Use of n-grams and K-means clustering to classify data from free text bone marrow reports by Richard F. Xiang

“…A natural language processing algorithm involving n-grams and K-means clustering was used to classify the text blocks into their appropriate bone marrow sections. …”
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Urban surface water bodies mapping using the automatic k-means based approach and sentinel-2 imagery by Mateo Gašparović, Sudhir Kumar Singh

“…AUWM was developed based on modified normalized difference water index, pansharpening techniques (MNDWIPS), and k-means clustering algorithm. Research was provided on three study sites. …”
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Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection by R. Hari Prasada Rao, Agam Das Goswami

“…In addition, the FC2R segmentation model combines the optimized fuzzy C-means clustering algorithm and the Resnet −101 deep learning approach that effectively improves the performance of the model. …”
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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means by Bo Li, Guochao Qian, Lijun Tang, Peng Sun, Zhensheng Wu

“…To solve the problem of high cost and low efficiency of measuring equipment in traditional distribution network fault location, a fault section location and line selection strategy combining dynamic binary particle swarm optimization (DBPSO) configuration and fuzzy C-means (FCM) clustering is proposed in this paper. …”
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Point-of-interest recommender model using geo-tagged photos in accordance with imperialist Fuzzy C-means clustering. by Ali Asghar Salehi Solaiman Abadi, Keyhan Khamforoosh, Vafa Maihami

“…This paper presents a travel recommender system by integrating the Imperialist Competitive Algorithm (ICA) and Fuzzy C-Means (FCM) Clustering algorithm. …”
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Domain Generalization Using Maximum Mean Discrepancy Loss for Remaining Useful Life Prediction of Lithium-Ion Batteries by Wenbin Li, Yue Yang, Stefan Pischinger

“…In this work, a data-driven algorithm based on stacked Long Short Term Memory (LSTM) encoder–decoders is proposed for RUL prediction. …”
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Optimal by criterion of minimum of average risk adaptive device for decision meaning considering correlation properties of the reflected signal by S. A. Gabets, S. U. Sedushev, R. A. Hutsau

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Penerapan Algoritma K-Means Dan Metode Marketing Mix dalam Segmentasi Mahasiswa dan Strategi Pemasaran by Siti Monalisa, Tengku Nurainun, Misra Hartati

“…The methodology in this research are student segmentations using K-Means Algorithm dan Clustering with Dunn Index Algorithm. …”
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2D DOA Estimation of Wideband and FH Signals Using Improved K-means Clustering and Implementation Considerations by Zahra Memarian, Mahdi Majidi

“…Additionally, a fast, modified K-means clustering algorithm is developed to refine DOA estimation for FH and WB signals across multiple active subchannels. …”
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ANFIS Models with Subtractive Clustering and Fuzzy C-Mean Clustering Techniques for Predicting Swelling Percentage of Expansive Soils by Mehdi Hashemi Jokar, Ali Heidaripanah

“…This study aims to optimize subtractive clustering and Fuzzy C-Mean Clustering (FCM) models for the most accurate prediction of swelling percentage in expansive soils. …”
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An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird by Sumandeep Kaur, Lakhwinder Kaur, Madan Lal

“…This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. …”
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Extraction and analysis of spatial heterodyne potassium signals based on principal component analysis and non-local means method by Wang XinQiang, Yang SiQian, Xiong Wei, Wang FangYuan, Ye Song

“…Principal Component Analysis (PCA) is then applied to separate the atmospheric background from the weak potassium lamp signals in the mixed signals, followed by the introduction of the Non-Local Means (NLM) denoising algorithm to suppress noise. …”
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Improved SOR signal detection algorithm in massive MIMO-TRDMA systems by Mingyue WANG, Fangwei LI, Xiaorong JING, Haibo ZHANG, Junzhou XIONG

“…In the massive multi-input multi-output time-reversal division multiple access (MIMO-TRDMA) systems, the traditional linear minimum mean square error (MMSE) algorithm achieved approximately the best performance.However, the matrix inversion of the MMSE algorithm was too complicated to ensure real-time processing of signal detection.To solve this problem, an improved successive over-relaxation (SOR) signal detection optimization algorithm was proposed.The proposed algorithm reasonably upgraded the solution of linear equations to prevent the complicated calculation of matrix inversion.Meanwhile, the steepest descent idea was used to provide an effective search direction for the SOR signal detection algorithm, achieving a rapid convergence rate and stronger inspection performance.The simulation results show that the proposed algorithm has the similar best performance with fewer update times compared with the traditional MMSE algorithm, and the calculation complexity is reduced from O(M³)to O(²).…”
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Improved SOR signal detection algorithm in massive MIMO-TRDMA systems by Mingyue WANG, Fangwei LI, Xiaorong JING, Haibo ZHANG, Junzhou XIONG

“…In the massive multi-input multi-output time-reversal division multiple access (MIMO-TRDMA) systems, the traditional linear minimum mean square error (MMSE) algorithm achieved approximately the best performance.However, the matrix inversion of the MMSE algorithm was too complicated to ensure real-time processing of signal detection.To solve this problem, an improved successive over-relaxation (SOR) signal detection optimization algorithm was proposed.The proposed algorithm reasonably upgraded the solution of linear equations to prevent the complicated calculation of matrix inversion.Meanwhile, the steepest descent idea was used to provide an effective search direction for the SOR signal detection algorithm, achieving a rapid convergence rate and stronger inspection performance.The simulation results show that the proposed algorithm has the similar best performance with fewer update times compared with the traditional MMSE algorithm, and the calculation complexity is reduced from O(M³)to O(²).…”
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Pattern recognition in SARS cases: insights from t-SNE and k-means clustering applied to COVID-19 symptomatology by Julliana Gonçalves Marques, Bruno Motta de Carvalho, Luiz Affonso Guedes, Márjory Da Costa-Abreu

“…This study proposes a dimensionality reduction approach combined with a clustering technique to visually analyse structural similarities among SARS-infected individuals, aiming to determine whether aspects such as case progression and diagnosis impact these patterns.MethodsThis analysis utilised the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm for dimensionality reduction, combined with Gower's distance to handle categorical data, and k-means clustering. …”
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Improved landslide susceptibility assessment: A new negative sample collection strategy and a comparative analysis of zoning methods by Jiani Wang, Yunqi Wang, Manyi Li, Zihan Qi, Cheng Li, Haimei Qi, Xiaoming Zhang

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Kombinasi K-Means dan Support Vector Machine (SVM) untuk Memprediksi Unsur Sara pada Tweet by Wiga Maulana Baihaqi, Muliasari Pinilih, Miftakhul Rohmah

“…This study aims to make sentence corpus containing SARA elements obtained from twitter, then label sentences with labels containing elements of SARA and not, and conduct group sentiments. The algorithm used for the labeling process is k-means, while Support Vector Machine (SVM) is used for the classification process. …”
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A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique by Vicka Rizqi Maulani, Mula Agung Barata, Pelangi Eka Yuwita

“…The 0.15 matrix result is relatively low, which is caused by the overlap of house price values in the dataset, but it has been shown that one-hot encoding can represent categorical data well in the data pre-processing process so that the data can be processed with the k-means algorithm.…”
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Using Artificial Intelligence Techniques For Intrusion Detection System by Manar Ahmed, Bayda Khaleel

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Measurement and Modeling of Spindle Thermal Error of Fiveaxis CNC Machine Tool with Double Turntable by LIU Xianli, SONG Houwang, WU Shi, YUE Caixu, Steven Y.Liang, LI Rongyi

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