113 Results Found

Lung cancer is one of the world’s lethal diseases and detecting it at an early stage is crucial and difficult. This paper proposes a computer-aided lung cancer diagnosis system using volatile organic compounds (VOCs) data. A silicon microreacto...

Scientific evaluation of partial discharge (PD) severity in gas-insulation switchgear (GIS) can assist in mastering the insulation condition of in-service GIS. Limited theoretical research on the laws of PD deterioration leads to a finite number of e...

This study addresses the challenges of high-dimensional data, such as the curse of dimensionality and feature redundancy, which can be viewed as an inherent asymmetry in the data space. To restore a balanced symmetry and build a more complete feature...

(1) Background: Traditional kinetic-based shelf-life prediction models have low fitting accuracy and inaccurate prediction results for blueberries. Therefore, this study aimed to develop a blueberry shelf-life prediction method based on a back propag...

In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain ar...

Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-L...

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients’ breathing th...

Heart rate is quite regular during sinus (normal) rhythm (SR) originating from the sinus node. In contrast, heart rate is usually irregular during atrial fibrillation (AF). Complete atrioventricular block with an escape rhythm, ventricular pacing, or...

Hydraulic multi-way valves as core components are widely applied in engineering machinery, mining machinery, and metallurgical industries. Due to the harsh working environment, faults in hydraulic multi-way valves are prone to occur, and the faults t...

Many biological or medical data have numerous features. Feature selection is one of the data preprocessing steps that can remove the noise from data as well as save the computing time when the dataset has several hundred thousand or more features. An...

Maximum Relevance Minimum Redundancy (mRMR) is a widely used feature selection method that is applied in a wide range of applications in various fields. mRMR adds to the optimal subset the features that have high relevance to the target variable whil...

Short-term building cooling load prediction is crucial for optimizing building energy management and promoting sustainability. While data-driven models excel in this task, their performance heavily depends on the input feature set. Feature selection...

In object-oriented remote sensing image classification experiments, the dimension of the feature space is often high, leading to the “dimension disaster”. If a reasonable feature selection method is adopted, the classification efficiency...

A novel short-term wind power forecasting method called mRMR-PSO-LSTM was proposed to address the limitations of traditional methods in ignoring the redundancy and temporal dynamics of meteorological features. The methods employed the Minimum Redunda...

Accurate recognition of complex human activities from wearable sensors plays a critical role in sports analytics and human performance monitoring. However, the high dimensionality and redundancy of raw inertial data can hinder model performance and i...

Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective mode...

Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Ai...

Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer’s disease (AD) through the development and comparison of ML models using Support Vecto...

In the context of the convergence of multiple energy systems, the risk of power quality degradation across different stages of energy generation and distribution has become increasingly significant. Accurate identification of power quality disturbanc...

This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A sim...

Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity sp...

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral...

Ransomware attacks against Industrial Internet of Things (IIoT) have catastrophic consequences not only to the targeted infrastructure, but also the services provided to the public. By encrypting the operational data, the ransomware attacks can disru...

In modern aerospace systems, effective sensor optimisation is essential for ensuring reliable diagnostics, efficient resource allocation, and proactive maintenance. This paper presents Normalised Diagnostic Contribution Index (NDCI) integration into...

Air pollution has become a global environmental problem, because it has a great adverse impact on human health and the climate. One way to explore this problem is to monitor and predict air quality index in an economical way. Accurate monitoring and...

We propose a novel feature selection method based on quadratic mutual information which has its roots in Cauchy–Schwarz divergence and Renyi entropy. The method uses the direct estimation of quadratic mutual information from data samples using Gaussi...

The modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eigh...

Background/Objectives: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. Methods: Both traditional machine learning and deep learning methods for clas...

Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this...

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of...

Due to the scarcity of modeling samples and the low prediction accuracy of the matte grade prediction model in the copper melting process, a new prediction method is proposed. This method is based on enhanced generative adversarial networks (EGANs) a...

Background: Robotic-based therapy has emerged as a prominent treatment modality for the rehabilitation of hand function impairment resulting from strokes. Aim: In this context, feature engineering becomes particularly important to estimate the intent...

Battery degradation is a complex nonlinear problem, and it is crucial to accurately predict the cycle life of lithium-ion batteries to optimize the usage of battery systems. However, diverse chemistries, designs, and degradation mechanisms, as well a...

In the rapid development of urbanization, the sustained and healthy development of transportation infrastructure has become a widely discussed topic. The inspection and maintenance of asphalt pavements not only concern road safety and efficiency but...

Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable...

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication,...

Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately...

In this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildin...

In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating...

A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. C...

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodolo...

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algo...

The increasing energy intensity of the economy has led us to look for ways to reduce this negative trend. One method is non-intrusive load monitoring (NILM). This paper presents the use of artificial intelligence methods for the selection of informat...

Radiomics has shown remarkable potential in predicting cancer prognosis by noninvasive and quantitative analysis of tumors through medical imaging. This review summarizes recent advances in the use of radiomics across various cancer types and imaging...

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispe...

This study presents a robust and extensible hybrid classification framework for accurately detecting diseases in citrus leaves by integrating transfer learning-based deep learning models with classical machine learning techniques. Features were extra...

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclea...

Obtaining necessary information (and even extracting hidden messages) from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years...

Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evo...

Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Pr...