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

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

Face Identification

A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Face recognition devices are ideal for such systems, since they have recently become faster, cheaper. When combined with voice-recognition, they are very robust against changes in the environment. Moreover, since humans primarily recognize each other by their faces and voices, they feel comfortable interacting with an environment that does the same. Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and create a unique file called a template. Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and pre-existing photos such as those in driver\u27s license databases. These systems depend on a recognition algorithm, such as the hidden Markov model. The first step for a facial recognition system is to recognize a human face and extract it for the rest of the scene. Next, the system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features. In this project, we describe Locality Preserving Projection (LPP), a new algorithm for learning a locality preserving subspace. The complete derivation and theoretical justifications of LPP can be traced back to. LPP is a general method for manifold learning. It is obtained by finding the optimal linear approximations to the Eigen functions of the Laplace Beltrami operator on the manifold. These nodal points are then compared to the nodal points computed from a database of pictures in order to find a match. Obviously, such a system is limited based on the angle of the face captured and the lighting conditions present

Machine Learning Based Method to Design a Facial Emotion Detection and Chat Bot System

One of the active areas of research trends is recognizing emotions in images. This project aims to identify facial emotions. The research concept in Emotion Recognition is included in the flow of our emotion recognition. These involve image acquisition, image pre-processing, face detection, feature extraction, and classification, with the machine being applied after the emotion have been classified.  Our framework relies on already-existing still images. This project aims to improve automated facial emotion recognition and build interaction between the system and the user (bot)

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

An Information-Theoretic Measure For Face Recognition: Comparison With Structural Similarity

Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information - theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in real-time environments. Performance of our information - theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM)