[1] Diagnostic and Statistical Manual of Mental Disorders (DMS-5-TR), 5th edn. Arlington, American Psychiatric Association, 2013.
[2] D. J. Morris-Rosendahl and M. A. Crocq, "Neurodevelopmental Disorders-the History and Future of a Diagnostic Concept," Dialogues in Clinical Neuroscience, vol. 22, no. 1, pp. 65–72, Mar. 2020.
[3] M. J. Maenner et al., "Prevalence and Characteristics of Autism Spectrum Disorder among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020," MMWR, Surveillance Summaries, vol. 72, no. 2, pp. 1–14, Mar. 2023.
[4] R. Alfred and J. H. Obit, "The Roles of Machine Learning Methods in Limiting the Spread of Deadly Diseases: A Systematic Review," Heliyon, vol. 7, no. 6, Elsevier Ltd., Jun. 01, 2021.
[5] B. Kitchenham et al., "Systematic Literature Reviews in Software Engineering: A Systematic Literature Review," Information and Software Technology, vol. 51, no. 1. pp. 7–15, Jan. 2009.
[6] D. Delisle-Rodriguez et al., "Multi-channel EEG-based BCI Using Regression and Classification Methods for Attention Training by Serious Game," Biomedical Signal Processing and Control, vol. 85, DOI: 10.1016/j.bspc.2023.104937, Aug. 2023.
[7] S. Shilaskar et al., "Prediction of Autism and Dyslexia Using Machine Learning and Clinical Data Balancing," Proc. of the 2023 IEEE Int. Conf. on Advances in Intelligent Computing and Applications (AICAPS 2023), DOI: 10.1109/AICAPS57044.2023.10074161, 2023.
[8] K. F. Kollias et al., "Autism Detection in High- functioning Adults with the Application of Eye-tracking Technology and Machine Learning," Proc. of the 2022 11th IEEE Int. Conf. on Modern Circuits and Systems Technologies (MOCAST 2022), DOI: 10.1109/MOCAST54814.2022.9837653, 2022.
[9] M. Bala, M. H. Ali, M. S. Satu, K. F. Hasan and M. A. Moni, "Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder," Algorithms, vol. 15, no. 5, DOI: 10.3390/a15050166, May 2022.
[10] R. M. Kannan and R. Sasikala, "Predicting Autism in Children at an Early Stage Using Eye Tracking," Proc. of the 2nd IEEE Int. Conf. on Vision towards Emerging Trends in Communication and Networking Technologies (ViTECoN 2023), DOI: 10.1109/ViTECoN58111.2023.10157663, 2023.
[11] A. R. Khan et al., "EXECUTE: Exploring Eye Tracking to Support E-learning," Proc. of the IEEE Global Engineering Education Conf. (EDUCON), pp. 670–676, Tunis, Tunisia, 2022.
[12] Ramanjot, D. Singh, M. Rakhra and S. Aggarwal, "Autism Spectrum Disorder Detection Using the Deep Learning Approaches," Proc. of IEEE Int. Conf. on Technological Advancements in Computational Sci. (ICTACS 2022), pp. 761– 766, Tashkent, Uzbekistan, 2022.
[13] N. Zaman, J. Ferdus and A. Sattar, "Autism Spectrum Disorder Detection Using Machine Learning Approach," Proc. of the 2021 IEEE 12th Int. Conf. on Computing Communication and Networking Technologies (ICCCNT 2021), DOI: 10.1109/ICCCNT51525.2021.9579522, 2021.
[14] V. Chakraborty et al., "Intelligent Dyslexia Prediction Empowered with Optimization Techniques," Proc. of the 2021 6th IEEE Int. Conf. on Recent Trends on Electronics, Inf., Comm. and Tech. (RTEICT 2021), pp. 412–415, DOI: 10.1109/RTEICT52294.2021.9573823, 2021.
[15] F. Thabtah and D. Peebles, "A New Machine Learning Model Based on Induction of Rules for Autism Detection," Health Informatics J., vol. 26, no. 1, pp. 264–286, Mar. 2020.
[16] F. Thabtah, "An Accessible and Efficient Autism Screening Method for Behavioural Data and Predictive Analyses," Health Informatics J., vol. 25, no. 4, pp. 1739–1755, Dec. 2019.
[17] O. Atila et al., "LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-based Features with Electroencephalogram Signals," Sensors, vol. 23, no. 16, DOI: 10.3390/s23167032, Aug. 2023.
[18] P. V. Redondo, R. Huser and H. Ombao, "Functional-coefficient Models for Multi-variate Time Series in Designed Experiments: With Applications to Brain Signals," arXiv: 2208.00292, [Online], Available: http://arxiv.org/abs/2208.00292, 2022.
[19] S. Subramaniam, "Enabling Innovative Technologies for Global Healthcare," 42nd Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society Proc., Institute of Electrical and Electronics Engineers and Canadian Medical and Biological Engineering Society, Montreal, Canada, July 2020.
[20] A. Ekhlasi, A. M. Nasrabadi and M. R. Mohammadi, "Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy," Iran J. Psychiatry, vol. 16, no. 4, pp. 374–382, DOI: 10.18502/ijps.v16i4.7224, Oct. 2021.
[21] D. Hai Shi Da Xue, J. Da Xue, M. IEEE Systems, M. IEEE Systems, B. International Federation of Automatic Control. Technical Committee on Economic and Institute of Electrical and Electronics Engineers, 2017 Int. Conf. on Information, Cybernetics and Computational Social Systems (IEEE ICCSS 2017), Dalian, Liaoning, China, July 24-26, 2017.
[22] Galgotias University, Universitatea "Aurel Vlaicu" din Arad, IEEE Industry Applications Society and Institute of Electrical and Electronics Engineers, 2020 IEEE 5th Int. Conf. on Computing Communication and Automation (ICCCA), Galgotias University, Noida, India, Oct. 30-31, 2020.
[23] S. S. Beriha, "Computer Aided Diagnosis System to Distinguish ADHD from Similar Behavioral Disorders," Biomedical and Pharmacology Journal, vol. 11, no. 2, pp. 1135–1141, Jun. 2018.
[24] L. Alice, B. K. S Jacob and S. Ramachandran, "ADHD Detection Based on Time Frequency Image and Deep- learning CNN from Event-related EEG," DOI: 10.21203/rs.3.rs-2986442/v1, 2023.
[25] D. Wang, D. Hong and Q. Wu, "Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning," IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 20, no. 2, pp. 1581–1586, Mar. 2023.
[26] S. M. Omar, M. Kimwele, A. Olowolayemo and D. M. Kaburu, "Enhancing EEG Signals Classification Using LSTM-CNN Architecture," Engineering Reports, DOI: 10.1002/eng2.12827, 2023.
[27] N. Gupte, M. Patel, T. Pen and S. Kurhade, "Early Detection of ADHD and Dyslexia from EEG Signals," Proc. of the 2023 IEEE 8th Int. Conf. for Convergence in Technology (I2CT 2023), DOI: 10.1109/I2CT57861.2023.10126272, 2023.
[28] M. Maniruzzaman et al., "Optimal Channels and Features Selection Based ADHD Detection from EEG Signal Using Statistical and Machine Learning Techniques," IEEE Access, vol. 11, pp. 33570–33583, DOI: 10.1109/ACCESS.2023.3264266, 2023.
[29] A. Afzali et al., "Automated Major Depressive Disorder Diagnosis Using a Dual-input Deep Learning Model and Image Generation from EEG Signals," Waves in Random and Complex Media, pp. 1–16, DOI: 10.1080/17455030.2023.2187237, Mar. 2023.
[30] Y. Sharma and B. K. Singh, "Classification of Children with Attention-Deficit Hyperactivity Disorder Using Wigner-Ville Time-Frequency and Deep expEEGNetwork Feature-based Computational Models," IEEE Trans. Med. Robot Bionics, vol. 5, no. 4, pp. 890–902, Nov. 2023.
[31] I. W. Huang et al., "Optimal EEG Data Segmentation in LSTM Networks for Learning Neural Dynamics of ADHD," Proc. of the IEEE 2022 Int. Conf. on System Science and Engineering (ICSSE 2022), pp. 33–38, DOI: 10.1109/ICSSE55923.2022.9948260, 2022.
[32] Y. Zhang et al., "ADHD Classification by Feature Space Separation with Sparse Representation," Proc. of the 2018 IEEE 23rd Int. Conf. on Digital Signal Processing (DSP), DOI: 10.1109/ICDSP.2018.8631658, Shanghai, China, 2019.
[33] B. Taghi Beyglou et al., "Detection of ADHD cases using CNN and Classical Classifiers of Raw EEG," Computer Methods and Programs in Biomedicine Update, vol. 2, p. 100080, Jan. 2022.
[34] C. Wang, "Indentification of Autism Spectrum Disorder Based on an Improved Convolutional Neural Networks," Proc. of the 2021 IEEE 3rd Int. Conf. on Machine Learning, Big Data and Business Intelligence (MLBDBI 2021), pp. 235–239, DOI: 10.1109/MLBDBI54094.2021.00051, 2021.
[35] M. Maniruzzaman et al., "Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal," Computers, Materials and Continua, vol. 72, no. 3, pp. 5179–5195, 2022.
[36] M. Mafi and S. Radfar, "High Dimensional Convolutional Neural Network for EEG Connectivity-based Diagnosis of ADHD," J. of Biomedical Physics and Engineering, vol. 12, no. 6, pp. 645–654, 2022.
[37] F. W. Alsaade and M. S. Alzahrani, "Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms," Computational Intelligence and Neuroscience, vol. 2022, DOI: 10.1155/2022/8709145, 2022.
[38] I. A. Ahmed et al., "Eye Tracking-based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques," Electronics (Switzerland), vol. 11, no. 4, DOI: 10.3390/electronics11040530, Feb. 2022.
[39] U. B. Mahadevaswamy and C. Manjunath, "f-MRI Based Detection of Autism Using CNN Algorithm," Proc. of the 2022 IEEE 2nd Mysore Sub Section Int. Conf. (MysuruCon 2022), DOI: 10.1109/MysuruCon55714.2022.9972394, 2022.
[40] B. R. G. Elshoky et al., "Comparing Automated and Non-automated Machine Learning for Autism Spectrum Disorders Classification Using Facial Images," ETRI J., vol. 44, no. 4, pp. 613–623, 2022.
[41] M. R. Kanhirakadavath et al., "Investigation of Eye-tracking Scan Path As a Biomarker for Autism Screening Using Machine Learning Algorithms," Diagnostics, vol. 12, no. 2, 2022.
[42] M. Saini, U. Satija and M. D. Upadhayay, "One-dimensional Convolutional Neural Network Architecture for Classification of Mental Tasks from Electroencephalogram," Biomed Signal Process Control, vol. 74, DOI: 10.1016/j.bspc.2022.103494, Apr. 2022.
[43] S. Altun, A. Alkan and H. Altun, "Automatic Diagnosis of Attention Deficit Hyperactivity Disorder with Continuous Wavelet Transform and Convolutional Neural Network," Clinical Psychopharmacology and Neuroscience, vol. 20, no. 4, pp. 715–724, 2022.
[44] S. Saini, R. Rani and N. Kalra, "Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning Techniques Based on Classification of EEG Signal," Proc. of the IEEE 8th Int. Conf. on Advanced Comp. and Comm. Systems (ICACCS 2022), pp. 782–786, Coimbatore, India, 2022.
[45] T. Tuncer, S. Dogan and A. Subasi, "LEDPatNet19: Automated Emotion Recognition Model Based on Non-linear LED Pattern Feature Extraction Function Using EEG Signals," Cognitive Neurodynamics, vol. 16, no. 4, pp. 779–790, Aug. 2022.
[46] N. A. Mashudi, N. Ahmad and N. M. Noor, "Classification of Adult Autistic Spectrum Disorder Using Machine Learning Approach," IAES Int. J. of Artificial Intelligence, vol. 10, no. 3, pp. 743–751, 2021.
[47] M. F. Rabbi et al., "A Convolutional Neural Network Model for Early-stage Detection of Autism Spectrum Disorder," Proc. of the 2021 IEEE Int. Conf. on Information and Communication Technology for Sustainable Development (ICICT4SD 2021), pp. 110–114, Dhaka, Bangladesh, Feb. 2021.
[48] G. P. Pralhad et al., "Dyslexia Prediction Using Machine Learning," Proc. of the 2021 1st IEEE Int. Conf. on Artificial Intelligence and Machine Vision (AIMV 2021), DOI: 10.1109/AIMV53313.2021.9671004, Gandhinagar, India, 2021.
[49] Y. H. Chen et al., "Early Detection of Autism Spectrum Disorder in Young Children with Machine Learning Using Medical Claims Data," BMJ Health Care Inform, vol. 29, no. 1, p. 100544, Sep. 2022.
[50] S. Karimi-Shahraki and M. Khezri, "Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-based Features of EEG Signals," JIPET J. of Intelligent Procedures in Electrical Technology, vol. 12, no. 47, pp. 29–40, [Online], Available: http://jipet.iaun.ac.ir/, 2021.
[51] N. Kaur and G. Gupta, "Refurbished and Improvised Model Using Convolution Network for Autism Disorder Detection in Facial Images," Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 2, pp. 883–889, DOI: 10.11591/ijeecs.v29.i2.pp883-889, Feb. 2023.
[52] S. Raj and S. Masood, "Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques," Procedia Computer Science, pp. 994–1004, DOI: 10.1016/j.procs.2020.03.399, 2020.
[53] Z. Sherkatghanad et al., "Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network," Frontiers in Neuroscience, vol. 13, DOI: 10.3389/fnins.2019.01325, Jan. 2020.
[54] M. Rakić, M. Cabezas, K. Kushibar, A. Oliver and X. Lladó, "Improving the Detection of Autism Spectrum Disorder by Combining Structural and Functional MRI Information," NeuroImage: Clinical, vol. 25, DOI: 10.1016/j.nicl.2020.102181, Jan. 2020.
[55] M. Moghaddari, M. Z. Lighvan and S. Danishvar, "Diagnose ADHD disorder in Children Using Convolutional Neural Network Based on Continuous Mental Task EEG," Computer Methods and Programs in Biomedicine, vol. 197, DOI: 10.1016/j.cmpb.2020.105738, Dec. 2020.
[56] Z. Zhao et al., "Applying Machine Learning to Identify Autism with Restricted Kinematic Features," IEEE Access, vol. 7, pp. 157614–157622, DOI: 10.1109/ACCESS.2019.2950030, 2019.
[57] R. Yaghoobi, S. Azadi and P. Keshavarzi, "Loss Detection of Recurrence Rate in the EEG Signals of Children with ADHD," NIScPR Online Periodicals Repository, JSIR, vol.78, no. 04, April 2019.
[58] G. A. Senthil et al., "A Novel Analysis and Detection of Autism Spectrum Disorder in Artificial Intelligence Using Hybrid Machine Learning," Proc. of the IEEE Int. Conf. on Innovative Data Communication Technologies and Application (ICIDCA), pp. 291–296, Uttarakhand, India, 2023.
[59] A. E. Alchalabi et al., "FOCUS: Detecting ADHD Patients by an EEG-based Serious Game," IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 7, pp. 1512–1520, Jul. 2018.
[60] M. R. Mohammadi et al., "EEG Classification of ADHD and Normal Children Using Non-linear Features and Neural Network," Biomedical Engineering Letters, vol. 6, no. 2, pp. 66–73, May 2016.
[61] A. J. Hao, B. Lianghua He and H. Yin, "Discrimination of ADHD Children Based on Deep Bayesian Network," Proc. of 2015 IET Int. Conf. on Biomedical Image and Signal Processing (ICBISP 2015), DOI: 10.1049/cp.2015.0764, Beijing, China, 2015.
[62] M. De Oliveira Meira et al., "Evaluating Brain Regions that Characterize Attention Deficit/Hyperactivity Disorder Based on SPECT Images and Machine Learning Models," Proc. of the IEEE Int. Joint Conf. on Neural Networks, DOI: 10.1109/IJCNN55064.2022.9892968, 2022.
[63] S. K. Khare, N. B. Gaikwad and V. Bajaj, "VHERS: A Novel Variational Mode Decomposition and Hilbert Transform-based EEG Rhythm Separation for Automatic ADHD Detection," IEEE Transactions on Instrumentation and Measurement, vol. 71, DOI: 10.1109/TIM.2022.3204076, 2022.
[64] M. Aceves-Fernandez and M. A. Aceves-Fernandez, "Methodology Proposal of ADHD Classification of Children Based on Cross Recurrence Plots Methodology Proposal of ADHD Classification of Children based on Cross Recurrence Plots," Non Linear Dynamics, vol. 104, pp. 1491-1505, 2021.
[65] S. Sangeetha, R. Uma and R. Valarmathi, "Dyslexia Biomarker Detection Using Machine Learning," Proc. of the 2022 Int. Conf. on Communication, Computing and Internet of Things (IC3IoT 2022), DOI: 10.1109/IC3IOT53935.2022.9767973, 2022.
[66] A. Parashar et al., "Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls," Intelligent Automation and Soft Computing, vol. 28, no. 3, pp. 669– 682, 2021.
[67] B. Wingfield et al., "A Predictive Model for Paediatric Autism Screening," Health Informatics J., vol. 26, no. 4, pp. 2538–2553, DOI: 10.1177/1460458219887823, Dec. 2020.
[68] S. Liu et al., "Deep Spatio-temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1–10, DOI: 10.1109/TNSRE.2020.3019063, 2021.
[69] R. Sujatha et al., "A Machine Learning Way to Classify Autism Spectrum Disorder," International Journal of Emerging Technologies in Learning, vol. 16, no. 6, pp. 182–200, 2021.
[70] T. Akter et al., "Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders," IEEE Access, vol. 7, pp. 166509–166527, DOI: 10.1109/ACCESS.2019.2952609, 2019.
[71] Y. Jayawardana et al., "Analysis of Temporal Relationships between ASD and Brain Activity through EEG and Machine Learning," Proc. of the 2019 IEEE 20th Int. Conf. on Information Reuse and Integration for Data Science (IRI), DOI: 10.1109/IRI.2019.00035, Los Angeles, USA, 2019.
[72] S. Pulido-Castro et al., "Ensemble of Machine Learning Models for an Improved Facial Emotion Recognition," Proc. of the 2021 IEEE URUCON, pp. 512–516, Montevideo, Uruguay, 2021.
[73] P. Thi Viet Huong et al., "Ensemble Learning in Detecting ADHD Children by Utilizing the Non-linear Features of EEG Signal*," Proc. of the 2nd Int. Conf. on Human-centered Artificial Intelligence (Computing4Human 2021), [Online], Available: http://ceur-ws.org, 2021.
[74] R. Holker and S. Susan, "Computer-aided Diagnosis Framework for ADHD Detection Using Quantitative EEG," Part of Book: Lecture Notes in Computer Science (Including Sub-series Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 229– 240, DOI: 10.1007/978-3-031-15037-1_19, Springer, 2022.
[75] S. Batsakis et al., "Data-driven Decision Support for Adult Autism Diagnosis Using Machine Learning," Digital, vol. 2, no. 2, pp. 224–243, DOI: 10.3390/digital2020014, Jun. 2022.
[76] N. Hamedi et al., "An Effective Connectomics Approach for Diagnosing ADHD Using Eyes-open Resting-state MEG," Proc. of the 11th IEEE Int. Conf. on Computer Engineering and Knowledge (ICCKE 2021), pp. 110–114, DOI: 10.1109/ICCKE54056.2021.9721443, Mashhad, Iran, 2021.
[77] A. JothiPrabha, R. Bhargavi and B. V. Deepa Rani, "Prediction of Dyslexia Severity Levels from Fixation and Saccadic Eye Movement Using Machine Learning," Biomed Signal Process Control, vol. 79, DOI: 10.1016/j.bspc.2022.104094, Jan. 2022.
[78] S.-F. Liang et al., "Differentiation between Resting-state fMRI Data from ADHD and Normal Subjects: Based on Functional Connectivity and Machine Learning," Proc. of the 2012 Int. Conf. on Fuzzy Theory and Its Applications (iFUZZY2012), DOI: 10.1109/iFUZZY.2012.6409719, Taiwan, 2012.
[79] Y. Ding, Y. Li, S. Li, Z. Fan and L. Wang, 2011 8th Int. Conf. on Fuzzy Systems and Knowledge Discovery (FSKD 2011) Proceedings, pp. 1349-2073, Shanghai, China, 26-28 July 2011.
[80] A. Alim and M. H. Imtiaz, "Automatic Identification of Children with ADHD from EEG Brain Waves," Signals, vol. 4, no. 1, pp. 193–205, DOI: 10.3390/signals4010010, Mar. 2023.
[81] EEE, IEEE Engineering in Medicine and Biology Society and IEEE Signal Processing Society, IEEE ISBI 2020 Int. Symposium on Biomedical Imaging (2020 Symposium Proc.), Iowa City, Iowa, 2020.
[82] IEEE, International Symposium on Biomedical Imaging, 2018 IEEE 15th Int. Symposium on Biomedical Imaging (ISBI 2018), DOI: 10.1109/ISBI39256.2018, 2018.
[83] H. Alkahtani et al., "Developing System-based Artificial Intelligence Models for Detecting the Deficit Hyperactivity Disorder," Mathematics, vol. 11, no. 22, DOI: 10.3390/math11224698, Nov. 2023.
[84] G. Sharma and A. M. Joshi, "SzHNN: A Novel and Scalable Deep Convolution Hybrid Neural Network Framework for Schizophrenia Detection Using Multichannel EEG," IEEE Transactions on Instrumentation, vol. 71, DOI: 10.1109/TIM.2022.3212040, 2022.
[85] A. Sharma and P. Tanwar, "Identification of Autism Spectrum Disorder (ASD) from Facial Expressions Using Deep Learning," Proc. of the 2022 IEEE Int. Conf. on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON 2022), pp. 478–484, Faridabad, India, 2022.
[86] S. Dalai, Jadavpur University, Institute of Electrical and Electronics Engineers, Kolkata Section and IEEE Signal Processing Society, Kolkata Chapter, Proc. of 2018 IEEE Applied Signal Processing Conference (ASPCON), Venue: Jadavpur University Main Campus, Kolkata, India, Dec. 7-9, 2018.
[87] A. A. Torres-García et al., Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications, 1st Edn., ISBN: 9780128201251, Elsevier, 2022.
