Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2074 2019 2019 Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq Noora Hani Sherif Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Eay Fahidhil Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Najlaa Nsrulaah Faris Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq 5Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq Hussein Alaa Diame Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq Raaid Alubady Department of Applied Data Science, Noroff University College, Kristiansand, Norway ; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon Seifedine Kadry Rapid changes in modern technology and sports have impacted society and lifestyle. Augmentative and Alternative Communication (AAC) technology helps to speak and play videos in various sports applications. In the current sports event, AAC's utilization to validate the players' complex moves exclusively has been considered a significant challenge that includes athlete moves in athletics and penalty shots in Soccer. Deep Learning-based Video Segmentation and Video mining (DL-VSVM) with eyeball tracking assistance are proposed to validate the task modeling of sports event video streaming in AAC. The user could select the specific event in the sport and sub-event using eyeball tracking assistance. The AAC is installed with unique icons to identify circumstances. A deep learning-based Sports Task model is created to recognize the required data to be streamed, and the model will help them view the specific sports event they need to watch. The numerical outcomes demonstrate that the suggested DL-VSVM model enhances the segmentation accuracy ratio of 95.3%, tracking ratio of 97.6%, prediction ratio of 98.7%, and reduces the cost function of 5.6% and the error rate of 20.1% compared to other existing models. 2023 2023 93 107 10.54216/JISIoT.090207 https://www.americaspg.com/articleinfo/18/show/2074