Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Real stock trading using soft computing models

Abstract: The main focus of this study is to compare different performances of soft computing paradigms for predicting the direction of individuals stocks. Three different artificial intelligence techniques were used to predict the direction of both Microsoft and Intel stock prices over a period of thirteen years. We explore the performance of artificial neural networks trained using backpropagation and conjugate gradient algorithm and a Mamdani and Takagi Sugeno fuzzy inference system learned using neural learning and genetic algorithm. Once all the different models were built the last part of the experiment was to determine how much profit can be made using these methods versus a simple buy and hold technique.

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

Abstract: In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches Soft computing approaches have the ability to adapt itself according to problem domain Another aspect is a good balance between exploration and exploitation processes These aspects make soft computing approaches more powerful, reliable and efficient The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases Second objective is to identify various diseases for which these approaches are applied Third objective is to categories the soft computing approaches for clinical support system In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc A detailed discussion on these approaches are presented in literature section This work summarizes various soft computing approaches used in healthcare domain in last one decade These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy

Approximation of phenol concentration using novel hybrid computational intelligence methods

Abstract: This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg–Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

Soft computing agents: new trends for designing autonomous systems

Abstract: 1: "Conscious" Software: A Computational View of Mind.- 2: Intelligent Agents in Granular Worlds.- 3: Controlling Effective Introns for Multi-Agent Learning by Means of Genetic Programming.- 4: TalkMine: A Soft Computing Approach to Adaptive Knowledge Recommendation.- 5: A Soft-Computing Distributed Artificial Intelligence Architecture for Intelligent Buildings.- 6: Towards a Multiagent Design Principle: Analyzing an Organizational-Learning Oriented Classifer System.- 7: A Human-Centered Approach for Intelligent Internet Applications.- 8: A Soft Computing Framework for Adaptive Agents.