@artikel{Hariharan201839,
tajuk = {Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification},
pengarang = {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&rakan kongsi = 40&md5=1f3b17817b00f07cadad6eb61c0f4bf9},
doi = {10.1016/j.cmpb.2017.11.021},
terbitan = {01692607},
tahun = {2018},
tarikh = {2018-01-01},
jurnal = {Computer Methods and Programs in Biomedicine},
isi padu = {155},
halaman = {39-51},
penerbit = {Elsevier Ireland Ltd},
abstrak = {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. Dalam kerja ini, 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) dan 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) dan 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 ciri-ciri), Linear Predictive Coding (LPC) based cepstral features (56 ciri-ciri), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 ciri-ciri). The combined feature set consists of 568 ciri-ciri. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Akhirnya, 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.},
nota = {dipetik oleh 21},
kata kunci = {Accidents, Algoritma, Artikel, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Pengelasan (maklumat), Classification Algorithm, Pengelas, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Pengekstrakan, Extreme Learning Machine, Factual, Factual Database, Pengekstrakan Ciri, Kaedah Pemilihan Ciri, Fuzzy System, Hearing Impairment, Manusia, Kelaparan, Bayi, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Belajar, Linear Predictive Coding, Pembelajaran Mesin, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Pekali Cepstral Frekuensi Mel, Multi-Class Classification, Rangkaian Neural, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Patofisiologi, Prematurity, Kebolehulangan, Kebolehulangan Keputusan, Pemprosesan isyarat, Pengenalan suara, Wavelet Analysis, Wavelet Packet, Paket Wavelet Berubah},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Hasan201774,
tajuk = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder},
pengarang = {C Z C Hasan and R Jailani and N Md Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&rakan kongsi = 40&md5=6f2ffe7c2f5daf9fd02d4456acb94438},
terbitan = {19984510},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {International Journal of Biology and Biomedical Engineering},
isi padu = {11},
halaman = {74-79},
penerbit = {North Atlantic University Union NAUN},
abstrak = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% ketepatan, 93.3% kepekaan, dan 90.0% kekhususan. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. Hak cipta terpelihara.},
nota = {dipetik oleh 1},
kata kunci = {Artikel, Artificial Neural Network, Autisme, Tinggi Badan, Berat badan, Anak-anak, Artikel Klinikal, Kajian Terkawal, Analisis Diskriminan, Early Diagnosis, Perempuan, Langkah, Analisis Gait, Gangguan Gaya Berjalan, Manusia, Belajar, Lelaki, Pediatrics, Budak sekolah, Statistical Analysis, Statistik, Time Series Analysis},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

@artikel{Gravier201649,
tajuk = {Neural network modelling of the influence of channelopathies on reflex visual attention},
pengarang = {A Gravier and C Quek and W Duch and A Wahab and J Gravier-Rymaszewska},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955207541&doi=10.1007%2fs11571-015-9365-x&rakan kongsi = 40&md5=52f56b25f1d05a2d8eb0249e67e49f45},
doi = {10.1007/s11571-015-9365-x},
terbitan = {18714080},
tahun = {2016},
tarikh = {2016-01-01},
jurnal = {Cognitive Neurodynamics},
isi padu = {10},
nombor = {1},
halaman = {49-72},
penerbit = {Springer Belanda},
abstrak = {This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network’s rate of failure to shift attention is lower than the control network’s, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children. © 2015, Springer Science+Business Media Dordrecht.},
nota = {dipetik oleh 8},
kata kunci = {Artikel, Artificial Neural Network, Perhatian, Autisme, Calcium Channelopathy, Cell Structure, Kognisi, Connectome, Electric Activity, Belajar, Mathematical Analysis, Mathematical Model, Nerve Cell, Simulation, Visual Reflex},
pubstate = {diterbitkan},
tppubtype = {artikel}
}