2025 May - 60 articles
Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space;...
Renewable energy (RE) is pivotal to achieving both environmental sustainability and long-term energy security, yet systematic evidence on the efficiency of RE investment across South and Southeast Asia remains sparse. This study introduces a rejoice&...
Large-language models (LLMs) show promise for automating evidence synthesis, yet head-to-head evaluations remain scarce. We benchmarked five state-of-the-art LLMs—openai/o1-mini, x-ai/grok-2-1212, meta-llama/Llama-3.3-70B-Instruct, google/Gemin...
The harmony search (HS) algorithm is a novel metaheuristic which has been widely used to solve both continuous and discrete optimization problems. In order to improve the performance and simplify the implementation of the HS algorithm for solving the...
Hyper-heuristics emerged as a broader metaheuristic framework to address the limitations of traditional optimization heuristics. By abstracting the design of low-level heuristics, hyper-heuristics offer a flexible and adaptable approach to solving co...
In this study, we develop a simulation of Contact Graph Routing (CGR) for data communication between Mars, Earth, and relay satellites. Due to the changing of the satellites’ distances to Mars and Earth, respectively, there are specific contact...
The offshore oil engineering jacket is a giant super-heavy steel frame structure with dimensions in the hundreds of meters. A high-precision free station control network is usually arranged around it to ensure construction accuracy. However, as the j...
The behavior decision control of autonomous vehicles is a critical aspect of advancing autonomous driving technology. However, current behavior decision algorithms based on deep reinforcement learning still face several challenges, such as insufficie...
Real-time tumor-tracked radiotherapy with a linear accelerator-magnetic resonance (linac-MR) hybrid system requires accurate tumor delineation at a fast MR imaging rate. Various autocontouring methods have been previously evaluated against “gol...
This article presents a comprehensive review of short text clustering using state-of-the-art methods: Bidirectional Encoder Representations from Transformers (BERT), Term Frequency-Inverse Document Frequency (TF-IDF), and the novel hybrid method Late...
Electromagnetic interference (EMI) remains a difficult task in the design and operation of contemporary power electronic systems, especially in those applications where signal quality has a direct impact on the overall performance and efficiency. Con...
Digital energy systems rely on actual data about power consumption and generation, which are not always available and, in certain situations, can be replaced with synthetic forms. This study presents a methodology for generating synthetic time-series...
The manuscript introduces an innovative framework for expiration date recognition aimed at improving accessibility for visually impaired individuals. The study underscores the pivotal role of convolutional neural networks (CNNs) in addressing complex...
The paper presents a hybrid machine learning model for the spatial segmentation of soils by salinity using multispectral satellite data from Sentinel-2 and climate parameters of the ERA5-Land model. The proposed method aims to solve the problem of ac...
Researchers have extensively explored learning analytics in online courses, primarily focusing on linear course structures where students progress sequentially through lessons and assessments. However, non-linear courses, which allow students to comp...
With the growing demand for sustainable energy solutions, biomass torrefaction has emerged as a crucial technology for converting agricultural waste into high-value biofuels. This work develops dual kinetic modeling using global and individual parame...
This paper introduces a novel method for the topology optimization of multi-material structures. The core innovation lies in minimizing an appropriate norm of the transfer matrix that links external loads to system outputs. Several formulations of st...
The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical for aiding physicians in real-time lesion identification and minimizing diagnostic errors such as false positives and missed lesions. Despite significant...
In this paper, we present a new distributed approximation algorithm for the minimum dominating set problem in planar triangle-free graphs. The algorithm operates in a constant number of rounds in the LOCAL model. Using the bunch technique, we prove t...
The steel strip is an important and ideal material for the automotive and aerospace industries due to its superior machinability, cost efficiency, and flexibility. However, surface defects such as inclusions, spots, and scratches can significantly im...
In high-dynamic battlefield environments, anti-ship missiles must perform intricate attitude adjustments and energy management within time constraints to hit a target accurately. Traditional optimization methods face challenges due to the high speed,...
In this paper, we study the 2D Shape Equipartition Problem (2D-SEP) with minimal boundaries, and we propose an efficient method that solves the problem with a low computational cost. The goal of 2D-SEP is to obtain a segmentation into N equal-area se...
Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core compon...
This paper introduces a novel approach to job recommendation systems by incorporating personality traits evaluated through the Gallup CliftonStrengths assessment, aiming to enhance the traditional matching process beyond skills and qualifications. Un...
Federated learning (FL) offers a privacy-preserving framework for distributed machine learning, enabling collaborative model training across diverse clients without centralizing sensitive data. However, statistical heterogeneity, characterized by non...
The density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points...
A growing and pressing need for Speech Enhancement Algorithms (SEAs) has emerged with the proliferation of hearing devices and mobile devices that aim to improve speech intelligibility without sacrificing speech quality. Recently, a tremendous number...
Hybrid quantum–classical (HQC) computing refers to the approach of executing algorithms coherently on both quantum and classical resources. This approach makes the best use of current or near-term quantum computers by sharing the workload with...
This study proposes an Evolutionary Algorithm (EA) to optimize the workstation layout in multi-product handcrafted furniture workshops with simultaneous manufacturing. The algorithm models the arrangement of workstations within a limited space, enhan...
As automotive technology advances, ensuring efficient and reliable vehicle storage systems becomes increasingly important to vehicle edges. Environmental factors, like extreme cold or intense heat, can greatly affect how well these critical component...
Adam (Adaptive Moment Estimation) is a well-known algorithm for the first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. As shown by computational experiments, with an increase...
This paper investigates the problem of ensuring the stable operation of multiple high-speed train systems under the threat of False Data Injection (FDI) attacks. Due to the wireless communication characteristics of railway networks, high-speed train...
Bioequivalence assessment of highly variable drugs (HVDs) remains a significant challenge, as the application of scaled approaches requires replicate designs, complex statistical analyses, and varies between regulatory authorities (e.g., FDA and EMA)...
This study analyzes cancer trends in Canada using machine learning techniques to extract insights from extensive cancer data sourced from the Canadian Cancer Society and Statistics Canada. It aims to enhance the understanding of cancer epidemiology a...
This study explores the existence and Mittag–Leffler stability of solutions for fuzzy fractional systems that include Caputo derivatives and ordinary derivatives with non-local conditions using the Schauder fixed-point theorem. Following this,...
With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and...
Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To addr...
In the present study, we focus on circulant graphs,
Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) tha...
With the present fault detection method for low-voltage distribution networks, it is difficult to detect single-phase grounding faults under complex working conditions. In this paper, a particle swarm optimization (PSO) support vector machine (SVM)-b...
Landslides are hazardous events that occur mainly in mountainous areas and cause substantial losses of various kinds worldwide; therefore, it is important to investigate them. In this study, a specific Machine Learning (ML) method was further analyze...
This study explores machine learning’s potential for early Acute Aortic Syndrome (AAS) prediction by integrating and cleaning extensive clinical datasets from 68 emergency departments in the USA, covering the medical histories of nearly 150,000...
Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generativ...
This paper provides a comprehensive overview of the state of the art non-destructive methods for detecting plant biochemical traits through spectral imaging of leafy greens. It offers insights into the various detection techniques and their effective...
Many important practical problems connected to energy efficiency in buildings, ecology, metallurgy, the development of wireless communication systems, the optimization of radar technology, quantum computing, pharmacology, and seismology are described...
Private consumers, small businesses, and even large enterprises are all at risk from botnets. These botnets are known for spearheading Distributed Denial-Of-Service (DDoS) attacks, spamming large populations of users, and causing critical harm to maj...
Functional data, including one-dimensional curves and higher-dimensional surfaces, have become increasingly prominent across scientific disciplines. They offer a continuous perspective that captures subtle dynamics and richer structures compared to d...
Amid global decarbonization mandates, urban distribution networks (UDNs) face escalating voltage volatility due to proliferating distributed energy resources (DERs) and emerging loads (e.g., 5G base stations and data centers). While virtual power pla...
As integrated circuit technology continues to advance, clock tree synthesis has become increasingly significant in the design of ultra-large-scale integrated circuits. Traditional clock tree synthesis methods often face challenges such as insufficien...
Fatigue driving is one of the crucial factors causing traffic accidents. Most existing fatigue driving detection algorithms overlook individual driver characteristics, potentially leading to misjudgments. This article presents a novel detection algor...
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