory sickness. The spread of the coronavirus is by having direct contact with infected saliva, mucus, or blood. Infected surfaces, such as those in hospitals or other healthcare facilities, can potentially spread the infection when touched. Now spreading globally is the Omicron version of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). According to preliminary studies, the Omicron variant of SARS-CoV-2 has a higher probability of re-infection. To recognize COVID-19 as well as omicron people in hospitals and primary health care, this review study focuses on the symptoms and indications. The data was collected and analyzed from more than one hundred high-impact original research papers to conclude their results and make a comparison between methods and findings.</p> Ali M.Hussein Hawry A. Majeed Naz R. Majeed Nza H.M. Ali Dashne A. Saeed Narmin A. Ali ##submission.copyrightStatement## 2024-02-05 2024-02-05 8 1 8 17 10.24086/cuesj.v8n1y2024.pp8-17