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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 problem with satisfactory efficiency; however, its accuracy is sacrificed when directly applying the traditional similarity measure based on Euclidean distance. Inspired by the Polar coordinate system and the quantum property, this work proposes a new similarity measure to replace the Euclidean distance, which is defined as Polar distance. Polar distance considers both angular and module length information, introducing a weight parameter adjusted to the specific application data. To validate the efficiency of Polar distance, we conducted various experiments using several typical datasets. For the conventional KNN algorithm, the accuracy performance is comparable when using Polar distance for similarity measurement, while for the QKNN algorithm, it significantly outperforms the Euclidean distance in terms of classification accuracy. Furthermore, the Polar distance shows scalability and robustness superior to the Euclidean distance, providing an opportunity for the large-scale application of QKNN in practice.

Recently, indoor localization has become an active area of research. Although there are various approaches to indoor localization, methods that utilize artificially generated magnetic fields from a target device are considered to be the best in terms of localization accuracy under non-line-of-sight conditions. In magnetic field-based localization, the target position must be calculated based on the magnetic field information detected by multiple sensors. The calculation process is equivalent to solving a nonlinear inverse problem. Recently, a machine-learning approach has been proposed to solve the inverse problem. Reportedly, adopting the k-nearest neighbor algorithm (k-NN) enabled the machine-learning approach to achieve fairly good performance in terms of both localization accuracy and computational speed. Moreover, it has been suggested that the localization accuracy can be further improved by adopting artificial neural networks (ANNs) instead of k-NN. However, the effectiveness of ANNs has not yet been demonstrated. In this study, we thoroughly investigated the effectiveness of ANNs for solving the inverse problem of magnetic field-based localization in comparison with k-NN. We demonstrate that despite taking longer to train, ANNs are superior to k-NN in terms of localization accuracy. The k-NN is still valid for predicting fairly accurate target positions within limited training times.

The article discusses the utilization of Pulsed Multifrequency Excitation and Spectrogram Eddy Current Testing (PMFES-ECT) in conjunction with the supervised learning method for the purpose of estimating defect parameters in conductive materials. To obtain estimates for these parameters, a three-dimensional finite element method model was developed for the sensor and specimen containing defects. The outcomes obtained from the simulation were employed as training data for the k-Nearest Neighbors (k-NN) algorithm. Subsequently, the k-NN algorithm was employed to determine the defect parameters by leveraging the available measurement outcomes. The evaluation of classification accuracy for different combinations of predictors derived from measured data is also presented in this study.

In recent years, food‐derived functional peptides have received more and more attention because of their low toxicity, which is different from drugs. This study was based on the theory that hybrid orbitals play an important role in chemical reactions. While preserving the original physical information, a descriptor called hybrid orbitals and atomic characteristics was designed to explore the relationship between hybrid orbitals, electronegative atoms, and the function of food‐derived peptides. The classification effects of support vector machines and K‐nearest neighbor are compared and selected by machine learning KNN algorithm to predict the function of food‐derived functional peptides, including inhibitors of angiotensin‐converting enzyme and dipeptidyl peptidase IV, antioxidant peptides, and antibacterial peptides, the accuracy of prediction and the area under curve value were about 0.8. Comparing the result of using hybrid orbital and electronegative atoms to predict the function of peptides, respectively, it was found that the hybrid orbital is more closely related to the function of the peptide. This study revealed that the application of hybrid orbital theory to the research of food‐derived peptides is of positive significance. This is a diagram summarizing the whole paper. The main tasks of this paper include obtaining data from the database, preprocessing the data, training the K‐nearest neighbor model, and testing the model by predicting the functions of peptides.

Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%.

Electrical conductivity and total dissolved solids are considered important parameters in determining quality of drinking and agricultural water because they directly represent total salt concentration in the water. Increases in these parameter values indicate a reduction in water quality. In this study, estimation of the two parameters in the Lighvan Chay River located in Eastern Azerbaijan, Iran, is studied using the k-nearest neighbors algorithm and support vector regression. Different sets of chemical parameters (i.e., phosphorus, chlorine, calcium, magnesium, sodium, sodium adsorption ratio, sulfate, bicarbonate) were considered as inputs while the total dissolved solids and electrical conductivity were the outputs. Three statistics—coefficient of determination (R 2), root mean square error, and mean absolute error—were used to verify accuracy of these models. Comparison of the results showed that both algorithms accurately estimated the total dissolved solids and electrical conductivity, but the support vector regression model is recommended because of better performance.

Spam usually has the characteristics of large data dimension and large numbers of samples. At present, the common classification learning algorithms have their own advantages and disadvantages. Aiming at the classification tendency of K nearest neighbor algorithm (KNN) in the face of unbalanced sample data and the long time consuming of support vector machine (SVM) training model, a fusion algorithm of KNN and SVM is proposed. Firstly, the content of spam is extracted, and then the text preprocessing is transformed into sparse matrix. The k nearest neighbor samples of the test sample set are screened out by KNN algorithm, and then the SVM algorithm is used to train these screened samples, establish the separation hyperplane, identify the mail, and judge whether it is spam or not. The results show that the KNN-SVM fusion algorithm can effectively reduce the impact of sample imbalance while maintaining the high classification accuracy of SVM algorithm, and its classification efficiency is slightly lower than that of KNN, algorithm but much higher than that of SVM algorithm.

Plagiarism is a problem that is becoming more prevalent as technology advances and the use of computer systems grows in comparison to previous generations. Plagiarism is the unauthorized use of another person’s work. Since manual plagiarism detection is difficult, this method should be automated. Plagiarism detection can be done using a variety of methods. Some of the research focuses on intrinsic plagiarism, while others focus on extrinsic plagiarism. Data mining is an area that can assist in both detecting plagiarism and improving the reliability of the operation. Plagiarism can be detected using a variety of data mining techniques. Text mining, clustering, bi-grams, tri-grams, and n-grams are some of the techniques that can assist with this. In this paper we will use the data mining techniques to increase the efficiency of detection of plagiarism.

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of the PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data from 30 MGEX stations in the Asia–Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by n-fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality.