Relevant bibliographies by topics / KNN CLASSIFIER

Academic literature on the topic 'KNN CLASSIFIER'

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Published: 11 September 2023

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Journal articles on the topic "KNN CLASSIFIER"

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Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms." Symmetry 13, no. 4 (April 7, 2021): 615. http://dx.doi.org/10.3390/sym13040615.

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The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training dataset are formed for the development of the kNN classifier: a variant that uses all objects from the original training dataset located inside the strip dividing the classes, and a variant that uses only those objects from the initial training dataset that are located inside the area containing all misclassified objects from the class dividing strip. In the second stage, the kNN classifier is developed using the new training dataset above-mentioned. The values of the parameters of the kNN classifier are determined during training to maximize the data classification quality. The data classification quality using the two-stage hybrid SVM-kNN classifier was assessed using various indicators on the test dataset. In the case of the improvement of the quality of classification near the class boundary defined by the SVM classifier using the kNN classifier, the two-stage hybrid SVM-kNN classifier is recommended for further use. The experimental results approve the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem. The experimental results obtained with the application of various datasets confirm the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem.
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Hu, Juan, Hong Peng, Jun Wang, and Wenping Yu. "kNN-P: A kNN classifier optimized by P systems." Theoretical Computer Science 817 (May 2020): 55–65. http://dx.doi.org/10.1016/j.tcs.2020.01.001.

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PAO, TSANG-LONG, YUN-MAW CHENG, YU-TE CHEN, and JUN-HENG YEH. "PERFORMANCE EVALUATION OF DIFFERENT WEIGHTING SCHEMES ON KNN-BASED EMOTION RECOGNITION IN MANDARIN SPEECH." International Journal of Information Acquisition 04, no. 04 (December 2007): 339–46. http://dx.doi.org/10.1142/s021987890700140x.

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Since emotion is important in influencing cognition, perception of daily activities such as learning, communication and even rational decision-making, it must be considered in human-computer interaction. In this paper, we compare four different weighting functions in weighted KNN-based classifiers to recognize five emotions, including anger, happiness, sadness, neutral and boredom, from Mandarin emotional speech. The classifiers studied include weighted KNN, weighted CAP, and weighted D-KNN. We use the result of traditional KNN classifier as the line performance measure. The experimental results show that the used Fibonacci weighting function outperforms others in all weighted classifiers. The highest accuracy achieves 81.4% with weighted D-KNN classifier.
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Murugan, S., Ganesh Babu T R, and Srinivasan C. "Underwater Object Recognition Using KNN Classifier." International Journal of MC Square Scientific Research 9, no. 3 (December 17, 2017): 48. http://dx.doi.org/10.20894/ijmsr.117.009.003.007.

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Mohamed, Taha M. "Pulsar selection using fuzzy knn classifier." Future Computing and Informatics Journal 3, no. 1 (June 2018): 1–6. http://dx.doi.org/10.1016/j.fcij.2017.11.001.

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Khan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam, and Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier." Diagnostics 12, no. 11 (October 26, 2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.

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Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called “Stacking Classifier” in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
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Widyadhana, Arya, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari, and Agus Zainal Arifin. "A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier." Jurnal Ilmu Komputer dan Informasi 14, no. 1 (February 28, 2021): 65–71. http://dx.doi.org/10.21609/jiki.v14i1.959.

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K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers. The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers.
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Zheng, Shuai, and Chris Ding. "A group lasso based sparse KNN classifier." Pattern Recognition Letters 131 (March 2020): 227–33. http://dx.doi.org/10.1016/j.patrec.2019.12.020.

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Wang, Zhiping, Junying Na, and Baoyou Zheng. "An Improved kNN Classifier for Epilepsy Diagnosis." IEEE Access 8 (2020): 100022–30. http://dx.doi.org/10.1109/access.2020.2996946.

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Taguelmimt, Redha, and Rachid Beghdad. "DS-kNN." International Journal of Information Security and Privacy 15, no. 2 (April 2021): 131–44. http://dx.doi.org/10.4018/ijisp.2021040107.

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On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.
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Dissertations / Theses on the topic "KNN CLASSIFIER"

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Mestre, Ricardo Jorge Palheira. "Improvements on the KNN classifier." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10923.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
The object classification is an important area within the artificial intelligence and its application extends to various areas, whether or not in the branch of science. Among the other classifiers, the K-nearest neighbor (KNN) is among the most simple and accurate especially in environments where the data distribution is unknown or apparently not parameterizable. This algorithm assigns the classifying element the major class in the K nearest neighbors. According to the original algorithm, this classification implies the calculation of the distances between the classifying instance and each one of the training objects. If on the one hand, having an extensive training set is an element of importance in order to obtain a high accuracy, on the other hand, it makes the classification of each object slower due to its lazy-learning algorithm nature. Indeed, this algorithm does not provide any means of storing information about the previous calculated classifications,making the calculation of the classification of two equal instances mandatory. In a way, it may be said that this classifier does not learn. This dissertation focuses on the lazy-learning fragility and intends to propose a solution that transforms the KNNinto an eager-learning classifier. In other words, it is intended that the algorithm learns effectively with the training set, thus avoiding redundant calculations. In the context of the proposed change in the algorithm, it is important to highlight the attributes that most characterize the objects according to their discriminating power. In this framework, there will be a study regarding the implementation of these transformations on data of different types: continuous and/or categorical.
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Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Bel, Haj Ali Wafa. "Minimisation de fonctions de perte calibrée pour la classification des images." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00934062.

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La classification des images est aujourd'hui un défi d'une grande ampleur puisque ça concerne d'un côté les millions voir des milliards d'images qui se trouvent partout sur le web et d'autre part des images pour des applications temps réel critiques. Cette classification fait appel en général à des méthodes d'apprentissage et à des classifieurs qui doivent répondre à la fois à la précision ainsi qu'à la rapidité. Ces problèmes d'apprentissage touchent aujourd'hui un grand nombre de domaines d'applications: à savoir, le web (profiling, ciblage, réseaux sociaux, moteurs de recherche), les "Big Data" et bien évidemment la vision par ordinateur tel que la reconnaissance d'objets et la classification des images. La présente thèse se situe dans cette dernière catégorie et présente des algorithmes d'apprentissage supervisé basés sur la minimisation de fonctions de perte (erreur) dites "calibrées" pour deux types de classifieurs: k-Plus Proches voisins (kNN) et classifieurs linéaires. Ces méthodes d'apprentissage ont été testées sur de grandes bases d'images et appliquées par la suite à des images biomédicales. Ainsi, cette thèse reformule dans une première étape un algorithme de Boosting des kNN et présente ensuite une deuxième méthode d'apprentissage de ces classifieurs NN mais avec une approche de descente de Newton pour une convergence plus rapide. Dans une seconde partie, cette thèse introduit un nouvel algorithme d'apprentissage par descente stochastique de Newton pour les classifieurs linéaires connus pour leur simplicité et leur rapidité de calcul. Enfin, ces trois méthodes ont été utilisées dans une application médicale qui concerne la classification de cellules en biologie et en pathologie.
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Mackových, Marek. "Analýza experimentálních EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241981.

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This thesis is focused on the analysis of experimental ECG records drawn up in isolated rabbit hearts and aims to describe changes in EKG caused by ischemia and left ventricular hypertrophy. It consists of a theoretical analysis of the problems in the evaluation of ECG during ischemia and hypertrophy, and describes an experimental ECG recording. Theoretical part is followed by a practical section which describes the method for calculating morphological parameters, followed by ROC analysis to evaluate their suitability for the classification of hypertrophy and at the end is focused on classification.
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Pavani, Sri-Kaushik. "Methods for face detection and adaptive face recognition." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7567.

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The focus of this thesis is on facial biometrics; specifically in the problems of face detection and face recognition. Despite intensive research over the last 20 years, the technology is not foolproof, which is why we do not see use of face recognition systems in critical sectors such as banking. In this thesis, we focus on three sub-problems in these two areas of research. Firstly, we propose methods to improve the speed-accuracy trade-off of the state-of-the-art face detector. Secondly, we consider a problem that is often ignored in the literature: to decrease the training time of the detectors. We propose two techniques to this end. Thirdly, we present a detailed large-scale study on self-updating face recognition systems in an attempt to answer if continuously changing facial appearance can be learnt automatically.
L'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.
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Marin, Rodenas Alfonso. "Comparison of Automatic Classifiers’ Performances using Word-based Feature Extraction Techniques in an E-government setting." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-32363.

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Nowadays email is commonly used by citizens to establish communication with their government. On the received emails, governments deal with some common queries and subjects which some handling officers have to manually answer. Automatic email classification of the incoming emails allows to increase the communication efficiency by decreasing the delay between the query and its response. This thesis takes part within the IMAIL project, which aims to provide an automatic answering solution to the Swedish Social Insurance Agency (SSIA) (“Försäkringskassan” in Swedish). The goal of this thesis is to analyze and compare the classification performance of different sets of features extracted from SSIA emails on different automatic classifiers. The features extracted from the emails will depend on the previous preprocessing that is carried out as well. Compound splitting, lemmatization, stop words removal, Part-of-Speech tagging and Ngrams are the processes used in the data set. Moreover, classifications will be performed using Support Vector Machines, k- Nearest Neighbors and Naive Bayes. For the analysis and comparison of different results, precision, recall and F-measure are used. From the results obtained in this thesis, SVM provides the best classification with a F-measure value of 0.787. However, Naive Bayes provides a better classification for most of the email categories than SVM. Thus, it can not be concluded whether SVM classify better than Naive Bayes or not. Furthermore, a comparison to Dalianis et al. (2011) is made. The results obtained in this approach outperformed the results obtained before. SVM provided a F-measure value of 0.858 when using PoS-tagging on original emails. This result improves by almost 3% the 0.83 obtained in Dalianis et al. (2011). In this case, SVM was clearly better than Naive Bayes.
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Lin, Ping-Min, and 林秉旻. "A real-time fall detection system using human body contours information and kNN classifier." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/17980118041306113240.

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碩士
國立交通大學
多媒體工程研究所
96
In the province of Human Computer Interaction, monitor system is an important study. As long as the situation of aging society becomes more and more serious, the care costs will increase plenty. That is the reason so many domestic and foreign scholars throw themselves into the research of elderly care monitor system in order to support the existing care system and reduce the huge expenditures of labor costs. This research used and integrated the human face detection system developed by our laboratory to get the characteristics of the human body and track that. And also used k-th Nearest Neighbor classification to classify the human postures. Then using the information of the changing rate collected by many experiments this research finally can develop a fall detection system.
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BHATT, PRASHANT. "CONTENT ACCESS USING FACE BIOMETRICS." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16578.

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In recent years, privacy and security have become a major challenge to all of us. Each type of data is easily available and accessible to all. This raises a concern that is everyone should have access to every kind of data? Unlike for adults, the access to the data contents for children should be limited and access to adult content like pornography etc. should be controlled and kept out of reach of the children until they become certain years of age. Therefore, we aim to design an approach that allows the access of data contents stored in a system based on the face biometrics of the person trying to access the data. We have thus created a system with data mainly videos that are segregated in two categories mainly adult videos and child videos. Our approach works in real time and allows access to the data to a particular age group only. It works for the prediction of the age group to allow access to the data contents. So, first, the age group of a person is predicted by using the Voila-Jones algorithm to detect the faces or face limitations. After detecting the face, features are extracted using HOG approach to get the feature vector, and then to train the system we have used KNN classifier. A publically available dataset is used for training purposes. We have thus implemented an approach that works in real time and gives efficient results.
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MANOJ, DIVI SAI. "COGNITIVE ASSESSMENT THROUGH THE ANALYSIS OF EEG SIGNALS." Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16577.

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With the advent of emerging technologies and methodologies of biomedical signal processing, research on the cognitive sciences has become one of the most innovative, enthralling and challenging researches in the field of biomedical engineering. The problem of cognitive assessment and enhancement has gained major importance amongst the today’s cognitive researches, as it aids in identification and treatment of cognitive related disorders like Attention Deficit Hyperactivity Disorder (ADHD), Spatial Navigation, Short Term Memory, Cross Modal Processing etc. In this work, as a part of Cognitive Assessment, we are concentrated towards the testing of Working Memory and Cognitive Workload through the analysis of the two channel ( - ) EEG signals obtained from the subjects when they were presented with some familiar stimulus. For this, the subjects were explained about the stimuli related to a situation and later were shown these stimuli and a model had been developed to differentiate these different types of stimuli responses. Several features in conjunction with classifiers have been explored and the corresponding results were analysed to decide the optimum feature and classifier that can be selected for obtaining the optimum classification accuracy. A maximum classification accuracy of 66.67% had been obtained when tried with the LDA+RBFFNN classifier for -channel when the feature of Hurst Exponent on 3-5 IMFs is used for the task of inter Stimuli Classification and a maximum classification accuracy of 100% had been obtained when tried with the LDA+KNN classifier for and channels when the feature of Hurst Exponent is used for the task of inter Subject Classification and the results show a very good overall Inter subject classification accuracies for the classifier LDA+KNN.
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Books on the topic "KNN CLASSIFIER"

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Pathak, Sudhir, and Soudamini Hota. KNN Classifier Based Approach for Multi-Class Sentiment Analysis of Twitter Data. Independently Published, 2017.

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Vidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.

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Book chapters on the topic "KNN CLASSIFIER"

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Aydede, Yigit. "Nonparametric Classifier - kNN." In Machine Learning Toolbox for Social Scientists, 137–55. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003381501-10.

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Shang, Wenqian, Houkuan Huang, Haibin Zhu, Yongmin Lin, Youli Qu, and Hongbin Dong. "An Adaptive Fuzzy kNN Text Classifier." In Computational Science – ICCS 2006, 216–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758532_30.

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Law, Kwok Ho, and Lam For Kwok. "IDS False Alarm Filtering Using KNN Classifier." In Information Security Applications, 114–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31815-6_10.

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Kępa, Marcin, and Julian Szymański. "Two Stage SVM and kNN Text Documents Classifier." In Lecture Notes in Computer Science, 279–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19941-2_27.

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Orczyk, Tomasz, Rafal Doroz, and Piotr Porwik. "Combined kNN Classifier for Classification of Incomplete Data." In Advances in Intelligent Systems and Computing, 21–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19738-4_3.

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Lu, Ruhua, Yueqing Mo, Weiqiao Yao, and Yalan Li. "A Leaf Recognition Algorithm Based on KNN Classifier." In Lecture Notes in Electrical Engineering, 1009–15. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6901-0_104.

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Chikmurge, Diptee, and R. Shriram. "Marathi Handwritten Character Recognition Using SVM and KNN Classifier." In Hybrid Intelligent Systems, 319–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_32.

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Zhou, Mu, Yusuke Tanimura, and Hidemoto Nakada. "One-Shot Learning Using Triplet Network with kNN Classifier." In Advances in Intelligent Systems and Computing, 227–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39878-1_21.

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Mukherjee, Rajendrani, Srestha Sadhu, and Aurghyadip Kundu. "Heart Disease Detection Using Feature Selection Based KNN Classifier." In Proceedings of Data Analytics and Management, 577–85. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6289-8_48.

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Mohurle, Savita, and Manoj Devare. "A Study of KNN Classifier to Predict Water Pollution Index." In Advances in Intelligent Systems and Computing, 457–66. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_44.

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Conference papers on the topic "KNN CLASSIFIER"

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Tsoukalas, Vassilis Th, Vassilis G. Kaburlasos, and Christos Skourlas. "A granular, parametric KNN classifier." In the 17th Panhellenic Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491845.2491892.

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Maniyath, Shima Ramesh, Ramachandra Hebbar, Akshatha K.N., Architha L.S., and S. Rama Subramoniam. "Soil Color Detection Using Knn Classifier." In 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C). IEEE, 2018. http://dx.doi.org/10.1109/icdi3c.2018.00019.

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Yigit, Halil. "A weighting approach for KNN classifier." In 2013 International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2013. http://dx.doi.org/10.1109/icecco.2013.6718270.

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Pichardo-Morales, Francisco D., Marco A. Acevedo-Mosqueda, and Sandra L. Gomez-Coronel. "Classification of Gunshots with KNN Classifier." In EATIS '18: Euro American Conference on Telematics and Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3293614.3293656.

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Guo, Xinyu. "A KNN Classifier for Face Recognition." In 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2021. http://dx.doi.org/10.1109/cisce52179.2021.9445908.

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Kaur, Manbir, Chintan Thacker, Laxmi Goswami, Thamizhvani TR, Imad Saeed Abdulrahman, and A. Stanley Raj. "Alzheimer’s Disease Detection using Weighted KNN Classifier in Comparison with Medium KNN Classifier with Improved Accuracy." In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2023. http://dx.doi.org/10.1109/icacite57410.2023.10183208.

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Wen, Ch J., and Y. Zh Zhan. "HMM+KNN classifier for facial expression recognition." In 2008 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2008. http://dx.doi.org/10.1109/iciea.2008.4582519.

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Jyothi, R., Sujit Hiwale, and Parvati V. Bhat. "Classification of labour contractions using KNN classifier." In 2016 International Conference on Systems in Medicine and Biology (ICSMB). IEEE, 2016. http://dx.doi.org/10.1109/icsmb.2016.7915100.

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Hu, Juan, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, and Xiaohui Luo. "A kNN classifier optimized by P systems." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393307.

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Manolakos, Elias S., and Ioannis Stamoulias. "IP-cores design for the kNN classifier." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537602.

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