Relevant bibliographies by topics / SVM(Support Vector Machine) KNN (K- nearest neighbor)

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Academic literature on the topic 'SVM(Support Vector Machine) KNN (K- nearest neighbor)'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "SVM(Support Vector Machine) KNN (K- nearest neighbor)"

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Assegie, Tsehay Admassu. "Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model." Indonesian Journal of electronics, electromedical engineering, and medical informatics 3, no. 1 (2021): 9–14. http://dx.doi.org/10.35882/ijeeemi.v3i1.2.

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Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.
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Srinivasulureddy, Ch, and N. S. Kumar. "Analysis and Comparison for Innovative Prediction Technique of Breast Cancer Tumor using k Nearest Neighbor Algorithm over Support Vector Machine Algorithm with Improved Accuracy." CARDIOMETRY, no. 25 (February 14, 2023): 878–94. http://dx.doi.org/10.18137/cardiometry.2022.25.878884.

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Aim: The main objective of this study is to compare the efficiency of the k-Nearest Neighbor (KNN) and Support vector machine (SVM) algorithms in detecting breast cancer tumors and to examine their improved accuracy, sensitivity, and precision. Materials and Methods: The data for the research of Innovative breast cancer prediction using machine learning algorithms is taken from UCI Machine Learning Repository. The sample size of the innovative technique involves two groups KNN (N=20) and SVM (N=20) according to clincalc.com by keeping alpha error-threshold at 0.05, confidence interval at 95%, enrollment ratio as 0:1, and power at 80%. The accuracy, sensitivity, and precision are calculated using MATLAB software. Result: Accuracy (%), sensitivity (%), precision (%) are compared using SPSS software using an independent sample t-test tool. The accuracy of the k-Nearest Neighbor is 93.38% (p<0.001) while the accuracy of the Support vector machine is 97.50%. The sensitivity rate is 90.85% (p<0.001) for k-Nearest Neighbor whereas the results of Support vector machine sensitivity is 95.83%. The precision of k-Nearest Neighbor is 98.48% (p<0.001) whereas the results of Support vector machine precision is 100%. Conclusion: The support vector machine algorithm appears to have performed better than the k-Nearest Neighbor with improved accuracy in Innovative breast cancer prediction.
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Solikin, Steven, Angga Dwinovantyo, Henry Munandar Manik, Sri Pujiyati, and Susilohadi Susilohadi. "Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data." Journal of Applied Geospatial Information 7, no. 2 (2023): 898–903. http://dx.doi.org/10.30871/jagi.v7i2.6363.

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Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093.
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Umar, Rusydi, Imam Riadi, and Dewi Astria Faroek. "A Komparasi Image Matching Menggunakan Metode K-Nearest Neightbor (KNN) dan Support Vector Machine (SVM)." Journal of Applied Informatics and Computing 4, no. 2 (2020): 124–31. http://dx.doi.org/10.30871/jaic.v4i2.2226.

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Pencocokan gambar adalah proses menemukan gambar digital yang memiliki tingkat kesamaan. mencocokkan gambar menggunakan metode klasifikasi. Dalam mengukur pencocokan gambar, gambar yang digunakan adalah gambar logo asli dan gambar logo hasil manipulasi. Perbandingan algoritma klasifikasi dari dua metode yaitu K-Nearest Neighbor (KNN) dan Support Vector Machine dengan optimasi Sequential Minimal Optimization (SMO) yang digunakan untuk menghitung kecocokan berdasarkan nilai akurasi. Metode klasifikasi K-Nearest Neighbor (KNN) didasarkan pada kedekatan atau perhitungan K sedangkan metode klasifikasi Support Vector Machine (SVM) mengukur jarak antara hyperplane dan data terdekat. Nilai kecocokan gambar diukur dengan Precision, Recall, F1-Score, dan Accuracy. Langkah-langkah pencocokan gambar mulai dari persiapan pemrosesan data, ekstraksi fitur dan bentuk warna HSV, kemudian tahap klasifikasi. Gambar digital digunakan sebanyak 10 gambar yang terdiri dari satu logo asli dan 9 logo yang dimanipulasi. Pada tahap pengujian klasifikasi, menggunakan aplikasi WEKA dengan menerapkan metode validasi silang 10 kali lipat. Dari hasil tes yang dilakukan bahwa metode klasifikasi k-neighbor (KNN) terdekat adalah 80% dan memiliki k = 0,889 yang cukup baik dalam mengukur kedekatan, sedangkan metode klasifikasi SVM adalah 70%. Hasil perbandingan pencocokan gambar ini dapat disimpulkan bahwa metode klasifikasi K-Nearest Neighbor bekerja lebih baik daripada SVM untuk pencocokan gambar.
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Tjikdaphia, Nadya Bethry Balqies, and Sulastri Sulastri. "COMPARISON OF NBC, SVM, KNN CLASSIFICATION RESULTS IN SENTIMENT ANALYSIS OF MOBILE JKN." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 9, no. 4 (2023): 665–72. http://dx.doi.org/10.33330/jurteksi.v9i4.2539.

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Abstract: The JKN Mobile application is a mobile application created to facilitate healthcare administration in Indonesia since 2017. The application has been downloaded by over 10 million users and has received 484,000 diverse reviews, including positive, negative, and neutral feedback. The average rating given by users is 4.5 out of 5 stars. This research aims to perform sentiment analysis on user reviews found in the Google Play Store review column. The methods used for sentiment analysis are Naive Bayes, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM). The test results show that with a 10% test data and 90% training data proportion, the SVM method achieves the highest accuracy of 95%. Naive Bayes follows with an accuracy of 87%, and K-NN with an accuracy of 75%. Keywords: JKN mobile application, sentiment analysis, naive bayes, k-nearest neighbor (K-NN), support vector machine (SVM). Abstrak: Aplikasi Mobile JKN adalah sebuah aplikasi yang dibuat untuk mempermudah administrasi kesehatan di Indonesia sejak tahun 2017. Aplikasi ini telah diunduh lebih dari 10 juta pengguna dengan 484 ribu ulasan beragam positif, negatif, dan netral. Rata-rata rating yang diberikan pengguna adalah 4,5 bintang dari 5 bintang. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan pengguna yang terdapat di kolom review Google Play Store. Metode yang digunakan untuk analisis sentimen adalah Naive Bayes, K-Nearest Neighbor (K-NN), dan Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa dengan menggunakan proporsi data uji sebesar 10% dan data training sebesar 90%, metode SVM mencapai akurasi tertinggi sebesar 95%. Diikuti oleh Naive Bayes dengan akurasi 87%, dan K-NN dengan akurasi 75%. Kata kunci: JKN mobile, analisis sentimen, naïve bayes, k-nearest neighbor (K-NN), support vector machine (SVM).
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Sopiatul Ulum, Rizal Fahmi Alifa, Putri Rizkika, and Chaerur Rozikin. "Perbandingan Performa Algoritma KNN dan SVM dalam Klasifikasi Kelayakan Air Minum." Generation Journal 7, no. 2 (2023): 141–46. http://dx.doi.org/10.29407/gj.v7i2.20270.

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Air menjadi kebutuhan mendasar bagi kelangsungan makhluk hidup dan pembangunan. Saat ini, kesadaran masyarakat terhadap pola konsumsi air yang berkualitas dan bermutu semakin tinggi sehingga diperlukan penelitian terhadap kelayakan air. Dalam penelitian air tersebut menggunakan metode klasifikasi objek. Pada penelitian ini membahas perbandingan antara 2 metode Machine Learning yaitu K-Nearest Neighbors (K-NN) dengan Support Vector Machine (SVM) berdasarkan parameter yang telah ditentukan. Penelitian ini menghasilkan tingkat akurasi algoritme K-Nearest Neighbors (K-NN) sebesar 65,341% dan algoritme Support Vector Machine (SVM) menghasilkan akurasi sebesar 69,764%. Dari hasil tersebut, dapat disimpulkan bahwa algoritme Support Vector Machine (SVM) memiliki akurasi lebih tinggi daripada algoritme K-Nearest Neighbors (K-NN).
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Minarno, Agus Eko, Fauzi Dwi Setiawan Sumadi, Hardianto Wibowo, and Yuda Munarko. "Classification of batik patterns using K-Nearest neighbor and support vector machine." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 1260–67. http://dx.doi.org/10.11591/eei.v9i3.1971.

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This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
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Agus, Eko Minarno, Dwi Setiawan Sumadi Fauzi, Wibowo Hardianto, and Munarko Yuda. "Classification of batik patterns using K-Nearest neighbor and support vector machine." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 1260–67. https://doi.org/10.11591/eei.v9i3.1971.

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This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and support vector machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include energy, entropy, contras, correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
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Basedt, Ngabdul, Eko Supriyadi, and Agus Susilo Nugroho. "Perbandingan Algoritma Klasifikasi dalam Analisis Sentimen Opini Masyarakat tentang Kenaikan Harga Bbm." Joined Journal (Journal of Informatics Education) 6, no. 2 (2024): 219. http://dx.doi.org/10.31331/joined.v6i2.2893.

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Kenaikan harga bahan bakar minyak (BBM) telah menjadi permasalahan yang cukup kompleks dan kontroversial . Peningkatan harga BBM memengaruhi berbagai aspek ekonomi dan sosial, termasuk inflasi, biaya produksi, dan tarif transportasi di Indonesia. Klasifikasi sentimen menggunakan algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan metode algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk menentukan algorimat klasifikasi sentimen manakah yang terbaik. Dengan melakukan perbangdingan algoritma klasifikasi sentimen menghasilkan akurasi yang paling tinggi didapatkan oleh algoritma Naive Bayes dengan akurasi sebesar 80,28%. Kedua adalah algoritma Support Vector Machine (SVM) dengan akurasi sebesar 73,89%. Algoritma yang memiliki nilai akurasi paling kecil adalah algorima K-Nearest Neighbor (KNN) dengan akurasi sebesar 50,00%.
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Alamsyah, Riza, Iman Permana, and Maharani Siti Aulieza. "Perbandingan Cacat Ubin Keramik dengan Metode K-Nearest Neighbor dan Support Vector Machine." Techno.Com 22, no. 4 (2023): 805–11. http://dx.doi.org/10.33633/tc.v22i4.8897.

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Penentuan kualitas ubin keramik sudah dilakukan secara otomatis dalam beberapa tahun terakhir. Kendala saat penentuan ubin keramik bercacat dapat berpengaruh terhadap penurunan kualitas produk akhir. Isu yang menjadi fokus dalam penelitian yaitu perbandingan metode antara KNN dengan SVM untuk mendeteksi cacat pada ubin keramik untuk mencapai hasil yang lebih akurat. Untuk mengatasi isu ini, proses yang dilakukan meliputi pengumpulan data gambar dari ubin keramik, yang kemudian diikuti oleh tahap preprocessing dan ekstraksi fitur berdasarkan tekstur. Data gambar tersebut kemudian diklasifikasikan dengan metode KNN dan SVM. Temuan dari penelitian ini menunjukkan bahwa pengklasifikasian dengan metode KNN pada k = 3 mampu memberikan hasil yang lebih unggul, yaitu mencapai akurasi 98.947%, sedangkan pengklasifikasian dengan metode SVM hanya mencapai akurasi 85.263%.
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Dissertations / Theses on the topic "SVM(Support Vector Machine) KNN (K- nearest neighbor)"

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VANCE, DANNY W. "AN ALL-ATTRIBUTES APPROACH TO SUPERVISED LEARNING." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1162335608.

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Alsouda, Yasser. "An IoT Solution for Urban Noise Identification in Smart Cities : Noise Measurement and Classification." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-80858.

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Noise is defined as any undesired sound. Urban noise and its effect on citizens area significant environmental problem, and the increasing level of noise has become a critical problem in some cities. Fortunately, noise pollution can be mitigated by better planning of urban areas or controlled by administrative regulations. However, the execution of such actions requires well-established systems for noise monitoring. In this thesis, we present a solution for noise measurement and classification using a low-power and inexpensive IoT unit. To measure the noise level, we implement an algorithm for calculating the sound pressure level in dB. We achieve a measurement error of less than 1 dB. Our machine learning-based method for noise classification uses Mel-frequency cepstral coefficients for audio feature extraction and four supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregating, and random forest). We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for the classification of sound samples in the dataset under study. We achieve noise classification accuracy in the range of 88% – 94%.
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Σαψάνης, Χρήστος. "Αναγνώριση βασικών κινήσεων του χεριού με χρήση ηλεκτρομυογραφήματος". Thesis, 2013. http://hdl.handle.net/10889/6420.

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Ο στόχος αυτής της εργασίας ήταν η αναγνώριση έξι βασικών κινήσεων του χεριού με χρήση δύο συστημάτων. Όντας θέμα διεπιστημονικού επιπέδου έγινε μελέτη της ανατομίας των μυών του πήχη, των βιοσημάτων, της μεθόδου της ηλεκτρομυογραφίας (ΗΜΓ) και μεθόδων αναγνώρισης προτύπων. Παράλληλα, το σήμα περιείχε αρκετό θόρυβο και έπρεπε να αναλυθεί, με χρήση του EMD, να εξαχθούν χαρακτηριστικά αλλά και να μειωθεί η διαστασιμότητά τους, με χρήση των RELIEF και PCA, για βελτίωση του ποσοστού επιτυχίας ταξινόμησης. Στο πρώτο μέρος γίνεται χρήση συστήματος ΗΜΓ της Delsys αρχικά σε ένα άτομο και στη συνέχεια σε έξι άτομα με το κατά μέσο όρο επιτυχημένης ταξινόμησης, για τις έξι αυτές κινήσεις, να αγγίζει ποσοστά άνω του 80%. Το δεύτερο μέρος περιλαμβάνει την κατασκευή αυτόνομου συστήματος ΗΜΓ με χρήση του Arduino μικροελεγκτή, αισθητήρων ΗΜΓ και ηλεκτροδίων, τα οποία είναι τοποθετημένα σε ένα ελαστικό γάντι. Τα αποτελέσματα ταξινόμησης σε αυτή την περίπτωση αγγίζουν το 75%.<br>The aim of this work was to identify six basic movements of the hand using two systems. Being an interdisciplinary topic, there has been conducted studying in the anatomy of forearm muscles, biosignals, the method of electromyography (EMG) and methods of pattern recognition. Moreover, the signal contained enough noise and had to be analyzed, using EMD, to extract features and to reduce its dimensionality, using RELIEF and PCA, to improve the success rate of classification. The first part uses an EMG system of Delsys initially for an individual and then for six people with the average successful classification, for these six movements at rates of over 80%. The second part involves the construction of an autonomous system EMG using an Arduino microcontroller, EMG sensors and electrodes, which are arranged in an elastic glove. Classification results in this case reached 75% of success.
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Book chapters on the topic "SVM(Support Vector Machine) KNN (K- nearest neighbor)"

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Bartz-Beielstein, Thomas, and Martin Zaefferer. "Models." In Hyperparameter Tuning for Machine and Deep Learning with R. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_3.

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AbstractThis chapter presents a unique overview and a comprehensive explanation of Machine Learning (ML) and Deep Learning (DL) methods. Frequently used ML and DL methods; their hyperparameter configurations; and their features such as types, their sensitivity, and robustness, as well as heuristics for their determination, constraints, and possible interactions are presented. In particular, we cover the following methods: $$k$$ k -Nearest Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and DL. This chapter in itself might serve as a stand-alone handbook already. It contains years of experience in transferring theoretical knowledge into a practical guide.
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Mitra, Srinithi, Utkarsh Patel, and Sricheta Parui. "Unraveling Autism Through FCN Using Hierarchical Support Vector Machine (H-SVM) and Interactive Embedding-K Nearest Neighbors (InEm-KNN)." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87657-8_14.

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Jachero, Bryan, and Karina Bermeo. "A Practical and Cost-Effective Combination of GPS Data and Machine Learning Tools for Detecting Transportation Modes." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_18.

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Abstract This work proposes a novel methodology to determine the modes of transportation used in the city of Cuenca, Ecuador, based on geolocation data and machine learning. For this purpose, 354,096 mobility samples from 40 people are collected via their mobile phones, with the respective identification of the transportation mode used: pedestrian, bicycle, bus, tram, taxi, and private vehicle. These samples are used to train and validate supervised learning architectures: classification trees, weighted k-nearest neighbor classifier, support vector machines, and two-layer neural networks. The classification tree achieved an accuracy of 99.5%, followed by 95.7% for the KNN model, 94.0% for SVM, and 93.2% for the BNN model. The trained, validated, and tested classification tree was applied to 110,242 samples obtained from the random mobility of 40 people, generating optimistic results. The findings indicate that the most used mode of transportation is the bus, followed by taxis and private vehicles. Pedestrians and bicycles, as well as trams, are predominantly used in the city center, while private transportation is more commonly used in rural areas. The obtained model can determine mobility patterns in the city, allowing for the effective establishment of origin-destination matrices. This facilitates public transportation planning, promotes alternatives to traditional mobility, and addresses the current problem of increasing traffic congestion, allowing for the reduction of pollutant emissions and mobilization costs, and aiding in the design of a new mobility plan.
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Dilliswar Reddy, Puli, and L. RamaParvathy. "Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220026.

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To predict the air pollution level in a particular region area using a K-Nearest Neighbor algorithm compared with the Support Vector Machine algorithm, The Novel K-Nearest Neighbor Algorithm and the Support Vector Machine Algorithm are two groupings. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N = 5 iterations for each method. G power is set at 80%. The confidence interval is 95%, and the threshold value is 0.05%. The G-power test is around 80% accurate. The K-Nearest Neighbor algorithm (97.44%) has better accuracy when compared with the Support Vector Machine (70.32%). The K-Nearest Neighbor algorithm has the highest accuracy compared to the Support Vector Machine algorithm. In the prediction of air pollution, K-Nearest Neighbor has better performance when compared to the Support Vector Machine Algorithm.
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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%). The statistical significance between K-Nearest Neighbour &amp; Support Vector Machine is (p=0.0094) where (p&lt;0.05).K-Nearest Neighbour algorithm helps in determining with more accuracy in erratic user behaviour over social media networks, and here KNN algorithm shows better accuracy than SVM 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 numerous heart disease prediction algorithms, including Support Vector Machines (SVM), Logistic Regression, KNN, Random Forest Classifier, and Deep Neural Networks. Prediction of IPL Data Using Machine Learning Techniques is the topic of Chapter 4. The following algorithms are covered in this chapter: decision trees, naive bayes, K-Nearest Neighbour Random Forest, data mining techniques, fuzzy clustering logic, support vector machines, reinforcement learning algorithms, data analytics approaches and Bayesian prediction techniques. Chapter Five: Software Error Prediction by means of machine learning- The AR model and the Known Power Model (POWM), as well as artificial neural networks (ANNs), particle swarm optimisation (PSO), decision trees (DT), Nave Bayes (NB), and linear classifiers, are among the approaches (K-nearest neighbours, Nave Bayes, C-4.5, and decision trees) Prediction of Rainfall Using Machine Learning Techniques, Chapter 6: The following are discussed: LASSO (Least Absolute Shrinkage and Selection Operator) Regression, ANN (Artificial Neural Network), Support Vector Machine, Multi-Layer Perception, Decision Tree, Adaptive Neuro-Fuzzy Inference System, Wavelet Neural Network, Ensemble Prediction Systems, ARIMA model, PCA and KMeans algorithms, Recurrent Neural Network (RNN), statistical KNN classifier, and neural SOM Weather Prediction Using Machine Learning Techniques that includes Bayesian Networks, Linear Regression, Logistic Regression, KNN Decision Tree, Random Forest, K-Means, and Apriori's Algorithm, as well as Linear Regression, Polynomial Regression, Random Forest Regression, Artificial Neural Networks, and Recurrent Neural Networks.
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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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Haro, Marco, Mariko Nakano-Miyatake, Jorge Cime-Castillo, Humberto Lanz-Mendoza, Mario Gonzalez-Lee, and Hector Perez-Meana. "Infected Mosquito Detection System Using Spectral Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220296.

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Considering that an accurate detection of infected mosquitos may directly avoid the propagation of mosquito-borne disease; in this paper, we propose a detection system of infected mosquitos by Dengue virus type II, that uses seven spectral feature measures, which are applied to the spectrogram estimated from wingbeat signal emitted by mosquito’s flight. To evaluate the proposed system, we construct our own dataset with 20 infected Aedes aegypti by Dengue and 20 healthy ones. Seven spectral analysis methods, such as Spectral Rolloff, Spectral Centroide, etc., are applied to the spectrogram obtained by using the Short Time Fourier Transform (STFT) to generate feature vectors with 15 elements. These are feed into common machine learning techniques, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression to detect the infected mosquitos differentiating form the healthy ones. Evaluation results show that, the best detection accuracy (84.32%) is provided by the KNN with K=3.
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Jain, Tarun, Payal Garg, Pradeep Kumar Tiwari, Vamsi Krishna Kuncham, Mrinal Sharma, and Vivek Kumar Verma. "Performance Prediction for Crop Irrigation Using Different Machine Learning Approaches." In Examining the Impact of Deep Learning and IoT on Multi-Industry Applications. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7511-6.ch005.

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Irrigation is an ancient practice that evolved over the years. Irrigation is the method through which a controlled amount of water is applied to plants making the most important recourse of irrigation. Earth is composed of 70% of water of which only 2.5% is fresh. Most of it trapped in snowfields and glaciers with only 0.007% of the earth's water for the needs of mankind. Limited water resources had become the main challenge in farming. In the chapter, machine learning algorithms and neural networks are used to reduce the usage of water in crop irrigation systems. This chapter focus on four mainstream machine learning calculations (KNN [k-nearest neighbor], GNB [Gauss Naive Bayes], SVM [support vector machine], DT [decision tree]) and a neural networks technique (artificial neural networks [ANN]) to expectation models utilizing the huge dataset (510 irrigation cases), bringing about productive and precise dynamic. The outcomes showed that k-nearest neighbors and artificial neural networks are the best indicators with the most elevated effectiveness of 98% and 90% respectively.
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Bang Tran Sy, Haruechaiyasak Choochart, and Sornlertlamvanich Virach. "Vietnamese Online Hotel Reviews Classification Bases on Term Features Selection." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-720-7-135.

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This paper aims to present the improved techniques to classify the user's feedbacks on hotel service qualities. The data were mainly collected from online feedback sources by PHP program. The training set was manually tagged as: NEGATIVE, POSITIVE, and NEUTRAL. In total, 2969 Vietnamese language terms were successfully collected. In the first part, the common machine learning techniques like K-Nearest Neighbor algorithm (KNN), Decision Tree, Naive Bayes (NB) and Support Vector Machines (SVM) were applying for classification. In the second part, we enhanced the efficiency of the text categorization by applying feature selection techniques, &amp;chi;2 (CHI). At the end of the paper, we concluded that the overall performance of general machine learning techniques was significantly improved by applying feature selection.
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Conference papers on the topic "SVM(Support Vector Machine) KNN (K- nearest neighbor)"

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Baykal, B. Aydin, Pelin B. Icer Baykal, and Preet M. Singh. "Modeling of Erosion-Corrosion in An Alkaline Environment by Machine Learning." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13450.

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Abstract Machine learning is a method that allows interpretations of new data using an established database of older data by referring to already-known results (known as “labels”) and extrapolating between them to estimate the label that would be assigned to a different experiment. This can be a powerful tool for corrosion prediction, because it makes it possible to estimate a range of corrosion rates for a certain family of materials in a specific range of environments without actually performing experiments. In this paper, the machine learning concept was applied to the erosion-corrosion of steels in white liquor, a strongly alkaline industrial chemical used for pulping wood chips. Previously obtained corrosion data in white liquor, which included different steel compositions, particle concentrations and sizes, temperatures and fluid properties such as viscosity were compiled and assigned labels based on previous assessments in the industry as passive, acceptable, marginal or unsuitable according to observed corrosion rate. Models using thirty selected variables were built based on this data using diverse machine learning methods, including support vector machines (SVM), decision trees, k-nearest neighbor methods (KNN). discriminant analysis etc. Feature selection was attempted for each model. The best accuracies for each method were compiled and assessed regarding their promise for predictive purposes in erosion-corrosion
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Seto, Emily, Meifeng Li, and Jing Liu. "Predicting Corrosion Severity of Pipeline Steels in Supercritical CO2 Environments Using Supervised Machine Learning." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-20803.

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Abstract The importance of effective corrosion management in carbon capture, utilization, and storage (CCUS) networks has significantly increased. Captured CO2 is often transported in the supercritical state (s-CO2) and can contain impurities like H2O, O2, SOx, or NOx. While repurposing existing oil and gas pipelines for s-CO2 transport has been suggested, further testing and risk assessment is required to validate this strategy and its associated risks. Given the substantial amount of corrosion data available from recent corrosion studies, machine learning (ML) has emerged as a promising tool for corrosion prediction and management. This study aims to utilize supervised ML techniques to predict the corrosion severity of pipeline steels operating in s-CO2 systems. The selected algorithms, random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) were trained on a comprehensive data set of X-series pipeline steels which includes corrosion rates, impurity levels, temperatures, pressures, and exposure times. Additional testing data set and error and accuracy scores were used to determine the most accurate algorithm. An additional experimental testing was performed to verify the predictions of the model. It was found that the RF model had the best accuracy of 65.3% out of the three tested models and KNN had the worst accuracy of 59.2%. In multiple impurity environments the RF model was able to accurately predict corrosion severity but overestimated corrosion severity in environments with short exposure times.
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Vieira, Ronald E., Farzin Darihaki, Jamie Li, and Siamack A. Shirazi. "Application of Machine Learning Techniques for Sand Erosion Prediction for Elbows in Multiphase Flow." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18995.

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Abstract The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall thickness loss data collected for different flow regimes (gas-sand, liquid-sand, dispersed-bubble, churn, annular, and low liquid loading multiphase flows), the methodology has been developed to predict the maximum erosion magnitudes in standard metallic elbows. In order to expand the range of application of the method to situations where data is not available, the erosion database has been expanded by including state-of- the-art validated CFD simulations and 2-dimensional CFD-based mechanistic model predictions. The ML algorithms, including elastic net (EN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and k-nearest neighbors (KNN) classification. The models are optimized using cross-validation and their performance is evaluated by different metrics. More than 650 case studies from previous literature as well as ongoing research have been used to train and test the ML models. The RF and XGB results show the overall best performance for a variety of flow conditions and pipe sizes. The resulting technique helps in saving time and resources to predict erosion in elbows and develop operational limits both within and beyond the current experimental domain while utilizing the most common production input parameters used by oil and gas production operators and other industrial applications.
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Saheed, Y. K., R. D. Magaji, A. Tosho, and O. B. Longe,. "Adopting Machine Learning Blockchain Intrusion Detection for Protecting Attacks on Internet of Things." In 27th iSTEAMS-ACity-IEEE International Conference. Society for Multidisciplinary and Advanced Research Techniques - Creative Research Publishers, 2021. http://dx.doi.org/10.22624/aims/isteams-2021/v27p30.

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Intrusion Detection Systems (IDSs) are widely used in various computer networks with the goal of spotting cyber threats and potential incidents. Collaborative intrusion detection networks (CIDSs) have been developed to augment the detection power of a single IDS by allowing IDS nodes to exchange data. The Internet of Things (IoT) can be thought of as a network or connectivity of sensors and actuators that share data in a unique way. Blockchain technology has been applied in a variety of fields to foster trust and data protection by enabling participants to trade transactions and communicate information while preserving a level of trust, integrity, and greater transparency. However, there are numerous security concerns associated with the implementation architectures and technologies that will form the Internet of Things' backbone. Hence, this paper proposes a machine learning technique leveraging on blockchain technology with IDS for detecting attacks on IoT. In this paper, we used Naïve Bayes (NB), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) models for performing the experiment on NSLKDD dataset. The experimental findings for KNN model achieved 99.6% detection rate with a false alarm rate of 0.4. The NB and SVM models also gave competitive results. Keywords: Machine Learning, Blockchain, Intrusion Detection System, Internet of Things, K-Nearest, Online Safety, Neighbor, Collaborative IDS
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Costa, Bernardo S., Aiko C. S. Bernardes, Julia V. A. Pereira, et al. "Artificial Intelligence in Automated Sorting in Trash Recycling." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4416.

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A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy around 93%.
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Dharmmesta, Rianta Athallah, I. Gede Pustika Jaya, Achmad Rizal, and Istiqomah. "Classification of Foot Kicks in Taekwondo Using SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) Algorithms." In 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2022. http://dx.doi.org/10.1109/iaict55358.2022.9887475.

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Siddartha, Yatham, and K. Malathi. "Improving accuracy in fake news detection using support vector machine (SVM) algorithm with K-nearest neighbors (KNN)." In INTERNATIONAL CONFERENCE ON NEWER ENGINEERING CONCEPTS AND TECHNOLOGY: ICONNECT-2024. AIP Publishing, 2025. https://doi.org/10.1063/5.0263248.

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Pugo-Mendez, Edisson, and Luis Serpa-Andrade. "Development of a platform based on artificial vision with SVM and KNN algorithms for the identification and classification of ceramic tiles." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001460.

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In the ceramic tile manufacturing industry, the quality of production achieved depends to a large extent on the quality of the tile, which is very important for its classification and price. Currently, this process is performed by human operators, but many industries aim to improve performance and production through automation of this process. In this work, we present the development of a platform based on an artificial vision that allows the identification of defects in ceramic tiles, so that we can classify them according to their quality. The algorithms chosen to develop the platform are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In order to implement these algorithms, the images are preprocessed, the descriptors for defect detection are obtained, then the algorithms are used and the results obtained
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Dias Martins, Leonardo, and Fabíola Pantoja Oliveira Araújo. "Mineração de Texto para a Análise do Perfil Emocional de Usuários de Jogo Empático." In Computer on the Beach. Universidade do Vale do Itajaí, 2021. http://dx.doi.org/10.14210/cotb.v12.p370-377.

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Daily, a large amount of data circulates on the Internet, producing a lot of information in the form of images, videos and texts. Then, it is necessary to analyze and extract these information automatically. Therefore, this work presents a case study that applies text mining to extract the emotional and sentimental profiles from the comments of the Last Day of June game users, where the results and the information extracted from the analysis of sentiments were presented. Three classification algorithms were used: Naive Bayes, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to predict the class of elements according to the emotions or feelings identified in the comments analysis. As a result, SVM with radial kernel was the one with the best accuracy, with 79%, followed by KNN with 3 closest neighbors, with 75%, and finally, Naive Bayes, with 62%.
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Carvalho, Lucas, Maycon Silva, Edimilson Santos, and Daniel Guidoni. "On the Analysis of Machine Learning Classifiers to Detect Traffic Congestion in Vehicular Networks." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9290.

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Problems related to traffic congestion and management have become common in many cities. Thus, vehicle re-routing methods have been proposed to minimize the congestion. Some of these methods have applied machine learning techniques, more specifically classifiers, to verify road conditions and detect congestion. However, better results may be obtained by applying a classifier more suitable to domain. In this sense, this paper presents an evaluation of different classifiers applied to the identification of the level of road congestion. Our main goal is to analyze the characteristics of each classifier in this task. The classifiers involved in the experiments here are: Multiple Layer Neural Network (MLP), K-Nearest Neighbors (KNN), Decision Trees (J48), Support Vector Machines (SVM), Naive Bayes and Tree Augment Naive Bayes.
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