Higher Order Centralised Scale-Invariants for Unconstrained Isolated Handwritten Digits

The works presented in this thesis are mainly involved in the study of global analysis of feature extractions. These include invariant moments for unequal scaling in x and y directions for handwritten digits, proposed method on scale-invariants and shearing invariants for unconstrained isolated handwritten digits. Classifications using Backpropagation model with its improved learning strategies are implemented in this study. Clustering technique with Self Organising Map (SOM) and dimension reduction with Principal Component Analysis (peA) on proposed invariant moments are also highlighted in this thesis. In feature extraction, a proposed improved formulation on scale-invariant moments is given mainly for unconstrained handwritten digits based on regular moments technique. Several types of features including algebraic and geometric invariants are also discussed. A computational comparison of these features found that the proposed method is superior than the existing feature techniques for unconstrained isolated handwritten digits. A proposed method on invariant moments with shearing parameters is also discussed. The formulation of this invariant shearing moments have been tested on unconstrained isolated handwritten digits. It is found that the proposed shearing moment invariants give good results for images which involved shearing parameters.peA is used in this study to reduce the dimension complexity of the proposed moments scale-invariants. The results show that the convergence rates of the proposed scaleinvariants are better after reduction process using peA. This implies that the peA is an alternative approach for dimension reduction of the moment invariants by using less variables for classification purposes. The results show that the memory storage can be saved by reducing the dimension of the moment invariants before sending them to the classifier. In addition, classifications of unconstrained isolated handwritten digits are extended using clustering technique with SOM methodology. The results of the study show that the clustering of the proposed moments scale-invariants is better visualised with SOM

Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization (TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature

OctaSOM - An octagonal based SOM lattice structure for biomedical problems

In this study, an octagonal-based self-organizing network’s lattice structure is proposed to allow more exploration and exploitation in updating the weights for better mapping and classification performances.The neighborhood of the octagonal-based lattice structure provides more nodes for the weights updating than standard hexagonal-based lattice structure. Based on our experiment, the octagonal-based lattice structure performance is better than standard hexagonal lattice structure on biomedical datasets for classification problem. This indicates that proposed algorithm is an alternative lattice structure for self-organizing network which give more wisdom to classification problems especially in the biomedical domains

Spectral properties from Matsubara Green's function approach - application to molecules

We present results for many-body perturbation theory for the one-body Green's function at finite temperatures using the Matsubara formalism. Our method relies on the accurate representation of the single-particle states in standard Gaussian basis sets, allowing to efficiently compute, among other observables, quasiparticle energies and Dyson orbitals of atoms and molecules. In particular, we challenge the second-order treatment of the Coulomb interaction by benchmarking its accuracy for a well-established test set of small molecules, which includes also systems where the usual Hartree-Fock treatment encounters difficulties. We discuss different schemes how to extract quasiparticle properties and assess their range of applicability. With an accurate solution and compact representation, our method is an ideal starting point to study electron dynamics in time-resolved experiments by the propagation of the Kadanoff-Baym equations.Comment: 12 pages, 8 figure

Penerapan Jaringan Syaraf Berbobot Tiga untuk Identifikasi Pembuat Tulisan Tangan

Tulisan tangan seseorang dapat dijadikan perantara untuk mengetahui identitas pembuatnya. Untuk mengetahui identitas personal ini memerlukan proses pembangkitan penciri dari seseorang tersebut melalui tahap ekstraksi ciri pada setiap kata tulisan tangan seseorang. Metode ekstrasi ciri yang digunakan adalah Zernike aspect moment invariants (ZAMI) sedangkan metode pelatihan dan pengenalan menggunakan Jaringan Syaraf Bobot Tiga (JSBT). Hasil ekstraksi ciri dari setiap tulisan tangan seorang dilakukan pelatihan menggunakan Prinsip Kontinuitas Homogen (PKH) sebagai dasar pengetahuan awal terhadap sampel tersebut. Tujuan artikel ini adalah menentukan kepemilikan tulisan tangan yang sah berdasarkan teks bebas menggunakan JSBT. Hasil eksperimen memperlihatkan bahwa kinerja model yang digunakan mencapai 98% dengan menggunakan berbagai variasi sampel uji dan latih

Improvement of authorship invarianceness for individuality representation in writer identification

Writer Identification (WI) is one of the areas in pattern recognition that have created a center of attention for many researchers to work in. Recently, its main focus is in forensics and biometric application, e.g. writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on feature extraction and classi?cation task in order to identify the handwritten authorship. However, additional steps need to be per- formed in order to have a better representation of input prior to the classi?cation task. Features extracted from the feature extraction task for a writer are in vari- ous representations, which degrades the classi?cation performance. This paper will discuss this additional process that can transform the various representations into a better representation of individual features for Individuality of Handwriting, in order to improve the performance of identification in WI

Predictive analytics in Malaysian dengue data from 2010 until 2015 using BigML

When era big data has reached to Malaysia, our government realized that all data are streaming all over the Internet from various data sources like sensors, social media data, excel spreadsheets, reviews, customer data, and etc. There are a lot data from our government need to be analysis which is can help decision making in the future. This Malaysia Open data can be analysis to help the government to predict what the next planning to do. In this paper, we use the Malaysia Open Data Government Portal about Malaysian Dengue Hotspot from 2010 until 2015. In the days, machine learning algorithms and technologies were mostly used by scientists, tech geeks or domain experts. However, several organizations are now using machine learning online and offline tool to make these technologies available to the masses to people outside. Online and offline tool make it easy for developers to apply machine learning to a dataset so as to add predictive features to their applications. In this paper, we used BigML which it provide online platform to integrate machine learning in real world applications and to predict the most popular place for Dengue to get an early warning and awareness to the people. BigML use the decision tree algorithms to do data analytics and prediction the popular place. In this case, we are using BigML to predict the place which always dengue occur in Malaysia which is also called as hotspot

Multilevel kohonen network learning for clustering problems

Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self-organising neural networks, known as multilevel learning, was proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM were evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis was to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM methods, predictions can be made for the unknown samples. The results showed that multilevel SOM learning gives better classification rate for small and medium scale datasets, but not for large scale dataset

Writer identification based on hyper sausage neuron

This paper proposes biomimetic pattern recognition (BPR) based on hyper sausage neuron (HSN) and applies it in writer identification. HSN is used to cover the training set.HSN’s coverage can be seen as a topological product of a one-dimensional line segment and an n-dimensional supersphere.The feature extraction is moment invariants such as united moment invariants (UMI) and aspect united moment invariants (AUMI).The experiments result show that AUMI-HSN method is more effective than UMI-HSN method for identifying the authorship of handwriting