European Journal of Artificial Intelligence and Machine Learning

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.

Illuminating the Future: Predictive Modelling of PV Output Using Machine Learning Techniques

2024-06-15T10:37:35+02:00

Leveraging solar energy will bring about a notable change in the fundamental production and use of power, and the parameters to achieve success in this area must be forecasted to aid steady production. This work entailed the use of advanced predictive machine learning models for optimal power output, reduced uncertainty, optimal resource planning, and a notably high degree of alignment with peak demands for energy for efficient power production from solar radiations. Models were generated by employing machine learning algorithms for data evaluation. The direct in-plane irradiance has the strongest correlation (1.00) with PV output, according to the results. Additionally, it indicated that the value of R²: 0.999567 of the Random Forest Regression was higher than all other regression models and the least Mean Squared Error (MSE) and Mean Absolute Error (MAE), 17.130680 and 2.28139, respectively. On the other hand, the Linear Regression’s Mean Squared Error (MSE), R², and Mean Absolute Error (MAE) values are, respectively, 20.645271, 0.999478, and 3.16270. Random Forest Regression is a stronger forecasting model because of its higher R² value, which also helps to explain variations in PV power output.

Assessment of the Accuracy of Various Machine Learning Algorithms for Classifying Urban Areas through Google Earth Engine: A Case Study of Kabul City, Afghanistan

2024-07-16T12:29:37+02:00

Accurate identification of urban land use and land cover (LULC) is important for successful urban planning and management. Although previous studies have explored the capabilities of machine learning (ML) algorithms for mapping urban LULC, identifying the best algorithm for extracting specific LULC classes in different time periods and locations remains a challenge. In this research, three machine learning algorithms were employed on a cloud-based system to categorize urban land use of Kabul city through satellite images from Landsat-8 and Sentinel-2 taken in 2023. The most advanced method of generating accurate and informative LULC maps from various satellite data and presenting accurate outcomes is the machine learning algorithm in Google Earth Engine (GEE). The objective of the research was to assess the precision and efficiency of various machine learning techniques, such as random forest (RF), support vector machine (SVM), and classification and regression tree (CART), in producing dependable LULC maps for urban regions by analyzing optical satellite images of sentinel and Landsat taken in 2023. The urban area was divided into five classes: built-up area, vegetation, bare-land, soil, and water bodies. The accuracy and validation of all three algorithms were evaluated. The RF classifier showed the highest overall accuracy of 93.99% and 94.42% for Landsat-8 and Sentinel-2, respectively, while SVM and CART had lower overall accuracies of 87.02%, 81.12%, and 91.52%, 87.77%, with Landsat-8 and Sentinel-2, respectively. The results of the present study revealed that in this classification and comparison, RF performed better than SVM and CART for classifying urban territory for Landsat-8 and Sentinel-2 using GEE. Furthermore, the study highlights the importance of comparing the performance of different algorithms before selecting one and suggests that using multiple methods simultaneously can lead to the most precise map.