This paper deals with the estimation of transfer entropy based on the k-nearest neighbors (k-NN) method. To this end, we first investigate the estimation of Shannon entropy involving a rectangular neighboring region, as suggested in already existing...
The k-nearest neighbors (KNN) algorithm has been widely used for classification analysis in machine learning. However, it suffers from noise samples that reduce its classification ability and therefore prediction accuracy. This article introduces the...
The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines th...
Geographically weighted regression (GWR) is a classical method for estimating nonstationary relationships. Notwithstanding the great potential of the model for processing geographic data, its large-scale application still faces the challenge of high...
Spike blunt bodies are a method to reduce drag when a body moves at speeds above sound. Several numerical works based on computational fluid dynamics (CFD) have deeply studied fluid performance and highlighted its advantages. However, most documentat...
The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential...
Contamination on insulators may increase the surface conductivity of the insulator, and as a consequence, electrical discharges occur more frequently, which can lead to interruptions in a power supply. To maintain reliability in an electrical distrib...
Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm...
This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grov...
We present a hybrid approach to improve the accuracy of Convolutional Neural Networks (CNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (kNN) algorithm for inference. Although this is a c...
The Evidential K-Nearest Neighbor (EK-NN) classifier has demonstrated robustness in handling incomplete and uncertain data; however, its application in high-dimensional big data for feature selection, such as genomic datasets with tens of thousands o...
This study introduces a novel k-nearest neighbors (kNN) method of forecasting precipitation at weather-observing stations. The method identifies numerous monthly temporal patterns to produce precipitation forecasts for a specific month. Compared to c...
The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural ne...
An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kin...
Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. Th...
COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results...
The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of in...
The global adoption of hybrid renewable energy systems (HRESs) is accelerating as a strategic response to escalating energy demands and the imperative to mitigate greenhouse gas emissions. Despite the development of various technological tools, such...
Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and...
The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms applied in many fields because it is very simple to understand and easy to design. However, one of the major problems encountered in using the kNN rule is that all...
Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically an...
(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta’s structure suffers dynamic modifications throughout the whole pregnancy and many of...
Water quality in Peru is an increasing concern, particularly in the upper Huarmey watershed, which is affected by heavy metal contamination and untreated wastewater. This study proposes an automated classification approach using three supervised mach...
In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on...
Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are somet...
The public’s support for emergency infrastructure projects, which will affect the government’s credibility, social stability, and development, is very important. However, there are few systematic research findings on public support for em...
Data-driven models have recently proved to be a very powerful tool to extract relevant information from different kinds of datasets. However, datasets are often subject to multiple anomalies, including the loss of important parts of entries. In the c...
Currently, one of the topical areas of application of machine learning methods in the construction industry is the prediction of the mechanical properties of various building materials. In the future, algorithms with elements of artificial intelligen...
Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected fr...
Because ships are typically operated for more than 25 years after construction, they can be considered mobile factories that require economic maintenance before being scrapped. Therefore, for stable and efficient ship operation, continuous maintenanc...
The k-nearest neighbor (kNN) method only uses samples’ paired distance to perform fault detection. It can overcome the nonlinearity, multimodality, and non-Gaussianity of process data. However, the nearest neighbors found by kNN on a data set c...
The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification perfo...
The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data. The quantum K-nearest neighbor (QKNN) algorithm can handle the above p...
k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distanc...
The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to sel...
Multi-label classification (MLC) plays a crucial role in various real-world scenarios. Prediction with nearest neighbors has achieved competitive performance in MLC. Hubness, a phenomenon in which a few points appear in the k-nearest neighbor (kNN) l...
Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) c...
Functional data, which provides information about curves, surfaces or anything else varying over a continuum, has become a commonly encountered type of data. The k-nearest neighbor (kNN) method, as a nonparametric method, has become one of the most p...
Electricity demand in residential areas is generally met by the local low-voltage grid or, alternatively, the national grid, which produces electricity using thermal power stations based on conventional sources. These generators are holding back the...
The prediction of ship location has become an increasingly popular research hotspot in the field of maritime transportation engineering, which benefits maritime safety supervision and security. Existing methods of ship location prediction based on mo...
Forecasting the output power of solar PV systems is required for the good operation of the power grid and the optimal management of energy fluxes occurring in the solar system. Before forecasting the solar system’s output, it is essential to focus on...
Text classification is a fundamental task in natural language processing (NLP). Deep-learning-based text classification methods usually have two stages: training and inference. However, the training dataset is only used in the training stage. To make...
k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k...
In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper pro...
Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is...
In this paper, we propose using particle swarm optimization (PSO) which can improve weighted k-nearest neighbors (PWKNN) to diagnose the failure of a wind power system. PWKNN adjusts weight to correctly reflect the importance of features and uses the...
A consistent entropy estimator for hyperspherical data is proposed based on the k-nearest neighbor (knn) approach. The asymptotic unbiasedness and consistency of the estimator are proved. Moreover, cross entropy and Kullback-Leibler (KL) divergence e...
Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing...
This paper redefines picture fuzzy soft matrices (pfs-matrices) because of some of their inconsistencies resulting from Cuong’s definition of picture fuzzy sets. Then, it introduces several distance measures of pfs-matrices. Afterward, this pap...
This paper deals with the problem of financial risk management using a new expected shortfall regression. The latter is based on the expectile model for financial risk-threshold. Unlike the VaR model, the expectile threshold is constructed by an asym...
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