Joint Diseases and Related Surgery (JDRS), the official journal of the Turkish Joint Diseases Foundation, is an international, peer-reviewed, immediate open access journal. All published articles in JDRS has been free for everyone, without embargo, since 1990.

Two novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell–Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov–Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods’ generalization ability and success.

The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving “Good” similarity and “Excellent” utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond.

As in any discipline, in Computer Engineering, students start learning the basic concepts of the discipline in their first year through an Introduction to Computer Engineering course. The topics toughed in this course can be grouped into two. The first group includes simple concepts like binary numbering system, hard disk, memory, and I/O devices. In the second group, an introduction to the courses that they will take in the next semesters of the program which includes programming, networking, software engineering, artificial intelligence and database systems. The main objective of this course is to give an introduction about the general concepts of the field to the first year Computer Engineering students and prepare them to understand the connections between them for their future studies. However, students and instructors face with many problems in this course. First, because of the diversity of the concepts given in the course, it is very difficult for the students to see the big picture of the Computer Engineering domain. Similarly, it is difficult for the instructors to prepare the course content in an integrated manner at the students' level. Additionally, the perception of the theory and practice behind the hardware and software topics and their connections is not an easy task for the beginners. Moreover, the topics are mostly abstract topics, which do not allow application of any laboratory sessions. Students usually find this course difficult to understand, which decreases their motivation about the department and success of the course. This study is established to propose a concept map approach to better visualize and discover all the connections between the concepts of Computer Engineering field which can be used in the curriculum of the programs and introductory courses of the field addressing the above problems. The proposed concept map helps to visualize the general picture of the field.