@article{Al-Hiyali201886,
title = {A review in modification food-intake behavior by brain stimulation: Excess weight cases},
author = {M I Al-Hiyali and A J Ishak and H Harun and S A Ahmad and W A Wan Sulaiman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062843670&doi=10.14704%2fnq.2018.16.12.1894&partnerID=40&md5=235f66cef05a144be23472641f70bd1d},
doi = {10.14704/nq.2018.16.12.1894},
issn = {13035150},
year = {2018},
date = {2018-01-01},
journal = {NeuroQuantology},
volume = {16},
number = {12},
pages = {86-97},
publisher = {Anka Publishers},
abstract = {Obesity and overweight are frequently prescribed for dysfunction in food-intake behavior. Due to the widely prevalence of obesity in last year’s, there is demand for more studies which are aimed to modify the food-intake behavior. For the past decades many researches has applied in modify food-intake by brain training or stimulation. This review for neuroscience studies in modifying food-intake behavior, it’s involved three sections; The first section explained the role of brain activity in food-intake regulation, general ideas about biomedical devices in food-intake behavior are discussed in second section and third section focused on brain-stimulation systems. Finally, this paper concluded with main points that need to be taken into account when designing experimental study for modification food-intake behavior by brain stimulation according to previous studies recommendation and challenges. © 2018, Anka Publishers. All Rights Reserved.},
note = {cited By 2},
keywords = {Amygdala, Anoxia, Article, Autism, Binge Eating Disorder, Body Mass, Body Weight, Brain Depth Stimulation, Depolarization, Dietary Intake, Drug Craving, Eating Disorder, Electric Current, Electroencephalogram, Electroencephalography, Energy Consumption, Energy Expenditure, Feeding Behavior, Food Intake, Functional Magnetic Resonance Imaging, Gender, Health Status, Homeostasis, Human, Hunger, Lifestyle, Nerve Cell Membrane Steady Potential, Nerve Excitability, Neurofeedback, Neuromodulation, Nutritional Assessment, Outcome Assessment, Questionnaires, Repetitive Transcranial Magnetic Stimulation, Signal Processing, Training, Transcranial Direct Current Stimulation, Transcranial Magnetic Stimulation, Underweight},
pubstate = {published},
tppubtype = {article}
}

@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}
}