http://ijci.uoitc.edu.iq/index.php/ijci/issue/feed Iraqi Journal for Computers and Informatics 2021-05-08T11:33:03+00:00 Prof. Dr. Safaa O. Al-mamory editor_ijci@uoitc.edu.iq Open Journal Systems <p>Iraqi Journal for Computers and Informatics is an open access ,double-blind peer reviewed, international academic journal published biannual (since 2017). It is institutional journal issued by the University of Information Technology and Communications (UoITC) in Baghdad- Iraq; UoITC is one of governmental universities which established by the Ministry of Higher Education in Iraq. IJCI aims to contribute to the constant scientific research and to provide a sober scientific journal that enriches scholars around the world and it deals with all aspects of computer science.</p> http://ijci.uoitc.edu.iq/index.php/ijci/article/view/277 CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW 2021-05-07T19:22:14+00:00 Aska Ezadeen Mehyadin aska.mehyadin@dpu.edu.krd Adnan Mohsin Abdulazeez adnan.mohsin@dpu.edu.krd <p><strong>Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serve in an occupation that is identified by their label. A complete audit without a supervisor is fairly easy to do, but nevertheless represents a significant risk to the enterprise, as there have been few chances to safely experiment with it so far. By utilizing a large number of unsupervised inputs along with the supervised inputs, the semi-supervised learning solves this issue, to create a good training sample. Since semi-supervised learning requires fewer human effort and allows greater precision, both theoretically or in practice, it is of critical interest.</strong></p> 2021-05-08T00:00:00+00:00 Copyright (c) 2021