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. |
Ujianadminnaacuitm2020-05-28T06:49:14+00:00
2018 |
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). |
2017 |
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). |
Analisis Diskriminasi Linear dalam Mengklasifikasikan Gait Berjalan Kanak-Kanak Autistik Persidangan Institut Jurutera Elektrik dan Elektronik Inc., 2017, ISBN: 9781538614099, (dipetik oleh 0). |