The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.

-Development of an accurate laboratory diagnostic tool, as recommended by WHO, is the key step to overcome the serious global health burden caused by malaria. This study aims to explore the possibility of computerized diagnosis of malaria and to develop a novel image processing algorithm to reliably detect the presence of malaria parasite from Plasmodium falciparum species in thin smears of Giemsa stained peripheral blood sample. The algorithm was designed as an expert system based on the method used by medical practitioner performing microscopy diagnosis of malaria. Digital images were acquired using a digital camera connected to a light microscope. Prior to processing, the images were subjected to gray-scale conversion to decrease image variability. Global thresholding were implemented to obtain erythrocyte and other blood cell components in each image. The segmented images were further processed to obtain possibly infected erythrocyte and the components of parasite inside the corresponding erythrocyte using multiple threshold. These parasite's constituents (nucleus and cytoplasm) were used as the preliminary basis for parasite/non parasite classification. Malaria samples prepared and provided by Eijkman Institute of Molecular Biology Indonesia were used to test the proposed algorithm.

No abstract

No abstract

<p>A novel human STR (Short Tandem Repeat) similarity method using cascade statistical fuzzy rules with tribal information inference is proposed. The proposed method consists of two cascade Fuzzy Inference Systems (FIS). The first FIS is to discriminate the tribal similarity, and the second FIS is to calculate the STR similarity. By using the allele marker’s statistical distribution probability density function as the membership function in the Fuzzy Rules of the first FIS, the new method makes it possible to tell the tribal similarity between two STR profiles. A 727 data acquired from tribal groups of Indonesia is used to examine the method produced promising result, being able to indicate higher tribal similarity score within a tribal group and lower similarity between tribal groups. In the light of Indonesia’s diverse tribal groups, these properties are able to be leveraged as a new way to improve the versatility of existing DNA matching algorithm.</p>