@article{Hariharan201839,
title = {Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification},
author = {M Hariharan and R Sindhu and V Vijean and H Yazid and T Nadarajaw and S Yaacob and K Polat},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036611215&doi=10.1016%2fj.cmpb.2017.11.021&partnerID=40&md5=1f3b17817b00f07cadad6eb61c0f4bf9},
doi = {10.1016/j.cmpb.2017.11.021},
issn = {01692607},
year = {2018},
date = {2018-01-01},
journal = {Computer Methods and Programs in Biomedicine},
volume = {155},
pages = {39-51},
publisher = {Elsevier Ireland Ltd},
abstract = {Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V.},
note = {cited By 21},
keywords = {Accidents, Algorithms, Article, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Classification (of information), Classification Algorithm, Classifier, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Extraction, Extreme Learning Machine, Factual, Factual Database, Feature Extraction, Feature Selection Methods, Fuzzy System, Hearing Impairment, Human, Hunger, Infant, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Learning, Linear Predictive Coding, Machine Learning, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficients, Multi-Class Classification, Neural Networks, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Pathophysiology, Prematurity, Reproducibility, Reproducibility of Results, Signal Processing, Speech Recognition, Wavelet Analysis, Wavelet Packet, Wavelet Packet Transforms},
pubstate = {published},
tppubtype = {article}
}

@article{Hameed2017,
title = {Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm},
author = {S S Hameed and R Hassan and F F Muhammad},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033361187&doi=10.1371%2fjournal.pone.0187371&partnerID=40&md5=f9260d41165145f229a3cf157699635e},
doi = {10.1371/journal.pone.0187371},
issn = {19326203},
year = {2017},
date = {2017-01-01},
journal = {PLoS ONE},
volume = {12},
number = {11},
publisher = {Public Library of Science},
abstract = {In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. © 2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.},
note = {cited By 11},
keywords = {Accuracy, Algorithms, Article, Autism, Autism Spectrum Disorders, CAPS2 Gene, Classification (of information), Classifier, Experimental Study, Gene, Gene Expression, Gene Identification, Genetic Association, Genetic Procedures, Genetic Risk, Genetics, Geometric Binary Particle Swarm Optimization Support Vector Machine Algorithm, Human, RIsk Assessment, Standardization, Statistical Filter, Statistical Parameters, Statistics, Support Vector Machines},
pubstate = {published},
tppubtype = {article}
}

@article{Bhat2014851,
title = {Automated diagnosis of autism: In search of a mathematical marker},
author = {S Bhat and U R Acharya and H Adeli and G M Bairy and A Adeli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925286949&doi=10.1515%2frevneuro-2014-0036&partnerID=40&md5=04858a5c9860e9027e3113835ca2e11f},
doi = {10.1515/revneuro-2014-0036},
issn = {03341763},
year = {2014},
date = {2014-01-01},
journal = {Reviews in the Neurosciences},
volume = {25},
number = {6},
pages = {851-861},
publisher = {Walter de Gruyter GmbH},
abstract = {Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH.},
note = {cited By 34},
keywords = {Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis},
pubstate = {published},
tppubtype = {article}
}

@article{SitiIradah2011193,
title = {EduTism: An assistive educational system for the treatment of autism children with intelligent approach},
author = {I Siti Iradah and A K Rabiah},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255214646&doi=10.1007%2f978-3-642-25200-6_19&partnerID=40&md5=85447136ace048f4543c86a103c8a786},
doi = {10.1007/978-3-642-25200-6_19},
issn = {03029743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7067 LNCS},
number = {PART 2},
pages = {193-204},
abstract = {This paper presents the development of an assistive educational system with intelligent approach which can be a basic electronic training and treatment tool to assist children with high-functioning autism. The plan is to bring these changes through the use of rules based algorithm as an approach to decide which level difficulty of the system should go according to the autism student performance based on the percentage of score. By applying this approach, the system will be able to monitor and analyze the performance of intelligent of autism student's capabilities. The system is capable to control the particular level of the autism students should play. It is capable to replace the teacher's responsibilities in terms of monitoring the student's progress and performance. Testing was conducted in Autism Intervention Programme of The National Autism Society of Malaysia (NASOM) at Malacca branch. Results and findings from this testing support the idea that educational software may be one of an effective and practical tool for teaching academic skills to autism children. Having programssuch asEduTism can improve effectiveness and efficiency of data collection tracking and reporting for the teachers and parents. © 2011 Springer-Verlag.},
note = {cited By 3},
keywords = {Algorithms, Assistive, Autism Intervention, Data Collection, Diseases, E-learning, Education, Educational Software, Educational Systems, High-Functioning Autism, Information Science, Intelligent Approach, Malaysia, Multimedia Systems, Rule Based, Software Testing, Student Performance, Students},
pubstate = {published},
tppubtype = {article}
}