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MAKE MDPI, Basel (2025)

21/08/2025

🚀 MAKE Journal’s Most Cited Papers of 2024 – Issue 2 📊

We are proud to highlight the top three most cited papers that are driving advancements in machine learning and knowledge extraction:

1️⃣ Cross-Validation Visualized: A Narrative Guide to Advanced Methods
– https://www.mdpi.com/2504-4990/6/2/65
2️⃣ A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition
– https://www.mdpi.com/2504-4990/6/2/40
3️⃣ Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review
– https://www.mdpi.com/2504-4990/6/2/58

These papers showcase the impactful research shaping the future of AI, ML, and data-driven innovation. Stay ahead by exploring the full list of top-cited research!

21/08/2025

🚀 MAKE Journal’s Most Cited Papers of 2024 – Issue 1 📊

We are proud to highlight the top three most cited papers that are driving advancements in machine learning and knowledge extraction:

1️⃣ Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models
– https://www.mdpi.com/2504-4990/6/1/18
2️⃣ SHapley Additive exPlanations (SHAP) for Efficient Feature Selection in Rolling Bearing Fault Diagnosis
– https://www.mdpi.com/2504-4990/6/1/16
3️⃣ An Ensemble-Based Multi-Classification Machine Learning Classifiers Approach to Detect Multiple Classes of Cyberbullying
– https://www.mdpi.com/2504-4990/6/1/9

These papers showcase the impactful research shaping the future of AI, ML, and data-driven innovation. Stay ahead by exploring the full list of top-cited research!

21/08/2025

📰 New Paper Published in MAKE

Evaluating Prompt Injection Attacks with LSTM-Based Generative Adversarial Networks: A Lightweight Alternative to Large Language Models

⚠️The mass generation of prompt attacks from low-cost models such as LSTM-based GANs and SLMs threatens current LLM defense systems. This paper analyzes these attacks and proposes novel countermeasures.

Authors: Sharaf Rashid, Edson Bollis, Lucas Pellicer, Darian Rabbani, Rafael Palacios, Aneesh Gupta and Amar Gupta

📖 Read the full paper here: https://www.mdpi.com/2504-4990/7/3/77 #

21/08/2025

New Research Published in MAKE 📰

Machine Learning Applied to Professional Football: Performance Improvement and Results Prediction

Authors: Diego Moya, Christian Tipantuña, Genesis Villa, Xavier Calderón-Hinojosa, Belén Rivadeneira and Belén Rivadeneira

⚽ Discover how hashtag enhances performance & predicts results in hashtag , with cases like penalties & hashtag milestones. hashtag hashtag

Read the full study at: https://www.mdpi.com/2504-4990/7/3/85

20/08/2025

🚀 Featured Paper in MAKE!

Explore the synergy between Large Language Models (LLMs) and Knowledge Graphs (KGs)! 🤖📊

This new study conducts a systematic review of 77 papers to show how LLMs can automate KG construction while KGs improve transparency, factual consistency, and reliability of AI applications. The research spans healthcare, finance, justice, and industrial automation, revealing both transformative potential and key challenges, including scalability, domain adaptation, explainability, and ethical considerations.

The paper also highlights strategies to optimize LLM–KG integration, providing actionable insights for robust and explainable AI systems. 🌐✨

Authors: Ramandeep Singh Dehal, Mehak Sharma and Reza (Enayat) Rajabi

📖 Read the full study here: https://www.mdpi.com/2504-4990/7/2/38

19/08/2025

🚀 MAKE Journal’s Most Cited Papers of 2023 – Issue 4 📊

We are proud to highlight the top three most cited papers that are driving advancements in machine learning and knowledge extraction:

1️⃣ A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
– http://www.mdpi.com/2504-4990/5/4/83
2️⃣ Human Pose Estimation Using Deep Learning: A Systematic Literature Review
– http://www.mdpi.com/2504-4990/5/4/81
3️⃣ Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks
– http://www.mdpi.com/2504-4990/5/4/68

These papers showcase the impactful research shaping the future of AI, ML, and data-driven innovation. Stay ahead by exploring the full list of top-cited research!

19/08/2025

🚀 MAKE Journal’s Most Cited Papers of 2023 – Issue 3 📊

We are proud to highlight the top three most cited papers that are driving advancements in machine learning and knowledge extraction:

1️⃣ Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate
– http://www.mdpi.com/2504-4990/5/3/53
2️⃣ Defining a Digital Twin: A Data Science-Based Unification
– http://www.mdpi.com/2504-4990/5/3/54
3️⃣ Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models – http://www.mdpi.com/2504-4990/5/3/59

These papers showcase the impactful research shaping the future of AI, ML, and data-driven innovation. Stay ahead by exploring the full list of top-cited research!

19/08/2025

🚀 Most Cited Paper in MAKE! 🚀

We are proud to highlight “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS”, now the most cited paper in MAKE with 1,343 citations and over 100k views! 📈✨

This review covers YOLO’s evolution in real-time object detection for robotics, driverless cars, and video monitoring. From YOLOv1 to YOLOv8, YOLO-NAS, and YOLO with transformers, it explores network architectures, training innovations, metrics, postprocessing, and future research directions. 🔍🤖

💡 Discover the key lessons that shape the next generation of object detection systems!

Authors: Juan Terven, Diana Margarita Córdova Esparza and Julio Alejandro Romero González

📖 Read the full paper here: http://bit.ly/4mL8EjB

14/08/2025

🚀 Discover MAKE’s Most Cited Papers of 2023! 📊

We are proud to spotlight the top three most cited papers from each of our first two issues in 2023, showcasing the impactful research driving advancements in machine learning.

📌 Issue 1 (2023)
1️⃣ XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process – https://brnw.ch/21wUZMw
2️⃣ Machine Learning and Prediction of Infectious Diseases: A Systematic Review – https://brnw.ch/21wUZMx
3️⃣ A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection – https://brnw.ch/21wUZME

📌 Issue 2 (2023)
4️⃣ Systematic Review of Recommendation Systems for Course Selection – https://brnw.ch/21wUZMF
5️⃣ Drug-Drug Interaction Extraction from Biomedical Text Using Relation BioBERT with BLSTM – https://brnw.ch/21wUZMH
6️⃣ Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education Research – https://brnw.ch/21wUZML

💡 These works push the boundaries of AI, deep learning, NLP, and predictive analytics, shaping the future of data-driven research.

🔍 Explore the full list and stay ahead in the rapidly evolving world of machine learning and knowledge extraction.

13/08/2025

🛡️MAKE | Cybersecurity Spotlight 🔐

AE-DTNN: Autoencoder–Dense–Transformer Neural Network Model for Efficient Anomaly-Based Intrusion Detection Systems 💻⚡

New in MAKE: "AE-DTNN: An Autoencoder-Dense-Transformer Neural Network for Intrusion Detection" by Maggie Mashaly et al. 🚀 A breakthrough DL model achieving 99.98% accuracy for Cybersecurity Systems. 🔥📊

📖 Read the full article: https://www.mdpi.com/2504-4990/7/3/78

13/08/2025

🌱 From the MAKE Archives | 2024 Research Spotlight

Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model🥔💻

(1) 🥔 Potatoes represent a key agricultural commodity with substantial influence on the global economy.
(2) 🤖 The study utilized the RegNetY-400MF deep learning model and transfer learning for detecting potato leaf diseases.
(3) ⚙️ Optimized parameters and data augmentation were employed to enhance model performance.

👩‍🔬 Authors: Chao-Yun Chang and Chih-Chin Lai - from National University of Kaohsiung

📖 Read the full article: https://www.mdpi.com/2504-4990/6/4/114

12/08/2025

🚨 Featured Paper from MAKE! 🚨

What ChatGPT Has to Say About Its Topological Structure: The Anyon Hypothesis 🧠✨

AI systems like ChatGPT are powerful but often act as “black boxes,” making it hard to know how they think. This paper is unique because it applies advanced concepts from quantum physics, like anyons and braids, to explain how AI models handle language and meaning. ⚛️🔗

It’s timely because there’s a global push for more explainable and trustworthy AI, and using topological ideas offers a novel path toward making AI’s inner workings clearer and possibly more stable. This study opens an exciting new frontier where physics and AI intersect. If quantum braids can help explain how models like ChatGPT operate, it could revolutionize how we design, interpret, and trust artificial intelligence. 🌐🤖

Future research might explore using these topological principles to create AI systems that are not only smarter but also more understandable and robust, potentially transforming fields from technology to cognitive science. 🔍🚀

👥 Authors: Michel Planat and Marcelo Amaral

📖 Read more at: https://www.mdpi.com/2504-4990/6/4/137

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