Abstract: Studying DNA (deoxyribonucleic acid) sequence is useful in biological processes and it is applied in the fields such as diagnostic and forensic research. DNA is the hereditary information in human and almost all other organisms. It is passed to their generations. Earlier stage detection of defective DNA sequence may lead to many developments in the field of Bioinformatics. Nowadays various tedious techniques are used to identify defective DNA. The proposed work is to analyze and identify the cancer-causing DNA motif in a given sequence. Initially the human DNA sequence is separated as k-mers using k-mer separation rule. The separated k-mers are clustered using Self Organizing Map (SOM). Using Levenshtein distance measure, cancer associated DNA motif is identified from the k-mer clusters. Experimental results of this work indicate the presence or absence of cancer causing DNA motif. If the cancer associated DNA motif is found in DNA, it is declared as the cancer disease causing DNA sequence. Otherwise the input human DNA is declared as normal sequence. Finally, elapsed time is calculated for finding the presence of cancer causing DNA motif using clustering formation. It is compared with normal process of finding cancer causing DNA motif. Locating cancer associated motif is easier in cluster formation process than the other one. The proposed work will be an initiative aid for finding genetic disease related research.
Abstract: A smart city project embraces benefits and costs which can be classified under direct and indirect categories. Externalities come into the picture, but they are often difficult to quantify. Despite this barrier, policy makers need to carry out cost-benefit analysis to justify the huge investments needed to make a city smart. The recent trend is towards the engagement of the private sector to utilize their resources and expertise, especially in the Information and Communication Technology (ICT) areas, where innovations blossom. This study focuses on the identification of costs (on a life cycle basis) and benefits associated with smart city project developments based on a comprehensive literature review and case studies, where public-private partnerships would warrant consideration, the related costs and benefits are highlighted. The findings will be useful for policy makers of cities.
Abstract: Bio-electrical responses obtained from freshwater
sediments by employing microbial fuel cell (MFC) technology were
investigated in this experimental study. During the electricity
generation, organic matter in the sediment was microbially oxidized
under anaerobic conditions with an electrode serving as a terminal
electron acceptor. It was found that the sediment organic matter
(SOM) associated with electrochemically-active electrodes became
more humified, aromatic, and polydispersed, and had a higher average
molecular weight, together with the decrease in the quantity of SOM.
The alteration of characteristics of the SOM was analogous to that
commonly observed in the early stage of SOM diagenetic process (i.e.,
humification). These findings including an elevation of the sediment
redox potential present a possibility of the MFC technology as a new
soil/sediment remediation technique based on its potential benefits:
non-destructive electricity generation and bioremediation.
Abstract: This paper proposes an improved approach based on
conventional particle swarm optimization (PSO) for solving an
economic dispatch(ED) problem with considering the generator
constraints. The mutation operators of the differential evolution (DE)
are used for improving diversity exploration of PSO, which called
particle swarm optimization with mutation operators (PSOM). The
mutation operators are activated if velocity values of PSO nearly to
zero or violated from the boundaries. Four scenarios of mutation
operators are implemented for PSOM. The simulation results of all
scenarios of the PSOM outperform over the PSO and other existing
approaches which appeared in literatures.
Abstract: Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data. Dynamic self-organizing maps such as Growing Self-organizing Map (GSOM) has been developed to overcome the problem of fixed structure in SOM to enable better representation of the discovered patterns. However, in mining large datasets or historical data the hierarchical structure of the data is also useful to view the cluster formation at different levels of abstraction. In this paper, we present a technique to generate concept trees from the GSOM. The formation of tree from different spread factor values of GSOM is also investigated and the quality of the trees analyzed. The results show that concept trees can be generated from GSOM, thus, eliminating the need for re-clustering of the data from scratch to obtain a hierarchical view of the data under study.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
Abstract: Self-organizing map (SOM) is a well known data
reduction technique used in data mining. It can reveal structure in
data sets through data visualization that is otherwise hard to detect
from raw data alone. However, interpretation through visual
inspection is prone to errors and can be very tedious. There are
several techniques for the automatic detection of clusters of code
vectors found by SOM, but they generally do not take into account
the distribution of code vectors; this may lead to unsatisfactory
clustering and poor definition of cluster boundaries, particularly
where the density of data points is low. In this paper, we propose the
use of an adaptive heuristic particle swarm optimization (PSO)
algorithm for finding cluster boundaries directly from the code
vectors obtained from SOM. The application of our method to
several standard data sets demonstrates its feasibility. PSO algorithm
utilizes a so-called U-matrix of SOM to determine cluster boundaries;
the results of this novel automatic method compare very favorably to
boundary detection through traditional algorithms namely k-means
and hierarchical based approach which are normally used to interpret
the output of SOM.