The goal of classifier combination can be briefly stated as combining the decisions of individual classifiers to obtain a better classifier. In this paper, we propose a method based on the combination of weak rank classifiers because rankings contain...
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a h...
This study empirically investigates the effects of a regional creative milieu on the migration inflows and outflows of the highly educated between urbanized areas in Korea. To estimate the push and pull effects, we use the 5% Population and Housing C...
Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estim...
Practitioners have used hidden Markov models (HMMs) in different problems for about sixty years. Moreover, conditional random fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propos...
Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart...
We propose and study a data-driven method that can interpolate between a classical and a modern approach to classification for a class of linear models. The class is the convex combinations of an average of the source task classifiers and a classifie...
Heavy metal concentrations that must be maintained in aquaponic environments for plant growth have been a source of concern for many decades, as they cannot be completely eliminated in a commercial set-up. Our goal was to create a low-cost real-time...
The detection of contaminants in food products after packaging by a non-invasive technique is a serious need for companies operating in the food industry. In recent years, many technologies have been investigated and developed to overcome the intrins...
As a key task in machine learning, data classification is essential to find a suitable coordinate system to represent the data features of different classes of samples. This paper proposes the mutual-energy inner product optimization method for const...
Machine learning tools can be applied to peptide-mediated biomineralization, which is an emerging biomimetic technique of creating functional nanomaterials. In particular, they can be used for the discovery of biomineralization peptides, which curren...
There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses...
Fourier Transform Infrared Spectroscopy (FTIR) provides valuable biochemical information for biomedical analysis. It aids in identifying cancerous tissues, diagnosing diseases like acute pancreatitis or Alzheimer’s, and has applications in geno...
Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classi...
Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the...
Existing research has revealed that auditory attention can be tracked from ongoing electroencephalography (EEG) signals. The aim of this novel study was to investigate the identification of peoples’ attention to a specific auditory object from...
This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy...
The intracardiac electrical activation maps are commonly used as a guide in the ablation of cardiac arrhythmias. The use of catheters with force sensors has been proposed in order to know if the electrode is in contact with the tissue during the regi...
To address the performance degradation of violation action recognition models due to changing operational scenes in power grid operations, this paper proposes a Few-shot Adaptive Network (FSA-Net). The method incorporates few-shot learning into the n...
(1) Background: Approximately 30% of schizophrenia patients are known to be treatment-resistant. For these cases, more personalized approaches must be developed. Virtual reality therapeutic approaches such as avatar therapy (AT) are currently undergo...
An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matri...
We present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution all...
Renewable-power-generating resources can provide unlimited clean energy and emit at most minute amounts of air pollutants and greenhouse gases, whereas fossil fuels are contributing to environmental pollution problems and climate change. The share of...
Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of thr...
In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computationa...
The Internet of Things (IoT) comprises diverse devices connected through heterogeneous communication protocols to deliver a wide range of services. However, the complexity and scale of IoT networks make them difficult to secure. Network intrusion det...
In this contribution, a comparison between different permutation entropies as classifiers of electroencephalogram (EEG) records corresponding to normal and pre-ictal states is made. A discrete probability distribution function derived from symbolizat...
The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteri...
Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for m...
Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there i...
Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus...
The ecological state of the Danube River, as the world’s most international river basin, will always be the focus of scientists in the field of ecology and environmental engineering. The concentration of orthophosphate anions in the river is on...
Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this wor...
One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors releva...
Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring c...
Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection...
Colorectal cancer is one of the most commonly diagnosed cancers in developed countries. Although the gold-standard diagnosis technique is the histological analysis of colon biopsies, it is important to investigate different diagnostic tools because t...
In this paper, the two-simultaneous-leak isolation problem in water distribution networks is addressed. This methodology relies on optimal sensor placement together with a leak location strategy using two well-known classifiers: k-NN and discriminant...
The author used the automatic proof procedure introduced in [1] and verified that the 4096 homomorphic recurrent double sequences with constant borders defined over Klein’s Vierergruppe K and the 4096 linear recurrent double sequences with constant b...
In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function...
We consider systems of bilinear forms and linear maps as representations of a graph with undirected and directed edges. Its vertices represent vector spaces; its undirected and directed edges represent bilinear forms and linear maps, respectively. We...
Quantum machine learning integrates quantum computing with classical machine learning techniques to enhance computational power and efficiency. A major challenge in quantum machine learning is developing robust quantum classifiers capable of accurate...
The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors re...
Background/Objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular h...
Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infes...
Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective o...
Classifiers are divided into linear and nonlinear classifiers. The linear classifiers are built on a basis of some hyper planes. The nonlinear classifiers are mainly neural networks. In this paper, we propose a novel neighborhood granule classifier b...
Accuracy, recall, specificity, and precision are key performance measures for binary classifiers. To obtain these measures, the probabilities generated by classifiers must be converted into deterministic labels using a threshold. Exhaustive search me...
Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed t...
Spider venoms are rich cocktails of bioactive peptides, proteins, and enzymes that are being intensively investigated over the years. In order to provide a better comprehension of that richness, we propose a three-level family classification system f...
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