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

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Journal articles 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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1

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. K

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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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Ardiada, I. Made Dwi, Made Sudarma, and Dwi Giriantari. "Text Mining pada Sosial Media untuk Mendeteksi Emosi Pengguna Menggunakan Metode Support Vector Machine dan K-Nearest Neighbour." Majalah Ilmiah Teknologi Elektro 18, no. 1 (2019): 55. http://dx.doi.org/10.24843/mite.2019.v18i01.p08.

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Twitter layanan jejaring sosial dan mikroblog yang memungkinkan penggunanya untuk mengirim dan membaca pesan berbasis teks hingga 140 karakter, yang dikenal dengan sebutan kicauan (tweet). Sebuah teks pada tweet tidak hanya menyampaikan keterangan dari suatu informasi, tetapi juga berisi informasi tentang perilaku manusia termasuk emosi. Untuk mendeteksi emosi dari teks pada layanan sosial media twitter dengan data yang tidak terstruktur maka perlu dilakukan analisis teks salah satunya dengan menggunakan Text Mining. Pada penelitian ini mengusulkan melakukan penelitian text mining pada Sosial

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Agrawal, Shweta, Sanjiv Kumar Jain, Ajay Khatri, Mohit Agarwal, Anshul Tripathi, and Yu-Chen Hu. "Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality." Mathematical Problems in Engineering 2022 (July 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/6445580.

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Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand

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Dr. G. Vijaya Lakshmi. "A Review on K nearest Neighbour Classification Technique in Machine Learning." International Journal of Scientific Research in Science, Engineering and Technology 12, no. 1 (2025): 257–60. https://doi.org/10.32628/ijsrset25121168.

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Classification is a Supervised Learning technique which is used to predict the correct category from the given input features. Logistic regression, decision trees, random forests, support vector machines (SVM), naive bayes, and K-nearest neighbors (KNN) are some of the several classification techniques.. This paper discusses the KNN classification technique, which uses the similarity measure of previously stored data points to classify new data points.

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Sunanthini, V., J. Deny, E. Govinda Kumar, et al. "Comparison of CNN Algorithms for Feature Extraction on Fundus Images to Detect Glaucoma." Journal of Healthcare Engineering 2022 (January 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/7873300.

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Glaucoma is a disease where the optic nerve of the eyes is smashed up due to the building up of pressure inside the vision point. This has no symptoms at the initial stages, and hence, patients with this disease cannot identify them at the beginning stage. It is explained as if the pressure in the eye increases, then it will hurt the optic nerve which sends images to the brain. This will lead to permanent vision loss or total blindness. The existing method used for the detection of glaucoma includes k-nearest neighbour and support vector machine algorithms. The k-nearest neighbour algorithm an

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Lefulebe, Bosiu E., Adriaan Van der Walt, and Sifiso Xulu. "Fine-Scale Classification of Urban Land Use and Land Cover with PlanetScope Imagery and Machine Learning Strategies in the City of Cape Town, South Africa." Sustainability 14, no. 15 (2022): 9139. http://dx.doi.org/10.3390/su14159139.

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Urban land use and land cover (LULC) change can be efficiently monitored with high-resolution satellite products for a variety of purposes, including sustainable planning. These, together with machine learning strategies, have great potential to detect even subtle changes with satisfactory accuracy. In this study, we used PlaneScope Imagery and machine learning strategies (Random Forests, Support Vector Machines, Naïve Bayes and K-Nearest Neighbour) to classify and detect LULC changes over the City of Cape Town between 2016 and 2021. Our results showed that K-Nearest Neighbour outperformed oth

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Todeschini, Roberto, and Cecile Valsecchi. "Evaluation of classification performances of minimum spanning trees by 13 different metrics." MATCH Communications in Mathematical and in Computer Chemistry 87, no. 2 (2021): 273–98. http://dx.doi.org/10.46793/match.87-2.273t.

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Minimum Spanning Tree (MST) is a well-known clustering algorithm that provides a graphical tree representation of the objects in a data set by exploiting local information to link each pair of similar objects. The a-posteriori analysis of this tree in terms of nodes and edges provides the basis to derive simple classifiers, namely semi-supervised classification approaches based on the minimum spanning tree approach. In this work, we propose different metrics to evaluate the MST ability to group objects of the same a-priori known classes. The classification capability of the proposed approach,

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Siriteerakul, Teera, Veera Boonjing, and Rutchanee Gullayanon. "Character classification framework based on support vector machine and k-nearest neighbour schemes." ScienceAsia 42, no. 1 (2016): 46. http://dx.doi.org/10.2306/scienceasia1513-1874.2016.42.046.

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de Feiter, Vincent S. de, Jessica M. I. Strickland, and Irene Garcia-Marti. "Advancing Data Quality Assurance with Machine Learning: A Case Study on Wind Vane Stalling Detection." Atmosphere 16, no. 2 (2025): 129. https://doi.org/10.3390/atmos16020129.

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High-quality observational datasets are essential for climate research and models, but validating and filtering decades of meteorological measurements is an enormous task. Advances in machine learning provide opportunities to expedite and improve quality control while offering insight into non-linear interactions between the meteorological variables. The Cabauw Experimental Site for Atmospheric Research in the Netherlands, known for its 213 m observation mast, has provided in situ observations for over 50 years. Despite high-quality instrumentation, measurement errors or non-representative dat

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Mondal, Shudipti Rani, Aafreen ., and Rakesh Pal. "PREDICTION OF BREAST CANCER USING MACHINE LEARNING." International Journal of Innovative Research in Advanced Engineering 8, no. 3 (2021): 28–33. http://dx.doi.org/10.26562/ijirae.2021.v0803.001.

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In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approache

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SAKTHIPRIYA, DHINAKARAN, and CHANDRAKUMAR THANGAVEL. "Comparison of machine learning classification algorithms based on weather variables and seed characteristics for the selection of paddy seed." Journal of Agrometeorology 26, no. 2 (2024): 209–14. http://dx.doi.org/10.54386/jam.v26i2.2553.

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Selection of seed is very crucial for the farmers before the start of the crop season. In this study therefore, an attempt has been made to compare various machine learning (ML) classification techniques for paddy seed forecast for cultivation in three major paddy producing taluk of Madurai district, Tamil Nadu viz Thirumangalam, Peraiyur, and Usilampatti. Five machine learning classification techniques viz. K-nearest neighbour (KNN), decision tree (DT), naive bayes (NB), support vector machine (SVM), and logistic regression (LR) used in this study were compared based on weather data and seed

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Cannings, Timothy I., Yingying Fan, and Richard J. Samworth. "Classification with imperfect training labels." Biometrika 107, no. 2 (2020): 311–30. http://dx.doi.org/10.1093/biomet/asaa011.

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Summary We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we deri

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Kazemi, Pooyan, Aldo Ghisi, and Stefano Mariani. "Classification of the Structural Behavior of Tall Buildings with a Diagrid Structure: A Machine Learning-Based Approach." Algorithms 15, no. 10 (2022): 349. http://dx.doi.org/10.3390/a15100349.

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We study the relationship between the architectural form of tall buildings and their structural response to a conventional seismic load. A series of models are generated by varying the top and bottom plan geometries of the buildings, and a steel diagrid structure is mapped onto their skin. A supervised machine learning approach is then adopted to learn the features of the aforementioned relationship. Six different classifiers, namely k-nearest neighbour, support vector machine, decision tree, ensemble method, discriminant analysis, and naive Bayes, are adopted to this aim, targeting the struct

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Vaishnavi, Ramathirtham. "Smart Crop Recommendation Using Machine Learning for Precision Agriculture." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49536.

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Abstract - Agriculture and its allied sectors are undoubtedly the largest providers of livelihoods in rural India. The agriculture sector is also a significant contributor factor to the country's Gross Domestic Product (GDP). Blessing to the country is the overwhelming size of the agricultural sector. However, regrettable is the yield per hectare of crops in comparison to international standards. This is one of the possible causes for a higher suicide rate among marginal farmers in India. This paper proposes a viable and user-friendly yield prediction system for the farmers. The proposed syste

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Joseph Adelakun, Adebowale, Boniface Kayode Alese, Aderonke Favour-Betty Thompson, and Olufunso Dayo Alowolodu. "Enhanced Machine Learning-based Diagnostic Model for Lassa Fever." Journal of Intelligent Data Analysis and Computational Statistics 1, no. 1 (2024): 20–30. http://dx.doi.org/10.46610/joidacs.2024.v01i01.003.

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The research presents a machine learning-based model for the diagnosis of Lassa fever. The development of the ML Model involves the collection of the Lassa fever dataset from the Infectious Diseases Control Centre of the Federal Medical Centre, Owo Ondo state, it was preprocessed and Five machine learning algorithms namely Naïve Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Decision Tree were trained and tested using 10-fold cross-validation technique and evaluated using standard metrics. Four of the classifiers had an accuracy of 90%

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Irfan, M., A. R. Nurhidayat, A. Wahana, D. S. Maylawati, and M. A. Ramdhani. "Comparison of K-Nearest Neighbour and support vector machine for choosing senior high school." Journal of Physics: Conference Series 1280 (November 2019): 022026. http://dx.doi.org/10.1088/1742-6596/1280/2/022026.

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Devak, Manjula, C. T. Dhanya, and A. K. Gosain. "Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall." Journal of Hydrology 525 (June 2015): 286–301. http://dx.doi.org/10.1016/j.jhydrol.2015.03.051.

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Chandrappa, Harshita, Swaathi BR, and Prof Swimpy Pahuja. "Prediction of Autism Spectrum Disorder based on Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3521–26. http://dx.doi.org/10.22214/ijraset.2023.52417.

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Abstract: In recent years, awareness of autism spectrum disorder (ASD) has grown faster than before. As everyone is aware, ASD is a disorder of neurodevelopment that also encompasses problems with conduct and social interaction. The degree of symptom severity and each individual's experience with ASD vary. Any age can be used to diagnose autism. According to research, violence, self-harm, elopement. tantrums, preoccupation, and lack of obedience are behaviour patterns most frequently observed in people with autism. Therefore, it is imperative to spot any sign of severe ASD as soon as possible.

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AIRCC. "LEVERAGING NAIVE BAYES FOR ENHANCED SURVIVAL ANALYSIS IN BREAST CANCER." International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 4 (2024): 47–56. https://doi.org/10.5121/ijaia.2024.15402.

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The study aims to predict breast cancer survival using Naïve Bayes techniques by comparing differentmachine learning models on a comprehensive dataset of patient records. The main classification groupswere survival and non-survival. The objective was to assess the performance of the Naïve Bayes classifierin the field of data mining and to achieve significant results in survival classification, aligning with currentacademic research. The Naive Bayes classifier attained an average accuracy of 91.08%, indicating consistent performance,though with some variability across different folds.

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Ichwan, Muhammad, Irma Amelia Dewi, and Zeni Muharom S. "Klasifikasi Support Vector Machine (SVM) Untuk Menentukan TingkatKemanisan Mangga Berdasarkan Fitur Warna." MIND Journal 3, no. 2 (2019): 16–23. http://dx.doi.org/10.26760/mindjournal.v3i2.16-23.

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Dalam proses penentuan mutu atau tingkat kemanisan buah mangga cengkir di pasaran pada umumnya dilakukan dengan dengan dua cara yaitu menggunakan pakar-pakar untuk pemilihan / sortasi kemanisan mangga atau menggunakan metode destruktif dengan cara pengambilan sampel, uji coba kemanisan mangga tersebut seperti menggunakan Refractometer. Permasalahan yang terjadi pada kedua proses tersebut yaitu memiliki cost yang relative besar dan tidak menghasilkan mutu yang seragam karena sortasi tingkat kemanisan mangga oleh pakar bersifat subjektif dan kemungkinan terjadinya kesalahan pengamatan sangat. Su

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Vaneesha, K. H., V. Srinivas, V. Abhishek, and Srinivas Sujay. "Comparative Analysis of Machine Learning Algorithms for Used Car Price Prediction." International Journal of Current Science Research and Review 07, no. 09 (2024): 7220–28. https://doi.org/10.5281/zenodo.13799018.

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Abstract : After 2021, over 90 million passenger automobiles were produced, marking a significant increase in auto production. This growth has led to a flourishing used car market, which has become a highly lucrative sector. One of the most critical and fascinating areas of research within this market is automobile price prediction. Accurate price prediction models can greatly benefit buyers, sellers, and businesses in the used car industry. This paper presents a detailed comparative analysis of two supervised machine learning models: K-Nearest Neighbour and Support Vector Machine regression t

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Earnest, Arul, Getayeneh Antehunegn Tesema, and Robert G. Stirling. "Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients." Healthcare 11, no. 20 (2023): 2756. http://dx.doi.org/10.3390/healthcare11202756.

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Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the Victorian Lung Cancer Registry (VLCR) between 2011 and 2022, in Victoria, Australia. Predictor variables included demographic, clinical, hospital, and geographical socio-economic indices. Machine learning methods such as random forests, k-nearest neighbour, neural networks, and support vector machines were implemented an

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Abdullah, Mohammad Nasir, Yap Bee Wah, Abu Bakar Abdul Majeed, et al. "Comparative Evaluation of Machine Learning Algorithms for Alzheimer’s Disease Classification using Synthetic Transcriptomics Dataset." Trends in Sciences 20, no. 11 (2023): 6881. http://dx.doi.org/10.48048/tis.2023.6881.

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Recent technological advancements have enabled the understanding of multi-omics data, including transcriptomics, proteomics, and metabolomics. Machine learning algorithms have shown promising results in classifying multi-omics data. The objective of this paper is to evaluate the performance of machine learning algorithms in classifying transcriptomics data for Alzheimer’s disease (AD) patients and healthy control (HC) individuals. A Synthetic dataset of varying sample sizes, dimensionalities, effect sizes, and correlations was generated based on actual transcriptomics data for AD patients. The

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Satria, M. Adhi, Kurniawan Nur Ramadhani, and Anditya Arifianto. "Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern." Indonesian Journal on Computing (Indo-JC) 3, no. 1 (2018): 75. http://dx.doi.org/10.21108/indojc.2018.3.1.215.

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<p>Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.</p>

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Aljameel, Sumayh S., Dorieh M. Alomari, Shatha Alismail, et al. "An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning." Computation 10, no. 8 (2022): 138. http://dx.doi.org/10.3390/computation10080138.

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Detection of minor leaks in oil or gas pipelines is a critical and persistent problem in the oil and gas industry. Many organisations have long relied on fixed hardware or manual assessments to monitor leaks. With rapid industrialisation and technological advancements, innovative engineering technologies that are cost-effective, faster, and easier to implement are essential. Herein, machine learning-based anomaly detection models are proposed to solve the problem of oil and gas pipeline leakage. Five machine learning algorithms, namely, random forest, support vector machine, k-nearest neighbou

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Trtak, Fatima Abdel Monem, and Kanita Karađuzović-Hadžiabdić. "Clinical decision making for prediction of otitis using machine learning approach." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 2 (2022): 138–46. https://doi.org/10.21533/pen.v10.i2.588.

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This study investigates the relationship between autoimmune disease otitis and gut microbial community abundance by using machine learning as an aid in the medical decision-making process. Stool samples of healthy and otitis diseased infants were obtained from the curatedMetagenomicData package. Class imbalance present in the dataset was handled by oversampling a minority class. Afterwards, we built several machine learning models (support vector machine, k-nearest neighbour, artificial neural networks, random forest and gradient boosting) to predict otitis from gut microbial samples. The best

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Akbar, Alvin Tarisa, Novanto Yudistira, and Achmad Ridok. "Identifikasi Gagal Ginjal Kronis dengan Mengimplementasikan Metode Support Vector Machine beserta K-Nearest Neighbour (SVM-KNN)." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 2 (2023): 301–8. https://doi.org/10.25126/jtiik.20236059.

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Ginjal merupakan bagian vital bagi manusia karena berfungsi untuk menyaring atau membersihkan cairan yang kita minum agar dapat dikonsumsi oleh tumbuh secara normal. Gagal ginjal adalah situasi dimana ginjal mengalami penurunan funsionalnya secara terus-menerus yang mana dapat mengakibatkan ketidakmampuan ginjal untuk berfungsi untuk semestinya. Untuk membantu pasien yang terjangkit penyakit gagal ginjal kronis hal yang terlebih dahulu dilakukan adalah mengindentifikasi penyakit tersebut. Indentifikasi gagal ginjal kronis dengan menggunakan dataset yang dibuat oleh L.Jerlin Rubini dkk. sudah d

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C, Pathanjali, Vimuktha E Salis, Jalaja G, and Latha A. "A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines." International Journal of Engineering & Technology 7, no. 3.12 (2018): 521. http://dx.doi.org/10.14419/ijet.v7i3.12.16171.

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Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency

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Akbar, Alvin Tarisa, Novanto Yudistira, and Achmad Ridok. "Identifikasi Gagal Ginjal Kronis dengan Mengimplementasikan Metode Support Vector Machine beserta K-Nearest Neighbour (SVM-KNN)." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 2 (2023): 301. http://dx.doi.org/10.25126/jtiik.20231026059.

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<p class="Judul2">Ginjal merupakan bagian vital bagi manusia karena berfungsi untuk menyaring atau membersihkan cairan yang kita minum agar dapat dikonsumsi oleh tumbuh secara normal. Gagal ginjal adalah situasi dimana ginjal mengalami penurunan funsionalnya secara terus-menerus yang mana dapat mengakibatkan ketidakmampuan ginjal untuk berfungsi untuk semestinya. Untuk membantu pasien yang terjangkit penyakit gagal ginjal kronis hal yang terlebih dahulu dilakukan adalah mengindentifikasi penyakit tersebut. Indentifikasi gagal ginjal kronis dengan menggunakan <em>dataset</em>

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Parodi, Stefano, Chiara Manneschi, Damiano Verda, Enrico Ferrari, and Marco Muselli. "Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables." Health Informatics Journal 24, no. 1 (2016): 54–65. http://dx.doi.org/10.1177/1460458216655188.

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This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin’s lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin’s lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperf

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Seimahura, Syarah, and Arina Selawati. "ANALISIS PERBANDINGAN KLASIFIKASI CITRA MYCROBACTERIUM TUBERCULOSIS." Akrab Juara : Jurnal Ilmu-ilmu Sosial 7, no. 1 (2022): 311. http://dx.doi.org/10.58487/akrabjuara.v7i1.1777.

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Tuberculosis (TB) is an infectious disease that remains a challenging health problem worldwide. This disease is caused by rod-shaped bacteria called Mycobacterium tuberculosis. These bacteria usually affect the lungs but can also spread to other parts of the body such as the eyes, bones and blood vessels. It was reported that around. 4.74 million new TB cases were identified and around eight hundred thousand people died from TB, during 2015 in Southeast Asia. In this study, color image segmentation techniques were carried out using classification methods by comparing several methods including

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CLASSIFICATION, AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR MACHINE AND K.-NEAREST NEIGHBOUR ALGORITHMS. "CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOUR ALGORITHMS." Advances in Engineering: an International Journal (ADEIJ) 2, no. 3 (2019): 01–13. https://doi.org/10.5281/zenodo.3237350.

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Remote sensing is collecting information about an object without any direct physical contact with the particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing uses the Satellite to detect and classify the particular object or area. They also classify the object on the earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the classifiers of previous images to decrease the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is pre

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Halim, Muhammad, Muslihah Wook, Nor Hasbullah, Noor Razali, and Hasmeda Hamid. "Comparative Assessment of Data Mining Techniques for Flash Flood Prediction." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (2022): 126–45. http://dx.doi.org/10.15849/ijasca.220328.09.

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Abstract Data mining techniques have recently drawn considerable attention from the research community for their ability to predict flash flood phenomena. These techniques can bring large-scale flood data into real practice and have become the necessary tools for impact assessment, societal resilience, and disaster control. Although numerous studies have been conducted on data mining techniques and flash flood predictions, domain-specific flash flood prediction models based on existing data mining techniques are still lacking. Notably, this study has focused on the performance of four data min

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Mathu sudhanan, S. R., K. Priya, and P. Uma Maheswari. "Deep Learnt Features and Machine Learning Classifier for Texture classification." Journal of Physics: Conference Series 2070, no. 1 (2021): 012108. http://dx.doi.org/10.1088/1742-6596/2070/1/012108.

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Abstract Texture classification plays a vital role in the emerging research field of image classification. This paper approaches the texture classification problem using significant features extracted from pre-trained Convolutional Neural Network (CNN) like Alexnet, VGG16, Resnet18, Googlenet, MobilenetV2, and Darknet19. These features are classified by machine learning classifiers such as Support Vector Machine (SVM), Ensemble, K Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), and Discriminant Analysis (DA). The performance of the work is evaluated with the texture databases na

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Zekić-Sušac, Marijana, Sanja Pfeifer, and Nataša Šarlija. "A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem." Business Systems Research Journal 5, no. 3 (2014): 82–96. http://dx.doi.org/10.2478/bsrj-2014-0021.

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Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in

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Kurniadi, Felix Indra. "Perbandingan Local Binary Pattern untuk Klasifikasi Sel Darah Putih." Jurnal ULTIMATICS 9, no. 2 (2018): 118–21. http://dx.doi.org/10.31937/ti.v9i2.663.

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In recent year, a lot of researches try to overcome problem in recognition and classify white blood cells to help hematologists diagnose white blood cells disease such blood cancer, leukemia and AIDS. This paper compares several methods Local Binary Pattern such as Local Binary Pattern Uniform, Local Binary Pattern Rotation Invariant and Local Binary Pattern Rotation Invariant Uniform to classify five types of white blood cells using two classifier: Support Vector Machine and K-Nearest Neighbour. Index Terms—LBP, LBP-U, LBP-RI, LBP-RIU, white blood cells

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Anupama, Y.K., S. Amutha., and Babu. D. R. Ramesh. "An Ensemble Classifier Approach for Diagnosis of Breast Cancer." Journal of Image Processing and Artificial Intelligence 6, no. 1 (2020): 7–12. https://doi.org/10.5281/zenodo.3607239.

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<em>Accurate and early diagnosis of breast cancer increases survival rate of patients. Diagnosis of Breast cancer involves identifying tumour as either benign or malignant. In this paper, proposed methodology is an integration of ensemble classifiers AdaBoost and Random Forest named as ADARF a prediction model for diagnosis of breast cancer. The main objective is to enhance the performance and to reduce error. Experimental result shows that the proposed approach has higher accuracy of 98.8% compared to Logistic Regression (LR), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classif

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Anupama, Y.K., S. Amutha., and Babu. D. R. Ramesh. "An Ensemble Classifier Approach for Diagnosis of Breast Cancer." Journal of Data Mining and Management 5, no. 1 (2020): 7–12. https://doi.org/10.5281/zenodo.3609348.

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<em>Accurate and early diagnosis of breast cancer increases survival rate of patients. Diagnosis of Breast cancer involves identifying tumour as either benign or malignant. In this paper, proposed methodology is an integration of ensemble classifiers AdaBoost and Random Forest named as ADARF a prediction model for diagnosis of breast cancer. The main objective is to enhance the performance and to reduce error. Experimental result shows that the proposed approach has higher accuracy of 98.8% compared to Logistic Regression (LR), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classif

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Tiitta, Markku, Valtteri Tiitta, Jorma Heikkinen, Reijo Lappalainen, and Laura Tomppo. "Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning." Sensors 20, no. 4 (2020): 1076. http://dx.doi.org/10.3390/s20041076.

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Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest ne

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Haeruddin, Haeruddin, Erick Erick, and Heru Wijayanto Aripradono. "Perbandingan Support Vector Machine, Random Forest Classifier, dan K-Nearest Neighbour dalam Pendeteksian Anomali pada Jaringan DDos." JTIM : Jurnal Teknologi Informasi dan Multimedia 7, no. 1 (2025): 23–33. https://doi.org/10.35746/jtim.v7i1.628.

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A Distributed Denial of Service (DDoS) attack poses a serious threat to network security and can disrupt online services by overwhelming the target server with excessive traffic. Effective detection of DDoS attacks requires a system capable of identifying anomalies in network traffic. In this context, Machine Learning (ML) offers an effective approach for classification and anomaly detection. However, different ML algorithms have varying strengths and weaknesses when processing large and complex network data. Therefore, this study aims to evaluate the performance of three ML algorithms: Suppor

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Sreevidya, Nelli, K. Akshaya, Md Hamza, and T. Pranay. "Fire Prediction Analysis Based on Ensemble Machine Learning Algorithms." Asian Journal of Research in Computer Science 17, no. 6 (2024): 74–84. http://dx.doi.org/10.9734/ajrcos/2024/v17i6457.

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A fire accident is the most tragic incident in human life. Particularly environmental hazards such as forest fires lead loss of wildlife, economy, wealth, human lives and pollution. our research purpose of predict the occurrence of fire incidents using ensemble machine learning models. The goal is to develop an accurate and reliable model that can forecast the occurrence of forest fires based on various environmental factors. The best performance is obtained by the ensemble machine learning model for this work. Comparative study of individual model and ensemble model. If you check all models D

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