European Journal of Artificial Intelligence and Machine Learning

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

Sai Dhiresh Kilari — 2025-04-19

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

Clifton Reddy — 2025-04-14

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.

Mitigating Demographic Bias in ImageNet: A Comprehensive Analysis of Disparities and Fairness in Deep Learning Models

Charles Kinyua Gitonga — 2025-04-18

Deep learning has transformed artificial intelligence (AI), yet fairness concerns persist due to biases in training datasets. ImageNet, a key dataset in computer vision, contains demographic imbalances in its “person” categories, raising concerns about biased AI models. This study is to examine these biases, evaluate their impact on model performance, and implement fairness aware mitigation strategies. Using a fine-tuned EfficientNet-B0 model, we achieved 98.44% accuracy. Subgroup analysis revealed higher error rates for darker-skinned individuals and women compared to lighter-skinned individuals and men. Mitigation techniques, including data augmentation and re-sampling, improved fairness metrics by 1.4% for underrepresented groups. Confidence analysis showed 99.25% accuracy for predictions with over 80% confidence. To enhance reproducibility, we deployed our demographic bias detection model on Hugging Face Spaces. The study’s limitations include a focus on “person” categories, computational constraints, and potential annotation biases. Future research should extend fairness-aware interventions across diverse datasets.

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

Shinji Kawakura — 2025-03-19

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

Dennis Kariuki Muriithi — 2025-02-28

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.