Journal of Information Systems Engineering and Management

Coronavirus disease (COVID-19) has emerged as a major global health challenge, making early and accurate diagnosis crucial, particularly for asymptomatic patients. Computed Tomography (CT) imaging has proven to be an effective modality for detecting COVID-19-related lung abnormalities.

In this paper, a hybrid framework integrating Wavelet Transform (WT), K-Means Cluste...

Empirical comparisons in safe reinforcement learning (RL) are usually reported at a single, fixed training budget, and the resulting method ranking is then treated as a property of the algorithms. We show that on a quadrotor hover task under wind disturbance this ranking is instead strongly budget-dependent. Using two runs of an identical five-method pipeline—an unconstrained...

The continuous increase in Internet of Things (IoT) devices in heterogeneous meta-computing environments has led to increased interest in more efficient task offloading methods to mitigate the effects of resource constraints in processing, memory, and battery life. Existing offloading methods often lack semantical offloading decisions and do not consider the heterogeneous and...

Introduction: Knowledge graphs power modern question-answering, recommendation, and entity-aware language models, but are perennially incomplete. The task of knowledge graph completion (KGC) infers missing triples and is increasingly approached by finetuning a large language model (LLM) to select the correct entity from a candidate list produced by a lightwei...

AutoML is used in this project to address the limitations of traditional machine learning (ML), which often requires expert knowledge for model selection, tuning, and validation. By automating these processes, AutoML makes data analysis more efficient and accessible to a broader audience, including non-experts. This research proposes an automatic learning machine (AutoML) sys...