Biologically inspired temporal sequence learning

We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation

Fuzzy Corporate Strategy Formulation Application System

Strategic analysis involves the decision-making process that analyzes business internal and external critical factors and plan the appropriate strategies based on the analysis of the factos. Decision-making is a dynamic process. It is complex and at times ambiguous Decision-makers encounter information search problem and detours, delayed feedback of results, uncertainty, ambiguity and in some cases conflict during decision-making. In many situations, strategies seem to engage in an informal causal analysis in a attempt to favourably influence decision outcomes. In this study a Fuzzy Corporate Strategy Formulation Application System was developed The system is an automated strategic formulation that embeds Artificial Intelligence technique : Fuzzy Logic for the purpose of improving the current practices of the automated strategic-formulation system. Fuzzy Logic allows computers to manipulate common sense in an uncertain world that involves knowledge of experts and other related references. The aim is to provide a business analysis tool that can assist the strategy decision-maker in a company to formulate strategies. The outcomes of the system have been validated with an expert in strategic management. The results reveal that the developed prototype is able to map the present situation of a company and to predict the possible future organization strategies, thus it has potential to assist in strategic decision-making

Face-voice association towards multimodal-based authentication using modulated spike-time dependent learning

We propose a reward based learning to associate face and voice stimuli. In particular, we implement learning in a spiking neural network paradigm using modulated spike-time dependent plasticity (STDP).The face and voice stimuli are paired with a temporal delay, and the network is trained to associate the paired face-voice with a target response.The learning rule is dependent on a reward policy in which the network is given a positive reward for a correct response to a face-voice stimulus pair, or the network receives a negative reward for an incorrect response. Despite a stochastic environment, the learning result of real images and sound indicates a good performance with 77.33% accuracy.The result demonstrates that a machine can be trained to associate a pair of biometric inputs to a target response

Fuzzy logic controller for roof sprinkler cooling system / Faizzudin Sahazudin , Nooraini Yusoff and Fadzilah Siraj

In Malaysia, a cooling system is very efficient in reducing the heat from outside temperature. Normally, for those who afford, the best equipment will be utilized to make homes in a cool and comfort temperature. This may take an equipment such as air-conditioner or super insulation to their house. However, those equipments are costly for their installation, maintenance and energy. In this study, we propose a design of roof sprinkler cooling system using fuzzy logic (FL). The water sprinkler works by triggering the controller to open the valves to release and spray water. FL could benefit the cooling system by intelligently controlling the actual water quantity based on the observed daily weather condition captured from a sensor. This technique is the key approach to saving both water and energy. The system has been simulated and tested accordingly with a number of conditions and parameters, and it has been shown that the proposed design is feasible and practical to be implemented

Exploring hidden relationships within students' data using neural network and logistic regression

Considerable attention has been given to the development of sophisticated techniques for exploring data sets.One of the most commonly used techniques is neural networks that have the abilities to detect nonlinear effects and/or interactions.Due to the reduced interpretability of the output model of neural networks the some data set has been analyzed using logistic regression.In this study both techniques have been applied to education data set.The study aims to provide some insight into fist year students undertaking undergraduate programs namely Bachelor of Information Technology (BIT),Bachelor of Multimedia (BMM) and Bachelor in Management of Technology (BMoT) at University Utara Malaysia. The Holland Personality Model was used to indicate the students personality traits in conjunction with students academic achievement of accuracies in both methods the methods were used in this exploratory study in a complementary manner

Fuzzy logic approach to corporate strategy mapping

Corporate strategy mapping involves an analysis of company's present situation based on strategic factors known as SWOT factors that represent Strengths, Weaknesses, Opportunities, and Threats. The company survival analysis aims to forecast appropriate strategies to undertake.For this purpose, Internal-External Matrix (I-E Matrix) is used to map a company's external and internal factors' scores to determine the overall corporate strategy of a company. Based on both scores, IE matrix recommends a company with three types of strategy; Grow and Build, Hold and Maintain, and Harvest and Divest. In allocating the strategies, there are regions whereby the coordinates of mapped IFA and EFA scores are not able to immediately indicate the appropriate strategy to be undertaken by a company.When such cases arise, an analyst opinion is required in order to determine which strategy implementation is most appropriate. Different analyst may provide different opinion based on his or her assumption, `market driven' or `resource-based'. There is no exact solution for the scores that fall in the ambiguous regions. As a solution, one possible approach is to integrate Fuzzy Logic technique with I-E Matrix in producing the automatic strategy formulation.This is due to the fact that Fuzzy Logic has shown to have ability to improve the intelligence of systems on uncertain, imprecise and noisy environment. In this study, Fuzzy Logic has been developed and tested on real cases data.The result shows that the proposed technique is able to forecast the strategic choice for the ambiguous locations that exists in the company

Deploying artifical intelligence techniques in loan application processing

The granting of loans by a financial institution is one of the important decisions that require insubstantial care. The institution usually employs loan officers to make credit decisions or recommendations for that particular institution. These officers are given some hard roles in evaluating the worthiness of each application. Some researchers recognize that the capability of humans to judge the worthiness of a loan is rather poor. Since business data warehouses store historical data from previous application, it is likely that there is knowledge hidden in this data may be useful in decision making. Unfortunately the task of discovering hidden information and useful relationship from data is difficult for human. This is due the fact that the data to be examined is very large and the nature of the relationship within the data is not obvious. To this end, Artificial Intelligence (AI) techniques can be beneficial to assist the decision maker in making decisions regarding loan application. AI provides a variety of useful tool for discovering the non-obvious relationships in historical data, while ensuring those relationships discovered will generalize to the future data. This knowledge is important and can be used by the loan officer in determining whether to accept or reject an application. This study suggests that loan application processing system integrates two components of computer-based information system namely, office automation system and AI system that comprises of intelligent decision support system and knowledge based system. In essence, the potential use of such a system can be accelerated to promote any organizations as an efficient and effective organization that has competitive advantage

Model peramalan pinjaman pendidikan Negeri Kedah menggunakan pendekatan rangkaian neural

Lembaga Biasiswa Negeri Kedah (LBNK) is a state government body that helps to provide a scholarship or educational loan to the individual who are born in Kedah.To approve an educational loan application is not an easy task and it will take a longer time if the manpower in not sufficient. To assist LBNK in reducing cost and processing time, this study aims to develop a forecasting model that can be used by the management to accelerate the loan application processing.In this study, the neural network based forecasting is selected.Such a technique has been chosen since this technique is able to recognize non linear patterns with the data set.For this purpose, a multilayer perceptron with backpropagation learning was employed to predict whether an application is suitable to be awarded a scholarship or loan by LBNK.A total of 1062 applications for the year 2002 was used to test the identified neural network model.The training and test results indicate that the highest result achieved is 99.06%.This indicates that neural network has the potential to be used as a forecasting model in education

Copy-Move Forgery Detection using Integrated DWT and SURF

In this study, we propose a combination of two feature extraction methods namely Discrete Wavelet Transform (DWT) and Speeded Up Robust Features (SURF) to detect a copy-move forgery in digital media.Copy-move is one of the most popular kinds of digital image tempering, in which one or more parts of a digital image are copied and pasted into different locations.DWT is used to reduce image dimension and SURF is superior in extracting the key features from the image.The method has been tested with BMP and JPG images consisting of genuine and counterfeited images.Furthermore, the method has also been tested with copied-moved images applied with a number of various geometric transformation attacks including rotation, translation, scaling or set of them.The experiments results prove that the proposed method is superior with overall accuracy 95% when compared with the existing method.The copy-move attacks in the digital image have been successfully detected