Intelligent System

Using intelligent agent-based systems to support information processing for executives has not been significantly advanced in both theory and practice. Research into this field tends to focus more on technical aspects than on social perspective. When executives are faced with increasing information availability and uncertainty in the business environment, using intelligent agent-based systems to enhance executives’ information processing capability appears

Biological processes and methods have been influencing science and technology for many decades. The ideas of feedback and control processes Norbert Wiener used in his cybernetics were based on observation of these phenomena in biological systems. Artificial intelligence and intelligent systems have been fundamentally interested in the phenomenology of living systems, namely perception, decision-making, action, and learning. Natural systems exhibit many properties that form fundamentals for a number of nature inspired applications – dynamics, flexibility, robustness, self-organisation, simplicity of basic elements, and decentralization. This paper reviews examples of nature inspired software applications focused on optimization problems, mostly drawing inspiration from collective behaviour of social colonies.

Chemical process control requires intelligent monitoring due to the dynamic nature of the chemical reactions and the non-linear functional relationship between the input and output variables involved. CSTR is one of the major processing unit in many chemical, pharmaceutical and petroleum industries as well as in environmental and waste management engineering. In spite of continuing advances in optimal solution techniques for optimization and control problems, many of such problems remain too complex to be solved by the known techniques. Thus, a heuristic approach is often a viable alternative. Neural Network models offer the most unified approach to building truly intelligent systems, which can provide good optimal solution for many applications. In this work we propose a hybrid (KohKal) neural network algorithm which is being used to model and solve a continuous stirred tank mixer/reactor (CSTM/R) problem which is non-linear and stochastic in nature. This hybrid algorithm is robust...

C4I (command, control, communications, computer and intelligence) system is a complex system of systems that enables the military commander to achieve decision superiority by affecting adversary's information and information based processes while protecting one`s own information systems. Owing to the sensitivity of military information the security threats to C4I systems are real and growing therefore security is a major challenge in order to maintain integrity, confidentiality, availability and accountability. Different Models and architectures frameworks e.g. DODAF, CDSA have been proposed which decompose the detail structure of C4I systems however these models and frameworks do not provide mechanism to capture associated security risks and issues. Threat modeling is the process to understand different ways system can be attacked which helps towards secure system design and implement countermeasures. This paper suggests that using threat modeling could help to eradicate or reduce the effect of advancing threats to C4I systems. In this paper we also identify how security can be modeled in top down manner e.g. from architecture level to lower level of modeling so that countermeasures can be incorporated on system level rather than on bit by bit basis.

Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily

Rule-based and case-based reasoning are two popular approaches used for problem solving in intelligent systems. They are natural alternatives in knowledge representation. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages. Due to their interchangeable nature, rules and cases can be easily integrated and thus produce effective knowledge representation formalisms, surpassing the disadvantages of each component method working individually. In this paper, we present a survey of approaches integrating rule-based reasoning with case-based reasoning. We describe a categorization scheme for the integrated formalisms and briefly present representative approaches of each category.

CONTROLAB is an environment which integrates intelligent systems and control algorithms aiming at applications in the area of robotics. Within CONTROLAB, two neural network architectures based on the backpropagation and the recursive models are proposed for the implementation of a robust speaker-independent word recognition system. The robustness of the system using the backpropagation network has been largely verified through use by children and adults in a totally uncontrolled environment such as large public halls for the exhibition of new technology products. Initial experimental results with the recursive network show that it will be able to overcome the backpropagation network major drawback, which is the frequent generation of false alarms. In addition, within CONTROLAB, a multivariable self-tuning algorithm is proposed to perform high-precision control of the trajectory, to be followed by a robot arm. High-precision control is achieved due to the ability of the algorithm to ...