Problem-based learning (PBL) is now incorporated into the curricula of most medical schools around the world. In comparison to the traditional curriculum, less is known about the influence of the adoption and implementation of a problem-based curricu...
Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spa...
This paper proposes a bi-objective approach to address the data-driven Bayesian network structure learning problem. The objectives considered for optimization are minimum description length (MDL) and misclassification. An algorithm based on the well-...
In the digital age, the abundant unstructured data on the Internet, particularly online news articles, provide opportunities for identifying social problems and understanding social systems for sustainability. However, the previous works have not pai...
The field of mathematics education research has been promoting problem-solving-based mathematics instruction (PS-based MI) to afford opportunities to develop students’ conceptual understanding and problem-solving abilities in mathematics. Given...
Educational approaches in structural engineering have focused on classical methods for solving problems with manual calculations through assignments, quizzes, and exams. The use of computational software to apply the learned knowledge has been ignore...
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using De...
Educators’ epistemological experience of facilitating medical students’ active learning is under-researched, especially concerning non-biomedical learning in integrated curricula. Longitudinal, qualitative research on problem-based learni...
In social science degree programs, where Statistics is not a core subject, students often experience anxiety and negative attitudes that influence their engagement and may hinder academic performance. This study examines the role of positive emotions...
The Max-cut problem is a well-known combinatorial optimization problem, which has many real-world applications. However, the problem has been proven to be non-deterministic polynomial-hard (NP-hard), which means that exact solution algorithms are not...
Since the turn of the 21st century, we have seen a surge of studies on the state of U.S. education addressing issues such as cost, graduation rates, retention, achievement, engagement, and curricular outcomes. There is an expectation that graduates s...
Problem-solving is central to STEM in higher education, yet evidence on effective learning strategies for problem-solving remains fragmented across cognitive psychology and discipline-specific research. This systematic literature review, conducted fo...
Reinforcement learning (RL) agents face significant challenges in sparse-reward environments, as insufficient exploration of the state space can result in inefficient training or incomplete policy learning. To address this challenge, this work propos...
A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and s...
Distributed production systems have to increasingly balance economic goals such as energy efficiency and productivity with critical technical requirements such as flexibility, real-time capability, and reliability. This paper presents a novel approac...
Insight problems—as a type of ill-defined problems—are often solved without an articulate plan, and finding their solution is accompanied by the Aha! experience (positive feeling from suddenly finding a solution). However, the solution of...
Based on Cognitive Load Theory (CLT), learning by studying worked examples, that is, step-by-step solutions, has been shown to reduce cognitive load and enhance learning outcomes more than learning by solving conventional problems. Two types of worke...
Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effectiv...
The purpose of this study is to analyze the learning outcomes and educational implications of introducing a new Problem-Based Learning project approach in a computing course for non-major students. The Problem-Based Learning project approach, designe...
Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning. Approximate solutions exist because exact learning is generally not applicable to networks o...
The optimal design of truss structures is one of the most complex problems, as it requires achieving high stiffness and stability while pursuing lightweight structures. With the recent advancements in AI technologies, machine learning-based approache...
11 February 2021
Problem-solving ability is necessary for the clinical reasoning and decision-making of nurses to solve patients’ health problems. This study aims to investigate the association between self-directed learning and problem-solving ability using the mult...
This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly...
Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships bet...
Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenan...
This study proposes a multi-objective stand structure optimization scheme based on deep reinforcement learning, demonstrating the strengths of deep reinforcement learning in solving multi-objective optimization problems and providing innovative insig...
Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. De...
Vector problem-solving abilities are fundamental to everyday life and higher education; thus, improving them is important in education and research. However, the role of cognitive and affective factors and learning engagement in vector problem-solvin...
Combinatorial optimization problems (COPs), including the Knapsack Problem (KP), the Traveling Salesman Problem (TSP), and regional variants such as the Close-Enough Traveling Salesman Problem (CETSP), constitute fundamental models for addressing com...
Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on...
This technical note applies active inference to the problem of learning goal-directed behaviour from scratch, namely, de novo learning. By de novo learning, we mean discovering, directly from observations, the structure and parameters of a discrete g...
Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can...
Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different...
The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap ne...
Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent way...
Higher education is undergoing a rapid transformation driven by advancements in artificial intelligence (AI), creating new opportunities to support the development of higher-order cognitive skills through carefully designed learning environments. Thi...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s na...
The evaluation of tunnel service conditions is a core problem in the maintenance of tunnel structures during their life cycles. To address this problem, machine learning algorithms were applied to the National Tunnel Inventory (NTI) database of the F...
A university course on mechanical design utilizing CAE structural analyses is reported. To develop students’ skills to solve practical open-ended engineering problems, a project-based learning (PBL) approach was adopted. The course taught not o...
Nonsmooth optimization refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. This Special Issue contains six research articles that collect together the most recent techniques and applications in the...
Vibration is a major problem that can cause structures to wear out prematurely and even fail. Smart structures are a promising solution to this problem because they can be equipped with actuators, sensors, and controllers to reduce or eliminate vibra...
The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approa...
This paper presents an adaptive learning structure based on neural networks (NNs) to solve the optimal robust control problem for nonlinear continuous-time systems with unknown dynamics and disturbances. First, a system identifier is introduced to ap...
Many scientific and engineering domains, including machine learning, software engineering, operations research, and logistics optimization, are characterized by a fragmented landscape of problem variants, methodological approaches, and application-sp...
High-quality, annotated datasets are crucial for enhancing the application of natural language processing tasks. Nevertheless, in the educational domain, they are limited compared to the vast amount of learning content. In the context of smart learni...
Artificial intelligence plays an indispensable role in improving productivity and promoting social development, and causal discovery is one of the extremely important research directions in this field. Acyclic directed graphs (DAGs) are the most comm...
Meaningful representation of large-scale non-Euclidean structured data, especially in complex domains like network security and IoT system, is one of the critical problems of contemporary machine learning and deep learning. Many successful cases of g...
1 August 2006
This paper describes the use of experiential learning theory in the development of an undergraduate subject in collaborative systems. The purpose of the subject was to introduce students to the design, development and use of collaborative systems in...
Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LS...
We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learne...
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