Senarai Penerbitan
Terdapat sebilangan besar penyelidikan berkaitan autisme yang boleh dijumpai di Malaysia yang umumnya menumpukan pada ASD, gangguan pembelajaran, alat bantu komunikasi, terapi dan banyak lagi. Senarai penerbitan disediakan di bawah:
- Klik ini untuk mencari menggunakan kata kunci yang ditentukan oleh pengguna.
- Ia akan membawa kepada laman web baru dengan kotak carian teks.
- Taip kata kunci anda di kotak carian
- Klik pada Kata kunci untuk mencari sebarang penerbitan. Kata yang lebih besar menunjukkan tanda yang paling banyak digunakan dan kata yang lebih kecil menunjukkan yang paling sedikit digunakan.
- Klik pada butang lungsur untuk memilih bertahun-tahun, jenis penerbitan atau pengarang pilihan anda.
- Klik pada perkataan bergaris bawah dalam perincian penerbitan untuk melihat lebih banyak maklumat.
2019 |
Abdullah, A A; Rijal, S; Sengkang, S R Penilaian ke atas Algoritma Pembelajaran Mesin untuk Klasifikasi Gangguan Spektrum Autisme (ASD) Persidangan 1372 (1), Institut Penerbitan Fizik, 2019, ISSN: 17426588, (dipetik oleh 0). Abstrak | Pautan | BibTeX | Tag: Gangguan Spektrum Autisme, Penilaian Tingkah Laku, Kejuruteraan Bioperubatan, Pemetaan Otak, Pengelasan (maklumat), Pokok Keputusan, Penyakit, Pengekstrakan Ciri, Kaedah Pemilihan Ciri, Pengesahan Silang K Lipat, Belajar, Operator Pengecutan dan Pemilihan Mutlak Paling Kurang, Anggaran Kuasa Dua Terkecil, Regresi Logistik, Pembelajaran Mesin, Kaedah Pembelajaran Mesin, Pengimejan Resonans Magnetik, Carian Jiran Terdekat, Analisis regresi, Pembelajaran yang diselia, Pembelajaran Mesin Diawasi @ persidangan{Abdullah2019, tajuk = {Penilaian ke atas Algoritma Pembelajaran Mesin untuk Klasifikasi Gangguan Spektrum Autisme (ASD)}, pengarang = {A A Abdullah and S Rijal and S R Dash}, penyunting = {Rahim Mustafa Zaaba Norali Noor S B A N B S K A N B A B M Fook C.Y. Yazid H.B.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076493636&doi=10.1088%2f1742-6596%2f1372%2f1%2f012052&rakan kongsi = 40&md5=2ec1bd9f6cf1e3afe965cc9e3792f536}, doi = {10.1088/1742-6596/1372/1/012052}, terbitan = {17426588}, tahun = {2019}, tarikh = {2019-01-01}, jurnal = {Journal of Physics: Conference Series}, isi padu = {1372}, nombor = {1}, penerbit = {Institut Penerbitan Fizik}, abstrak = {Gangguan Spektrum Autisme (ASD) dicirikan oleh kelewatan dalam pembangunan interaksi sosial, tingkah laku berulang dan minat yang sempit, yang biasanya didiagnosis dengan alat diagnostik standard seperti Jadual Pemerhatian Diagnostik Autisme (Remaja) dan Temuduga Diagnostik Autisme-Disemak (ADIR-R). Kerja sebelumnya telah melaksanakan kaedah pembelajaran mesin untuk klasifikasi ASD, namun mereka menggunakan jenis set data yang berbeza seperti imej otak untuk MRI dan EEG, gen risiko dalam profil genetik dan penilaian tingkah laku berdasarkan ADOS dan ADI-R. Di sini percubaan menggunakan Soalan Spektrum Autisme (AQ) untuk membina model yang mempunyai potensi yang lebih tinggi untuk mengklasifikasikan ASD telah dibangunkan. Dalam penyelidikan ini, Chi-square dan Operator Pengecutan dan Pemilihan Mutlak Terkecil (LASSO) telah dipilih sebagai kaedah pemilihan ciri untuk memilih ciri yang paling penting 3 algoritma pembelajaran mesin yang diselia, iaitu Hutan Rawak, Regresi Logistik dan K-Nearest Neighbours dengan pengesahan silang K-fold. Prestasi dinilai di mana keputusan Regresi Logistik mendapat ketepatan tertinggi dengan 97.541% menggunakan model dengan 13 ciri yang dipilih berdasarkan kaedah pemilihan Khi kuasa dua. © 2019 IOP Publishing Ltd. Hak cipta terpelihara.}, nota = {dipetik oleh 0}, kata kunci = {Gangguan Spektrum Autisme, Penilaian Tingkah Laku, Kejuruteraan Bioperubatan, Pemetaan Otak, Pengelasan (maklumat), Pokok Keputusan, Penyakit, Pengekstrakan Ciri, Kaedah Pemilihan Ciri, Pengesahan Silang K Lipat, Belajar, Operator Pengecutan dan Pemilihan Mutlak Paling Kurang, Anggaran Kuasa Dua Terkecil, Regresi Logistik, Pembelajaran Mesin, Kaedah Pembelajaran Mesin, Pengimejan Resonans Magnetik, Carian Jiran Terdekat, Analisis regresi, Pembelajaran yang diselia, Pembelajaran Mesin Diawasi}, pubstate = {diterbitkan}, tppubtype = {persidangan} } Gangguan Spektrum Autisme (ASD) dicirikan oleh kelewatan dalam pembangunan interaksi sosial, tingkah laku berulang dan minat yang sempit, yang biasanya didiagnosis dengan alat diagnostik standard seperti Jadual Pemerhatian Diagnostik Autisme (Remaja) dan Temuduga Diagnostik Autisme-Disemak (ADIR-R). Kerja sebelumnya telah melaksanakan kaedah pembelajaran mesin untuk klasifikasi ASD, namun mereka menggunakan jenis set data yang berbeza seperti imej otak untuk MRI dan EEG, gen risiko dalam profil genetik dan penilaian tingkah laku berdasarkan ADOS dan ADI-R. Di sini percubaan menggunakan Soalan Spektrum Autisme (AQ) untuk membina model yang mempunyai potensi yang lebih tinggi untuk mengklasifikasikan ASD telah dibangunkan. Dalam penyelidikan ini, Chi-square dan Operator Pengecutan dan Pemilihan Mutlak Terkecil (LASSO) telah dipilih sebagai kaedah pemilihan ciri untuk memilih ciri yang paling penting 3 algoritma pembelajaran mesin yang diselia, iaitu Hutan Rawak, Regresi Logistik dan K-Nearest Neighbours dengan pengesahan silang K-fold. Prestasi dinilai di mana keputusan Regresi Logistik mendapat ketepatan tertinggi dengan 97.541% menggunakan model dengan 13 ciri yang dipilih berdasarkan kaedah pemilihan Khi kuasa dua. © 2019 IOP Publishing Ltd. Hak cipta terpelihara. |
2018 |
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} } 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. |
2017 |
Ilias, S; Tahir, N M; Jailani, R Feature extraction of autism gait data using principal component analysis and linear discriminant analysis Persidangan Institut Jurutera Elektrik dan Elektronik Inc., 2017, ISBN: 9781509009251, (dipetik oleh 0). Abstrak | Pautan | BibTeX | Tag: 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 @ 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} } 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. |
Ilias, S; Tahir, N M; Jailani, R; Hasan, C Z C Analisis Diskriminasi Linear dalam Mengklasifikasikan Gait Berjalan Kanak-Kanak Autistik Persidangan Institut Jurutera Elektrik dan Elektronik Inc., 2017, ISBN: 9781538614099, (dipetik oleh 0). Abstrak | Pautan | BibTeX | Tag: 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 @ 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} } 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. |
2016 |
Rusli, N; Yusof, H M; Sidek, S N; Latif, M H Hottest pixel segmentation based thermal image analysis for children Persidangan Institut Jurutera Elektrik dan Elektronik Inc., 2016, ISBN: 9781467377911, (dipetik oleh 0). Abstrak | Pautan | BibTeX | Tag: Affective State, Gangguan Spektrum Autisme, Kejuruteraan Bioperubatan, Penyakit, Pengekstrakan Ciri, First-Order Statistics, Forehead Region, Gray Level Intensity, Image Analysis, Image Segmentation, Pixels, Segmentation Techniques, Thermal Image Analysis, Thermal Images @ persidangan{Rusli2016274, tajuk = {Hottest pixel segmentation based thermal image analysis for children}, pengarang = {N Rusli and H M Yusof and S N Sidek and M H Latif}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015712176&doi=10.1109%2fIECBES.2016.7843457&rakan kongsi = 40&md5=847e69c597caab24e0cd0f4e2cf558c6}, doi = {10.1109/IECBES.2016.7843457}, isbn = {9781467377911}, tahun = {2016}, tarikh = {2016-01-01}, jurnal = {IECBES 2016 - Persidangan IEEE-EMBS mengenai Kejuruteraan dan Sains Bioperubatan}, halaman = {274-279}, penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.}, abstrak = {Dalam kertas ini, the first order statistics for gray level intensity defined from thermal image is implemented to govern the significant and distinguishable characteristic pattern in thermal image of affective states. The impact of thresholding mechanism is studied to differentiate between positive affective states (happy) and negative affective states (sad) analysis in response to the stimuli adopted from International Affective Pictures System (IAPS) database. The hottest pixel segmentation technique is applied where it identifies the threshold level in a way to classify the hottest pixel area. The region of interest is narrowed to a forehead region with result of separation analysis made to left and right area. Two experiments have been conducted by using different set of stimuli and the results depicts of asymmetry and differed in culmination pattern for these two affective states. This conclusive result from this study suggests that this feature can be used as one of the important feature to give information of affective states on individuals with autism spectrum disorder (ASD) with least of facial expressions and perhaps would-be use in non-verbal means. © 2016 IEEE.}, nota = {dipetik oleh 0}, kata kunci = {Affective State, Gangguan Spektrum Autisme, Kejuruteraan Bioperubatan, Penyakit, Pengekstrakan Ciri, First-Order Statistics, Forehead Region, Gray Level Intensity, Image Analysis, Image Segmentation, Pixels, Segmentation Techniques, Thermal Image Analysis, Thermal Images}, pubstate = {diterbitkan}, tppubtype = {persidangan} } Dalam kertas ini, the first order statistics for gray level intensity defined from thermal image is implemented to govern the significant and distinguishable characteristic pattern in thermal image of affective states. The impact of thresholding mechanism is studied to differentiate between positive affective states (happy) and negative affective states (sad) analysis in response to the stimuli adopted from International Affective Pictures System (IAPS) database. The hottest pixel segmentation technique is applied where it identifies the threshold level in a way to classify the hottest pixel area. The region of interest is narrowed to a forehead region with result of separation analysis made to left and right area. Two experiments have been conducted by using different set of stimuli and the results depicts of asymmetry and differed in culmination pattern for these two affective states. This conclusive result from this study suggests that this feature can be used as one of the important feature to give information of affective states on individuals with autism spectrum disorder (ASD) with least of facial expressions and perhaps would-be use in non-verbal means. © 2016 IEEE. |
2015 |
Khosrowabadi, R; Quek, C; Ang, K K; Wahab, A; Chen, Annabel S -H Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions Artikel Jurnal Applied Soft Computing Journal, 32 , hlm. 335-346, 2015, ISSN: 15684946, (dipetik oleh 6). Abstrak | Pautan | BibTeX | Tag: Gangguan Spektrum Autisme, Pemprosesan Isyarat Bioperubatan, Otak, Connectivity Feature, Connectivity Pattern, Penyakit, Elektroensefalografi, Face Perceptions, Pengekstrakan Ciri, Functional Connectivity, Pengecaman Corak, Pattern Recognition Techniques @artikel{Khosrowabadi2015335, tajuk = {Dynamic screening of autistic children in various mental states using pattern of connectivity between brain regions}, pengarang = {R Khosrowabadi and C Quek and K K Ang and A Wahab and S -H Annabel Chen}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927922520&doi=10.1016%2fj.asoc.2015.03.030&rakan kongsi = 40&md5=5973f80db5649e5c61e344907819a18b}, doi = {10.1016/j.asoc.2015.03.030}, terbitan = {15684946}, tahun = {2015}, tarikh = {2015-01-01}, jurnal = {Applied Soft Computing Journal}, isi padu = {32}, halaman = {335-346}, penerbit = {Elsevier Ltd.}, abstrak = {Dalam kajian ini, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Oleh itu, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (LIHAT) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 kanak-kanak autistik (age matched in 7-10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods. © 2015 Published by Elsevier B.V.}, nota = {dipetik oleh 6}, kata kunci = {Gangguan Spektrum Autisme, Pemprosesan Isyarat Bioperubatan, Otak, Connectivity Feature, Connectivity Pattern, Penyakit, Elektroensefalografi, Face Perceptions, Pengekstrakan Ciri, Functional Connectivity, Pengecaman Corak, Pattern Recognition Techniques}, pubstate = {diterbitkan}, tppubtype = {artikel} } Dalam kajian ini, a dynamic screening strategy is proposed to discriminate subjects with autistic spectrum disorder (ASD) from healthy controls. The ASD is defined as a neurodevelopmental disorder that disrupts normal patterns of connectivity between the brain regions. Oleh itu, the potential use of such abnormality for autism screening is investigated. The connectivity patterns are estimated from electroencephalogram (LIHAT) data collected from 8 brain regions under various mental states. The EEG data of 12 healthy controls and 6 kanak-kanak autistik (age matched in 7-10) were collected during eyes-open and eyes-close resting states as well as when subjects were exposed to affective faces (happy, sad and calm). Subsequently, the subjects were classified as autistic or healthy groups based on their brain connectivity patterns using pattern recognition techniques. Performance of the proposed system in each mental state is separately evaluated. The results present higher recognition rates using functional connectivity features when compared against other existing feature extraction methods. © 2015 Published by Elsevier B.V. |
2011 |
Razali, N; Wahab, A 2Model Ruang Afektif (ASM) untuk mengesan kanak-kanak autistik Persidangan 2011, ISBN: 9781612848433, (dipetik oleh 8). Abstrak | Pautan | BibTeX | Tag: Kanak-kanak Autistik, Gangguan Otak, Pengimejan Otak, Teknik Pengimejan Otak, Isyarat Otak, Kanak-kanak dengan Autisme, Elektronik Pengguna, Pengumpulan data, Penyakit, Elektroencephalogram, Elektroensefalografi, Pengekstrakan Ciri, Domain Kekerapan, Pengimejan Resonans Magnetik Berfungsi, Model Campuran Gaussian, Pengimejan Resonans Magnetik, Perceptron Pelbagai Lapisan, Perceptron pelbagai lapisan, Pelbagai lapisan, Tomografi Pelepasan Positron, Resonans, Model Ruang, Hasil Pengesahan @ persidangan{Razali2011536, tajuk = {2Model Ruang Afektif (ASM) untuk mengesan kanak-kanak autistik}, pengarang = {N Razali and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid = 2-s2.0-80052392399&doi = 10.1109% 2fISCE.2011.5973888&rakan kongsi = 40&md5 = f6ea401148e6558b861e4df6407e527e}, doi = {10.1109/ISCE.2011.5973888}, isbn = {9781612848433}, tahun = {2011}, tarikh = {2011-01-01}, jurnal = {Prosiding Simposium Antarabangsa mengenai Elektronik Pengguna, ISCE}, halaman = {536-541}, abstrak = {Terdapat banyak kajian yang dilakukan terhadap kes autisme menggunakan teknik pencitraan otak. Dalam kertas ini, Electroencephalogram (LIHAT) digunakan untuk memahami dan menganalisis fungsi otak untuk mengenal pasti atau mengesan gangguan otak untuk autisme dari segi peniruan motor. Oleh itu, kebolehpasaran dan kemampuan peralatan EEG menjadikannya pilihan yang lebih baik jika dibandingkan dengan alat pengimejan otak lain seperti pengimejan resonans magnetik yang berfungsi (fMRI), tomografi pelepasan positron (PET) dan megnetoencephalography (MEG). Pengumpulan data terdiri daripada kanak-kanak autis dan normal dengan jumlah keseluruhan 6 kanak-kanak untuk setiap kumpulan. Semua subjek diminta mengepal tangan mereka dengan mengikuti rangsangan video yang disajikan 1 masa minit. Model campuran Gaussian digunakan sebagai kaedah pengekstrakan ciri untuk menganalisis isyarat otak dalam domain frekuensi. Kemudian, data pengekstrakan dikelaskan menggunakan perceptron pelbagai lapisan (MLP). Menurut hasil pengesahan, peratusan diskriminasi antara kedua-dua kumpulan adalah hingga 85% secara purata dengan menggunakan pengesahan k-kali ganda. © 2011 IEEE.}, nota = {dipetik oleh 8}, kata kunci = {Kanak-kanak Autistik, Gangguan Otak, Pengimejan Otak, Teknik Pengimejan Otak, Isyarat Otak, Kanak-kanak dengan Autisme, Elektronik Pengguna, Pengumpulan data, Penyakit, Elektroencephalogram, Elektroensefalografi, Pengekstrakan Ciri, Domain Kekerapan, Pengimejan Resonans Magnetik Berfungsi, Model Campuran Gaussian, Pengimejan Resonans Magnetik, Perceptron Pelbagai Lapisan, Perceptron pelbagai lapisan, Pelbagai lapisan, Tomografi Pelepasan Positron, Resonans, Model Ruang, Hasil Pengesahan}, pubstate = {diterbitkan}, tppubtype = {persidangan} } Terdapat banyak kajian yang dilakukan terhadap kes autisme menggunakan teknik pencitraan otak. Dalam kertas ini, Electroencephalogram (LIHAT) digunakan untuk memahami dan menganalisis fungsi otak untuk mengenal pasti atau mengesan gangguan otak untuk autisme dari segi peniruan motor. Oleh itu, kebolehpasaran dan kemampuan peralatan EEG menjadikannya pilihan yang lebih baik jika dibandingkan dengan alat pengimejan otak lain seperti pengimejan resonans magnetik yang berfungsi (fMRI), tomografi pelepasan positron (PET) dan megnetoencephalography (MEG). Pengumpulan data terdiri daripada kanak-kanak autis dan normal dengan jumlah keseluruhan 6 kanak-kanak untuk setiap kumpulan. Semua subjek diminta mengepal tangan mereka dengan mengikuti rangsangan video yang disajikan 1 masa minit. Model campuran Gaussian digunakan sebagai kaedah pengekstrakan ciri untuk menganalisis isyarat otak dalam domain frekuensi. Kemudian, data pengekstrakan dikelaskan menggunakan perceptron pelbagai lapisan (MLP). Menurut hasil pengesahan, peratusan diskriminasi antara kedua-dua kumpulan adalah hingga 85% secara purata dengan menggunakan pengesahan k-kali ganda. © 2011 IEEE. |