280 research outputs found
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
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