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Most Cited

  • Data Descriptor
  • Open Access
149 Citations
37,850 Views
10 Pages

A Dataset of Scalp EEG Recordings of Alzheimer’s Disease, Frontotemporal Dementia and Healthy Subjects from Routine EEG

  • Andreas Miltiadous,
  • Katerina D. Tzimourta,
  • Theodora Afrantou,
  • Panagiotis Ioannidis,
  • Nikolaos Grigoriadis,
  • Dimitrios G. Tsalikakis,
  • Pantelis Angelidis,
  • Markos G. Tsipouras,
  • Euripidis Glavas and
  • Alexandros T. Tzallas
  • + 1 author

27 May 2023

Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article provides a detailed description of a resting-state EEG dataset of individu...

  • Data Descriptor
  • Open Access
51 Citations
12,361 Views
9 Pages

A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma

  • Félix Quinton,
  • Romain Popoff,
  • Benoît Presles,
  • Sarah Leclerc,
  • Fabrice Meriaudeau,
  • Guillaume Nodari,
  • Olivier Lopez,
  • Julie Pellegrinelli,
  • Olivier Chevallier and
  • Jean-Louis Alberini
  • + 2 authors

27 April 2023

Liver cancer is the sixth most common cancer in the world and the fourth leading cause of cancer mortality. In unresectable liver cancers, especially hepatocellular carcinoma (HCC), transarterial radioembolisation (TARE) can be considered for treatme...

  • Article
  • Open Access
43 Citations
9,205 Views
12 Pages

Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

  • Mohamed Ebrahim,
  • Ahmed Ahmed Hesham Sedky and
  • Saleh Mesbah

2 February 2023

Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learn...

  • Article
  • Open Access
40 Citations
36,477 Views
17 Pages

Machine Learning for Credit Risk Prediction: A Systematic Literature Review

  • Jomark Pablo Noriega,
  • Luis Antonio Rivera and
  • José Alfredo Herrera

7 November 2023

In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of informati...

  • Data Descriptor
  • Open Access
40 Citations
20,855 Views
16 Pages

Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases

  • Sachin Panchal,
  • Ankita Naik,
  • Manesh Kokare,
  • Samiksha Pachade,
  • Rushikesh Naigaonkar,
  • Prerana Phadnis and
  • Archana Bhange

28 January 2023

Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis system to detect retinal fundus diseases is extremely useful and serviceable to ophthalmologists. Early detection, diagnosis, and correct treatment could save the e...

  • Article
  • Open Access
32 Citations
9,491 Views
16 Pages

Federated Learning for Data Analytics in Education

  • Christian Fachola,
  • Agustín Tornaría,
  • Paola Bermolen,
  • Germán Capdehourat,
  • Lorena Etcheverry and
  • María Inés Fariello

20 February 2023

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices while avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without tr...

  • Article
  • Open Access
30 Citations
5,299 Views
17 Pages

Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models

  • Denis Krivoguz,
  • Sergei G. Chernyi,
  • Elena Zinchenko,
  • Artem Silkin and
  • Anton Zinchenko

30 August 2023

This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model...

  • Article
  • Open Access
28 Citations
11,215 Views
27 Pages

Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders

  • Zeyad A. T. Ahmed,
  • Eid Albalawi,
  • Theyazn H. H. Aldhyani,
  • Mukti E. Jadhav,
  • Prachi Janrao and
  • Mansour Ratib Mohammad Obeidat

3 November 2023

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This...

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Data - ISSN 2306-5729