@artikel{Shams2012372,
tajuk = {Fuzzy model for detection and estimation of the degree of autism spectrum disorder},
pengarang = {W K Shams and A Wahab and U A Qidwai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869038189&doi=10.1007%2f978-3-642-34478-7_46&rakan kongsi = 40&md5=98929aba468010a02f652994b0da2a54},
doi = {10.1007/978-3-642-34478-7_46},
terbitan = {03029743},
tahun = {2012},
tarikh = {2012-01-01},
jurnal = {Nota Kuliah dalam Sains Komputer (termasuk subseries Nota Kuliah dalam Artificial Intelligence dan Lecture Notes dalam Bioinformatics)},
isi padu = {7666 LNCS},
nombor = {PART 4},
halaman = {372-379},
abstrak = {Early detection of autism spectrum disorder (ASD) is of great significance for early intervention. Selain itu, knowing the degree of severity in ASD and how it changes with the intervention is imperative for the treatment process. This study proposes Takagi- Sugeno-Kang (TSK) fuzzy modeling approach that is based on subtractive clustering to classify autism spectrum disorder and to estimate the degree of prognosis. The study has been carried out using Electroencephalography (LIHAT) signal on two groups of control and ASD children age-matched between seven to nine years old. EEG signals are quantized to temporal-time domain using Short Time Frequency Transformation (STFT). Spectrum energy is extracted as features for alpha band. The proposed system is modeled to estimate the degree in which subject is autistic, normal or uncertain. The results show accuracy in range (70-97) % when using fuzzy model .Also this system is modeled to generate crisp decision; the results show accuracy in the range (80-100) %. The proposed model can be adapted to help psychiatrist for diagnosis and intervention process. © 2012 Springer-Verlag.},
nota = {dipetik oleh 2},
kata kunci = {Gangguan Spektrum Autisme, Pengelasan (maklumat), Data Processing, Detection and Estimation, Penyakit, Campur Tangan Awal, EEG Signals, Elektrofisiologi, Fuzzy Approach, Fuzzy Modeling, Spectrum Energy, Subtractive Clustering, Time-Frequency Transformation, Treatment Process},
pubstate = {diterbitkan},
tppubtype = {artikel}
}