Bi-objective optimization seeks to obtain Pareto optimal solutions that balance two trade-off objectives, providing guidance for decision making in various fields, particularly in the field of transportation. The novelty of this study lies in two asp...
This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the...
Feature selection has been a fundamental research area for both conventional and contemporary machine learning since the beginning of predictive analytics. From early statistical methods, such as principal component analysis, to more recent and data-...
Feature selection aims to identify a relevant subset of features from the original feature set to enhance the performance of machine learning models, which is crucial for improvig model accuracy. However, this task is highly challenging due to the en...
Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of...
Given a graph whose nodes and edges are associated with a profit, a visiting (or traversing) time and an admittance time window, the Mixed Team Orienteering Problem with Time Windows (MTOPTW) seeks for a specific number of walks spanning a subset of...
An Informed RRT* (IRRT*) algorithm is one of the optimized versions of a Rapidly-exploring Random Trees (RRT) algorithm which finds near-optimal solutions faster than RRT and RRT* algorithms by restricting the search area to an ellipsoidal subset of...
The graph theory is one of the fundamental structures in computer science used to model various scientific and engineering problems. Many problems within the graph theory are categorized as NP-hard and NP-complete. One such problem is the minimum dom...
This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and...
Fuzzy c-means (FCM), the fuzzy variant of the popular k-means, has been used for data clustering when cluster boundaries are not well defined. The choice of initial cluster prototypes (or the initialization of cluster memberships), and the fact that...
An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominatin...
The capacitated dispersion problem is a variant of the maximum diversity problem in which a set of elements in a network must be determined. These elements might represent, for instance, facilities in a logistics network or transmission devices in a...
While collecting training data, even with the manual verification of experts from crowdsourcing platforms, eliminating incorrect annotations (noisy labels) completely is difficult and expensive. In dealing with datasets that contain noisy labels, ove...
The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced dat...
Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to det...
This paper investigates the optimal design of base-isolated structures equipped with tuned inerter dampers (TIDs) subjected to various ground motions. The Clough–Penzien model is employed to simulate the power spectrum of three types of ground...
Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combini...
Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the li...
Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost)...
Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehic...
Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To s...
The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature measurements are made at the skin surface, a measurement that is impacted by the amb...
We consider fault detection in a hydraulic system that maintains multivariate time-series sensor data. Such a real-world industrial environment could suffer from noisy data resulting from inaccuracies in hardware sensing or external interference. Thu...
Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibil...
In this study, a 4-D, computerized ionospheric tomography algorithm, IONOLAB-Fusion, is developed to reconstruct electron density using both actual and virtual vertical and horizontal paths for all ionospheric states. The user-friendly algorithm only...
Although surface drip irrigation allows an efficient use of water in agriculture, the heterogeneous distribution of soil water complicates its optimal usage. Mathematical models can be used to simulate the dynamics of water in the soil below a drippe...
This paper presents an approach to protecting deep neural network privacy on edge devices using ARM TrustZone. We propose a selective layer protection technique that balances performance and privacy. Rather than executing entire layers within the Tru...
This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples wer...
In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features...
This study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 s...
The simultaneous acquisition of multi-spectral images on a single sensor can be efficiently performed by single shot capture using a mutli-spectral filter array. This paper focused on the demosaicing of color and near-infrared bands and relied on a c...
This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including hum...
The accurate and rapid determination of olefin content in gasoline is crucial for fuel quality control. While near-infrared spectroscopy (NIR) offers a rapid analytical solution, multiple parameters in the conventional partial least squares regressio...
The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network tra...
The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field vali...
Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases l...
Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top...
Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection...
Organic fertilizer is a key component of agricultural sustainability and significantly contributes to the improvement of soil fertility. The values of nutrients such as organic matter and nitrogen in organic fertilizers positively affect plant growth...
Diversity is crucial for metaheuristic algorithms. It prevents early convergence, balances exploration and exploitation, and helps to avoid local optima. Traditional metaheuristic algorithms tend to rely on a single strategy for generating new soluti...
Differential Interferometric SAR (DInSAR) has been used to monitor surface deformations in open pit mines and tailings dams. In this paper, ground deformations have been detected on the area of tailings Dam-I at the Córrego do Feijão Mi...
Background: The external carotid artery (ECA) plays a vital role in facial perfusion and acts as a collateral pathway for cerebral blood flow during internal carotid artery (ICA) stenosis. In cases of carotid near-occlusion (CNO), characterized by se...
Soil organic carbon (SOC) is an essential indicator of soil fertility, health, and carbon sequestration capacity. Its proper management improves soil structure, productivity, and resilience to climate change, making rapid and reliable SOC assessment...
Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize t...
To address the demand for precise sorghum tannin control in liquor brewing, and to overcome the inefficiency and high cost of traditional methods, this study developed a non-destructive approach by fusing features from dual hyperspectral sensors. Bas...
Polyphenol oxidase (PPO) is the primary biochemical driver of browning and the subsequent decline of market quality in harvested fruit. In this work, a fully non-invasive analytical framework was built using Visible/Near-Infrared (VIS/NIR) spectrosco...
Financial time-series labeling remains fundamentally limited by three critical deficiencies: temporal rigidity (fixed horizons regardless of market conditions), scale blindness (single-resolution analysis), and correlation-causation conflation. These...
Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackl...
Background/Objective: This study aims to evaluate the predictive performance of three supervised machine learning algorithms—decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) in forecasting key visual skills releva...
Traffic sign detection is critically important for intelligent transportation systems, yet persistent challenges like multi-scale variation and complex background interference severely degrade detection accuracy and real-time performance. To address...
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