[Al-Muhanna, M.K., Alghamdi, A.A., Alrfaei, B.M., Afzal, M., Al-Subaiee, R. and Haddadi, R. (2024). An attention-based hybrid optimized residual memory network (AHRML) method for autism spectrum disorder (ASD) detection, Journal of Disability Research 3(3): 20240030.]Search in Google Scholar
[Alhafedh, M.A.A. and Qasim, O.S. (2019). Two-stage gene selection in microarray dataset using fuzzy mutual information and binary particle swarm optimization, Indian Journal of Forensic Medicine & Toxicology 13(4): 1162–1171.]Search in Google Scholar
[Almars, A.M., Badawy, M. and Elhosseini, M.A. (2023). ASD2-TL* GTO: Autism spectrum disorders detection via transfer learning with gorilla troops optimizer framework, Heliyon 9(11): e21530.]Search in Google Scholar
[Arora, S. and Anand, P. (2019). Binary butterfly optimization approaches for feature selection, Expert Systems with Applications 116: 147–160.]Search in Google Scholar
[Banaie-Dezfouli, M., Nadimi-Shahraki, M.H. and Beheshti, Z. (2023). BE-GWO: Binary extremum-based grey wolf optimizer for discrete optimization problems, Applied Soft Computing 146: 110583.]Search in Google Scholar
[Chen, R.-C., Dewi, C., Huang, S.-W. and Caraka, R.E. (2020). Selecting critical features for data classification based on machine learning methods, Journal of Big Data 7(1): 52–78.]Search in Google Scholar
[Czmil, S., Kluska, J. and Czmil, A. (2024). An empirical study of a simple incremental classifier based on vector quantization and adaptive resonance theory, International Journal of Applied Mathematics and Computer Science 34(1): 149–165, DOI: 10.61822/amcs-2024-0011.]Search in Google Scholar
[Dong, H., Sun, J., Sun, X. and Ding, R. (2020). A many-objective feature selection for multi-label classification, Knowledge-Based Systems 208: 106456.]Search in Google Scholar
[Duan, L., Liu, J., Yin, H., Wang, W., Liu, L., Shen, J. and Wang, Z. (2022). Dynamic changes in spatiotemporal transcriptome reveal maternal immune dysregulation of autism spectrum disorder, Computers in Biology and Medicine 151: 106334.]Search in Google Scholar
[Gaspar, A., Oliva, D., Hinojosa, S., Aranguren, I. and Zaldivar, D. (2022). An optimized kernel extreme learning machine for the classification of the autism spectrum disorder by using gaze tracking images, Applied Soft Computing 120: 108654.]Search in Google Scholar
[Helen Josephine, V., Vedaiyan, R., Xavier, V., Winston, J., Jegatheesan, A., Lakshmi, D. and Raj, J.S. (2023). Hybrid bacterial foraging optimization with sparse autoencoder for energy systems, Computer Systems Science and Engineering 45(1): 701–714.]Search in Google Scholar
[Hirota, T. and King, B.H. (2023). Autism spectrum disorder: A review, Jama 329(2): 157–168.]Search in Google Scholar
[Jiménez-Cordero, A., Morales, J.M. and Pineda, S. (2021). A novel embedded min-max approach for feature selection in nonlinear support vector machine classification, European Journal of Operational Research 293(1): 24–35.]Search in Google Scholar
[Li, G., Han, D., Wang, C., Hu, W., Calhoun, V.D. and Wang, Y.-P. (2020). Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia, Computer Methods and Programs in Biomedicine 183: 105073.]Search in Google Scholar
[Li, J., Chen, Z., Li, G., Ouyang, G. and Li, X. (2022a). Automatic classification of ASD children using appearance-based features from videos, Neurocomputing 470: 40–50.]Search in Google Scholar
[Li, L., Wang, Y., Xu, Y. and Lin, K.-Y. (2022b). Meta-learning based industrial intelligence of feature nearest algorithm selection framework for classification problems, Journal of Manufacturing Systems 62: 767–776.]Search in Google Scholar
[Liu, Y., Ju, S., Wang, J. and Su, C. (2020). A new feature selection method for text classification based on independent feature space search, Mathematical Problems in Engineering 2020(1): 6076272.]Search in Google Scholar
[Loganathan, S., Geetha, C., Nazaren, A.R. and Fernandez, M.H.F. (2023). Autism spectrum disorder detection and classification using chaotic optimization based Bi-GRU network: An weighted average ensemble model, Expert Systems with Applications 230: 120613.]Search in Google Scholar
[Majidpour, J., Rashid, T.A., Thinakaran, R., Batumalay, M., Dewi, D.A., Hassan, B.A., Dadgar, H. and Arabi, H. (2024). NSGA-II-DL: Metaheuristic optimal feature selection with deep learning framework for HER2 classification in breast cancer, IEEE Access 2(12): 38885–38898.]Search in Google Scholar
[Mengash, H.A., Alqahtani, H., Maray, M., Nour, M.K., Marzouk, R., Al-Hagery, M.A., Mohsen, H. and Al Duhayyim, M. (2023). Automated autism spectral disorder classification using optimal machine learning model, Computers, Materials and Continua 74(3): 5251–5265.]Search in Google Scholar
[Mirjalili, S. and Hashim, S.Z.M. (2012). BMOA: Binary magnetic optimization algorithm, International Journal of Machine Learning and Computing 2(3): 204–208.]Search in Google Scholar
[Mohammed, M.B., Salsabil, L., Shahriar, M., Tanaaz, S.S. and Fahmin, A. (2021). Identification of autism spectrum disorder through feature selection-based machine learning, 2021 24th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, pp. 1–6.]Search in Google Scholar
[Nogay, H.S. and Adeli, H. (2023). Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks, Biomedical Signal Processing and Control 79: 104234.]Search in Google Scholar
[Omuya, E.O., Okeyo, G.O. and Kimwele, M.W. (2021). Feature selection for classification using principal component analysis and information gain, Expert Systems with Applications 174: 114765.]Search in Google Scholar
[Parlett-Pelleriti, C.M., Stevens, E., Dixon, D. and Linstead, E.J. (2023). Applications of unsupervised machine learning in autism spectrum disorder research: A review, Review Journal of Autism and Developmental Disorders 10(3): 406–421.]Search in Google Scholar
[Paul, D., Jain, A., Saha, S. and Mathew, J. (2021). Multi-objective PSO based online feature selection for multi-label classification, Knowledge-Based Systems 222: 106966.]Search in Google Scholar
[Qasim, O.S., Al-Thanoon, N.A. and Algamal, Z.Y. (2020). Feature selection based on chaotic binary black hole algorithm for data classification, Chemometrics and Intelligent Laboratory Systems 204: 104104.]Search in Google Scholar
[Qi, X., Zhu, Y. and Zhang, H. (2017). A new meta-heuristic butterfly-inspired algorithm, Journal of Computational Science 23: 226–239. ]Search in Google Scholar
[Rahman, M.M., Usman, O.L., Muniyandi, R.C., Sahran, S., Mohamed, S. and Razak, R.A. (2020). A review of machine learning methods of feature selection and classification for autism spectrum disorder, Brain Sciences 10(12): 949.]Search in Google Scholar
[Raj, S. and Masood, S. (2020). Analysis and detection of autism spectrum disorder using machine learning techniques, Procedia Computer Science 167: 994–1004.]Search in Google Scholar
[Sahu, S.K. and Verma, P. (2022). Classification of autistic spectrum disorder using deep neural network with particle swarm optimization, International Journal of Computer Vision and Image Processing 12(1): 1–11.]Search in Google Scholar
[Sayed, G.I., Hassanien, A.E. and Azar, A.T. (2019). Feature selection via a novel chaotic crow search algorithm, Neural Computing and Applications 31: 171–188.]Search in Google Scholar
[Shami, T.M., Mirjalili, S., Al-Eryani, Y., Daoudi, K., Izadi, S. and Abualigah, L. (2023). Velocity pausing particle swarm optimization: A novel variant for global optimization, Neural Computing and Applications 35(12): 9193–9223.]Search in Google Scholar
[Spencer, R., Thabtah, F., Abdelhamid, N. and Thompson, M. (2020). Exploring feature selection and classification methods for predicting heart disease, Digital Health 6, Article no. 205507620914777, DOI: 10.1177/2055207620914777.]Search in Google Scholar
[Thirumoorthy, K. and Muneeswaran, K. (2021). Feature selection using hybrid poor and rich optimization algorithm for text classification, Pattern Recognition Letters 147: 63–70.]Search in Google Scholar
[Uddin, M.J., Ahamad, M.M., Sarker, P.K., Aktar, S., Alotaibi, N., Alyami, S.A., Kabir, M.A. and Moni, M.A. (2023). An integrated statistical and clinically applicable machine learning framework for the detection of autism spectrum disorder, Computers 12(5): 92.]Search in Google Scholar
[Umamaheswari, K. and Parthiban, L. (2020). Improved whale optimization algorithm based feature selection with fuzzy rule base classifier for autism spectrum disorder diagnosis, International Journal of Advanced Research in Engineering and Technology 11(11): 41–55.]Search in Google Scholar
[Wang, H., Jing, X. and Niu, B. (2014). A weighted bacterial colony optimization for feature selection, Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, pp. 379–389.]Search in Google Scholar
[Wang, H., Tan, L. and Niu, B. (2019). Feature selection for classification of microarray gene expression cancers using bacterial colony optimization with multi-dimensional population, Swarm and Evolutionary Computation 48: 172–181.]Search in Google Scholar
[Zhang, F., Wei, Y., Liu, J., Wang, Y., Xi, W. and Pan, Y. (2022). Identification of autism spectrum disorder based on a novel feature selection method and variational autoencoder, Computers in Biology and Medicine 148: 105854.]Search in Google Scholar
[Zhao, J., Zhang, D., He, Q. and Li, L. (2023). A hybrid-strategy-improved dragonfly algorithm for the parameter identification of an SDM, Sustainability 15(15): 11791.]Search in Google Scholar
