The paper considers a time-efficient implementation of the nearest neighbours (kNN) algorithm. A well-known approach for accelerating the kNN algorithm is to utilise dimensionality reduction methods based on the use of space-filling curves. In this paper, we take this approach further and propose an algorithm that employs multiple space-filling curves and is faster (with comparable quality) compared with the kNN algorithm, which uses kd-trees to determine the nearest neighbours. A specific method for constructing multiple Peano curves is outlined, and statements are given about the preservation of object proximity information in the course of dimensionality reduction. An experimental comparison with known kNN implementations using kd-trees was performed using test and real-life data.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223091 | PMC |
| http://dx.doi.org/10.3390/e24060767 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.
Support Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
View Article and Find Full Text PDFIntegrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.
Sci Rep
January 2025
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.
View Article and Find Full Text PDFUnveiling shadows: A data-driven insight on depression among Bangladeshi university students.
Heliyon
January 2025
Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Depression is more than just feeling sad. It is a severe and multifaceted mental health condition that impacts millions of individuals around the globe. Regrettably, it can even be more prevalent in university students of underdeveloped and developing countries like Bangladesh because of academic pressure, family and societal expectations, financial limitations, stigmatized social and cultural norms, unemployment concerns, lack of mental health awareness, etc.
View Article and Find Full Text PDFArtificial intelligence with wrapper multioutput regression to identify chemical contents of fresh and dried manure samples using NIR spectroscopy.
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
School of Physical and Applied Sciences, Goa University, Taleigao Plateau, Goa 403206, India. Electronic address:
Analyzing manure nutrients such as total ammonium nitrogen (NH), dry matter (DM), calcium oxide (CaO), total nitrogen (-N), phosphorus pentoxide (PO), magnesium oxide (MgO), and potassium oxide (KO) helps in fulfilling crop nutritional needs while improving the profitability and a lower risk of pollutants. This study used two Near Infra Red (NIR) spectral datasets of fresh and dried manure. The freshly prepared NHCl, CaO, Ca(OH), PO, MgO, and KO samples were used for spectral signature peak identification and calibration.
View Article and Find Full Text PDFEnhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.
Sensors (Basel)
December 2024
Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!