@ 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}
}

@ persidangan{Ishak2019156,
tajuk = {Pemilihan robot dalam campur tangan robot untuk kanak-kanak ASD},
pengarang = {N I Ishak and H.Md. Yusof and S N Sidek and N Rusli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062769148&doi=10.1109%2fIECBES.2018.8626679&rakan kongsi = 40&md5=4ab38d1996ff4c48913864199d814cc6},
doi = {10.1109/IECBES.2018.8626679},
isbn = {9781538624715},
tahun = {2019},
tarikh = {2019-01-01},
jurnal = {2018 Persidangan IEEE EMBS mengenai Kejuruteraan dan Sains Bioperubatan, IECBES 2018 - Prosiding},
halaman = {156-160},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Kertas kerja ini menerangkan tentang pemilihan robot yang sesuai untuk penglibatan dengan Autism Spectrum Disorder (ASD) kanak-kanak. Banyak robot sedang dibangunkan untuk membantu kanak-kanak ini memperbaiki tingkah laku mereka, kemahiran komunikasi, interaksi sosial, perhatian dan kepekaan bersama. Penyelidikan terkini menunjukkan bahawa robot yang mengkomersialkan adalah lebih baik dalam terapi intervensi awal untuk kanak-kanak kerana keteguhannya dan mudah diprogramkan oleh ibu bapa dan guru.. Sebaliknya, rupa fizikal robot juga memainkan ciri penting untuk pemilihan robot. Kajian perbandingan telah dibuat antara robot prototaip yang kini digunakan dalam Interaksi Manusia-Robot (HR) dan robot komersial. Akibatnya, kami mencadangkan untuk mempunyai robot komersial yang teguh, ringkas, jimat, tahan lama dan fleksibel untuk ditukar kepada sebarang bentuk yang diingini sebagai medium interaksi kami. © 2018 IEEE},
nota = {dipetik oleh 1},
kata kunci = {Gangguan Spektrum Autisme, Kejuruteraan Bioperubatan, Robot Komersil, Kemahiran komunikasi, Campur Tangan Awal, Interaksi Robot Manusia, Ciri-ciri Penting, Penyelidikan Terkini, Robotik, Interaksi Sosial},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Hong201651,
tajuk = {Kajian mengenai alat bantu untuk pesakit autistik},
pengarang = {T S Hong dan S Mohamaddan dan S T S Shazali dan N A A Mohtadzar dan R A Bakar},
url = {https://www.scopus.com/inward/record.uri?eid = 2-s2.0-85015656147&doi = 10.1109% 2fIECBES.2016.7843413&rakan kongsi = 40&md5 = ayah70fbb2785ec386d3c3f8e3134ad1c},
doi = {10.1109/IECBES.2016.7843413},
isbn = {9781467377911},
tahun = {2016},
tarikh = {2016-01-01},
jurnal = {IECBES 2016 - Persidangan IEEE-EMBS mengenai Kejuruteraan dan Sains Bioperubatan},
halaman = {51-56},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Kesukaran berterusan dalam kemahiran sosial dan interaksi sosial menimbulkan cabaran yang besar bagi individu yang didiagnosis dengan gangguan spektrum autisme (ASD). Tinjauan literatur semasa menyediakan penyelidikan menyeluruh mengenai kajian yang difokuskan pada alat bantu untuk kekurangan dalam kemahiran sosial atau interaksi sosial pada mereka yang mempunyai ASD. Dua belas kajian yang memenuhi kriteria kemasukan dipilih. Kajian dikategorikan berdasarkan Intervensi Berbasis Komputer (CBI) dan Intervensi Tingkah Laku Berbantu Robot (RBI). Setiap kajian kemudian dinilai berdasarkan beberapa aspek. Kekuatan, batasan dan hasil dibincangkan. Semua kajian menunjukkan hasil yang positif. © 2016 IEEE.},
nota = {dipetik oleh 2},
kata kunci = {Kejuruteraan Bioperubatan, Komponen, Kejuruteraan, Memformat, Kejuruteraan Perindustrian, Masukkan, Gaya, Gaya},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ 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}
}

@ persidangan{Rusli2016226,
tajuk = {Non-invasive assessment of affective states on individual with autism spectrum disorder: A review},
pengarang = {N B Rusli and S N Sidek and H Md Yusof and M H Abd Latif},
penyunting = {Ahmad Usman M Y J Ibrahim F. Mohktar M.S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952767451&doi=10.1007%2f978-981-10-0266-3_47&rakan kongsi = 40&md5=f4aafb2216ef5c9d03ae7a1db352e4bd},
doi = {10.1007/978-981-10-0266-3_47},
terbitan = {16800737},
tahun = {2016},
tarikh = {2016-01-01},
jurnal = {IFMBE Proceedings},
isi padu = {56},
halaman = {226-230},
penerbit = {Springer Verlag},
abstrak = {Individuals with Autism Spectrum Disorder (ASD) are identified as a group of people who have social interaction and communication impairment. They have difficulty in producing speech and explaining what they meant. They also suffer from emotional or cognitive states requirement that stance challenges to their interest in communicating and socializing. Oleh itu, it is vital to know their emotion to help them develop better skills in social interaction. Emotion can be derived from affective states and can be detected through physical reaction and physiological signals. There are numerous known modalities available to detect the affective states either through invasive and non-invasive methods. In order to evaluate the affective states of individuals with ASD, amongst the methods used are through electrodermal activity (EDA), electromyographic (EMG) activity, and cardiovascular activity (ECG) and blood flow analyses. Though considered non invasive, these methods require sensor to be patched on to the skin causing discomfort to the subjects and might distract their true emotion. We propose non-invasive methods which is also contactless to address the problem to detect emotion of individual with ASD that is through thermal imaging. Through the impact of cutaneous temperature in blood flow, thermal imprint is radiated and can be detected in this method. Sehingga kini, no research has been reported of the use of thermal imaging analysis of facial skin temperature on the individuals with ASD. In this paper we will justify the method and also discuss the merits and demerits of other methods. © International Federation for Medical and Biological Engineering 2016.},
nota = {dipetik oleh 1},
kata kunci = {Affective State, Autisme, Kejuruteraan Bioperubatan, Blood, Penyakit, Emosi, Ekspresi wajah, Hemodynamics, Infrared Imaging, Noninvasive Medical Procedures, Physiological Signals, Fisiologi, Signal Detection, Skin, Sains Sosial},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Hoole2012541,
tajuk = {Autisme, EEG and brain electromagnetics research},
pengarang = {P R P Hoole and K Pirapaharan and S A Basar and R Ismail and D L D A Liyanage and S S H M U L Senanayake and S R H Hoole},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876771339&doi=10.1109%2fIECBES.2012.6498036&rakan kongsi = 40&md5=9f9390b30b859a90936c66699c1a5115},
doi = {10.1109/IECBES.2012.6498036},
isbn = {9781467316668},
tahun = {2012},
tarikh = {2012-01-01},
jurnal = {2012 Persidangan IEEE-EMBS mengenai Kejuruteraan dan Sains Bioperubatan, IECBES 2012},
halaman = {541-543},
abstrak = {There has been a significant increase in the incidence of autism. We report the work on autism by our international group, on the growing attention paid to EEG based diagnosis and the interest in tracing EEG changes to brain electromagnetic signals (BEMS), seeking the cause of autism and the brain regions of its origin. The time- and frequency domain and principal component analysis (PCA) of these EEG signals with a Multilayer Perception Neural Network (MLP) identifies an autistic subject and helps improve classification accuracy. We show differences between a working brain and a relaxed brain, especially in the Alpha waves used for diagnosis. © 2012 IEEE.},
nota = {dipetik oleh 11},
kata kunci = {Kejuruteraan Bioperubatan, Otak, Brain Regions, Ketepatan Pengelasan, Penyakit, EEG Signals, Electromagnetic Signals, Electromagnetics, Electromagnetism, Domain Kekerapan, International Group, Multilayer Perception Neural Networks, Neuroimaging, Analisis Komponen Utama},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Abdullah2012144,
tajuk = {Perinatal, maternal and antenatal associated factors for autism: A case control study},
pengarang = {M N Abdullah and W M Z W Mohamad and M R Abdullah and M J Yaacob and M S Baharuddin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876762294&doi=10.1109%2fIECBES.2012.6498121&rakan kongsi = 40&md5=b14466b2341cc29599332d94d866ea9a},
doi = {10.1109/IECBES.2012.6498121},
isbn = {9781467316668},
tahun = {2012},
tarikh = {2012-01-01},
jurnal = {2012 Persidangan IEEE-EMBS mengenai Kejuruteraan dan Sains Bioperubatan, IECBES 2012},
halaman = {144-148},
abstrak = {Autism disorders are a group of neurodevelopmental disorders which characterized into three main domains which are social interaction impairment, communication delay and repetitive or stereotypic behavior. Many studies had suggested that the risk factors for autism derive from three big factors namely environmental factors, genetic predisposition and vaccine induced. The aim of this study was to investigate the perinatal, maternal and antenatal associated factors on autistic disorder children at Hospital Pulau Pinang and Hospital Bukit Mertajam, Pulau Pinang. A case control study involving 312 cases and control was conducted using data retrieved from hospital records at Pulau Pinang hospital and Bukit Mertajam hospital from 2001 ke 2008. The departments involved were Psychiatric, Obstetrics and Gynecology and Record and Management Department. All cases which met the inclusion and exclusion criteria were included in the study. Univariable and multivariable logistic regression were used to explore the perinatal, maternal and antenatal associated factors associated with autistic disorder children. There were seven associated factors contributed most to autistic disorder determination. The factors were maternal age [Adjusted Odds Ratio (OR): 1.41; 95% Confidence Interval (CI): (1.27, 1.57)], maternal smoking reported at first antenatal visit [Adjusted OR: 13.61; 95% CI: (1.87, 99.35)], birth asphyxia [Adjusted OR: 0.35; 95% CI: (0.11, 1.08)], psychiatric history [Adjusted OR: 54.94; 95% CI: (12.07, 250.04)], multiple gestation [Adjusted OR: 4.81; 95% CI: (1.86, 12.45)], parity for more than 4 [Adjusted OR: 0.11; 95% CI: (0.03, 0.47)], parity between 0 dan 1 [Adjusted OR: 0.19; 95% CI: (0.07,0.55)], Chinese race compared to the Malay race [Adjusted OR: 10.11; 95% CI: (3.61, 28.30)] and Indian race compared to the Malay race [Adjusted OR: 5.14; 95% CI: (1.38, 19.16)]. The results suggested that autistic disorders were associated with perinatal, maternal and antenatal factors such as delivery, pregnancy and maternal characteristics. © 2012 IEEE.},
nota = {dipetik oleh 0},
kata kunci = {Antenatal, ASD, Autisme, Autistik, Kejuruteraan Bioperubatan, Case-Control Studies, Delivery, Penyakit, Hospital, Logistics, Maternal, Obstetrics, Ibu bapa, Perinatal, Pregnancy, pranatal, Retrospective, Faktor risiko},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}