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...
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, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean...
The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, ba...
The suggested solution in this work makes use of the parallel processing capability of FPGA to enhance the efficiency of the K-Nearest Neighbor (KNN) algorithm on encrypted data. The suggested technique was assessed utilizing the breast cancer datase...
Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classif...
With the widespread use of the Internet, network security issues have attracted more and more attention, and network intrusion detection has become one of the main security technologies. As for network intrusion detection, the original data source al...
In this study, a joint modulation format identification and optical signal-to-noise ratio (OSNR) monitoring algorithm is proposed and experimentally demonstrated using the k-nearest neighbor algorithm for intensity modulation and direct detection (IM...
Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and smal...
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...
Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (M...
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...
The gravity-aided inertial navigation system is a technique using geophysical information, which has broad application prospects, and the gravity-map-matching algorithm is one of its key technologies. A novel gravity-matching algorithm based on the K...
(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...
Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning effici...
Accurate prediction of PM2.5 concentration is essential for public health and environmental protection, and specifically crucial for the management of the availability of sufficient health personnel during adverse health episodes. However, its nonlin...
Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning...
Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex...
The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which req...
An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to com...
The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weight...
We present a quantum-enhanced version of the classic k-Nearest Neighbors (kNN) classification algorithm, applied to the detection of arterial hypertension. The traditional Euclidean distance metric of the kNN algorithm is replaced with a fidelity-der...
The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms alwa...
Wireless indoor localization technology is a hot research field at present. Its basic principle is to estimate the geometric position of the mobile node by measuring the characteristic parameters of the propagation signal between the mobile node and...
A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more...
Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, result...
The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully stu...
Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the ti...
Carbon nanotubes (CNTs) are extensively utilized in the fabrication of high-performance composites due to their exceptional mechanical, electrical, and thermal characteristics. To investigate the mechanical properties of CNTs filled polymers accurate...
The work presents a tool enabling the selection of powder for 3D printing. The project focused on three types of powders, such as steel, nickel- and cobalt-based and aluminum-based. An important aspect during the research was the possibility of obtai...
Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of...
Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging. Recent maj...
In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method t...
Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue...
We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm-spe...
The back projection (BP) algorithm is characterized by its high performance for multi-receiver synthetic aperture sonar (SAS). For this reason, it is usually used to evaluate the imaging performance of Fourier-domain methods. However, this algorithm...
The traveling salesman problem (TSP) is one of the typical NP–Hard problems of combinatorial optimization area. This paper proposes a new hyper heuristic algorithm named Parametric Hybrid Method (PHM) based on The Farthest Vertex (FV) and Greedy heur...
Accurate estimation of forest biomass to enable the mapping of forest C stocks over large areas is of considerable interest nowadays. Airborne laser scanning (ALS) systems bring a new perspective to forest inventories and subsequent biomass estimatio...
Stratiform and convective rain types are associated with different cloud physical processes, vertical structures, thermodynamic influences and precipitation types. Distinguishing convective and stratiform systems is beneficial to meteorology research...
A reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k closest points. In recent years, considerable research has been conducted into monitoring reverse k nearest neighbor queries. In this paper, we stud...
The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa...
In this paper, we propose a crossover operator for genetic algorithms with binary chromosomes populations based on the cellular automata (
Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact...
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting d...
Side weirs have been widely used since ancient times in many hydraulic works. Their operation can be analyzed following different approaches. However, almost all possible analysis approaches require knowledge of the discharge coefficient, which depen...
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...
Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data ge...
This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunc...
The ability to continuously learn over time by incorporating new information while holding onto previously acquired expertise is known as incremental learning (IL). Although this concept is fundamental to human learning, existing machine learning tec...
of 53