Relevant bibliographies by topics / Support Vector Machine; NaiveBayes; K-Nearest Neighbour

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Support Vector Machine; NaiveBayes; K-Nearest Neighbour'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Support Vector Machine; NaiveBayes; K-Nearest Neighbour"

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Nausheen, S., Kumar M. Anil, and K. K. Amrutha. "SURVEY ON SENTIMENT ANALYSIS OF STOCK MARKET." International Journal of Research - Granthaalayah 5, no. 4 (2017): 69–75. https://doi.org/10.5281/zenodo.572298.

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Sentiment analysis has seen a tremendous growth in the past few years. Sentiment analysis or opinion mining is a process of collecting users’ opinion from user generated content. It has various applications, such as stock market prediction, products’ review collection, etc. a large amount of work has been done in this field by applying sentiment analysis to various applications. The main goal of this paper is to study the various methods used for sentiment analysis. Further we explain the overview of various related papers and their performances.
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Rezaei-Hachesu, Peyman, Taha Samad-Soltani, Ruhollah Khara, Mehdi Gheibi, and Nazila Moftian. "192: PREDICTION OF ASTHMA CONTROL LEVELS USING DATA MINING METHODS: AN EVIDENCE-BASED APPROACH." BMJ Open 7, Suppl 1 (2017): bmjopen—2016–015415.192. http://dx.doi.org/10.1136/bmjopen-2016-015415.192.

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Background and aims:Asthma is a chronic lung disease and has a raising worldwide prevalence. Lack of timely and appropriate control for this condition leads to financial and physical injuries. The aim of this study is to prediction of asthma control levels by applying data mining algorithms.Methods:This is a cross-sectional study carried out in the city of Sanandaj in Iran. Samples consist of 600 referred patient patients who live with asthma to Tohid pulmonary clinic in Sanandaj In a period of two months in 2015. Data were collected based on the study's inclusion criteria. Preprocessing was p
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Qie, Shuai, Xin Zhang, Jiusong Luan, Zhelun Song, Jingyun Li, and Jingyu Wang. "Model development and validation for predicting small-cell lung cancer bone metastasis utilizing diverse machine learning algorithms based on the SEER database." Medicine 104, no. 12 (2025): e41987. https://doi.org/10.1097/md.0000000000041987.

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The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cancer (SCLC) and create a straightforward web-based predictor based on the developed algorithm. Data comprising demographic and clinicopathological characteristics of patients with SCLC and their potential BM were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. This data was then utilized to develop 12 machine learning algorithm models: support vector machine, logistic regression, NaiveBayes, extreme gra
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Ehsan, Muhmammad. "Comparison of the Predictive Models of Human Activity Recognition (HAR) in Smartphones." UMT Artificial Intelligence Review 1, no. 2 (2021): 27–35. http://dx.doi.org/10.32350/air.0102.03.

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This report compared the performance of different classification algorithms such as decision tree, K-Nearest Neighbour (KNN), logistic regression, Support Vector Machine (SVM) and random forest. The dataset comprised smartphones’ accelerometer and gyroscope readings of the participants while performing different activities, such as walking, walking downstairs, walking upstairs, standing, sitting, and laying. Different machine learning algorithms were applied to this dataset for classification and their accuracy rates were compared. KNN and SVM were found to be the most accurate of all.
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Muhammad, Abba Bello, Ishaq O. Olawoyin, Abubakar Yahaya, S. U. Gulumbe, Abdullahi A. Muhammad, and Iliyasu Abubakar Salisu. "CREDIT RISK ANALYSIS: AN ASSESSMENT OF THE PERFORMANCE OF SIX MACHINE LEARNING TECHNIQUES IN CREDIT SCORING MODELLING." FUDMA JOURNAL OF SCIENCES 8, no. 6 (2024): 163–73. https://doi.org/10.33003/fjs-2024-0806-2893.

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This study checked the credit risk analysis domain, concentrating on assessing the efficacy of six distinct credit scoring methodologies: linear discriminant analysis, logistic regression, artificial neural networks, support vector machine, decision tree and, K-nearest neighbour on microcredit applicant’s data. Two performance metrics were used: Area under the receiver operative characteristic curve and, Precision. The results obtained from the experimentation phase reveal distinct performance levels for each technique. Specifically, K-nearest neighbour and artificial neural networks showcase
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Jati, Agung Nugroho, Astri Novianty, Nanda Septiana, and Leni Widia Nasution. "Comparison Analysis of Gait Classification For Human Motion Identification Using Embedded Computer." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 5014. http://dx.doi.org/10.11591/ijece.v8i6.pp5014-5020.

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In this paper, it will be discussed about comparison between two kinds of classification methods in order to improve security system based of human gait. Gait is one of biometric methods which can be used to identify person. K-Nearest Neighbour has parallelly implemented with Support Vector Machine for classifying human gait in same basic system. Generally, system has been built using Histogram and Principal Component Analysis for gait detection and its feature extraction. Then, the result of the simulation showed that K-Nearest Neighbour is slower in processing and less accurate than Support
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Maureen I. Akazue, Nkiru Queen Muka, and Abel E. Edje. "Mitigating insider’s threats using support vector machine and k-nearest Neighbour." International Journal of Science and Research Archive 12, no. 1 (2024): 2626–35. http://dx.doi.org/10.30574/ijsra.2024.12.1.1110.

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Addressing insider’s threats is a critical challenge in organizational security. This study presents the development and evaluation of a hybrid machine learning model aimed at enhancing insider’s threat detection effectiveness. The escalating risks associated with insider’s threats necessitated advance detection mechanisms to mitigate potential breaches. Leveraging the strengths of multiple individual models, including Support Vector Machine (SVM) and K-nearest Neighbour (KNN), the hybrid model addressed this challenge by improving detection accuracy while minimizing false positives. Through r
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Moosavian, Ashkan, Hojat Ahmadi, Babak Sakhaei, and Reza Labbafi. "Support vector machine and K-nearest neighbour for unbalanced fault detection." Journal of Quality in Maintenance Engineering 20, no. 1 (2014): 65–75. http://dx.doi.org/10.1108/jqme-04-2012-0016.

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Purpose – The purpose of this paper is to develop an appropriate approach for detecting unbalanced fault in rotating machines using KNN and SVM classifiers. Design/methodology/approach – To fulfil this goal, a fault diagnosis approach based on signal processing, feature extraction and fault classification, was used. Vibration signals were acquired from a designed experimental system with three conditions, namely, no load, balanced load and unbalanced load. FFT technique was applied to transform the vibration signals from time-domain into frequency-domain. In total, 29 feature parameters were e
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Wijaya, Aditya Surya, Nurul Chamidah, and Mayanda Mega Santoni. "Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 1 (2019): 33. http://dx.doi.org/10.22146/ijeis.38729.

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Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features red
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Raj, Veena, Sam-Quarcoo Dotse, Mathew Sathyajith, M. I. Petra, and Hayati Yassin. "Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems." Energies 16, no. 2 (2023): 671. http://dx.doi.org/10.3390/en16020671.

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In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and
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Dissertations / Theses on the topic "Support Vector Machine; NaiveBayes; K-Nearest Neighbour"

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Linton, Thomas. "Forecasting hourly electricity consumption for sets of households using machine learning algorithms." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186592.

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To address inefficiency, waste, and the negative consequences of electricity generation, companies and government entities are looking to behavioural change among residential consumers. To drive behavioural change, consumers need better feedback about their electricity consumption. A monthly or quarterly bill provides the consumer with almost no useful information about the relationship between their behaviours and their electricity consumption. Smart meters are now widely dispersed in developed countries and they are capable of providing electricity consumption readings at an hourly resolutio
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Andersson, Fanny, and Anna Furugård. "Detektion och klassificering av äppelmognad i hyperspektrala bilder." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178848.

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Detta arbete presenterar en icke-destruktiv metod för att detektera och klassificera mognadsgraden hos äpplen med användning av hyperspektrala bilder. Fastställning av mognadsgraden hos äpplen är intressant för bland annat äppelodlare och musterier vid lagring och beredning. Äpplens mognadsgrad är även intressant inom växtförädling. För att fastställa mognadsgraden idag krävs att det skärs i frukten, en så kallad destruktiv metod. Hyperspektrala bilder kan idag användas inom områden som jordbruk, miljöövervakning och militär spaning.<br><p>Examensarbetet är utfört vid Institutionen för teknik
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Creemers, Warren. "On the Recognition of Emotion from Physiological Data." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/680.

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This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (E
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Klimeš, Filip. "Zpracování obrazových sekvencí sítnice z fundus kamery." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220975.

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Cílem mé diplomové práce bylo navrhnout metodu analýzy retinálních sekvencí, která bude hodnotit kvalitu jednotlivých snímků. V teoretické části se také zabývám vlastnostmi retinálních sekvencí a způsobem registrace snímků z fundus kamery. V praktické části je implementována metoda hodnocení kvality snímků, která je otestována na reálných retinálních sekvencích a vyhodnocena její úspěšnost. Práce hodnotí i vliv této metody na registraci retinálních snímků.
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Sharma, Govind. "Sentiment-Driven Topic Analysis Of Song Lyrics." Thesis, 2012. https://etd.iisc.ac.in/handle/2005/2472.

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Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA
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Sharma, Govind. "Sentiment-Driven Topic Analysis Of Song Lyrics." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2472.

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Sentiment Analysis is an area of Computer Science that deals with the impact a document makes on a user. The very field is further sub-divided into Opinion Mining and Emotion Analysis, the latter of which is the basis for the present work. Work on songs is aimed at building affective interactive applications such as music recommendation engines. Using song lyrics, we are interested in both supervised and unsupervised analyses, each of which has its own pros and cons. For an unsupervised analysis (clustering), we use a standard probabilistic topic model called Latent Dirichlet Allocation (LDA)
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Book chapters on the topic "Support Vector Machine; NaiveBayes; K-Nearest Neighbour"

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Devak, Manjula, and C. T. Dhanya. "Downscaling of Precipitation in Mahanadi Basin, India Using Support Vector Machine, K-Nearest Neighbour and Hybrid of Support Vector Machine with K-Nearest Neighbour." In Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-18663-4_100.

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Sandeep, V., and A. Shri Vindhya. "Poor Accuracy in Determining Erratic User Behavior in Social Media Networks Using KNN Algorithm Comparing SVM Algorithm." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220034.

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The main aim of this research is to determine the erratic user behaviour over social media using machine learning classifiers by comparing Novel K-Nearest Neighbour algorithm and Support Vector Machine algorithm. Classification is performed using K-Nearest Neighbour with sample size (N=10) and Support Vector Machine sample size (N=10), and results were compared based on the accuracy of both algorithms. The KNN is used to determine the accuracy of erratic user behaviour with the help of social media network reviews with twitter data. The accuracy achieved for KNN is (95.30%) and SVM is (92.67%)
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Biswas, Neepa, Suchismita Maiti, Sujata Kundu, and Sudarsan Biswas. "Advancing Mental Health Insights Through Machine Learning on EEG Data." In Integrative Machine Learning and Optimization Algorithms for Disease Prediction. IGI Global Scientific Publishing, 2025. https://doi.org/10.4018/979-8-3373-1087-9.ch011.

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Machine learning techniques have shown promise in classifying mental states based on electroencephalography (EEG) data. This has implications for neuroscience, cognitive psychology, and human-computer interaction. The study applied seven machine learning algorithms, including decision tree, random forest, AdaBoost, K Nearest Neighbour, Naïve Bayes, Support Vector Machine, and Artificial Neural Network, on a publicly available EEG dataset. The random forest algorithm had the best accuracy of 96%, followed by decision tree and K Nearest Neighbour at 90%. These techniques hold great potential for
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Nisha, M., and J. Jebathagam. "Analysis of Machine Learning Algorithms in Healthcare." In Intelligent Technologies for Automated Electronic Systems. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815179514124010018.

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Machine learning entails making changes to the systems that carry out artificial intelligence (AI)-related tasks. It displays the many ML kinds and applications. It also explains the fundamental ideas behind feature selection methods and how they can be applied to a variety of machine learning (ML) techniques, including artificial neural networks (ANN), Naive Bayes classifiers (probabilistic classifiers), support vector machines (SVM), K Nearest Neighbour (KNN), and decision trees, also known as the greedy algorithm.
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Vijaya Lakshmi, Adluri, Sowmya Gudipati Sri, Ponnuru Sowjanya, and K. Vedavathi. "Prediction using Machine Learning." In Handbook of Artificial Intelligence. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124514123010005.

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This chapter begins with a concise introduction to machine learning and the classification of machine learning systems (supervised learning, unsupervised learning, and reinforcement learning). ‘Breast Cancer Prediction Using ML Techniques’ is the topic of Chapter 2. This chapter describes various breast cancer prediction algorithms, including convolutional neural networks (CNN), support vector machines, Nave Bayesian classification, and weighted Nave Bayesian classification. Prediction of Heart Disease Using Machine Learning Techniques is the topic of Chapter 3. This chapter describes the nume
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Falomir Zoe, Museros Lledo, Sanz Ismael, and Gonzalez-Abril Luis. "Guessing Art Styles using Qualitative Colour Descriptors, SVMs and Logics." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-578-4-227.

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An approach for style painting categorization based on their Qualitative Colour Descriptors (QCD) and their similarity (SimQCD) is presented in this paper. For learning the colour features of paintings from Baroque, Impressionism and Post-Impressionism styles, two classifiers are built based on k-Nearest Neighbour (k-NN) and support vector machine (SVM) methods. The results showed accuracies of categorization higher than 75%. Colour logics in art styles are identified from the literature in order to improve and explain the categorization obtained.
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Muthulakshmi, K., and K. Valarmathi. "Attaining Cloud Security Solution Over Machine Learning Techniques." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210045.

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Cloud computing provides physical and logical computation resource on demand for the set of service. Cloud environment reduce the infrastructure cost and easy to use without any extra burden. Cloud storage an access raised the several security issues like data privacy, access control, authentication, virtual machine security, web security etc., In one side hackers, breaches, cloud security issues and threats get expanded. But in another side many technologies are keep increased to secure cloud data. Technology may be cryptographic technique, anonymization technique, machine learning technique
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Alegeh, Nurudeen, Munavar Thottoli, Naeem Mian, Andrew Longstaff, and Simon Fletcher. "Feature Extraction of Time-Series Data Using DWT and FFT for Ballscrew Condition Monitoring." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210069.

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This paper investigates the use of the discrete wavelet transform (DWT) and Fast Fourier Transform (FFT) to improve the quality of extracted features for machine learning. The case study in this paper is detecting the health state of the ballscrew of a gantry type machine tool. For the implementation of the algorithm for feature extraction, wavelet is first applied to the data, followed by FFT and then useful features are extracted from the resultant signal. The extracted features were then used in various machine learning algorithms like decision tree, K-nearest neighbour (KNN) and support ve
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Kazi, Kutubuddin Sayyad Liyakat. "Machine Learning-Driven Internet of Medical Things (ML-IoMT)-Based Healthcare Monitoring System." In Advances in Healthcare Information Systems and Administration. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6294-5.ch003.

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In order to forecast nine deadly diseases, including blood pressure, diabetes, hepatitis, and kidney disorders, seven machine learning classification algorithms were utilised in this work: adaptive boosting, Random Forest, Decision Trees, Support Vector Machines, Naïve Bayes, Artificial Neural Networks, and K-Nearest Neighbour. Performance criteria such as Accuracy, Precision, and Recall are employed to evaluate the efficacy of the proposed model. Four measures are used to assess the classifiers' performance: accuracy, precision, recall, and precision. The present healthcare model reaches a mi
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-, Anjali, Pinki -, and Varun Sharma. "Sentiment Analysis on Movie “Kashmir Files” Using Machine Learning." In Emerging Trends In Engineering And Management, 2023rd ed. Soft Computing Research Society, 2023. http://dx.doi.org/10.56155/978-81-955020-3-5-02.

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Sentiment Analysis which is also known as “Opinion Mining” is a process of analyzing, emotions, sentiments, attitudes and expressions expressed in written language. Now, these opinion can be mined from any social media platform like Twitter, Facebook as people are more likely to share their opinions on these platforms. Twitter is one such platform which is very popular &amp; most people are using this to express their opinion. The research addresses the sentiment analysis of movie “Kashmir files” by using trending hash tages on Twitter. Sentiment can be of three types-1) Positive 2) Negative 3
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Conference papers on the topic "Support Vector Machine; NaiveBayes; K-Nearest Neighbour"

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Yeshvant.V and Smitha G.L. "Enhancing the Accuracy in Predicting Snow Avalanche with Support Vector Machine Algorithm Compared with K-Nearest Neighbour." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877515.

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Saikumar, Dara DVVNS, Uma Priyadarsini P. S, and I. Meignana Arumugam. "Expression of Concern for: Prediction of Heart Disease Using K-Nearest Neighbour Algorithm in Comparison with Support Vector Machine Algorithm." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703554.

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Fayaz Aashiq, Shaik, A. Mary Joy Kinol, and Pratibha Ramani. "Expression of Concern for: Detection of Diabetics Using Support Vector Machine Algorithm in Comparison With K Nearest Neighbour Algorithm to Measure Accuracy, Sensitivity and Specificity." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703550.

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Kothe, R. S., D. G. Bhalke, and P. P. Gutal. "Musical instrument recognition using k-nearest neighbour and Support Vector Machine." In 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT). IEEE, 2016. http://dx.doi.org/10.1109/icaecct.2016.7942604.

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Li Zhang, Ning Ye, Weida Zhou, and Licheng Jiao. "Support vectors pre-extracting for support vector machine based on K nearest neighbour method." In 2008 International Conference on Information and Automation (ICIA). IEEE, 2008. http://dx.doi.org/10.1109/icinfa.2008.4608212.

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Lathish, A., and T. Devi. "Comparison of Support Vector Machine and K-Nearest Neighbour Algorithm for Accurate text Classification." In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2022. http://dx.doi.org/10.1109/icirca54612.2022.9985594.

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Saikumar, Dara DVVNS, Uma Priyadarsini P. S, and I. Meignana Arumugam. "Prediction of Heart Disease Using K-Nearest Neighbour Algorithm in Comparison with Support Vector Machine Algorithm." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10023034.

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Reddy, P. Ram Kiran, and J. Mohana. "Enhancement of accuracy in face recognition system using K-Nearest Neighbour algorithm over support vector machine." In 5TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES (ICMS5). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0227887.

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Reddy, M. Guru Karthik, and P. Jagadeesh. "Innovative detection of brain tumor using support vector machine classifier and comparison with K-nearest neighbour classifier." In THE 4TH INTERNATIONAL CONFERENCE ON MATERIAL SCIENCE AND APPLICATIONS. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0173033.

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Arani, Adithya, G. Uganya, and T. Sathish. "Comparative analysis of accuracy in prediction of loan sanction using k-nearest neighbour and support vector machine." In FIFTH INTERNATIONAL CONFERENCE ON APPLIED SCIENCES: ICAS2023. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0198469.

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