European Journal of Artificial Intelligence and Machine Learning

Use Artificial Intelligence into Facility Design and Layout Planning Work in Manufacturing Facility

2025-04-22T01:43:15+02:00

The integration of artificial intelligence (AI) into facility design and layout planning has revolutionized manufacturing by enhancing precision, efficiency, and adaptability. Traditional facility planning methods, reliant on static, rule-based approaches, are increasingly being replaced by AI-driven solutions that optimize spatial arrangements, improve workflow, and balance human-machine interactions. This paper explores the application of AI tools such as Process Planning AI, AutoCAD AI, and Space & Machine Design AI in manufacturing facility design. These technologies leverage predictive modeling, real-time analytics, and generative design to optimize process planning, enhance production layouts, and facilitate adaptive decision-making. Additionally, AI-driven simulations and digital modeling enable manufacturers to anticipate design challenges, reduce bottlenecks, and maximize resource utilization. As AI adoption grows, its role in smart factories and dynamic production environments continues to evolve, fostering a more data-driven, efficient, and automated approach to facility layout and design.

Adaptive Anomaly Detection in Database Transactions: Bridging Security Gaps with Reinforcement Learning

2025-04-22T01:43:24+02:00

Anomaly detection in database transactions is critical for safeguarding sensitive information and ensuring the integrity of operations in industries like finance, healthcare, and e-commerce. Existing techniques, including rule-based, machine learning, and deep learning methods, face challenges such as high false positive rates, poor adaptability to evolving patterns, and limited scalability in imbalanced datasets. This research proposes a novel Reinforcement Learning (RL)-based anomaly detection system to address these limitations. The model employs a dynamic reward mechanism and anomaly scoring system to classify transactions accurately while reducing false positives. It leverages the Kaggle Anomaly Detection in Transactions Dataset and a synthetically generated dataset for training and evaluation. Experimental results show that the RL-based model outperforms traditional methods, achieving a precision of 95.2%, recall of 92.4%, and an AUC-ROC score of 97.2%, significantly higher than Autoencoders, Isolation Forest, and Support Vector Machines. The proposed model’s adaptability and robustness make it a scalable solution for real-time anomaly detection, addressing critical gaps in existing techniques. This study advances database security by offering a highly accurate, adaptive, and efficient system for detecting anomalies in complex transactional environments.

Suggestion for Aquaphotomics-Oriented Skin Data Analysis using Explainable Artificial Intelligence: Applications of SHAP, LIME, Lightgbm, ELI5, PDPbox, and Skater for Dataset Categorization and Process Interpretation

2025-04-09T01:11:06+02:00

In recent years, research has been active in various fields to measure and collect spectrum data on the moisture content of a wide variety of plants and animals, beauty products, concrete, cement, etc., and to clearly display this data using a display method known as an aquagram. In light of this trend, in this thesis study, we propose a method for the automatic classification of aquagrams using various exploitable artificial intelligence (XAI)-based programming techniques. In doing so, we show and explain the process of their classification and the fact that it is possible to show the indicative value of the validity and rationale of the classification, to a certain extent. We have selected XAI based on Explain Like SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Light Gradient Boosting Machine (LightGBM), I’m 5 (ELI5), Partial Dependency Plot box (PDPbox), and Skater to analyze diverse datasets, in this study, in particular, aquagram datasets. We intend to thereby present the field with a numerical method to illustrate the seemingly obscure processes and arguments of machine learning, particularly deep learning, classification, which will be useful for future research. Concretely, after investigating the previously obtained matrix-formed aquagram data, we describe the case of explicit classification by machine learning for four different groups of datasets on skin moisture content and moisture transpiration. The programs we use for these are all coded in Python and import and use packages such as pandas, pickle, etc.

Explainable Artificial Intelligence Models for Predicting Malaria Risk in Kenya

2025-03-25T00:30:20+01:00

The article aims to develop interpretable Machine Learning models using R statistical programming language for malaria risk prediction in Kenya, emphasizing leveraging Explainable AI (XAI) techniques to support targeted interventions and improve early detection mechanisms. The methodology involved using synthetic data with 1000 observations, employing over-sampling to address class imbalance, utilizing two machine learning algorithms (Random Forest and Extreme Gradient Boosting), applying cross-validation techniques, Hyper-parameter tuning and implementing feature importance and SHAP (Shapley Additive Explanations) for model interpretability. The findings revealed that Random Forest outperformed Extreme Gradient Boosting with 98% accuracy. Critical prediction features included clinical symptoms such as nausea, muscle aches, and fever, plasmodium species identification, and environmental factors like rainfall and temperature. Both models demonstrated strong sensitivity in detecting malaria cases. This promotes trust in model predictions by clearly outlining the decision process for individual outcomes. The research concluded that integrating Explainable AI into malaria risk prediction represents a transformative approach to public health management. Through providing transparent, interpretable models, the research offers a robust, data-driven approach to predicting malaria risks, potentially empowering healthcare providers and policymakers to deploy resources more effectively and reduce the disease burden in endemic regions.

Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

2024-08-24T14:16:57+02:00

Artificial Intelligence (AI) and Machine Learning (ML) have the potential to revolutionize education, with applications ranging from personalized learning systems to teaching students about AI concepts. Beyond utilizing and integrating these technologies, it is crucial to comprehend the fundamental principles governing the field. Choosing an “attractive” area of AI suitable for students and engaging them is essential to introducing difficult Computer Science concepts. In particular, introducing these concepts in elementary and secondary (K-12) Education is not a simple task, as it involves complex algorithms and theories that could overwhelm young learners. To overcome this challenge, we can rely on nature-inspired or bio-inspired algorithms such as Swarm Intelligence (SI) family, and leverage block-based programming environments (like MIT Scratch or other Logo-like environments) to make AI concepts more accessible and intuitive for students. This article proposes the creation and implementation of simplified simulations inspired by the Artificial Fish Swarm Optimization Algorithm (AFSO)-namely how fish behave collectively in the ocean–as an educational tool for both elementary and secondary school students. The proposed educational methodology combines the integration of Constructionist Learning principles, as the “Creative Thinking Spiral” learning model, with the inquiry-based approach of the 5Es Instructional Model.