Machine Learning and Knowledge Extraction, Volume 7, Issue 2

A large number of bug reports are submitted by users and developers in bug-tracking system every day. It is time-consuming for software maintainers to assign bug reports to appropriate developers for fixing manually. Many bug-triaging methods have be...

In vitro fertilization (IVF) is a well-established and efficient assisted reproductive technology (ART). However, it requires a series of costly and non-trivial procedures, and the success rate still needs improvement. Thus, increasing the success ra...

COVID-19 mortality is a complex phenomenon influenced by multiple factors. This study aimed to identify factors associated with death in COVID-19 patients by considering clinical, demographic, environmental, and socioeconomic conditions, using machin...

Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. Unfortunately, they still repres...

This study introduces MediaWatchers4Climate, a methodological framework that leverages machine learning to evaluate the accuracy and rhetorical framing of climate change narratives in Greek online media. The model is designed to analyze large-scale t...

Question answering over domain-specific knowledge graphs implies several challenges. It requires sufficient knowledge of the world and the domain to understand what is being asked, familiarity with the knowledge graph’s structure to build a cor...

Effectively leveraging cognitive load predictions helps optimize collaborative learning design and implementation. This study explored the feasibility of predicting individual learners’ cognitive load during collaborative learning using a combi...

Brain tumor classification poses significant challenges in medical imaging, largely due to the heterogeneity and structural complexity of tumors. With Magnetic Resonance Imaging (MRI) serving as a cornerstone for diagnosis, manual interpretation by r...

Autonomous trains require reliable and accurate environmental perception to take over safety-critical tasks from the driver. This paper investigates the application of N-version architectures to rail track detection using Deep Neural Networks (DNNs)...

The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on extending it to the family of Bregman dive...

This study introduces artificial intelligence as a powerful tool to transform bioequivalence (BE) trials. We apply advanced generative models, specifically Wasserstein Generative Adversarial Networks (WGANs), to create virtual subjects and reduce the...

Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques&md...

Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach fo...

We present a model of classical binary time series derived from a matrix product state (MPS) Ansatz widely used in one-dimensional quantum systems. We discuss how this quantum Ansatz allows us to generate classical time series in a sequential manner....

Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. Membership inference attacks (MIAs) exploit this to obtain confidential information about the data used for training, aimi...

With the evolution of data collection technologies, sensor-generated data have become the norm. However, decades of manually recorded maintenance data still hold untapped value. Natural Language Processing (NLP) offers new ways to extract insights fr...

As programming education becomes increasingly complex, grading student code has become a challenging task. Traditional methods, such as dynamic and static analysis, offer foundational approaches but often fail to provide granular insights, leading to...

Knee osteoarthritis (KOA) is a highly prevalent muscoloskeletal joint disorder affecting a significant portion of the population worldwide. Accurate predictions of KOA progression can assist clinicians in drawing preventive strategies for patients. I...

The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative...

This paper introduces a novel framework enabling natural language question answering on Piping and Instrumentation Diagrams (P&IDs), addressing a critical gap between engineering design documentation and intuitive information retrieval. Our appro...

Particle tracking velocimetry (PTV) forms the basis for many fluid dynamic experiments, in which individual particles are tracked across multiple successive images. However, when the experimental setup involves high-speed, high-density particles that...

We used machine learning (ML) to characterize 894,154 medical records of outpatient visits from the Veterans Administration Central Data Warehouse (VA CDW) by the likelihood of assignment of 200 International Classification of Diseases (ICD) code blo...

Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges...

In electron microscopy, obtaining low-noise images is often difficult, especially when examining biological samples or delicate materials. Therefore, the suppression of noise is essential for the analysis of such noisy images. State-of-the-art image...

Regression is a fundamental task in machine learning, and neural networks have been successfully employed in many applications to identify underlying regression patterns. However, they are often criticised for their lack of interpretability and commo...

Due to their considerable costs, deep neural networks (DNNs) are valuable assets that need to be protected in terms of intellectual property (IP). From this statement, DNN watermarking gains significant interest since it allows DNN owners to prove th...

This paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend...

In today’s rapidly evolving transportation infrastructure, developing long-lasting, high-performance pavement materials remains a significant priority. Integrating machine learning (ML) techniques provides a transformative approach to optimizin...

Waste heat recovery is a critical strategy for optimizing energy consumption and reducing greenhouse gas emissions. In this context, the circular economy highlights the importance of this practice as a key tool to enhance energy efficiency, minimize...

As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these mod...

(1) Background: This paper intends to accomplish a comparative study and analysis regarding the multiclass classification of facial thermal images, i.e., in three classes corresponding to predefined emotional states (neutral, happy and sad). By carry...