Ujian - National Autism Resource Centre (NARC)

Liang, S; Loo, C K; Sabri, Md A Q

Klasifikasi Gangguan Spektrum Autisme dalam Video: Hibrid Rangkaian Jauh Koherensi Temporal dan Pendekatan Memori Dual yang mengatur sendiri Artikel Jurnal

Nota Kuliah dalam Kejuruteraan Elektrik, 621 , hlm. 421-430, 2020, ISSN: 18761100, (dipetik oleh 0).

Abstrak | Pautan | BibTeX | Tag: Kecerdasan Buatan, Gangguan Spektrum Autisme, Kanak-kanak Autistik, Melupakan Bencana, Kanak-kanak dengan Autisme, Diagnosis, Penyakit, E-pembelajaran, Pendekatan Hibrid, Belajar, Rangkaian Neural, Objektif Utama, Sistem Berskala, Koherensi Temporal, Pembelajaran Dalam Talian Tanpa Pengawasan

Razali, N N C; Ghani, N A; Hisham, S Saya

Pemeriksaan Autisme Pintar Menggunakan Ejen Fuzzy Artikel Jurnal

Nota Kuliah dalam Kejuruteraan Elektrik, 632 , hlm. 495-503, 2020, ISSN: 18761100, (dipetik oleh 0).

Abstrak | Pautan | BibTeX | Tag: Kanak-kanak Autistik, Kanak-kanak dengan Autisme, Proses Kompleks, Diagnosis, Penyakit, Motor Halus, Ejen Fuzzy, Input kabur, Model Saringan, Pendapat Kedua

@artikel{Razali2020495,
tajuk = {Pemeriksaan Autisme Pintar Menggunakan Ejen Fuzzy},
pengarang = {N N C Razali dan N A Ghani dan S I Hisham},
penyunting = {Najib Abdul Wahab Othman Abd Ghani Irawan Khatun Raja Ismail Saari Daud Mohd Faudzi M S Y N A N M A S R M T M M M R A A Kasruddin Nasir A.N. Ahmad M.A.},
url = {https://www.scopus.com/inward/record.uri?eid = 2-s2.0-85083080538&doi = 10.1007% 2f978-981-15-2317-5_42&rakan kongsi = 40&md5 = c37fc825e41384f79ae2dd3361a504a7},
doi = {10.1007/978-981-15-2317-5_42},
terbitan = {18761100},
tahun = {2020},
tarikh = {2020-01-01},
jurnal = {Nota Kuliah dalam Kejuruteraan Elektrik},
isi padu = {632},
halaman = {495-503},
penerbit = {Pemecut},
abstrak = {Dalam diagnosis penyakit, sama ada fizikal atau psikologi, terdapat situasi yang menyebabkan sukar untuk memperoleh pendapat kedua. Ini benar terutamanya dalam diagnosis Autisme kerana proses diagnosis yang rumit. Terlepas dari proses yang kompleks, cabarannya merangkumi kos dan ketersediaan pakar. Ini, namun begitu, tidak mengubah hakikat bahawa mempunyai pendapat kedua bebas secara berkala sangat penting. Oleh itu, kajian ini mencadangkan model saringan autisme pintar menggunakan ejen kabur, untuk membantu pakar dan bukan pakar dalam membuat diagnosis. Dalam kajian ini, input kabur diberikan berdasarkan lima kategori, iaitu Komunikasi, Motor Kasar, Motor Halus, Penyelesaian masalah, dan Sosial Peribadi, dan khusus untuk kanak-kanak berumur tiga tahun sahaja. Model yang dicadangkan akan dapat menghasilkan output dalam bentuk urutan berdasarkan markah skor terendah hingga tertinggi bagi setiap kategori. Hasil ini kemudiannya berkaitan dengan cadangan aktiviti kepada kanak-kanak autistik mengikut keutamaan (berdasarkan skor yang diperoleh). © 2020, Springer Nature Singapore Pte Ltd.},
nota = {dipetik oleh 0},
kata kunci = {Kanak-kanak Autistik, Kanak-kanak dengan Autisme, Proses Kompleks, Diagnosis, Penyakit, Motor Halus, Ejen Fuzzy, Input kabur, Model Saringan, Pendapat Kedua},
pubstate = {diterbitkan},
tppubtype = {artikel}
}

Tutup

Mohd, C K N C K; Shahbodin, F; Azni, A H; Jano, DENGAN

Permainan persepsi visual untuk pelajar autistik: Reka bentuk & pembangunan Persidangan

Persatuan untuk Jentera Pengkomputeran, 2019, ISBN: 9781450366212, (dipetik oleh 0).

Abstrak | Pautan | BibTeX | Tag: Gangguan Spektrum Autisme, Kanak-kanak Autistik, Kanak-kanak dengan Autisme, Reka Bentuk dan Pembangunan, Diagnosis, Produk Diagnostik, Alat diagnostik, Penyakit, Pendidikan, Pengkomputeran Pendidikan, Insiden Tinggi, Teknologi komunikasi dan maklumat, Penggunaan Maklumat, Rawatan Pesakit, Permainan yang serius, Pendidikan Khas, Pelajar, Penglihatan, Persepsi Visual

@ persidangan{Mohd20195b,
tajuk = {Permainan persepsi visual untuk pelajar autistik: Reka bentuk & pembangunan},
pengarang = {C K N C K Mohd dan F Shahbodin dan A H Azni dan Z Jano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064918100&doi=10.1145/3314527.3314533&rakan kongsi = 40&md5=a3c6394e2cd86d31a30ed2d1f3b6e1e6},
doi = {10.1145/3314527.3314533},
isbn = {9781450366212},
tahun = {2019},
tarikh = {2019-01-01},
jurnal = {Siri Prosiding Persidangan Antarabangsa ACM},
halaman = {5-11},
penerbit = {Persatuan untuk Jentera Pengkomputeran},
abstrak = {Penggunaan teknologi komunikasi maklumat (ICT) dalam terapi menawarkan perspektif baharu untuk merawat banyak domain dalam individu yang mengalami gangguan spektrum autisme (ASD) kerana ia digunakan dalam pelbagai cara dan tetapan dan ia menarik kepada pesakit. Pelajar autisme mempunyai masalah persepsi visual yang tinggi. Mereka sering mengalami kesukaran untuk mengenali, mengingati, menyusun dan mentafsir imej visual. Akibatnya, mereka mudah keliru dalam situasi di mana simbol bertulis atau bergambar digunakan untuk pembelajaran. Kertas itu melaporkan reka bentuk dan pembangunan alat diagnostik autisme menggunakan teknik permainan yang serius. Ia adalah alat untuk guru pendidikan khas untuk mendiagnosis masalah persepsi visual dalam kalangan pelajar autisme. Permainan ini dikenali sebagai Vi-Per Games. Alat diagnostik yang dikenali sebagai Vi-Per Games telah dibangunkan berdasarkan model ADDIE. Vi-Per Games mampu mendiagnosis pelajar autistik tanpa memerlukan guru mempunyai sedikit pengalaman dan pengetahuan dalam mendiagnosis persepsi visual. Prototaip ini akan menjadi penyelesaian berteknologi tinggi untuk mendiagnosis masalah persepsi visual yang direka untuk kanak-kanak autistik. © 2019 Persatuan untuk Jentera Pengkomputeran.},
nota = {dipetik oleh 0},
kata kunci = {Gangguan Spektrum Autisme, Kanak-kanak Autistik, Kanak-kanak dengan Autisme, Reka Bentuk dan Pembangunan, Diagnosis, Produk Diagnostik, Alat diagnostik, Penyakit, Pendidikan, Pengkomputeran Pendidikan, Insiden Tinggi, Teknologi komunikasi dan maklumat, Penggunaan Maklumat, Rawatan Pesakit, Permainan yang serius, Pendidikan Khas, Pelajar, Penglihatan, Persepsi Visual},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

Tutup

Diurut, R

Autisme dan trikotilomania dalam remaja lelaki Artikel Jurnal

Laporan Kes BMJ, 2018 , 2018, ISSN: 1757790X, (dipetik oleh 0).

Abstrak | Pautan | BibTeX | Tag: Remaja, Alopecia, Keresahan, Artikel, Gangguan Defisit Perhatian, Gangguan Hiperaktifan Kekurangan Perhatian, Autisme, Gangguan Spektrum Autisme, Gangguan Tingkah Laku, Berat badan, Laporan kes, Perangsang Sistem Saraf Pusat, Senarai Semak Tingkah Laku Kanak-kanak, Artikel Klinikal, komorbiditi, Komplikasi, Diagnosis, Berbeza, Diagnosis pembezaan, Pentitratan Dos Ubat, Toleransi Dadah, DSM-5, Echolalia, Fluvoxamine, Susulan, Manusia, Hiperaktif, Kemerosotan Intelektual, Lelaki, Metilfenidat, Obesiti, Terapi pekerjaan, Indeks Penaakulan Persepsi, Jurnal Keutamaan, Indeks Kelajuan Pemprosesan, Skala Penarafan Status Psikiatri, Skala Penarafan Psikologi, Skala penilaian, Kegelisahan, Ganjaran, Perencat Serapan Serotonin, Perencat Serapan Serotonin, Pendidikan Khas, Kelewatan Pertuturan, Gangguan Pertuturan, Terapi ucapan, Trikotilomania, Indeks Kefahaman Lisan, Skala Kepintaran Wechsler, Indeks Memori Bekerja

Hariharan, M; Sindhu, R; Vijean, V; Yazid, H; Nadarajaw, T; Yaacob, S; Polat, K

Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification Artikel Jurnal

Computer Methods and Programs in Biomedicine, 155 , hlm. 39-51, 2018, ISSN: 01692607, (dipetik oleh 21).

Abstrak | Pautan | BibTeX | Tag: 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

@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}
}

Tutup

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.

Tutup

Adly, Helmi M N; Faaizah, S; Naim, C P

Serious game for autism children: Conceptual framework Persidangan

58 VOL I , WITPress, 2014, ISSN: 17433517, (dipetik oleh 1).

Abstrak | Pautan | BibTeX | Tag: Autisme, Communication Systems, Computer Games, Kerangka Konseptual, Diagnosis, Digital Games, Penyakit, Pendidikan, Teknologi maklumat, Prototype Development, Penyelidikan, Software Prototyping, Technical Solutions, Penglihatan, Persepsi Visual