K-nearest Neighbor Classification Algorithm Research Articles

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic mode decomposition with control is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k ) is input into the k-nearest neighbor classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and Vk (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k , and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

An Integrated Approach for Accurate 3D Point Cloud Recognition of Substation Equipment Using PSO and kNN Classification

Low efficiency and poor accuracy are caused by missing data in traditional 3D reconstruction methods. This study suggests a new 3D point cloud recognition technique for substation equipment based on 3D laser scanning point clouds, which combines the k-nearest neighbour (KNN) classification algorithm and particle swarm optimisation (PSO) algorithm, to address these issues. The particle swarm optimisation algorithm optimises the coefficient weights of each subspace feature. The k-nearest neighbour classification algorithm is then used to finish the classification. To confirm the superiority and accuracy of the suggested approach, the impact of the point cloud subspace’s size and loss rate on the recognition effect is examined experimentally and contrasted with the enhanced iterative nearest point algorithm. With an average recognition time of 0.19 seconds and a recognition accuracy of over 95\%, the experimental results demonstrate the method’s good performance in terms of efficiency and accuracy, opening up a wide range of potential applications.

An efficient dynamic migration and consolidation method of VMs based on improved K-nearest neighbor algorithm and attribute weighting

Virtual machine (VM) consolidation and migration that only consider current workload can result in excessive unnecessary migrations. To address this issue, a VM consolidation algorithm based on resource utilization prediction is proposed. An improved K-nearest neighbor (KNN) classification algorithm weighted by attribute inconsistency is proposed to predict the workload of both the host and the VMs. Firstly, two distributions are partitioned according to the neighboring relationship for comparing consistency. Then, an inconsistency evaluation function based on earth mover’s distance (EMD) is designed to measure the inconsistency between the neighboring sample set of each sample under each attribute and the equivalent partition refined by the decision attribute. Finally, the inconsistency level of the neighboring samples is transformed into the importance of the corresponding attribute to implement the attribute weighting KNN classifier. When selecting the source host and target host for VM migration, both current and predicted overloads are considered to avoid unnecessary VM migrations. Simulation tests were performed with random and realistic workloads, and the results show that the proposed method can reduce the overall energy consumption of the host, while also reducing service level agreement (SLA) violations and VM migration.

Research On Emotion Management Based on Speech Analysis for Nursing Homes

Speech emotion recognition is an important research area in artificial intelligence aimed at identifying the emotional states of speakers. This paper provides an overview of the current state and key algorithms of speech emotion recognition, focusing particularly on the emotional well-being of elderly residents in nursing homes. Firstly, the paper introduces the background and application areas of speech emotion recognition, emphasizing its significance in human-computer interaction, psychological health monitoring, and emotional intelligent systems. The paper extensively discusses methods of extracting emotional features, it adopts the K-Nearest Neighbors (KNN) classification algorithm, which serves as a simple yet effective classification method with potential in emotional speech recognition. This study aims to explore the design, implementation, and future directions of an emotion recognition system based on KNN. This algorithm exhibits a high fitting performance for emotional speech data, presenting advantages such as ease of implementation and alignment with the distribution characteristics of emotional speech data. Through multiple comparative experiments, the optimal K value for recognition has been determined, achieving a straightforward and convenient emotional recognition simulation. Finally, the paper summarizes the current state of speech emotion recognition, emphasizing its potential and significance in practical applications.

Classification and selection of the main features for the identification of toxicity in Agaricus and Lepiota with machine learning algorithms.

The occurrence of fungi is cosmopolitan, and while some mushroom species are beneficial to human health, others can be toxic and cause illness problems. This study aimed to analyze the organoleptic, ecological, and morphological characteristics of a group of fungal specimens and identify the most significant features to develop models for fungal toxicity classification using genetic algorithms and LASSO regression. The results of the study indicated that odor, spore print color, and habitat were the most significant characteristics identified by the genetic algorithm GALGO. Meanwhile, odor, gill size, stalk shape, and twelve other features were the relevant characteristics identified by LASSO regression. The importance score of the odor variable was 99.99%, gill size obtained 73.7%, stalk shape scored 39.9%, and the remaining variables did not score higher than 18%. Logistic regression, k-nearest neighbor (KNN), and XG-Boost classification algorithms were used to develop models using the features selected by both GALGO and LASSO. The models were evaluated using sensitivity, specificity, and accuracy metrics. The models with the highest AUC values were XGBoost, with a maximum value of 0.99 using the features selected by LASSO, followed by KNN with a maximum value of 0.99. The GALGO selection resulted in a maximum AUC of 0.98 in KNN and XGBoost. The models developed in this study have the potential to aid in the accurate identification of toxic fungi, which can prevent health problems caused by their consumption.

Test Cost Reduction for VLSI Adaptive Test With K-Nearest Neighbor Classification Algorithm

Test Cost Reduction for VLSI Adaptive Test With K-Nearest Neighbor Classification Algorithm

Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models

This study aims to determine whether there is an increase in accuracy results for predicting pregnancy risk with a classification algorithm that goes through and without going through the clustering stage. After that, compare which classification algorithm gets the best improvement. This study uses the K-Means clustering approach, as well as the SVM, Naive Bayes, and K-Nearest Neighbor (KNN) classification algorithms. The pregnancy risk dataset used comes from the UCI Machine Learning Repository. Evaluation metrics used include accuracy, precision, recall, and F1-score. The results of the study revealed that the K-Means model with KNN provided the highest performance compared to the other two, with an accuracy of 79.53% and an average F1-score of 0.8. The implementation of K-Means resulted in an increase in accuracy of 0.4%, 1.57%, and 2.76% on KNN, SVM, and Naive Bayes respectively, which confirms the impact of clustering in improving classification performance. The resulting model can be used in real-time via a website built using the Flask API, and offers tools that can help health practitioners to plan treatments effectively and minimize the risk of pregnancy.

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann-Whitney Test.

This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann-Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes. To address these concerns, a new centrifugal pump fault indicator (CPFI) that does not rely on previous knowledge is developed based on a novel fault-specific Mann-Whitney test. The new fault indicator is obtained by decomposing the vibration signature (VS) of the centrifugal pump hierarchically into its respective time-frequency representation using the wavelet packet transform (WPT) in the first step. The node containing the fault-specific frequency band is selected, and the Mann-Whitney test statistic is calculated from it. The combination of hierarchical decomposition of the vibration signal for fault-specific frequency band selection and the Mann-Whitney test form the new fault-specific Mann-Whitney test. The test output statistic yields the centrifugal pump fault indicator, which shows sensitivity toward the health condition of the centrifugal pump. This indicator changes according to the working conditions of the centrifugal pump. To further enhance fault detection, a new effect ratio (ER) is introduced. The KNN algorithm is employed to classify the fault type, resulting in promising improvements in fault classification accuracy, particularly under variable operating conditions.

Klasifikasi Buah Jeruk Segar dan Busuk Berdasarkan RGB dan HSV Menggunakan Metode KNN

Fruits are a group of agricultural commodities in Indonesia. The demand for domestic fruit commodities is quite high, this is indicated by the large number of fruits available in modern markets and traditional markets. In this research, a classification process will be carried out between fresh oranges and rotten oranges based on RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) color extraction. This study uses the K-Nearest Neighbor classification algorithm with a value of k = 1; 2; 3; 4; 5; 6; and 7. The dataset used consists of 146 training data and 88 testing data. The purpose and benefits of this research are to save time and facilitate classification according to the wishes of fruit growers. The final result of the test accuracy is 88.95%. Based on the test, this system can be said to be quite good at classifying fresh and rotten citrus fruits.

A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning

Abstract Wind turbines generate clean and renewable energy for the international market. The most ‎‎important aspect of wind turbine maintenance is reducing failures, downtime, and operating and maintenance expenses. ‎This study aims to detect multiple faults exhibited by wind turbine blades; failures such as cracks (tip crack, mid-span crack, and crack ‎near the root) were observed in the blades at different locations. The research suggests a new approach, incorporating vibration signals and machine learning techniques to identify various failures in wind turbine blades. The technology of ranking features such as ReliefF algorithms, chi-squares, and information gains was adopted to discuss a method framework to diagnose several problems in wind turbine blades, such as cracks in different locations. The k-nearest neighbors (KNNs), support vector machines, and random forests are used to classify data based on measured vibration signals. The eight main time-domain features are calculated from the vibration signals. The proposed methodology was validated using four databases. The results showed good classification accuracy in four databases, with at least three non-conventional features in each database’s top nine features of the three classification techniques. The results also showed that when the ReliefF selection algorithm is applied with the KNN classification algorithm, it generates the highest classification accuracy under all failure conditions, and the value is 97%. Finally, the performance of the proposed classification model is compared with other machine learning classification models, and a promising result is obtained. ‎

Bearing Fault Classification Using Multi-Class Machine Learning (ML) Techniques

Bearing elements are widely used in rotating machines and their failure results in a considerable amount of downtime of the machines. The aim of this work is to classify defects in a bearing. Three types of classification have been done: (i) Binary classification: classification as non-defective or defective bearing, (ii) 3-class classification such as non-defective, defective with inner ring defect and defective with roller defect and finally (iii) 7-class classification corresponding to no defect condition, three ring defect conditions pertaining to indentations of three different sizes on the inner ring and three roller defect conditions corresponding to indentations of three different sizes on the roller. The open-access data generated using a rolling bearing test rig from the Politecnico Di Torino, Italy, has been used for this work. The data had been obtained using 2 accelerometers on two bearing housings for multiple load and speed combinations. For classification, in the present work, classical ML algorithms such as logistic regression (LR), K-Nearest Neighbour (K-NN) classification algorithm, random forest (RF), support vector classifier (SVC) and kernel support vector machine (KSVM) have been used. All these techniques gave very promising results, the classification accuracy varying from 0.7969 to 0.9996 for all speed-load conditions. Such classification work across multiple operational conditions, with multiple fault conditions and multiple signatures with faulty components, has not been reported.

Süper Çözünürlük Yönteminin Uydu İmgelerinin Sınıflandırma Performansına Etkisi

The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution.

A wearable nanoscale heart sound sensor based on P(VDF-TrFE)/ZnO/GR and its application in cardiac disease detection.

This paper describes a method for preparing flexible composite piezoelectric nanofilms of P(VDF-TrFE)/ZnO/graphene using a high-voltage electrospinning method. Composition and β-phase content of the piezoelectric composite films were analyzed using X-ray diffraction. The morphology of the composite film fibers was observed through scanning electron microscopy. Finally, the P(VDF-TrFE)/ZnO/graphene composite film was encapsulated in a sandwich-structure heart sound sensor, and a visual heart sound acquisition and classification system was designed using LabVIEW. A heart sound classification model was trained based on a fine K-nearest neighbor classification algorithm to predict whether the collected heart sounds are normal or abnormal. The heart sound detection system designed in this paper can collect heart sound signals in real time and predict whether the heart sounds are normal or abnormal, providing a new solution for the diagnosis of heart diseases.

Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization

Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.

Analyzing the Level of Anxiety Disorders of Final-Year Students by Applying the Fuzzy Mamdani Method

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still relatively high, coupled with the COVID-19 pandemic, which has impacted the economic sector. For this reason, research on stunting is still a critical topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm and build a website-based early detection application for toddler stunting cases. The research results using the k-Nearest Neighbor Algorithm trial obtained a relatively high accuracy of 92.45%. Implementing an early detection system for stunting cases has proven to help health workers classify toddlers as stunted or not. This application is also helpful as an archive and facilitates data reporting. The application has eight main menus: the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, and stunting early warning menu, which contains malnourished and stunted toddlers.

Stunting Early Warning Application Using KNN Machine Learning Method

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still relatively high, coupled with the COVID-19 pandemic, which has impacted the economic sector. For this reason, research on stunting is still a critical topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm and build a website-based early detection application for toddler stunting cases. The research results using the k-Nearest Neighbor Algorithm trial obtained a relatively high accuracy of 92.45%. Implementing an early detection system for stunting cases has proven to help health workers classify toddlers as stunted or not. This application is also helpful as an archive and facilitates data reporting. The application has eight main menus: the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, and stunting early warning menu, which contains malnourished and stunted toddlers.

Stunting Early Warning Application Using KNN Machine Learning Method

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still quite high, coupled with the COVID-19 pandemic which has had quite an impact on the economic sector. For this reason, research on stunting is still a very important topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm, as well as build a website-based early detection application for stunting toddler cases using the CodeIgniter framework with the PHP programming language. The results of the research using the k-Nearest Neighbor Algorithm trial obtained a fairly high accuracy of 92.45%. The implementation of an early detection system for stunting cases has proven to help and facilitate health workers in classifying toddlers as stunted or not. This application is also useful as an archive and facilitates data reporting. In the application there are 8 main menus, namely the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, stunting early warning menu which contains malnourished toddlers, stunted toddlers.

Big data analysis based on the correlation between live-streaming with goods, perceived value and consumer repurchase

Abstract Clarifying the correlation between live banding, perceived value, and consumer repurchase is to enhance the economic benefits of live banding better. In this paper, a K-nearest neighbor classification algorithm is proposed in the context of big data analysis technology, and the principle and distance criterion of the algorithm are explained. Then the KNN algorithm is optimized using the Gaussian kernel density function, and the optimization process of the algorithm is given. Finally, the optimized KNN algorithm is used to mine and analyze the indicators of Taobao live banding data, and the performance evaluation is also done for the algorithm. Regarding live-streaming with goods and sensory value, the ratings of A, B, C and D accounted for 38.41%, 36.73%, 34.54% and 35.4%, respectively. In terms of the association between live banding and consumer repurchase, the average value of the data is 33.46%, the maximum value is 43.65%, and the minimum value is 18.16%. Big data analysis shows a strong correlation between live banding, sensory value and consumer repurchase. Live banding enhances consumer sensory value, sensory value influences consumer repurchase, and consumer repurchase behavior promotes live banding optimization, which continuously improves the quality of the product and marketing atmosphere of live banding.

A Parallel Approach for Optimizing KNN Classification Algorithm in Big Data

Big data classification is the study of how to classify large amounts of data, which conventional data mining methods typically find challenging to handle. The most popular data mining technique is the K-Nearest Neighbor(KNN) classifier because of its efficiency and simplicity. The sequential KNN classifier can't handle a huge amount of data due to its highly required calculation nature, so it is improved by using a parallel technique supported by Map Reduce. This model enables us to classify the massive amount of data that exceeds terabytes. Similar to the original KNN model, this parallel implementation offers the same classification rate but less time complexity. In this paper, parallel KNN with Map Reduce has been proposed using a Hadoop multi-data node cluster. First, the dataset split into n blocks, and in each node, the mapper and reducer will be executed. The mapper phase is responsible for calculating the Euclidean distance between the training set and the targeted point, and the output of the mapper is a set of pairs of <Distance, Class> that serve as the reducer's input. In reducer, the minimum k distances are determined, and the class with maximum occurrence will represent the predicted class. The results showed a significant improvement in time complexity for the proposed approach over the traditional one. A New York criminal data set with a size of 6.5 million records was used in this work. The tuples Latitude and Longitude were used to determine the nearest neighbors, while the Patrol-BORO was used as class label. 

A new design method for the openings of shell structures under the dual control of principal stress lines and stress contours

Current design software lacks high precision and effective control in handling complex shell opening designs. This study develops a new workflow for generating shell openings based on the exceptional modeling advantages of Grasshopper, a visual programming language integrated with the Rhino software. The process contains two critical subroutines developed in Python for data processing and transformation. One is a selection tool based on the K-nearest neighbors classification algorithm for processing principal stress line data. The classification result is the guideline for shell meshing, which substantially improves the structural rationality of mesh shells generated based on dynamic equilibrium methods and particle spring systems. Another tool developed in this study based on the computer vision library OpenCV provides the functionality to read and process stress image data. In addition, this subroutine allows multi-level control of shell opening dimensions according to stress contours. In contrast with the conventional method, the new method efficiently brings shell form under the dual control of principal stress lines and stress contours for the first time. The obtained shell-opening results indicate increased freedom in layout, shape, and smoothness. The method is applied to a free-form shell, and three typical practical examples are presented to demonstrate its structural efficiency and visual effect.