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Conference

Intelligent Systems Design and Applications 

About: Intelligent Systems Design and Applications is an academic conference. The conference publishes majorly in the area(s): Fuzzy logic & Artificial neural network. Over the lifetime, 2991 publication(s) have been published by the conference receiving 19151 citation(s).
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Journal ArticleDOI
01 Apr 1996
TL;DR: This paper illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics, and applies the AIS to a real-world problem: the recognition of promoters in DNA sequences.
Abstract: In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to «forget» little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan's ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not require effort to optimize any system parameters.

377 citations


Proceedings ArticleDOI
08 Sep 2005
TL;DR: HMM offers a new paradigm for stock market forecasting, an area that has been of much research interest lately, and is presented for forecasting stock price for interrelated markets.
Abstract: This paper presents hidden Markov models (HMM) approach for forecasting stock price for interrelated markets. We apply HMM to forecast some of the airlines stock. HMMs have been extensively used for pattern recognition and classification problems because of its proven suitability for modelling dynamic systems. However, using HMM for predicting future events is not straightforward. Here we use only one HMM that is trained on the past dataset of the chosen airlines. The trained HMM is used to search for the variable of interest behavioural data pattern from the past dataset. By interpolating the neighbouring values of these datasets forecasts are prepared. The results obtained using HMM are encouraging and HMM offers a new paradigm for stock market forecasting, an area that has been of much research interest lately.

233 citations


Proceedings ArticleDOI
01 Nov 2011
TL;DR: A novel strategy to discover the community structure of (possibly, large) networks by exploiting a novel measure of edge centrality, based on the κ-paths, which allows to efficiently compute a edge ranking in large networks in near linear time.
Abstract: In this paper we present a novel strategy to discover the community structure of (possibly, large) networks This approach is based on the well-know concept of network modularity optimization To do so, our algorithm exploits a novel measure of edge centrality, based on the κ-paths This technique allows to efficiently compute a edge ranking in large networks in near linear time Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM

202 citations


Proceedings ArticleDOI
30 Nov 2009
TL;DR: This work proposes a simple way to turn standard measures for OR into ones robust to imbalance, and shows that, once used on balanced datasets, the two versions of each measure coincide, and argues that these measures should become the standard choice for OR.
Abstract: Ordinal regression (OR -- also known as ordinal classification) has received increasing attention in recent times, due to its importance in IR applications such as learning to rank and product review rating. However, research has not paid attention to the fact that typical applications of OR often involve datasets that are highly imbalanced. An imbalanced dataset has the consequence that, when testing a system with an evaluation measure conceived for balanced datasets, a trivial system assigning all items to a single class (typically, the majority class) may even outperform genuinely engineered systems. Moreover, if this evaluation measure is used for parameter optimization, a parameter choice may result that makes the system behave very much like a trivial system. In order to avoid this, evaluation measures that can handle imbalance must be used. We propose a simple way to turn standard measures for OR into ones robust to imbalance. We also show that, once used on balanced datasets, the two versions of each measure coincide, and therefore argue that our measures should become the standard choice for OR.

171 citations


Proceedings ArticleDOI
30 Nov 2009
TL;DR: This paper describes the algorithm design and implementation of GAs on Hadoop, an open source implementation of MapReduce, and demonstrates the convergence and scalability up to 10^5 variable problems.
Abstract: Genetic algorithms(GAs) are increasingly being applied to large scale problems. The traditional MPI-based parallel GAs require detailed knowledge about machine architecture. On the other hand, MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. In this paper, we show how genetic algorithms can be modeled into the MapReduce model. We describe the algorithm design and implementation of GAs on Hadoop, an open source implementation of MapReduce. Our experiments demonstrate the convergence and scalability up to 10^5 variable problems. Adding more resources would enable us to solve even larger problems without any changes in the algorithms and implementation since we do not introduce any performance bottlenecks.

167 citations


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Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
2020130
201963
2018214
2017100
2016106
2015117