@ persidangan{Ilias2017275,
tajuk = {Feature extraction of autism gait data using principal component analysis and linear discriminant analysis},
pengarang = {S Ilias and N M Tahir and R Jailani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034081031&doi=10.1109%2fIEACON.2016.8067391&rakan kongsi = 40&md5=7deaef6538413df7bfaf7cf723001d72},
doi = {10.1109/IEACON.2016.8067391},
isbn = {9781509009251},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference},
halaman = {275-279},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Dalam penyelidikan ini, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Di sini, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Selanjutnya, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE.},
nota = {dipetik oleh 0},
kata kunci = {Pengelasan (maklumat), Analisis Diskriminan, Penyakit, Pengekstrakan, Pengekstrakan Ciri, Analisis Gait, Klasifikasi Gait, Image Retrieval, Elektronik Perindustrian, Kernel Function, Kinematic Parameters, Kinematik, Belajar, Analisis Diskriminasi Linear, Machine Learning Approaches, Sistem Analisis Pergerakan, Polynomial Functions, Analisis Komponen Utama, Mesin Vektor Sokongan, SVM Classifiers},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Nor2016226,
tajuk = {Analisis isyarat EMG otot TA dan GAS semasa berjalan di Autism Spectrum Disorder (ASD) kanak-kanak},
pengarang = {M N M Nor dan R Jailani dan N M Tahir},
url = {https://www.scopus.com/inward/record.uri?eid = 2-s2.0-84992153602&doi = 10.1109% 2fISCAIE.2016.7575068&rakan kongsi = 40&md5 = 7aaa147660a67bf4c2ddaa31f0e78717},
doi = {10.1109/ISCAIE.2016.7575068},
isbn = {9781509015436},
tahun = {2016},
tarikh = {2016-01-01},
jurnal = {ISCA 2016 - 2016 Simposium IEEE mengenai Aplikasi Komputer dan Elektronik Industri},
halaman = {226-230},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Makalah ini membentangkan analisis Elektromiografi (EMG) isyarat otot anggota bawah semasa berjalan di kalangan kanak-kanak. Jumlah 18 kanak-kanak terdiri daripada 8 Gangguan Spektrum Autisme (ASD) kanak-kanak dan 10 Perkembangan Khas (TD) kanak-kanak berumur antara 6 ke 13 berumur tahun telah mengambil bahagian dalam kajian ini. Otot Tibialis Anterior (TA) dan Gastrocnemius (GAS) diperiksa dan data isyarat EMG diperoleh menggunakan Trigno Wireless EMG System di Human Motion and Analysis Laboratory, UiTM Shah Alam. Corak isyarat EMG untuk otot TA dan GAS akan dijelaskan dan ujian-t bebas akan dianalisis untuk menyiasat perbezaan berjalan kaki pada kanak-kanak ASD dan kanak-kanak TD. Hasilnya menunjukkan bahawa terdapat perbezaan yang signifikan pada Gastrocnemius (GAS) otot antara kanak-kanak ASD dan TD semasa pertengahan di mana nilai p sama dengan 0.042. Dari kajian ini, isyarat EMG untuk otot GAS memainkan peranan penting dalam membezakan antara kanak-kanak ASD dan TD. © 2016 IEEE.},
nota = {dipetik oleh 4},
kata kunci = {Kanak-kanak ASD, Gangguan Spektrum Autisme, Penyakit, Elektromiografi, Gas, Gastrocnemius, Gerakan Manusia, Elektronik Perindustrian, Otot, Tibialis Anterior, Perkembangan Khas, Berjalan Gait, Corak Berjalan},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Ilias201652,
tajuk = {Classification of autism children gait patterns using Neural Network and Support Vector Machine},
pengarang = {S Ilias and N M Tahir and R Jailani and C Z C Hasan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992135613&doi=10.1109%2fISCAIE.2016.7575036&rakan kongsi = 40&md5=55c6d166768ed5fa3b504a2bd3441829},
doi = {10.1109/ISCAIE.2016.7575036},
isbn = {9781509015436},
tahun = {2016},
tarikh = {2016-01-01},
jurnal = {ISCA 2016 - 2016 Simposium IEEE mengenai Aplikasi Komputer dan Elektronik Industri},
halaman = {52-56},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Dalam kajian ini, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Pertama, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Seterusnya, these three category gait parameters acted as inputs to both classifiers. Results showed that fusion of temporal spatial and kinematic contributed the highest accuracy rate for NN classifier specifically 95% whilst SVM with polynomial as kernel, 95% accuracy rate is contributed by fusion of all gait parameters as inputs to the classifier. Sebagai tambahan, the classifiers performance is validated by computing both value of sensitivity and specificity. With SVM using polynomial as kernel, sensitivity attained is 100% indicated that the classifier's ability to perfectly discriminate normal subjects from autism subjects whilst 85% specificity showed that SVM is able to identify autism subjects as autism based on their gait patterns at 85% rate. © 2016 IEEE.},
nota = {dipetik oleh 5},
kata kunci = {Accuracy Rate, Autisme, Pengelasan (maklumat), Penyakit, Analisis Gait, Gait Parameters, Corak Gait, Elektronik Perindustrian, Kinematik, Rangkaian Neural, NN Classifiers, Kepekaan dan Kekhususan, Mesin Vektor Sokongan, Three Categories},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{KhazaalShams2011653,
tajuk = {Characterizing autistic disorder based on principle component analysis},
pengarang = {W Khazaal Shams and A W Abdul Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855644760&doi=10.1109%2fISIEA.2011.6108797&rakan kongsi = 40&md5=c486566e2d7ff404d830704c0b404067},
doi = {10.1109/ISIEA.2011.6108797},
isbn = {9781457714184},
tahun = {2011},
tarikh = {2011-01-01},
jurnal = {2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011},
halaman = {653-657},
abstrak = {Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. Walau bagaimanapun, Elektroencephalogram (LIHAT) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE.},
nota = {dipetik oleh 6},
kata kunci = {Autisme, Brain Function, Isyarat Otak, Classification Process, Data Dimensions, Penyakit, Electroencephalogram Signals, Elektroensefalografi, Frequency Domain Analysis, Elektronik Perindustrian, Pergerakan Motor, Motor Tasks, PCA, Analisis Komponen Utama, Signal Detection, Time Frequency Domain},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Sudirman2010401,
tajuk = {Study of electroencephalography signal of autism and down syndrome children using FFT},
pengarang = {Sudirman and S Saidin and N Mat Safri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251542066&doi=10.1109%2fISIEA.2010.5679434&rakan kongsi = 40&md5=17fce4f69b27a3cc644f36c118b6ec6e},
doi = {10.1109/ISIEA.2010.5679434},
isbn = {9781424476473},
tahun = {2010},
tarikh = {2010-01-01},
jurnal = {ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications},
halaman = {401-406},
abstrak = {Elektroensefalografi (LIHAT) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. Akibatnya, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE.},
nota = {dipetik oleh 15},
kata kunci = {Alpha Value, Autisme, Sindrom Down, EEG Signals, Elektroensefalografi, Elektrofisiologi, Fast Fourier Transforms, Elektronik Perindustrian, Metadata, Antara Muka Pengguna, Visual Evoked Potential, Visualization},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}