Journal of Electronic & Information Systems

Evaluating Maximum Diameters of Tumor Sub-regions for Survival Prediction in Glioblastoma Patients via Machine Learning, Considering Resection Status

Reza Babaei — 2024-03-22

In recent decades, there have been significant advancements in medical diagnosis and treatment techniques. However, there is still much progress to be made in effectively managing a wide range of diseases, particularly cancer. Timely diagnosis of cancer remains a critical step towards successful treatment, as it significantly impacts patients’ chances of survival. Among various types of cancer, glioma stands out as the most common primary brain tumor, exhibiting different levels of aggressiveness. One of the monitoring techniques is magnetic resonance imaging (MRI) that provides a precise visual representation of the tumor and its sub-regions (edema (ED), enhancing tumor (ET), and non-enhancing necrotic tumor core (NEC)), enabling monitoring of its location, shape, and sub- regional characteristics. In this study, we aim to investigate the underlying relationship between the maximumdiameters of tumor sub-regions and patients’ overall survival (OS) in glioblastoma cases. Using an MRI dataset of glioblastoma patients, we categorized them based on resection status: gross total resection (GTR) and unknown (NA). By employing the Euclidean distance algorithm, we estimated sub-regions’ maximum diameters. Machine learning algorithms were used to explore the correlation between sub-regions’ maximum diameters and survival outcomes. The results of the univariate prediction models showed that tumor sub-regions’ maximum diameters have a noticeable correlation with the survival rates among patients with unknown resection status with the average spearman correlation of -0.254. Also, addition of the sub-regions’ maximum diameter feature to the radiomics increased the accuracy of ML algorithms in predicting the survival rates with an average of 4.58%.

Attribute-specific Cyberbullying Detection Using Artificial Intelligence

Adeyinka Orelaja — 2024-02-28

Cyberbullying, a pervasive issue in the digital age, poses threats to individuals’ well-being across various attributes such as religion, age, ethnicity, and gender. This research employs artificial intelligence to detect cyberbullying instances in Twitter data, utilizing both traditional and deep learning models. The study repurposes the Sentiment140 dataset, originally intended for sentiment analysis, for the nuanced task of cyberbullying detection. Ethical considerations guide the dataset transformation process, ensuring responsible AI development. The Naive Bayes algorithm demonstrates commendable precision, recall, and accuracy, showcasing its efficacy. The Bi-LSTM model, leveraging deep learning capabilities, exhibits nuanced cyberbullying detection. The study also underscores limitations, emphasizing the need for refined models and diverse datasets.

Sliding Mode-Based Distributed Trajectory Tracking Control of Four-body Train Systems

Yueheng Ding — 2024-01-25

This paper considers the speed tracking of a four-body train system modelled mathematically based on Newton’s second law, which is described by a large-scale interconnected system with four subsystems. Uncertainties are included in the systems to represent the potential impacts on system performance caused by mechanical wear and external environmental changes. An adaptive sliding mode technique is employed to design a distributed control scheme to guarantee tracking accuracy. Coordinate transformations are introduced to transfer the model of train systems to a system in regular form to facilitate the design of the hyperplane and controllers. The Barbashin-Krasovskii theorem is employed to show the reachability of the hyperplane. In simulations, the Gaussian function is chosen as the desired signal, representing time-varying characteristics relevant to real-world situations, and the result demonstrates the feasibility of the proposed control strategy.