2025 March - 58 articles
Quantum computing has the potential to solve certain compute-intensive problems faster than classical computing by leveraging the quantum mechanical properties of superposition and entanglement. This capability can be particularly useful for solving...
Electric vehicles can serve as controllable loads, storing energy during off-peak periods and acting as generation units during peak periods or periods with high electricity prices. They function as distributed generation resources within distributio...
In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definit...
The transformation of healthcare organizations is essential to address their inherent complexity and dynamic nature. This study emphasizes the role of Data Science, with the incorporation of Artificial Intelligence tools, in enabling data-driven and...
3D object detection plays a pivotal role in achieving accurate environmental perception, particularly in complex traffic scenarios where single-modal detection methods often fail to meet precision requirements. This highlights the necessity of multi-...
The reduction in mass and surface of antennas and scattering structures is an important task in designing electromagnetic devices. To solve this task, we present a novel algorithm based on the optimal current grid approximation for generating sparse...
This paper investigates the design and MATLAB/Simulink implementation of two intelligent neural reinforcement learning control algorithms based on deep learning neural network structures (RL DLNNs), for a complex Heating Ventilation Air Conditioning...
Poisson regression is used to model count response variables. The method has a strict assumption that the mean and variance of the response variable are equal, while, in practice, the case of overdispersion is common. Also, in multicollinearity, the...
Natural language processing (NLP) has numerous applications and has been extensively developed in deep learning. In recent years, language models such as Transformer, BERT, and GPT have frequently been the foundation for related research. However, re...
With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious. Accurate prediction of PM2.5 concentration is of great significance to environmental protection and public health. Our study takes Nanni...
A Cyber-Physical System (CPS) incorporates communication dynamics and software into phsical processes, providing abstractions, modeling, design, and analytical techniques for the system. Based on spatial temporal graph neural networks (STGNNs), anoma...
For large-scale multi-objective optimization, it is particularly challenging for evolutionary algorithms to converge to the Pareto Front. Most existing multi-objective evolutionary algorithms (MOEAs) handle convergence and diversity in a mutually dep...
One of the best heuristic algorithms for solving the capacitated vehicle routing problem is a hybrid genetic search. A critical component of the search is a splitting procedure, where a solution encoded as a giant tour of nodes is optimally split int...
With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of compre...
This work analyzes high-power wind turbines (WTs) from the Oravita region, Romania. These WTs are based on slip ring induction generator with wound rotor and we propose a modified architecture with two power converters on both the stator and on the r...
This paper explores the application of deep neural networks (DNNs) as an alternative to the traditional Black–Scholes model for predicting European put option prices. Using synthetic datasets generated under the Black–Scholes framework, t...
This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these com...
In graph theory, the study of domination sets has garnered significant interest due to its applications in network design and analysis. Consider a graph
The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, stu...
Incorporating Artificial Intelligence (AI) in healthcare has transformed disease diagnosis and treatment by offering unprecedented benefits. However, it has also revealed critical cybersecurity vulnerabilities in Deep Learning (DL) models, which rais...
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug d...
The lack of extensive, varied, and thoroughly annotated datasets impedes the advancement of artificial intelligence (AI) for medical applications, especially colorectal cancer detection. Models trained with limited diversity often display biases, esp...
Dimensionality reduction is essential in machine learning, reducing dataset dimensions while enhancing classification performance. Feature Selection, a key subset of dimensionality reduction, identifies the most relevant features. Genetic Algorithms...
The Euler-Type Universal Numerical Integrator (E-TUNI) is a discrete numerical structure that couples a first-order Euler-type numerical integrator with some feed-forward neural network architecture. Thus, E-TUNI can be used to model non-linear dynam...
For networks that process 3D data, estimating the orientation and position of 3D objects is a challenging task. This is because the traditional networks are not robust to the rotation of the data, and their internal workings are largely opaque and un...
The Fourth Industrial Revolution (4IR) has significantly impacted healthcare, including sexually transmitted infection (STI) management in Sub-Saharan Africa (SSA), particularly among key populations (KPs) with limited access to health services. This...
In this paper, we introduce an advanced Fuzzy Record Similarity Metric (FRMS) that improves approximate record matching and models human perception of record similarity. The FRMS utilizes a newly developed similarity space with favorable properties c...
A method for obtaining integer sequences is presented by defining simple rules for the evolution of a discrete dynamical system. This paper demonstrates that various Catalan-like recurrences of known integer sequences can be obtained from a single st...
Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this prob...
The Greiner–Hormann algorithm is a commonly used polygon overlay analysis algorithm. It uses a double-linked list structure to store vertex data, and its intersection calculation step has a significant effect on the overall operating efficiency...
Dynamic path planning enables vehicles to autonomously navigate in unknown or continuously changing environments, thereby reducing reliance on fixed maps. Deep reinforcement learning (DRL), with its superior performance in handling high-dimensional s...
Statistical and machine learning modelling techniques have been effectively used in the healthcare domain and the prediction of epidemiological chronic diseases such as diabetes, which is classified as an epidemic due to its high rates of global prev...
The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning e...
Nowadays, portrait drawing has become increasingly popular as a means of developing artistic skills and nurturing emotional expression. However, it is challenging for novices to start learning it, as they usually lack a solid grasp of proportions and...
The coordination of microgrid (MG) and distribution is an emerging trend for future development. This paper proposes an uncertainty feasible region (UFR) analysis method based on outer approximation cutting (OAC) under the coordination of MG and dist...
While classical skyline queries identify interesting data within large datasets, flexible skylines introduce preferences through constraints on attribute weights, and further reduce the data returned. However, computing these queries can be time-cons...
Integer linear programs (ILPs) and mixed integer programs (MIPs) often have multiple distinct optimal solutions, yet the widely used Gurobi optimization solver returns certain solutions at disproportionately high frequencies. This behavior is disadva...
We describe GridMet as open-source software that automatically measures the spatial tuning parameters of grid cells, such as firing field size, spacing, and orientation angles. Applying these metrics to experimental data can help quantify changes in...
The flexible job shop scheduling problem is relevant in many different areas. However, the usual deterministic approach sees its usefulness limited, as uncertainty plays a paramount role in real-world processes. Considering processing times in the fo...
There is a close correlation between brain aging and age. However, traditional neural networks cannot fully capture the potential correlation between age and brain aging due to the limited receptive field. Furthermore, they are more concerned with de...
The Patient Rule Induction Method is a data mining technique used for identifying patterns in datasets, particularly focusing on discovering regions of the chosen input space where the response variable is unusually high or low. It falls in the subgr...
In this paper, we explore a framework to identify an optimal choice of compression algorithms that enables the best allocation of computing resources in a large-scale data storage environment: our goal is to maximize the efficiency of data compressio...
X-ray computed tomography (CT) is vital for medical diagnostics, but frequent radiation exposure raises concerns, driving the adoption of low-dose CT (LDCT) to mitigate risks. However, LDCT often introduces noise, compromising diagnostic accuracy. Th...
This paper presents an adaptive Machine Learning (ML)-based framework for automatic load optimization in Connected Smart Green Townhouses (CSGTs) The system dynamically optimizes load consumption and transitions between grid-connected and island mode...
Abandoned objects on highways seriously threaten traffic safety, and their prompt identification and removal are crucial. Existing methods struggle to balance computational cost and detection accuracy due to the significant scale differences of aband...
For daily activity recognition in smart homes, it is possible to reduce the effort required for labeling by transferring a trained model. This involves utilizing a labeled daily activity dataset from one smart home to recognize other activities in an...
Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especiall...
Background: Principal component analysis (PCA) is a method that identifies common directions within multivariate data and presents the data in as few dimensions as possible. One of the advantages of PCA is its objectivity, as the same results can be...
The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it...
Road event detection is critical for tasks such as monitoring, anomaly detection, and optimization. Traditional approaches often require complex feature engineering or the use of machine learning models, which can be computationally intensive, especi...
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