Algorithms, Volume 18, Issue 10

Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective op...

Driver emotion recognition is vital for intelligent driver assistance systems, where the accurate detection of emotional states enhances both safety and user experience. Current approaches, however, require extensive labeled datasets, perform poorly...

The generation of high-quality test cases remains challenging due to combinatorial explosion and difficulty balancing exploration-exploitation in complex parameter spaces. This paper presents a novel Hybrid Artificial Bee Colony (ABC) algorithm that...

The aging of the Portuguese population is a multifaceted challenge that requires a coordinated and comprehensive response from society. In this context, social service institutions play a fundamental role in providing aid and support to the elderly,...

Network robustness optimization is crucial for enhancing the resilience of industrial networks and social systems against malicious attacks. Existing studies typically evaluate the robustness by simulating the sequential removal of nodes or edges and...

In graph theory and network design, the minimum cut is a fundamental measure of system connectivity and communication capacity. While prior research has largely focused on computing the minimum cut for a fixed source–sink pair, practical scenar...

To address the limitations of the single-modal electroencephalogram (EEG), such as its single physiological dimension, weak anti-interference ability, and inability to fully reflect emotional states, this paper proposes a gated multi-head cross-atten...

This paper presents TVAE-SSL, a novel semi-supervised learning (SSL) paradigm that involves Tabular Variational Autoencoder (TVAE)-sampled synthetic data injection into the training process to enhance model performance under low-label data conditions...

Student dropout remains a persistent challenge in higher education, with substantial personal, institutional, and societal costs. We developed a modular dropout prediction pipeline that couples data preprocessing with multi-model benchmarking and a g...

The Hamiltonian Monte Carlo (HMC) method is effective for Bayesian inference but suffers from synchronization overhead in distributed settings. We propose two variants: a distributed HMC (DHMC) baseline with synchronized, globally exact gradient eval...

Soil chemicalization, involving the use of synthetic chemicals like fertilizers, pesticides, and herbicides, has been crucial in modern agriculture but has raised concerns about soil degradation, environmental pollution, and long-term sustainability....

This paper presents an optimized hybrid deep learning model for power load forecasting—QR-FMD-CNN-BiGRU-Attention—that integrates similar day selection, load decomposition, and deep learning to address the nonlinearity and volatility of p...

In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (K...

Classifying multiband images acquired by advanced sensors, including those mounted on satellites, is a central task in remote sensing and environmental monitoring. These sensors generate high-dimensional outputs rich in spectral and spatial informati...

The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, hig...

In this work we present strategies for parallelization of algorithms for representing integers as a sum of positive integers using OpenMP (Open Multi-Processing). We consider different types of algorithms—a non-recursive algorithm, a recursive...

Non-coding RNAs (ncRNAs) are involved in many biological processes, making their identification and functional characterization a priority. Among them, long non-coding RNAs (lncRNAs) have been shown to regulate diverse cellular processes, such as cel...

Aiming at the shortcomings of traditional ACO algorithms in indoor localization applications, a high-performance improved ant colony algorithm (HIPACO) based on dynamic hybrid pheromone strategy is proposed. The algorithm divides the ant colony into...

This study presents the first quantitative meta-analysis in cooperative multi-agent reinforcement learning (MARL). Undertaken on the StarCraft Multi-Agent Challenge (SMAC) benchmark, we quantify reproducibility and statistical heterogeneity across st...

Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to tra...

Reverse logistics (RL) plays a crucial role in promoting circularity and sustainability in supply chains, particularly in the face of increasing waste generation and growing environmental demands. In recent years, machine learning (ML) has emerged as...

Although current 3D semantic segmentation methods have achieved significant success, they suffer from catastrophic forgetting when confronted with dynamic, open environments. To address this issue, class incremental learning is introduced to update m...

In the detection of small targets such as insulator defects and flashovers, the existing YOLOv11 has problems such as insufficient feature extraction and difficulty in balancing model lightweight and detection accuracy. We propose a lightweight archi...

Artificial intelligence and machine learning have increasingly transformed optometry, enabling automated classification and predictive modeling of eye conditions. In this study, we introduce Optometry Random Forest, an artificial intelligence-based s...

Conventional audio and video codecs are designed for human perception, often discarding subtle spectral cues that are essential for machine-based analysis. To overcome this limitation, we propose a machine-oriented compression framework that reinterp...

This paper provides a detailed breakdown of a minimalist, fundamental Transformer-based architecture for forecasting univariate time series. It describes each processing step in detail, from input embedding and positional encoding to self-attention m...

This paper introduces an effective approach for rotatory fault diagnosis, specifically focusing on centrifugal pumps, by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and feature-level integration. Centrifugal...

Data-driven evolutionary algorithms (DDEAs) are essential computational intelligent methods for solving expensive optimization problems (EOPs). The management of surrogate models for fitness predictions, particularly the selection and integration of...

This paper introduces Contextual Object Grouping (COG), a specific computer vision framework that enables automatic interpretation of technical security diagrams through dynamic legend learning for intelligent sensing applications. Unlike traditional...

This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal module...

Transmembrane proteins (TMPs) constitute approximately 30% of the mammalian proteome and are critical targets in biomedical research due to their involvement in signaling, transport, and drug interactions. However, their unique structural characteris...

The rapid expansion of AI applications in various domains demands models that balance predictive power with human interpretability, a requirement that has catalyzed the development of hybrid algorithms combining high accuracy with human-readable outp...

Defect detection in textile manufacturing is critically hampered by the inefficiency of manual inspection and the dual constraints of deep learning (DL) approaches. Specifically, DL models suffer from poor generalization, as the rapid iteration of fa...

Pinning control is a key strategy for stabilizing complex networks through a limited set of nodes. However, determining the optimal number and location of pinned nodes under dynamic and structural constraints remains a computational challenge. This w...

Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate...

Spatially selective nerve stimulation is an active area of research, with the capability to reduce side effects and increase the clinical efficacy of nerve stimulation technologies. Several research groups have demonstrated proof-of-concept devices c...

This study investigates the utilisation of AI tools, including Grammarly Free, QuillBot Free, Canva Free Individual, and others, to enhance learning outcomes for 180 s-year telecommunications engineering students at Universidad Politécnica de...

This paper proposes TWISS (TOPSIS + Wrapper Incremental Subset Selection), a novel hybrid feature selection framework designed for electromyographic (EMG) pattern recognition in upper-limb prosthetic control. TWISS integrates the multi-criteria decis...

Graph Neural Networks (GNNs) capture complex information in graph-structured data by integrating node features with iterative updates of graph topology. However, they inherently rely on the homophily assumption—that nodes of the same class tend...

This study evaluates the impact of Generative AI (Artificial Intelligence) algorithms on human decision making in complex problem-solving tasks. Rather than assessing the algorithms in isolation, we focus on how their use shapes three critical cognit...

Diesel engines power much of the heavy-duty equipment used in underground mines, where exhaust emissions pose acute environmental and occupational health challenges. However, predicting the amount of air required to dilute these emissions is difficul...

In our study, we examine how the temporal window shift—the step between consecutive analysis windows—affects EEG-based cognitive fatigue detection while keeping the window length fixed. Using a reference workload dataset and a pipeline th...

With the escalating global cyber threats, Distributed Denial of Service (DDoS) attacks have become one of the most disruptive and prevalent network attacks. Traditional DDoS detection systems face significant challenges due to the unpredictable natur...

Managing resource use in cloud and edge environments is crucial for optimizing performance and efficiency. Traditionally, this process is performed with detailed knowledge of the available infrastructure while being application-specific. However, it...

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representat...

Smart cities are an emerging technology that is receiving new ethical attention due to recent advancements in artificial intelligence. This paper provides an overview of smart city ethics while simultaneously performing novel theorization about the d...

Accurate brain tumor classification from MRI is often constrained by limited labeled data. We systematically compare conventional machine learning, deep learning, and few-shot learning (FSL) for four classes (glioma, meningioma, pituitary, no tumor)...

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictio...

Cancer classification using high-dimensional genomic data presents significant challenges in feature selection, particularly when dealing with datasets containing tens of thousands of features. This study presents a new application of the Simultaneou...

Evolutionary Algorithms (EAs) are those based on the phenomenon of survival of the fittest. Differential Evolution (DE) is a member of this family, and it is well-suited for handling problems with real-valued variables. However, to use DE to solve bi...