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Journal articles on the topic 'Incremental KNN'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Yu, Cui, Rui Zhang, Yaochun Huang, and Hui Xiong. "High-dimensional kNN joins with incremental updates." GeoInformatica 14, no. 1 (2009): 55–82. http://dx.doi.org/10.1007/s10707-009-0076-5.

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Ajjaj, Souad, Souad El Houssaini, Mustapha Hain, and Mohammed-Alamine El Houssaini. "Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring." Telecom 4, no. 3 (2023): 629–48. http://dx.doi.org/10.3390/telecom4030028.

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Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machi

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Huo, Dexuan, Jilin Zhang, Xinyu Dai, et al. "A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition." Sensors 23, no. 5 (2023): 2433. http://dx.doi.org/10.3390/s23052433.

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The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and humidity changes), which lead to a serious decline in gas recognition accuracy or even invalidation. To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be re

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Zaki, Ahmed Mohamed, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, and El-Sayed M. El El-Kenawy. "Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization." Journal of Cybersecurity and Information Management 13, no. 1 (2024): 76–84. http://dx.doi.org/10.54216/jcim.130108.

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The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusi

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ZHAO, GENG, KEFENG XUAN, DAVID TANIAR, and BALA SRINIVASAN. "INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS." Journal of Interconnection Networks 09, no. 04 (2008): 455–70. http://dx.doi.org/10.1142/s0219265908002382.

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Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objec

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Li, Huiqin, Yanling Li, Chuan He, Hui Zhang, and Jianwei Zhan. "Radar Working State Recognition Based on the Unsupervised and Incremental Method." Journal of Sensors 2021 (October 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/8673046.

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Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm

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Patel, Damodar, and Amit Kumar Saxena. "Feature Selection in High Dimension Datasets using Incremental Feature Clustering." Indian Journal Of Science And Technology 17, no. 32 (2024): 3318–26. http://dx.doi.org/10.17485/ijst/v17i32.2077.

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Objectives: To develop a machine learning-based model to select the most important features from a high-dimensional dataset to classify the patterns at high accuracy and reduce their dimensionality. Methods: The proposed feature selection method (FSIFC) forms and combines feature clusters incrementally and produces feature subsets each time. The method uses K-means clustering and Mutual Information (MI) to refine the feature selection process iteratively. Initially, two clusters of features are formed using K-means clustering (K=2) by taking features as the basis of clustering instead of takin

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Sachin, B. Jadhav, R. Udupi Vishwanath, and B. Patil Sanjay. "Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4077–91. https://doi.org/10.11591/ijece.v9i5.pp4077-4091.

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Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work presents a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by

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Chen, Jiusheng, Xingkai Xu, and Xiaoyu Zhang. "Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine." Journal of Electrical and Computer Engineering 2020 (August 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/9898546.

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Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient tra

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Damodar, Patel, and Kumar Saxena Amit. "Feature Selection in High Dimension Datasets using Incremental Feature Clustering." Indian Journal of Science and Technology 17, no. 32 (2024): 3318–26. https://doi.org/10.17485/IJST/v17i32.2077.

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Abstract <strong>Objectives:</strong> To develop a machine learning-based model to select the most important features from a high-dimensional dataset to classify the patterns at high accuracy and reduce their dimensionality. <strong>Methods:</strong> The proposed feature selection method (FSIFC) forms and combines feature clusters incrementally and produces feature subsets each time. The method uses K-means clustering and Mutual Information (MI) to refine the feature selection process iteratively. Initially, two clusters of features are formed using K-means clustering (K=2) by t

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Safar, Maytham. "Spatial Queries in Road Networks Based on PINE." JUCS - Journal of Universal Computer Science 14, no. (4) (2008): 590–611. https://doi.org/10.3217/jucs-014-04-0590.

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Over the last decade, due to the rapid developments in information technology (IT), a new breed of information systems has appeared such as geographic information systems that introduced new challenges for researchers, developers and users. One of its applications is the car navigation system, which allows drivers to receive navigation instructions without taking their eyes off the road. Using a Global Positioning System (GPS) in the car navigation system enables the driver to perform a wide range of queries, from locating the car position, to finding a route from a source to a destination, or

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Liu, Shuo, Heng Li, Qiankun Yu, Yun Zhao, and Wen Zhang. "Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays." Journal of Physics: Conference Series 2718, no. 1 (2024): 012069. http://dx.doi.org/10.1088/1742-6596/2718/1/012069.

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Abstract This paper studies a sound source localization method of multiple fixed targets based on machine learning and distributed arrays. In an outdoor open field, a three-line array was applied to collect array data and calculate latency characteristics. Then multiple classification models were established and trained. Finally, the locations of the sound source points were predicted by those models, in which the support vector machine (SVM), the nearest node (KNN), and the naive Bayesian model achieved 100% localization accuracy. Compared to the conventional method, this method has three sig

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Wan, Ping, Chaozhong Wu, Yingzi Lin, and Xiaofeng Ma. "Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine." Discrete Dynamics in Nature and Society 2019 (March 26, 2019): 1–17. http://dx.doi.org/10.1155/2019/2745381.

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Driving anger, known as “road rage”, has gradually become a serious traffic psychology issue. Although driving anger identification is solved in some studies, there is still a gap in driving anger grading which is helpful to take different intervening measures for different anger intensity, especially in real traffic environment. The main objectives of this study are: (1) explore a novel driving anger induction method based on various elicitation events, e.g., traffic congestion, vehicles weaving/cutting in line, jaywalking and red light waiting in real traffic environment; (2) apply increment

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14

Evwiekpaefe, A. E., and S. M. Umar. "Predicting road traffic crash severity in Kaduna Metropolis using some selected machine learning techniques." Nigerian Journal of Technology 40, no. 5 (2022): 888–900. http://dx.doi.org/10.4314/njt.v40i5.15.

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Road Traffic Crash (RTC) is among the leading causes of death in the world and has a significant impact on the socio–economic development in a society. Generally, RTC can be caused by one or a combination of the following factors: Human, environment and vehicle. This study utilized five data mining algorithm classifiers (Decision Tree (DT), K-Nearest Neighbor (KNN), J-Repeated Incremental Pruning to Produce Error Reduction (JRIP), Naïve Bayes (NB), and Multi-layer Perceptron (MLP)) to classify the severity of RTC and identify the significant causes of RTC in Kaduna State, Nigeria. The RTC data

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Moni, Vidya. "Human Papillomavirus Targeted Immunotherapy Outcome Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3598–611. http://dx.doi.org/10.22214/ijraset.2021.37197.

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Warts caused by the Human Papillomavirus (HPV) is a highly contagious disease, and affects several million people across the globe every year, in the form of small lesions on the skin, commonly known as warts. Warts can be treated effectively with several methods, the most effective being Immunotherapy and Cryotherapy. Our research is focused on the performance comparison of modern Machine Learning classification techniques to predict the outcome (positive or negative) of Immunotherapy treatment given to a patient, by using patient data as input features to our classifiers. The precision, reca

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Wu, Chengyan, Qianzhong Li, Ru Xing, and Guo-Liang Fan. "Using the Chou’s Pseudo Component to Predict the ncRNA Locations Based on the Improved K-Nearest Neighbor (iKNN) Classifier." Current Bioinformatics 15, no. 6 (2020): 563–73. http://dx.doi.org/10.2174/1574893614666191003142406.

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Background: The non-coding RNA identification at the organelle genome level is a challenging task. In our previous work, an ncRNA dataset with less than 80% sequence identity was built, and a method incorporating an increment of diversity combining with support vector machine method was proposed. Objective: Based on the ncRNA_361 dataset, a novel decision-making method-an improved KNN (iKNN) classifier was proposed. Methods: In this paper, based on the iKNN algorithm, the physicochemical features of nucleotides, the degeneracy of genetic codons, and topological secondary structure were selecte

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Oladepo, Abdulfatai Ganiyu, Amos Orenyi Bajeh, Abdullateef Oluwagbemiga Balogun, Hammed Adeleye Mojeed, Abdulsalam Abiodun Salman, and Abdullateef Iyanda Bako. "Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 17 (2021): 84. http://dx.doi.org/10.3991/ijim.v15i17.19915.

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This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. T

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Ramakrishna, Mahesh Thyluru, Vinoth Kumar Venkatesan, Rajat Bhardwaj, et al. "HCoF: Hybrid Collaborative Filtering Using Social and Semantic Suggestions for Friend Recommendation." Electronics 12, no. 6 (2023): 1365. http://dx.doi.org/10.3390/electronics12061365.

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Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has

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19

Al-batah, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh, Mohammed Hasan Abu-Arqoub, and Rashiq Rafiq Marie. "Classifying Dental Care Providers Through Machine Learning with Features Ranking." Data and Metadata 4 (April 7, 2025): 755. https://doi.org/10.56294/dm2025755.

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This study investigates the application of machine learning (ML) models for classifying dental providers into two categories—standard rendering providers and safety net clinic (SNC) providers—using a 2018 dataset of 24,300 instances with 20 features. The dataset, characterized by high missing values (38.1%), includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), and beneficiary demographics. Feature ranking methods such as information gain, Gini index, and ANOVA were employed to identify critical predictors, revealing treatment-related metrics (TXMT_USER_

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Duan, Xindong. "Computer Network Intrusion Anomaly Detection Based on Rough Fourier Fast Algorithm." Mathematical Problems in Engineering 2022 (September 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/4751844.

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Since there is a close relationship between network information security attack events and time complexity, it is necessary to count the degree of correlation between the current connection record and the connection record within a certain period of time before. Only in this way can the relationship between network connection data and network information security attack events be better reflected. In this paper, a rough Fourier fast algorithm based on rough set theory is proposed. Based on the characteristic attributes of the intrusion detection data set with the most value of character attrib

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Abdel-Jaber, Fayez, and Kim N. Dirks. "Thermal Load Prediction in Residential Buildings Using Interpretable Classification." Buildings 14, no. 7 (2024): 1989. http://dx.doi.org/10.3390/buildings14071989.

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Energy efficiency is a critical aspect of engineering due to the associated monetary and environmental benefits it can bring. One aspect in particular, namely, the prediction of heating and cooling loads, plays a significant role in reducing energy use costs and in minimising the risks associated with climate change. Recently, data-driven approaches, such as artificial intelligence (AI) and machine learning (ML), have provided cost-effective and high-quality solutions for the prediction of heating and cooling loads. However, few studies have focused on interpretable classifiers that can genera

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Liu, Yansong, Shuang Wang, He Sui, and Li Zhu. "An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift." PLOS ONE 19, no. 1 (2024): e0292140. http://dx.doi.org/10.1371/journal.pone.0292140.

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A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehen

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Cohen, Talia R., Gaylen E. Fronk, Kent A. Kiehl, John J. Curtin, and Michael Koenigs. "Clarifying the relationship between mental illness and recidivism using machine learning: A retrospective study." PLOS ONE 19, no. 2 (2024): e0297448. http://dx.doi.org/10.1371/journal.pone.0297448.

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Objective There is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States. Method Participants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understan

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El, Ghouch Nihad, El Mokhtar En-Naimi, Abdelhamid Zouhair, and Achhab Mohammed Al. "Hybrid approach of the fuzzy C-means and the K-nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized follow-up of learners in real time." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4939–50. https://doi.org/10.11591/ijece.v9i6.pp4939-4950.

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The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Ca

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Narváez, Miryan Estela, Raquel Johanna Moyano Ariasy Moyano Arias, Diego Bernardo Palacios Campana, and Geovanny Augusto Izurieta Guamán. "Predicción de clientes potenciales utilizando K vecino más cercano en el área de negocios de la Cooperativa Riobamba." Revista Perspectivas 4, no. 1 (2022): 28–33. http://dx.doi.org/10.47187/perspectivas.vol4iss1.pp28-33.2022.

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K vecino más cercano (KNN) es uno de los algoritmos que permite el diagnóstico en tiempo real y apoya la toma de decisiones. Para esta investigación, se consideró la base de datos del área de negocio de la Cooperativa de Ahorro y Crédito Riobamba Ltda., un banco de datos que almacena una gran cantidad de información de los clientes. Estos datos se utilizaron para seleccionar información relevante manteniendo y respetando la confidencialidad de los clientes. El objetivo principal del proyecto es predecir clientes potenciales aplicando el algoritmo KNN. Los resultados demuestran que el algoritmo

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Narváez, Miryan Estela, Raquel Johanna Moyano Ariasy Moyano Arias, Diego Bernardo Palacios Campana, and Geovanny Augusto Izurieta Guamán. "Predicción de clientes potenciales utilizando K vecino más cercano en el área de negocios de la Cooperativa Riobamba." Revista Perspectivas 4, no. 1 (2022): 28–33. http://dx.doi.org/10.47187/perspectivas.4.1.150.

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K vecino más cercano (KNN) es uno de los algoritmos que permite el diagnóstico en tiempo real y apoya la toma de decisiones. Para esta investigación, se consideró la base de datos del área de negocio de la Cooperativa de Ahorro y Crédito Riobamba Ltda., un banco de datos que almacena una gran cantidad de información de los clientes. Estos datos se utilizaron para seleccionar información relevante manteniendo y respetando la confidencialidad de los clientes. El objetivo principal del proyecto es predecir clientes potenciales aplicando el algoritmo KNN. Los resultados demuestran que el algoritmo

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Zhang, Yan, Anxiang Wang, Chaolong Zhang, et al. "State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model." Batteries 11, no. 6 (2025): 217. https://doi.org/10.3390/batteries11060217.

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Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). First, the battery’s voltage, current, temperature, and other data during the charging stage were measured and recorded through experiments. Incremen

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Jiang, Jiaxi, Tianyu Fu, Jiaqi Liu, et al. "Incremental energy-based recurrent transformer-KAN for time series deformation simulation of soft tissue." Expert Systems with Applications 271 (May 2025): 126619. https://doi.org/10.1016/j.eswa.2025.126619.

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Zhang, Xiaofeng, Bo Tao, Du Jiang, Baojia Chen, Dalai Tang, and Xin Liu. "Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW." Machines 12, no. 5 (2024): 321. http://dx.doi.org/10.3390/machines12050321.

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Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbo

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HSU, PING-HUI, KUAN-YU CHEN, and KUN-MAO CHAO. "FINDING ALL APPROXIMATE GAPPED PALINDROMES." International Journal of Foundations of Computer Science 21, no. 06 (2010): 925–39. http://dx.doi.org/10.1142/s0129054110007647.

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We study the problem of finding all maximal approximate gapped palindromes in a string. More specifically, given a string S of length n, a parameter q ≥ 0 and a threshold k > 0, the problem is to identify all substrings in S of the form uvw such that (1) the Levenshtein distance between ur and w is at most k, where ur is the reverse of u and (2) v is a string of length q. The best previous work requires O(k2n) time. In this paper, we propose an O(kn)-time algorithm for this problem by utilizing an incremental string comparison technique. It turns out that the core technique actually solves

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Peng, Jun, Xuan Zhao, Jian Ma, et al. "State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks." Batteries 10, no. 9 (2024): 315. http://dx.doi.org/10.3390/batteries10090315.

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An accurate estimation of the state of health (SOH) of Li-ion batteries is critical for the efficient and safe operation of battery-powered systems. Traditional methods for SOH estimation, such as Coulomb counting, often struggle with sensitivity to measurement noise and time-consuming tests. This study addresses this issue by combining incremental capacity (IC) analysis and a novel neural network, Kolmogorov–Arnold Networks (KANs). Fifteen features were extracted from IC curves and a 2RC equivalent circuit model was used to identify the internal resistance of batteries. Recursive least square

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Kangas, Michael James, Christina L. Wilson, Raychelle M. Burks, et al. "An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays." International Journal of Chemistry 10, no. 2 (2018): 36. http://dx.doi.org/10.5539/ijc.v10n2p36.

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Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods

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Liu, Tie Bin. "A K-Nearest-Neighbour Based Classifier for Securities Text Categorization." Advanced Materials Research 989-994 (July 2014): 1541–46. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1541.

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Event-driven investments have gained great importance and popularity. Due to the importance of the timely and effective messages for successful investment, the automated categorization of documents into predefined labels has received an ever-increased attention in the recent years. This paper implements a new text document classifier by integrating the K-nearest neighbour (KNN) classification approach with the VSM vector space model. By screening the feature items and weighted key items, the proposed classifier turns the financial information text into N-dimensional vector and identified the p

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Sánchez-Ruiz, Marcos, Jonathan Flores-Monroy, Enrique Escamilla-Hernández, Mariko Nakano-Miyatake, and Hector Perez-Meana. "Detección de estados de ánimo en ambientes no restringidos." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 10, Especial4 (2022): 110–15. http://dx.doi.org/10.29057/icbi.v10iespecial4.9142.

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Uno de los factores importantes que influyen en los accidentes automovilísticos es el manejar bajo condiciones no-óptimas, tales como estrés, ira, miedo, depresión entre otros, en las cuales la posibilidad de sufrir un accidente durante manejo se incrementa. Por lo tanto, hasta la fecha han sido propuestos varios esquemas que detectan la emoción del conductor basado en su expresión facial. La mayoría de ellos usan solo un fotograma (una imagen) y operan en condiciones restringidas que rara vez se presentan en condiciones reales de manejo. Con el fin de poder resolver este problema, este artícu

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Li, Ying, Yuxing Ye, Zhiwei Zhang, and Long Wen. "A New Incremental Learning Method Based on Rainbow Memory for Fault Diagnosis of AUV." Sensors 25, no. 15 (2025): 4539. https://doi.org/10.3390/s25154539.

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Autonomous Underwater Vehicles (AUVs) are gradually becoming some of the most important equipment in deep-sea exploration. However, in the dynamic nature of the deep-sea environment, any unplanned fault of AUVs would cause serious accidents. Traditional fault diagnosis models are trained in static and fixed datasets, making them difficult to adopt in new and unknown deep-sea environments. To address these issues, this study explores incremental learning to enable AUVs to continuously adapt to new fault scenarios while preserving previously learned diagnostic knowledge, named the RM-MFKAN model

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Järv, Risto. "The Singing Wolf Meets His Kin Abroad. Web-based databases of the Estonian Folklore Archives." Estudis de Literatura Oral Popular / Studies in Oral Folk Literature, no. 5 (June 22, 2017): 29. http://dx.doi.org/10.17345/elop201629-44.

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Els folkloristes estonians han creat diverses bases de dades en local, a més d’un parell de desenes de bases de dades en línia per facilitar la tasca de la recerca i incrementar l’accessibilitat al material d’arxiu. Aquest treball proporciona una visió general de quatre bases de dades en línia gestionades pels investigadors dels Arxius de Folklore d’Estònia: cançons rúniques, el folklore local, jocs infantils i rondalles. D’una banda, aquestes bases de dades es poden fer servir per a finalitats de recerca i per preparar textos per ser publicats; de l’altra, poden ser d’interès per a molta altr

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Castro Villarreal, J. S., A. Beltrán-Ostos, and C. F. Valencia Arboleda. "SAT0649-HPR DIRECT COSTS OF SYSTEMIC LUPUS ERYTHEMATOSUS IN COLOMBIA." Annals of the Rheumatic Diseases 79, Suppl 1 (2020): 1283.2–1284. http://dx.doi.org/10.1136/annrheumdis-2020-eular.5785.

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Background:Treatment burden of Systemic Lupus Erythematosus (SLE) is considered high. There are no studies in Colombia that includes the estimation of an incremental cost associated to SLE.Objectives:To estimate the direct cost associated to SLE in contributory healthcare scheme in Colombia. To estimate prevalence and characterize SLE population affiliated in the contributory healthcare scheme in Colombia. To estimate the direct healthcare cost in patients with and without SLE and the effect of being diagnosed with SLE in the total direct cost during a period of two years.Methods:The present s

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Tchorz, JP, R. Doll, M. Wolkewitz, E. Hellwig, and C. Hannig. "Microhardness of Composite Materials With Different Organic Phases in Deep Class II Cavities: An In Vitro Study." Operative Dentistry 36, no. 5 (2011): 502–11. http://dx.doi.org/10.2341/10-325-l.

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SUMMARY Objective: This study compared the microhardness of three composite resins with different organic matrices in deep class II cavities. Materials and Method: A total of 36 extracted molars were randomly assigned to six groups and standardized class II cavities were prepared. The cavity design comprised three steps in a mesiodistal direction with an increasing depth (2, 4, and 6 mm). Twelve cavities each were restored using Filtek Supreme (FS), Quixfil (QF), and Filtek Silorane (SI). The materials were applied in incremental layers of 2 mm and cured either with Halogen Translux Energy (HT

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Bernal-Flores, Álvaro, Paulino Pérez-Rodríguez, Alejandrina Robledo-Paz, and Adrián R. Quero-Carrillo. "PRETRATAMIENTO CON CITOCININAS EN Bouteloua curtipendula (MICHX.) TORR. PARA INCREMENTAR LA REGENERACIÓN DE PLÁNTULAS." Revista Fitotecnia Mexicana 44, no. 3 (2021): 391. http://dx.doi.org/10.35196/rfm.2021.3.391.

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El pasto banderita [Bouteloua curtipendula (Michx.) Torr.] es una de las forrajeras más cultivadas en zonas semiáridas de EUA y norte de México; es una especie apomíctica recalcitrante con grandes atributos forrajeros. El objetivo del presente estudio fue evaluar tratamientos de pre-regeneración usando citocininas para incrementar la frecuencia de regeneración. Callos morfogénicos inducidos de cariópsides maduros de Banderita cv. El Reno, cultivados en medio basal de Murashige y Skoog (MS) y suplementados con tres niveles de ácido 2,4 diclorofenoxiacético (2,4-D; 2, 3 y 4 mg L-1) en presencia

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Kenny, G. P., F. D. Reardon, W. Zaleski, M. L. Reardon, F. Haman, and M. B. Ducharme. "Muscle temperature transients before, during, and after exercise measured using an intramuscular multisensor probe." Journal of Applied Physiology 94, no. 6 (2003): 2350–57. http://dx.doi.org/10.1152/japplphysiol.01107.2002.

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Seven subjects (1 woman) performed an incremental isotonic test on a Kin-Com isokinetic apparatus to determine their maximal oxygen consumption during bilateral knee extensions (V˙o 2 sp). A multisensor thermal probe was inserted into the left vastus medialis (middiaphysis) under ultrasound guidance. The deepest sensor (tip) was located ∼10 mm from the femur and deep femoral artery (Tmu 10), with additional sensors located 15 (Tmu 25) and 30 mm (Tmu 40) from the tip. Esophageal temperature (Tes) was measured as an index of core temperature. Subjects rested in an upright seated position for 60

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MARFO, K. A., P. T. DORWARD, P. Q. CRAUFURD, F. ANSERE-BIOH, J. HALEEGOAH, and R. BAM. "IDENTIFYING SEED UPTAKE PATHWAYS: THE SPREAD OFAGYA AMOAHRICE CULTIVAR IN SOUTHWESTERN GHANA." Experimental Agriculture 44, no. 2 (2008): 257–69. http://dx.doi.org/10.1017/s0014479708006170.

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SUMMARYA study was carried out to identify the factors that contributed to the natural spread and uptake of a rainfed rice variety namedAgya Amoahin the Western Region of Ghana after introduction of a small amount (0.5 kg) of seed in 1987 by a small-scale farmer. Fifteen years after its introduction over 73% of rice farmers had grown the variety in the Western Region. Initial awareness of the variety was created by information provided mainly by friends, seeing the variety grown in fields and from relatives. Seed for initial planting of the variety was purchased from other farmers by 67% of fa

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Huda, Abil. "PENINGKATAN EFISIENSI PANEL SURYAMENGGUNAKAN TEKNOLOGI MAXIMUM POWER POINT TRACKING (MPPT) BERBASIS INCREMENT CONDUCTANCE MENGGUNAKAN BOOST CONVERTER." MEDIA ELEKTRIKA 15, no. 2 (2022): 137. http://dx.doi.org/10.26714/me.v15i2.11001.

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Panel surya merupakan suatu pembangkit listrik terbarukan yang rama tama pada lingkungan. namun panel surya tersebut juga mempunyai kekurangan yaitu ketika panel surya tidak menerima suhu dan cahaya yang baik dengan faktor curah hujan serta terlindungi dari awan adapun dapat diliahat dari karakteristik pada panel surya dimana daya yang dihasil kan tersebut tidak maksimal, oleh karena itu penulis ingin menggunakan MPPT (Maksimum Power Point Tracking) dengan metode Increment Conductance dimana daya keluaran pada panel surya yang diharapkan dapat lebih maksimal dan hasil yang optimal dalam berbag

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Kenny, Glen P., Frank D. Reardon, Michel B. Ducharme, Mark L. Reardon, and Wytek Zaleski. "Tissue Temperature Transients in Resting Contra-Lateral Leg Muscle Tissue During Isolated Knee Extension." Canadian Journal of Applied Physiology 27, no. 6 (2002): 535–50. http://dx.doi.org/10.1139/h02-030.

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This study was designed to evaluate the role of non-active tissue in the retention and dissipation of heat during and following intense isolated muscle activity. Six subjects performed an incremental isotonic test (constant angular velocity, increases in force output) on a KIN-COM' isokinetic apparatus to determine their maximal oxygen consumption during single knee extensions [Formula: see text] In a subsequent session, a thin wire multi-sensor temperature probe was inserted into the left vastus medialis under ultrasound guidance at a specific internal marker. The deepest temperature sensor (

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Bueno, Reginaldo, Reinaldo Bianchi, and Renato Giacomini. "Estimating the State of Health of Lithium-Ion Batteries: Comparing the Computational Cost and Precision of Kolmogorov-Arnold Networks and Multi-Layer Perceptron Artificial Neural Networks." ECS Meeting Abstracts MA2025-01, no. 5 (2025): 609. https://doi.org/10.1149/ma2025-015609mtgabs.

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Lithium-ion batteries are currently the primary energy storage for various devices, from smartphones to electric vehicles. Although this technology has numerous advantages over its predecessors, lithium batteries still have some points of concern. In an electric vehicle, the battery price accounts for a large portion of the total vehicle cost, and its production requires the metallic element lithium, whose deposits are concentrated in a few countries worldwide. These points lead to the need to accurately estimate the battery's State of Health (SoH) to utilize its full potential before discardi

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NAGANO, Masayuki. "EVALUATION OF STRONG GROUND MOTIONS IN KOBE AREA DURING THE 2000 TOTTORI-KEN SEIBU EARTHQUAKE USING 3D FINITE DIFFERENCE METHOD WITH VARIABLE GRID SPACING AND TIME INCREMENT." Journal of Structural and Construction Engineering (Transactions of AIJ) 69, no. 580 (2004): 43–50. http://dx.doi.org/10.3130/aijs.69.43_3.

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Kenny, Glen P., and Ollie Jay. "Sex differences in postexercise esophageal and muscle tissue temperature response." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 292, no. 4 (2007): R1632—R1640. http://dx.doi.org/10.1152/ajpregu.00638.2006.

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Factors associated with blood pressure regulation during recovery from exercise dramatically influence core temperature regulation. However, it is unknown whether sex-related differences in postexercise hemodynamics affect core and muscle temperature response. Sixteen participants (8 males, 8 females) completed an incremental isotonic test on a Kin-Com isokinetic apparatus to determine their activity-specific peak oxygen consumption during bilateral knee extensions (V̇o2sp). On a separate day, participants performed 15 min of isolated bilateral knee extensions at a moderate (60% V̇o2sp) exerci

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Kenny, Glen P., Ollie Jay, Wytek M. Zaleski, et al. "Postexercise hypotension causes a prolonged perturbation in esophageal and active muscle temperature recovery." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 291, no. 3 (2006): R580—R588. http://dx.doi.org/10.1152/ajpregu.00918.2005.

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We examined the effect of two levels of exercise-induced hypotension on esophageal (Tes) and active and nonactive muscle temperatures during and following exercise. Seven males performed an incremental isotonic test on a Kin-Com isokinetic apparatus to determine their peak oxygen consumption during bilateral knee extensions (V̇o2sp). This was followed on separate days by 15-min of isolated bilateral knee extensions at moderate (60% V̇o2sp) (MEI) and high (80% V̇o2sp) (HEI) exercise intensities, followed by 90 min of recovery. Muscle temperature was measured with an intramuscular probe inserted

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L.E Wenno, Gorgeous, Suryo Prakoso, and Puri Wijayanti. "Studi Simulasi Reservoir pada Reservoir Strong Water Drive untuk Meningkatkan Oil Recovery." Journal of Engineering Environtmental Energy and Science 2, no. 1 (2024): 1–16. http://dx.doi.org/10.31599/5rddd439.

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Lapangan “DMH” memiliki tenaga dorong utama yang berasal dari lapisan akuifer besar sehingga lapangan ini tergolong sebagai strong water drive reservoir. Jumlah kumulatif produksi minyak lapangan DMH sejak tahun 1998 sebesar 2.796 MMSTB atau sekitar 43,5% dari oil in place. Maksud dilakukannya penelitian ini adalah melakukan pemodelan dinamik reservoir X lapangan DMH adapun tujuannya adalah mendapatkan skenario pengembangan lapangan yang paling memungkinkan diterapkan untuk memperoleh incremental recovery yang paling optimum. Pemodelan reservoir dinamik dilakukan menggunakan simulator dengan m

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Sakai, Hiroko M., Hildred Machuca, and Ken-Ichi Naka. "Processing of Color- and Noncolor-Coded Signals in the Gourami Retina. I. Horizontal Cells." Journal of Neurophysiology 78, no. 4 (1997): 2002–17. http://dx.doi.org/10.1152/jn.1997.78.4.2002.

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Sakai, Hiroko M., Hildred Machuca, and Ken-Ichi Naka. Processing of color- and noncolor-coded signals in the gourami retina. I. Horizontal cells. J. Neurophysiol. 78: 2002–2017, 1997. There are two types of horizontal cells, the luminosity and the chromaticity cells, in the retina of the kissing gourami, Helostoma rudolfi. Luminosity cells occupy the outermost layer proximal to the receptor terminals, whereas chromaticity cells form a layer proximal to the layer of luminosity cells. Neither type of cell has axons. Responses were evoked by light from red and green light-emitting diodes. The two

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Miller, Tanja, and Trine Lolk Haslam. "Udsatte familiers perspektiv på koordinerede, helhedsorienterede sociale indsatser." Forskning i Pædagogers Profession og Uddannelse 5, no. 1 (2021): 17. http://dx.doi.org/10.7146/fppu.v5i1.125708.

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ResumeFormålet med denne artikel er at søge svar på, hvordan deltagelse i et helhedsorienteret tilbud kan styrke familiers indflydelse på eget liv. Det forekommer at være lidt af et paradoks, at større indblanding i privatsfæren kan føre til øget indflydelse på eget liv. Artiklen har til formål, i et borgerperspektiv, at undersøge, hvordan paradokset opleves. Artiklens empiriske grundlag og fund stammer fra udforskning af projektet Familiens Trivsel – Barnets Trivsel over en periode på tre år. Et af formålene med projektet er at styrke udsatte familiers motivation og involvering – og give dem

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