Relevant bibliographies by topics / MACHINE LARNING

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'MACHINE LARNING'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "MACHINE LARNING"

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Chn, Yuchn, Hongling Wang, Yuxuan Han, Yuxuan Fng, and Hongchi Lu. "Comparison of machine learning models in credit risk assessment." Applied and Computational Engineering 74, no. 1 (2024): 278–88. http://dx.doi.org/10.54254/2755-2721/74/20240495.

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Crdit risk plays an important rol in financ. In a sns, crdit risk rflcts th stability of financial institutions and th protction of invstors. Du to th impact of th pidmic on th world conomy, w nd to rassss th crdit risk. For a long tim, machin larning and dp larning in statistics hav bn vry ffctiv in prdicting crdit risk. But in machin larning and dp larning, whn prdicting crdit risk, w citd four modls, namly XGBoost, Dcision Tr, Random Forst and Convolutional Nural Ntwork (CNN) modls. Analyz th advantags and disadvantags of ths modls: XGBoost can optimiz th tr modl and prvnt ovrfitting throug
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Cahyanti, F. Lia Dwi, Fajar Sarasati, Widi Astuti, and Elly Firasari. "KLASIFIKASI DATA MINING DENGAN ALGORITMA MACHINE LARNING UNTUK PREDIKSI PENYAKIT LIVER." Technologia : Jurnal Ilmiah 14, no. 2 (2023): 134. http://dx.doi.org/10.31602/tji.v14i2.10093.

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Liver merupakan organ tubuh manusia yang memiliki peranan sangat penting seperti mencerna, menyerap, membantu proses pencernaan makanan serta menghancurkan racun di dalam darah. Penyakit hati atau liver yang sudah akut sangat mempengaruhi fungsi-fungsi hati, penyakit hati dapat diketahui dari munculnya gejala klinis maupun fisik yang timbul pada pasien. Penelitian ini membahas tentang klasifikasi penyakit liver pada dataset ILPD yang diambil dari UCI Machine learning Repository menggunakan algoritma machine learning. Dataset terdiri dari 583 record data, 10 kriteria, dan 1 variable kelas berje
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Pinheiro, Mayara, and Hamilton Oliveira. "Artificial Intelligence." Revista Ibero-Americana de Ciência da Informação 15, no. 3 (2022): 950–68. http://dx.doi.org/10.26512/rici.v15.n3.2022.42767.

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A pesquisa aborda o uso e os estudos sobre Inteligência Artificial (IA) voltada para as tarefas da Ciência da Informação (CI), seu objetivo é analisar os últimos 20 anos da produção científica brasileira a respeito da IA, os objetivos específicos são identificar as abordagens da IA, da CI, os autores, as instituições, as revistas cientificas, as áreas profissionais, o volume de publicações e as metodologias empregadas nos estudos por meio de uma análise quantitativa. Os trabalhos foram recuperados nas bases: Brapci, Peri e no Repositório da Federação Brasileira das Associações de Bibliotecário
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Abbasi, Muhammad Ahmed Ahmed, Hafza Faiza Abbasi, Xiaojun Yu, Muhammad Zulkifal Aziz, Nicole Tye June Yih Yih та Zeming Fan. "E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks". Journal of Neural Engineering, 7 жовтня 2024. http://dx.doi.org/10.1088/1741-2552/ad83f4.

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Abstract The advancements in Brain-Computer Interface (BCI) have substantially evolved people’s lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues,
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Dissertations / Theses on the topic "MACHINE LARNING"

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SHARMA, DIVYA. "APPLICATION OF ML TO MAKE SENCE OF BIOLOGICAL BIG DATA IN DRUG DISCOVERY PROCESS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18378.

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Scientists have been working over years to assemble and accumulate data from biological sources to find solutions for many principal questions. Since a tremendous amount of data has been collected over the past and still increasing at an exponential rate, hence it now becomes unachievable for a human being alone to handle or analyze this data. Most of the data collection and maintenance is now done in digitalized format and hence requires an organization to have better data management and analysis to convert the vast data resource into insights to achieve their objectives. The continuous
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Borovikova, Mariya. "Domain Adaptation of Named Entity Recognition for Plant Health Monitoring." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG105.

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La complexité croissante des écosystèmes agricoles et le La complexité croissante des écosystèmes agricoles et le besoin urgent de surveillance efficace de la santé des plantes rendent nécessaires des solutions technologiques avancées pour traiter les données textuelles. Située dans le cadre du projet BEYOND, cette thèse répond à ces besoins en améliorant les systèmes de reconnaissance d'entités nommées (REN) adaptés au domaine de la santé des plantes. Reconnaissant les limites des approches traditionnelles, cette recherche intègre des stratégies d'adaptation au domaine.La principale contribut
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Conference papers on the topic "MACHINE LARNING"

1

Hasan, Sayed Salah Ahmed, Hussein Abdel Atty Elsayed Mohamed, and Ayman M. Bahaa-Eldin. "An Enhanced Machine Larning based Threat Hunter An Intelligent Network Intrusion Detection System." In 2019 14th International Conference on Computer Engineering and Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces48960.2019.9068160.

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Kocevska, Teodora, Tomaž Javornik, Aleš Švigelj, Ke Guan, Aleksandra Rashkovska, and Andrej Hrovat. "Impact of Room Size on Machine Larning-based Material Prediction using Channel Impulse Response." In 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2023. http://dx.doi.org/10.1109/iwssip58668.2023.10180272.

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