An intelligent forex monitoring system

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network

An agent-based intelligent system for information gathering from world wide web environment

To use the vast amount of information efficiently and effectively from Web sites is very important for making informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by users. In this paper, an information gathering system is develop by means of multiple agents to solve those problems. We employed some ideas of Gaia's methodology and an open agent architecture to analyze and design the system. The system consists of a query preprocessing agent, information retrieval agent, information filtering agent, and information management agent. The filtering agent is trained with categorized documents and can provide users with the necessary information. The experimental results show that all agents in the system can work cooperatively to retrieve relevant information from the World Wide Web environment.

Decision model for BDI-based intelligent agents

The very challenging issue for intelligent agents is “do what they should do”. The BDI architecture is presented to solve this challenge. The main difficult for this architecture is the formalizing problem. In this paper, we discuss the procedure descriptive framework, which presents a method for formalizing BDI architecture. We a present decision model for intelligent agents in this procedure descriptive framework. This research shows how an agent to generate beliefs and make decisions by using its current beliefs.

A proposed hybrid evaluation methodology for intelligent transport system deployment

The rapid deployment of intelligent transport systems in practice requires serious evaluation of new construction projects. Traditional transportation evaluation methods are insufficient to capture the new features of intelligent transport systems, particularly the applications of recently developed information technologies. This research aims to develop a new conceptual framework to evaluate the social, economic and environmental impacts of intelligent transport systems deployment, with explicit consideration of impacts of information technologies on variant travel behaviours. The research would provide a systematic evaluation methodology to enable decision makers and publics better evaluating and understanding the social, economic and environmental benefits of implementation of intelligent transport systems.

Intelligent consumer purchase intention prediction system for green products

In this paper the authors model green behaviour by predicting consumers’ purchase intention using Kohonen’s LVQ technique. It is envisaged that such a model may facilitate better understanding of green consumers’ market segments. The model employs cognitive, affective, and situational attributes of consumers to predict their purchase intention. The model can, potentially, provide a more direct method for companies to gauge consumers’ intention to purchase green products. The results indicate that consumers are more strongly resistant to lower quality than higher prices of green products in comparison to other alternative non-green products.

Resilience : when coping is emotionally intelligent

The potential for resilient behaviors and emotional intelligence involves an interplay between the individual and his or her broader environment. Studies that have examined resilience explore factors or characteristics that assist individuals to thrive from and in adversity. These protective factors are part of an individual’s general makeup. Some of these factors are considered genetic, such as a personality that is outgoing and social; however, many protective behaviors can be learned. Coping in the face of adversity involves emotional intelligence and resilience, both of which can be developed through support and education. In this context, fostering resilience and emotional intelligence has the potential to improve clinical outcomes for mental health consumers.

Detecting and tracing DDoS attacks by intelligent decision prototype

Over the last couple of months a large number of distributed denial of service (DDoS) attacks have occurred across the world, especially targeting those who provide Web services. IP traceback, a counter measure against DDoS, is the ability to trace IP packets back to the true source/s of the attack. In this paper, an IP traceback scheme using a machine learning technique called intelligent decision prototype (IDP), is proposed. IDP can be used on both probabilistic packet marking (PPM) and deterministic packet marking (DPM) traceback schemes to identify DDoS attacks. This will greatly reduce the packets that are marked and in effect make the system more efficient and effective at tracing the source of an attack compared with other methods. IDP can be applied to many security systems such as data mining, forensic analysis, intrusion detection systems (IDS) and DDoS defense systems.

An intelligent energy management model for a parallel hybrid vehicle under combined loads

To exploit the benefits offered by parallel HEVs, an intelligent energy management model is developed and evaluated in this paper. Despite most existing works, the developed model incorporates combined wind/drag, slope, rolling, and accessories loads to minimise the fuel consumption under varying driving conditions. A slope prediction unit is also employed. The engine and the electric motor can output power simultaneously under a heavy-load or a slopped road condition. Two simulation were conducted namely slopped-windy-prediction and slopped-windy-prediction-hybrid. The results indicate that the vehicle speed and acceleration is smoother where the hybrid component was included. The average fuel consumption for the first and second simulations were 7.94 and 7.46 liter/100 km, respectively.

A rule-based intelligent energy management system for an internal combustion engine vehicle

An intelligent energy management system (IEMS) is developed to improve fuel efficiency of an internal combustion engine vehicle. It helps determine the best approach to run the engine system through dynamically analysing various factors relating to vehicle. The energy balance technique is implemented and utilised. The simulation outcome of the IEMS is compared against that of a conventional system under the same driving factors. The results show that the IEMS reduces the fuel consumption around 5.6% for the tested conditions.

A low-cost intelligent gas sensing device for military applications

The field of electronic noses and gas sensing has been developing rapidly since the introduction of the silicon based sensors. There are numerous systems that can detect and indicate the level of a specific gas. We introduce here a system that is low power, small and cheap enough to be used in mobile robotic platforms while still being accurate and reliable enough for confident use. The design is based around a small circuit board mounted in a plastic case with holes to allow the sensors to protrude through the top and allow the natural flow of gas evenly across them. The main control board consists of a microcontroller PCB with surface mount components for low cost and power consumption. The firmware of the device is based on an algorithm that uses an Artificial Neural Network (ANN) which receives input from an array of gas sensors. The various sensors feeding the ANN allow the microcontroller to determine the gas type and quantity. The Testing of the device involves the training of the ANN with a number of different target gases to determine the weightings for the ANN. Accuracy and reliability of the ANN is validated through testing in a specific gas filled environment.