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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and adolescents
Jyotismita Talukdar, Deba Kanta Gogoi, Thipendra P. Singh
Healthcare Analytics (2023) Vol. 3, pp. 100178-100178
Open Access | Times Cited: 22

Showing 22 citing articles:

The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications
i b, P. M. Durai Raj Vincent
Computer Science Review (2025) Vol. 56, pp. 100718-100718
Closed Access | Times Cited: 1
ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach
Franklin Parrales–Bravo, Rosangela Caicedo–Quiroz, Elianne Rodríguez Larraburu, et al.
Informatics (2024) Vol. 11, Iss. 2, pp. 31-31
Open Access | Times Cited: 7
Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
Mayuri Sharma, Chandan Jyoti Kumar, Jyotismita Talukdar, et al.
Open Life Sciences (2023) Vol. 18, Iss. 1
Open Access | Times Cited: 17
An exploration of machine learning approaches for early Autism Spectrum Disorder detection
Nawshin Haque, Tania Islam, Md. Erfan
Healthcare Analytics (2025), pp. 100379-100379
Open Access
Diagnosis of autism spectrum disorder—prediction and analysis of enhanced classification model based on machine learning
M.S. Sankari, A. Kannammal
Elsevier eBooks (2025), pp. 241-257
Closed Access
Optimization of Machine Learning with Feature Selection Techniques in Autism Spectrum Disorder Detection
R Nivetha, N. Rajathi, V. Vanitha
Lecture notes in electrical engineering (2025), pp. 371-382
Closed Access
Machine Learning Methods for the Timely Identification of Autism Spectrum Disorder in Toddlers
Shweta Jain, Hrudaya Kumar Tripathy
(2024), pp. 515-520
Closed Access | Times Cited: 4
An intelligent recommender system using machine learning association rules and rough set for disease prediction from incomplete symptom set
Kamakhya Narain Singh, Jibendu Kumar Mantri
Decision Analytics Journal (2024) Vol. 11, pp. 100468-100468
Open Access | Times Cited: 4
Identifying diseases symptoms and general rules using supervised and unsupervised machine learning
Fatemeh Sogandi
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Aspectos del Trastorno Espectro Autista Relevantes para la Psicopedagogía: Una Revisión Sistemática
Franklin Cerrud Álvarez, Judith Eneida Valdés Agrazal, César Antonio Pérez Castillo, et al.
Ciencia Latina Revista Científica Multidisciplinar (2024) Vol. 7, Iss. 6, pp. 5821-5834
Open Access | Times Cited: 1
Leveraging Transfer Learning with Stacked Ensemble Learning for theDetection of Autism Spectrum Disorder using Eye-tracking
R. Asmetha Jeyarani, Radha Senthilkumar, R. Gowri, et al.
2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (2024), pp. 1-8
Closed Access | Times Cited: 1
Efficient machine learning models across multiple datasets for autism spectrum disorder diagnoses
Amr E. Eldin Rashed, Waleed M. Bahgat, Ali Mohammed Saleh Ahmed, et al.
Biomedical Signal Processing and Control (2024) Vol. 100, pp. 106949-106949
Closed Access | Times Cited: 1
WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for Enhanced Autism Spectrum Disorder Diagnosis
Kainat Khan, Rahul Katarya
Journal of Neuroscience Methods (2024) Vol. 413, pp. 110319-110319
Closed Access | Times Cited: 1
A Deep Learning Approach to Classify Autism Spectrum Disorder using MRI Images
Deba Kanta Gogoi, Jyotismita Talukdar, Dhruba K. Bhattacharyya, et al.
(2023)
Open Access | Times Cited: 3
Bayesian optimization of one-dimensional convolutional neural networks (1D CNN) for early diagnosis of autistic spectrum disorder
Temidayo Oluwatosin Omotehinwa, Morolake Oladayo Lawrence, David Opeoluwa Oyewola, et al.
Journal of Computational Mathematics and Data Science (2024), pp. 100105-100105
Open Access
An Efficient Autism Spectrum Disorder Classification in Different Age Groups using Machine Learning Models
Ambika Rani Subhash, Ashwin Kumar U Motagi
International Journal of Online and Biomedical Engineering (iJOE) (2024) Vol. 20, Iss. 09, pp. 17-38
Open Access
Intelligent Diagnosis of Autism Spectrum Disorder: Harnessing Machine Learning for Enhanced Early Detection
Tushar Taggar, Subhag Sharma, Pooja Sharma
Lecture notes in networks and systems (2024), pp. 369-378
Closed Access
Graphical attention networks for autism spectrum disorder classification
Menaka Radhakrishnan, Vagisan Govindan, R. Karthik, et al.
DELETED (2024)
Closed Access
Early Autism Spectrum Disorder Prediction Using Fine-Tuned Bernoulli’s Naive Bayes Algorithm
Kanav Gupta, Chayan Paul, Nishant Jain
Communications in computer and information science (2024), pp. 278-289
Closed Access
An interpretable clustering classification approach for assessing and adjusting hospital service lines
Esmaeil Bahalkeh, Tze C. Chiam, Yuehwern Yih
Healthcare Analytics (2023) Vol. 4, pp. 100255-100255
Open Access | Times Cited: 1
Implementation and Analysis of Various Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder Using Matlab
Hindumathi Voruganti, Ameya Endla, Bhople Vaibhavi, et al.
Practice, progress, and proficiency in sustainability (2023), pp. 246-261
Closed Access
Comparative Performance Analysis of Logistic Regression and Artificial Neural Network in Autism Spectrum Disorder Detection
M.S. Sankari, A. Kannammal
(2023), pp. 219-225
Closed Access
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