40 research outputs found
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
Advancements in Image Classification using Convolutional Neural Network
Convolutional Neural Network (CNN) is the state-of-the-art for image
classification task. Here we have briefly discussed different components of
CNN. In this paper, We have explained different CNN architectures for image
classification. Through this paper, we have shown advancements in CNN from
LeNet-5 to latest SENet model. We have discussed the model description and
training details of each model. We have also drawn a comparison among those
models.Comment: 9 pages, 15 figures, 3 Tables. Submitted to 2018 Fourth International
Conference on Research in Computational Intelligence and Communication
Networks(ICRCICN 2018
Reputation-Based Attack-Resistant Cooperation Stimulation (RACS) For Mobile Ad hoc Networks
In mobile ad hoc networks (MANET), nodes usually belong to different
authorities and pursue different goals. In order to maximize their own
performance, nodes in such networks tend to be selfish and are not willing to
forward packets for benefit of others. Meanwhile, some nodes may behave
maliciously and try to disrupt the network through wasting other nodes
resources in a very large scale. In this article, we present a reputation-based
attack resistant cooperation stimulation (RACS) system which ensures that
damage caused by malicious nodes can be bounded and cooperation among the
selfish nodes can be enforced. Mathematical analyses of the system as well as
the simulation results have confirmed effectiveness of our proposed system.
RACS is completely self-organizing and distributed. It does not require any
tamper-proof hardware or central management policy.Comment: 20 pages, 4 figure
Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting Proportion Of Components In Manhole Gas Mixture
The article presents performance analysis of a real valued neuro genetic
algorithm applied for the detection of proportion of the gases found in manhole
gas mixture. The neural network (NN) trained using genetic algorithm (GA) leads
to concept of neuro genetic algorithm, which is used for implementing an
intelligent sensory system for the detection of component gases present in
manhole gas mixture Usually a manhole gas mixture contains several toxic gases
like Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and
Carbon Monoxide. A semiconductor based gas sensor array used for sensing
manhole gas components is an integral part of the proposed intelligent system.
It consists of many sensor elements, where each sensor element is responsible
for sensing particular gas component. Multiple sensors of different gases used
for detecting gas mixture of multiple gases, results in cross-sensitivity. The
cross-sensitivity is a major issue and the problem is viewed as pattern
recognition problem. The objective of this article is to present performance
analysis of the real valued neuro genetic algorithm which is applied for
multiple gas detection.Comment: 16 pages,11 figure
Vision-based Human Fall Detection Systems using Deep Learning: A Review
Human fall is one of the very critical health issues, especially for elders
and disabled people living alone. The number of elder populations is increasing
steadily worldwide. Therefore, human fall detection is becoming an effective
technique for assistive living for those people. For assistive living, deep
learning and computer vision have been used largely. In this review article, we
discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based)
fall detection techniques. We also present a survey on fall detection benchmark
datasets. For a clear understanding, we briefly discuss different metrics which
are used to evaluate the performance of the fall detection systems. This
article also gives a future direction on vision-based human fall detection
techniques
Temporal Modeling of Node Mobility in Mobile Ad hoc Network
Ad-hoc network consists of a set of identical nodes that move freely and
independently and communicate via wireless links. The most interesting
feature of this network is that it does not require any predefined
infrastructure or central administration and hence it is very suitable for
establishing temporary communication links in emergency situations. This
flexibility however is achieved at the price of communication link
uncertainties due to frequent topology changes. In this article we describe
the system dynamics using the proven concept of time series
modeling. Specifically, we analyze variations of the number of neighbor nodes
of a particular node over a geographical area and for given total number of
nodes assuming different values of (i) the speeds of nodes, (ii) the
transmission powers, (iii) sampling periods and (iv) different mobility
patterns. We consider three different mobility models: (i) Gaussian mobility
model, (ii) random walk mobility model and (iii) random way point mobility
model. The number of neighbor nodes of a particular node behaves as a random
variable for any mobility pattern. Through our analysis we find that the
variation of the number of neibhbor nodes can be well modeled by an
autoregressive AR model. The values of evaluated for different
scenarios are found to be in the range between and . Moreover, we also
investigate the relationship between the speed and the time of measurements,
and the transmission range of a specific node under various mobility
patterns
Temporal Modeling of Link Characteristic in Mobile Ad hoc Network
Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence is very suitable for temporary communication links in an emergency situation. This flexibility, however, is achieved at a price of communication uncertainty induced due to frequent topology changes. In this article, we have tried to identify the system dynamics using the proven concepts of time series modeling. Here, we have analyzed variation of link utilization between any two particular nodes over a fixed area for differentmobility patterns under different routing algorithm. We have considered four different mobility models – (i) Gauss-Markov mobility model, (ii) Manhattan Grid Mobility model and (iii) Random Way Point mobility model and (iv) Reference Point Group mobility model. The routing protocols under which, we carried out our experiments are (i) Ad hoc On demand Distance Vector routing (AODV), (ii) Destination Sequenced Distance Vector routing (DSDV) and (iii) Dynamic Source Routing (DSR). The value of link load between two particular nodes behaves as a random variable for any mobility pattern under a routing algorithm. The pattern of link load for every combination of mobility model and for every routing protocol can be well modeled as an autoregressive model of order p i.e. AR(p). The order of p is estimated and it is found that most of them are of order 1 only
Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study
In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors