Keywords : Python

Disease Diagnosis Systems Using Machine Learning and Deep learning Techniques Based on TensorFlow Toolkit: A review

Firdews A.Alsalman; Shler Farhad Khorshid; Amira Bibo Sallow

AL-Rafidain Journal of Computer Sciences and Mathematics, 2022, Volume 16, Issue 1, Pages 111-120
DOI: 10.33899/csmj.2022.174415

Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing.  TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework), a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework- for disease classification is discussed.

Blind Steganalysis using One-Class Classification

Mohammed A. Karem M.; Ahmed Sami Nori

AL-Rafidain Journal of Computer Sciences and Mathematics, 2019, Volume 13, Issue 2, Pages 28-41
DOI: 10.33899/csmj.2020.163518

Steganography is the science/art of hiding information in a way that must not draw attention to the message hidden in the transmitted media, if a suspicion is raised then there is no meaning to the purpose of steganography. Then appeared its counterpart, Steganalysis, which aims to suspect and analyze the transmitted media to decide wither it contain an embedded data or not which we present in a blind Steganalysis way. One-Class Classification (OCC) machine learning algorithms aim to build classification models depending on positive class only when the negative class is not available or poorly sampled. Here in this paper we depend on a one-class support vector machines (OCSVM) which has been trained on only one class of images that is clean images class, so that the trained classifier can classify new reviews to their correct class i.e. clean or stego. Training an OCC turned to be hard work and required long execution time since classifier parameters tuning, data separation and model evaluation needed to be done manually in a brute force way. A powerful programming language (Python) with the powerful machine learning library (Scikit-Learn) gave a promising classification results in deciding whether an input image is clean or stego image.