Volume 4 Issue 1
Authors: Molood Noghrehabadi; Media Aminian; Azam Davahli
Abstract: To increase efficiency, speed, accuracy and to decrease cost, the recent approaches in developing decision support systems for agriculture, and more generally for environmental problems management, tend to adopt a “systemic” approach. This paper presents a new methodology for mechanization of mushroom cultivation which can be applied to other agricultural products. We are using a multi-agent system to parallelize most of mechanization steps which can considerably increase the speed and efficiency of cultivation or production. This system consists of six agents, namely, Monitoring of environmental condition, Preparation of Compost, Day counter, Temperature regulator, Sprinkler and Harvest. The paper provides a review on the studies on mechanization with multi-agent and the other methods, the multi-agent model, functionality of each agent, the overall efficiency of this methodology and compares it with some other existing works.
Keywords: Agriculture; Mushroom Cultivation; Mechanization; Agent; Multi-Agent System; Parallelism
Authors: G. V.R. Sagar; K. Anitha Sheela
Abstract: This paper improves the role of adaptive nature of new Evolutionary Algorithm (EA) [19] in designing Artificial Neural Network (ANN) using the proper selection mechanism. The proposed EA has been used for two purposes. One is generalization of architecture. In this, the optimal adaptive architecture is achieved by using evolutionary crossover and mutation. The adaptive strategy increased in the stage of selection process. This algorithm used the tournament selection method with minimum hamming distance. Unlike most previous studies, proposed EA puts emphasis on autonomous functioning in the design process of ANNs. The mathematical frame work is discussed in [19]. The proposed EA has been tested on a number of benchmark problems in machine learning and ANNs, including breast cancer, diabetes, heart problems and for time complexity N-Bit Parity is used. The experimental results show that proposed EA can design compact ANN architectures with good generalization ability, compared to other algorithms with good time complexity.
Keywords: Evolutionary Aalgorithm; Crossover; Mutation; Tournament Selection; Time Complexity
Authors: Helgi Pall Helgason; Kristinn R. Thorisson; Deon Garrett
Abstract: In the domain of intelligent systems the management of system resources is typically called “attention”. Attention mechanisms exist because even environments of moderate complexity are a source of vastly more information than available cognitive resources of any known intelligence can handle. Cognitive resource management has not been of much concern in artificial intelligence (AI) work that builds relatively simple systems for particular targeted problems. For systems capable of a wide range of actions in complex environments, explicit management of time and cognitive resources is not only useful, it is a necessity. We have designed a general attention mechanism for intelligent systems. While a full implementation remains to be realized, the architectural principles on which our work rests have already been implemented. Here we examine some prior work that we find relevant to engineered systems, describe our design, and how it derives from constructivist AI principles.
Keywords: Attention; Resource Management; Architecture; Artificial Intelligence