@ persidangan{Ilias2017540,
tajuk = {Development of three dimensional gait pattern in autism children - a review},
pengarang = {S Ilias and N M Tahir and R Jailani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019000981&doi=10.1109%2fICCSCE.2016.7893635&rakan kongsi = 40&md5=37aaf5f94b177ecfa164c432d32b5dfe},
doi = {10.1109/ICCSCE.2016.7893635},
isbn = {9781509011780},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {Prosiding - 6th IEEE International Conference on Control System, Pengkomputeran dan Kejuruteraan, ICCSCE 2016},
halaman = {540-545},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Baru-baru ini, gait patterns of children with autism is of interest in the gait community in order to identify significant gait parameter namely the three dimensional (3D) gait features such as spatial temporal, kinematic and kinetic. This is because gait pattern provides clinicians and researchers in understanding the trajectory of gait development. Understanding the characteristics and identifying gait pattern is essential in order to distinguish normal as well as abnormal gait pattern. Hence the purpose of this review is to identify deviations gait in children with autism based on criteria specifically subject character; measurement, type of gait variables measured; method of classification and major findings. Several gait variables from different instrumentation for gait analysis is reviewed too. Development of gait patterns via assessing gait deviations in children with ASD could assist clinician and researchers to differentiate gait pattern abnormality in diagnosing, clinical decision-making and treatment planning as well. © 2016 IEEE.},
nota = {dipetik oleh 0},
kata kunci = {Abnormal Gait, Kanak-kanak dengan Autisme, Membuat Keputusan Klinikal, Sistem Kawalan, Membuat keputusan, Penyakit, Enzyme Kinetics, Analisis Gait, Klasifikasi Gait, Kinematik, Spatial Temporals, Temporal Spatial, Tiga Dimensi, Three-Dimensional Computer Graphics, Treatment Planning},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ 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{elias201767,
tajuk = {Analisis Diskriminasi Linear dalam Mengklasifikasikan Gait Berjalan Kanak-Kanak Autistik},
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-s2.0-85048377850&doi = 10.1109% 2fEMS.2017.22&rakan kongsi = 40&md5=06de53be2b4f3976ddcc420067ab6e44},
doi = {10.1109/EMS.2017.22},
isbn = {9781538614099},
tahun = {2017},
tarikh = {2017-01-01},
jurnal = {Prosiding - Simposium Permodelan Eropah ke-11 UKSim-AMSS mengenai Permodelan dan Simulasi Komputer, EMS 2017},
halaman = {67-72},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Matlamat penyelidikan ini adalah untuk menyiasat keberkesanan antara Analisis Komponen Utama (PCA) dan Analisis Diskriminasi Linear (LDA) bersama dengan rangkaian saraf (NN) dalam mengklasifikasikan gaya berjalan kanak-kanak autisme berbanding kumpulan kawalan. Dua belas kanak-kanak autistik dan tiga puluh dua kanak-kanak normal mengambil bahagian dalam kajian ini. Pertama, gaya berjalan kedua-dua kumpulan ini diperoleh menggunakan Sistem Analisis Gerakan VICON untuk mengekstrak tiga dimensi (3D) ciri gait yang terdiri daripada 21 ciri gait iaitu lima ciri daripada spatial temporal asas, lima ciri mewakili parameter kinetik dan dua belas ciri dari kinematik. Selanjutnya, PCA dan LDA digunakan sebagai pengekstrakan ciri dalam menentukan ciri penting antara ciri gaya berjalan ini. Dengan NN sebagai pengelas, keputusan menunjukkan bahawa LDA sebagai pengekstrakan ciri mengatasi PCA untuk klasifikasi autisme berbanding kanak-kanak normal iaitu corak gaya berjalan kinematik yang dicapai 98.44% ketepatan diikuti oleh ciri gaya berjalan spatial temporal asas dengan ketepatan 87.5%. © 2017 IEEE.},
nota = {dipetik oleh 0},
kata kunci = {Autisme, Kanak-kanak Autistik, Kanak-kanak dengan Autisme, Analisis Diskriminan, Penyakit, Pengekstrakan, Pengekstrakan Ciri, Analisis Gait, Klasifikasi Gait, Kinematik, Analisis Diskriminasi Linear, Sistem Analisis Pergerakan, Rangkaian Neural, Analisis Komponen Utama, Tiga Dimensi},
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}
}