8 research outputs found
Hubungan gaya pembelajaran dengan pencapaian akademik pelajar aliran vokasional
Analisis keputusan Sijil Pelajaran Malaysia (SPM) 2011 menunjukkan penurunan
pencapaian bagi Sekolah Menengah Vokasional. Oleh itu, kajian ini dilaksanakan
bertujuan untuk mengkaji hubungan di antara gaya pembelajaran dengan pencapaian
akademik pelajar. Kajian ini juga ingin mengenalpasti gaya pembelajaran paling
dominan yang diamalkan oleh pelajar serta melihat perbezaan gaya pembelajaran
dengan jantina pelajar. Seramai 131 orang Pelajar Tingkatan Empat Kursus
Vokasional Di Sekolah Menengah Vokasional Segamat di Johor telah terlibat dalam
kajian ini. Soal selidik Index of Learning Style (ILS) yang dibangunkan oleh Felder
dan Silverman (1991) yang mengandungi 44 soalan telah digunakan untukh
menjalankan kajian ini. Gaya pembelajaran pelajar dapat dilihat melalui empat
dimensi gaya pembelajaran yang terdiri dari dua sub-skala yang bertentangan iaitu
dimensi pelajar Aktif dan Reflektif, dimensi pelajar Konkrit dan Intuitif, dimensi
pelajar Verbal dan Visual, serta dimensi pelajar Tersusun dan Global. Data yang
diperolehi dianalisis dengan menggunakan perisian Statistical Package for Social
Science for WINDOW release 20.0 (SPSS.20.0). Ujian Korelasi Pearson digunakan
untuk menganalisis data dalam mengkaji hubungan gaya pembelajaran dengan
pencapaian akademik pelajar. Nilai pekali p yang diperolehi di antara gaya
pembelajaran dengan pencapaian pelajar adalah (p=0.1 hingga 0.4). Ini menunjukkan
tidak terdapat hubungan yang signifikan di antara dua pembolehubah tersebut. Kajian
ini juga mendapati bahawa gaya pembelajaran yang menjadi amalan pelajar ialah
gaya pembelajaran Tersusun. Hasil kajian juga mendapati bahawa tidak terdapat
perbezaan yang signifikan di antara gaya pembelajaran dengan jantina pelajar
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DWT/PCA face recognition using automatic coefficient selection
In PCA-based face recognition, there is often a trade-off between selecting the most relevant parts of a face image for recognition and not discarding information which may be useful. The work presented in this paper proposes a method to automatically determine the most discriminative coefficients in a DWT/PCA-based face recognition system, based on their inter-class and intra-class standard deviations. In addition, the eigenfaces used for recognition are generally chosen based on the value of their associated eigenvalues. However, the variance indicated by the eigenvalues may be due to factors such as variation in illumination levels between training set faces, rather than differences that are useful for identification. The work presented proposes a method to automatically determine the most discriminative eigenfaces, based on the inter-class and intra-class standard deviations of the training set eigenface weight vectors. The results obtained using the AT&T database show an improvement over existing DWT/PCA coefficient selection techniques
Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition
Noise, corruptions and variations in face images can seriously hurt the
performance of face recognition systems. To make such systems robust,
multiclass neuralnetwork classifiers capable of learning from noisy data have
been suggested. However on large face data sets such systems cannot provide the
robustness at a high level. In this paper we explore a pairwise neural-network
system as an alternative approach to improving the robustness of face
recognition. In our experiments this approach is shown to outperform the
multiclass neural-network system in terms of the predictive accuracy on the
face images corrupted by noise
A statistical multiresolution approach for face recognition using structural hidden Markov models
This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy
Hybrid learning-based model for exaggeration style of facial caricature
Prediction of facial caricature based on exaggeration style of a particular artist is a significant task in computer generated caricature in order to produce an artistic facial caricature that is very similar to the real artist’s work without the need for skilled user (artist) input. The exaggeration style of an artist is difficult to be coded in algorithmic method. Fortunately, artificial neural network, which possesses self-learning and generalization ability, has shown great promise in addressing the problem of capturing and learning an artist’s style to predict a facial caricature. However, one of the main issues faced by this study is inconsistent artist style due to human factors and limited collection on image-caricature pair data. Thus, this study proposes facial caricature dataset preparation process to get good quality dataset which captures the artist’s exaggeration style and a hybrid model to generalize the inconsistent style so that a better, more accurate prediction can be obtained even using small amount of dataset. The proposed data preparation process involves facial features parameter extraction based on landmark-based geometric morphometric and modified data normalization method based on Procrustes superimposition method. The proposed hybrid model (BP-GANN) combines Backpropagation Neural Network (BPNN) and Genetic Algorithm Neural Network (GANN). The experimental result shows that the proposed hybrid BP-GANN model is outperform the traditional hybrid GA-BPNN model, individual BPNN model and individual GANN model. The modified Procrustes superimposition method also produces a better quality dataset than the original one