Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenario...
This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. For this, we introduce methods to optimize M...
The stable operation of aerospace equipment is important for space safety, and the fault diagnosis of aerospace equipment is of practical significance. A fault diagnosis system needs to establish clear causal relationships and provide interpretable d...
Although machine learning models are widely used in critical domains, their complexity and poor interpretability remain problematic. Decision trees (DTs) and rule-based models are known for their interpretability, and numerous studies have investigat...
Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniq...
Efficient energy management relies on uncovering meaningful consumption patterns from large-scale electricity load demand profiles. With the widespread adoption of sensor technologies such as smart meters and IoT-based monitoring systems, granular an...
Machine learning models such as artificial neural networks (ANNs) are becoming increasingly popular in short-term water demand forecasting. This is because, despite their lack of interpretability, ANNs are able to capture complex interactions between...
Recommender systems are widely deployed across digital platforms, yet their opacity raises concerns about auditability, fairness, and user trust. To address the gap between predictive accuracy and model interpretability, this study proposes a glass-b...
Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced...
In distributed data environments, classification tasks are challenged by inconsistencies across independently maintained sources. These environments are inherently characterized by high informational uncertainty. Our framework addresses this challeng...
The integration of machine learning (ML) in marine engineering has been increasingly subjected to stringent regulatory scrutiny. While environmental regulations aim to reduce harmful emissions and energy consumption, there is also a growing demand fo...
Agriculture is a major source of greenhouse gas emissions, yet predicting temperature increases associated with specific CO2 sources remains challenging due to the heterogeneity of agri-environmental systems. In response, this study presents a machin...
Subarachnoid hemorrhage (SAH) and its major complication, cerebral vasospasm (CVS), present significant challenges for early diagnosis and risk stratification. In this study, we developed interpretable decision tree models to differentiate between he...
The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binar...
Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and...
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 c...
Psycholinguistic properties such as concept familiarity and concreteness have been investigated in relation to technological innovations in teaching and learning. Due to ongoing advances in semantic representation and machine learning technologies, t...
The negative impact of absenteeism on organizations’ productivity and profitability is well established. To decrease absenteeism, it is imperative to understand its underlying causes and to identify susceptible employee subgroups. Most research...
In contemporary 5G network environments, intrusion detection systems must balance detection accuracy with operational efficiency, as improvements in one dimension are often achieved at the expense of the other. This study addresses this trade-off by...
In multi-agent systems, the interactions between autonomous agents within dynamic and uncertain environments are crucial for achieving their objectives. Current research leverages model checking techniques to verify these interactions, with social ac...
This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central met...
Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous...
The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human mi...
This article describes solutions to control problems using fuzzy logic, which facilitates the development of decision support systems across various fields. However, addressing this task through the manual creation of rules in specific fields necessi...
In uncertain battlefield environments, rapid and accurate detection, identification of hostile targets, and assessment of threat levels are crucial for supporting effective decision-making. Despite offering the advantage of structural transparency, t...
More than 50% of the national lands in Japan have been surveyed by airborne laser scanning (ALS) data with different point densities; and developing an effective approach to take full advantage of these ALS data for forest management has thus become...
This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolu...
Advancements in high–throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in–depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufa...
Tree crown diameter (CD) values, relating to the rate of material exchange between the forest and the atmosphere, can be used to evaluate forest biomass and carbon stock. To map tree CD values using meter-level optical remote sensing images, we propo...
This paper applies hierarchical clustering and Hamming Distance to analyze the temporal trends of infectious diseases across different regions of Uzbekistan. By leveraging hierarchical clustering, we effectively group regions based on disease similar...
Mitigation of the heat island effect is critical due to the frequency of extremely hot weather. Urban street greening can achieve this mitigation and improve the quality of urban spaces and people’s welfare. However, a clear definition of stree...
Balancing the accuracy and the complexity of models is a well established and ongoing challenge. Models can be misleading if they are not accurate, but models may be incomprehensible if their accuracy depends upon their being complex. In this paper,...
Explainable artificial intelligence provides tools to better understand predictive models and their decisions, but many such methods are limited to producing insights with respect to a single class. When generating explanations for several classes, r...
Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field...
Urban emergency medical services (EMSs) depend on time-critical accessibility, spatial demand distribution, and resilient transport networks. This study examines how built-environment characteristics shape spatiotemporal population intensity (as a pr...
Background: The identification of the optimal management for blunt splenic trauma—angioembolization (AE), splenectomy, or observation—remains a challenge. This study applies Optimal Policy Trees (OPT), an artificial intelligence (AI) mode...
One of the priorities in sustainable forest management is monitoring the health status of trees and stands. From the aspect of remote sensing (RS), the best way of doing this is by interpreting color infrared (CIR) aerial photographs; however, this r...
This paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset...
Surface soil moisture (SSM) plays a critical role in climate change, hydrological processes, and agricultural production. Decision trees and deep learning are widely applied to SSM retrieval. The former excels in interpretability while the latter out...
Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maxi...
Time series of repeat aerial photographs currently span decades in many regions. However, the lack of calibration data limits their use in forest change analysis. We propose an approach where we combine repeat aerial photography, tree-ring reconstruc...
Machine learning (ML) techniques are increasingly used to diagnose faults in aerospace applications, but diagnosing multiple faults in aircraft fuel systems (AFSs) remains challenging due to complex component interactions. This paper evaluates the ac...
In human–robot collaborative environments, predicting and explaining robotic grasp failures is crucial for effective operation. While machine learning models can predict failures accurately, they often lack transparency, limiting their utility...
Macroevolutionary studies which focus on the development of traits in a phylogenetic context are increasingly used to explore the evolutionary mechanisms and processes that have led to the diversity in species we see today. This includes the study of...
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task em...
6 December 2022
Exposure to greenness has been studied through objective measures of remote visualization of greenspace; however, the link to how individuals interpret spaces as green is missing. We examined the associations between three objective greenspace measur...
Severe low-level wind shear (S-LLWS) in the vicinity of airport runways (25 knots or more) is a growing concern for the safety of civil aviation. By comprehending the causes of S-LLWS events, aviation safety can be enhanced. S-LLWS is a rare occurren...
Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about glob...
Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with...
In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation....
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