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Machine Learning and Knowledge Extraction (ISSN 2504-4990) is a peer-reviewed, scholarly open access

08/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "Benchmarking with a Language Model Initial Selection for Text Classification Tasks" by Agus Riyadi, Mate Kovacs, Uwe Serdült and Victor Kryssanov.

Read it here: https://www.mdpi.com/2504-4990/7/1/3

MDPI 立命館大学／Ritsumeikan University Kementerian PPN/Bappenas Universität Zürich

The now-globally recognized concerns of AI’s environmental implications resulted in a growing awareness of the need to reduce AI carbon footprints, as well as to carry out AI processes responsibly and in an environmentally friendly manner. Benchmarking, a critical step when evaluating AI solutions...

08/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "Quantifying Interdisciplinarity in Scientific Articles Using Deep Learning Toward a TRIZ-Based Framework for Cross-Disciplinary Innovation" by Nicolas Douard, Ahmed Samet, George Giakos and Denis Cavallucci.

Read it here: https://www.mdpi.com/2504-4990/7/1/7

MDPI Université de Strasbourg Manhattan University

Interdisciplinary research (IDR) is essential for addressing complex global challenges that surpass the capabilities of any single discipline. However, measuring interdisciplinarity remains challenging due to conceptual ambiguities and inconsistent methodologies. To overcome these challenges, we pro...

08/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "Unsupervised Word Sense Disambiguation Using Transformer’s Attention Mechanism" by Radu Ion, Vasile Păiș, Verginica Barbu Mititelu, Elena Irimia, Maria Mitrofan, Valentin Badea and Dan Tufiș.

Read it here: https://www.mdpi.com/2504-4990/7/1/10

MDPI

Transformer models produce advanced text representations that have been used to break through the hard challenge of natural language understanding. Using the Transformer’s attention mechanism, which acts as a language learning memory, trained on tens of billions of words, a word sense disambiguati...

07/10/2025

📢 2024 High Quality Paper 📰

Welcome to read the highly cited paper "Fine-Tuning Artificial Neural Networks to Predict Pest Numbers in Grain Crops: A Case Study in Kazakhstan" by Galiya Anarbekova, Luis Gonzaga Baca Ruiz, Akerke Akanova, Saltanat Sharipova and Nazira Ospanova.

Read it here: https://www.mdpi.com/2504-4990/6/2/54

MDPI Казахский Агротехнический Университет Universidad de Granada Astana It University Toraighyrov University - Торайғыров университеті

This study investigates the application of different ML methods for predicting pest outbreaks in Kazakhstan for grain crops. Comprehensive data spanning from 2005 to 2022, including pest population metrics, meteorological data, and geographical parameters, were employed to train the neural network f...

07/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs" by Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu and Kang Qin.

Read it here: https://www.mdpi.com/2504-4990/7/2/35

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 when applied to high-dimensional and domain-speci...

07/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms" by Kianeh Kandi and Antonio García-Dopico.

Read it here: https://www.mdpi.com/2504-4990/7/1/20

MDPI Universidad Politécnica de Madrid (Oficial)

This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to un...

06/10/2025

📢 2024 High Quality Paper 📰

Welcome to read the highly cited paper "Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI" by Ding Li, Yan Liu and Jun Huang.

Read it here: https://www.mdpi.com/2504-4990/6/2/50

MDPI Concordia University

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-b...

06/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "A Hybrid Gradient Boosting and Neural Network Model for Predicting Urban Happiness: Integrating Ensemble Learning with Deep Representation for Enhanced Accuracy" by Gregorius Airlangga and Alan Liu.

Read it here: https://www.mdpi.com/2504-4990/7/1/4

MDPI Unika Atma Jaya 國立中正大學（National Chung Cheng University）

Urban happiness prediction presents a complex challenge, due to the nonlinear and multifaceted relationships among socio-economic, environmental, and infrastructural factors. This study introduces an advanced hybrid model combining a gradient boosting machine (GBM) and neural network (NN) to address...

06/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "Leveraging Failure Modes and Effect Analysis for Technical Language Processing" by Mathieu Payette, Georges Abdul-Nour, Toualith Jean-Marc Meango, Miguel Diago and Alain Côté.

Decades of maintenance data often go underused—buried in free-text fields. Our study presents a novel approach that combines Failure Modes and Effects Analysis with LLMs to extract reliability insights from these texts.

Read it here: https://www.mdpi.com/2504-4990/7/2/42

MDPI UQTR - Université du Québec à Trois-Rivières

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 from these historical records, especially from short...

03/10/2025

📢 2024 High Quality Paper 📰

Welcome to read the highly cited paper "A Cognitive Load Theory (CLT) Analysis of Machine Learning Explainability, Transparency, Interpretability, and Shared Interpretability" by Stephen Fox and Vitor Fortes Rey.

Read it here: https://www.mdpi.com/2504-4990/6/3/71

MDPI VTT Technical Research Centre of Finland Deutsches Forschungszentrum für Künstliche Intelligenz RPTU

Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which can involve selective attention to new information....

03/10/2025

📢 2024 High Quality Paper 📰

Welcome to read the highly cited paper "Application of Bayesian Neural Networks in Healthcare: Three Case Studies" by Lebede Ngartera, Mahamat Ali Issaka and Saralees Nadarajah.

Read it here: https://www.mdpi.com/2504-4990/6/4/127

MDPI The University of Manchester

This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses t...

02/10/2025

📢 New in MAKE 📰

Welcome to read the recent paper "Enhancing Soundscape Characterization and Pattern Analysis Using Low-Dimensional Deep Embeddings on a Large-Scale Dataset" by Daniel Alexis Nieto Mora, Leonardo Duque-Muñoz and Juan David Martínez Vargas.

Read it here: https://www.mdpi.com/2504-4990/7/4/109

MDPI Institución Universitaria ITM Universidad EAFIT

Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in understanding the soundscape as a whole while consi...

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