Parallel Implementation on Improved Error Signal of Backpropagation Algorithm

The research work presented in this thesis is a continuation of Shamsuddin's work regarding proposed error signal for the backpropagation (BP) algorithm. The main focus is to parallelise Shamsuddin's work in order to improve the speedup of the BP algorithm. The experiments are implemented using the Sequent Symmetry SE30 parallel machine. The BP algorithm uses the data partitioning method with columnwise block striped and the batch mode weight updating strategy. Twenty-six patterns consisting of uppercase letters from 'A' to 'Z' are tested in the experiments. Two main factors taken into consideration in this, experiments are the execution time and speedup and the recognition rates. Shamsuddin's proposed BP parallel version, is compared with the sequential version. Experimental results shows that the execution time of the parallel version is much less than the execution time of the sequential version. The parallel version produces a good speedup as the number of processors, are increased due to the value that is near the ideal value. Experiments for testing the recognition rates involves the twenty-six trained sample data with perfect pattern and untrained sample data with 10% corrupted pattern. The recognition rates results show 100% accuracy for the trained and untrained data using the standard BP and Shamsuddin's proposed BP running sequentially

Object-Oriented Programming semantics representation utilizing agents

Comprehending Object-Oriented Programming (OOP) is not an easy task especially by novice students. The problem occurs during the transition from learning fundamental programming language concept to OOP concept. It is very important to handle this problem from the beginning before novices learn more advanced OOP concepts like encapsulation, inheritance, and polymorphism. Learning programming from source code examples is a common behavior among novices. Novices tend to refer to source codes examples and adapt the source codes to the problem given in their assignments. To cater the problems faced by these novices, a novel agent-based model have been designed to assist them in comprehending OOP concepts through source codes examples. The instructor needs to provide two related source codes that are similar but in different domain. Generally, these source codes go through the preprocessing, comparison, extraction, generate program semantics and classification processes. A formal algorithm that can be applied to any two related Java-based source codes examples is invented to generate the semantics of these source codes. The algorithm requires source codes comparison based on keyword similarity to extract the words that exist in the two related source codes. Three agents namely SemanticAgentGUI, semanticAgent and noviceAgent are designed in the proposed model. The running system shows an OOP semantic knowledge representation by intelligent agents

A multi-agent system for computational problem solving - a review

Computational problem solving is taken into account as the first step before source code development. However, novice students face difficulties in understanding problem statements and transforming them to computational problem solving techniques. This requires the understanding of fundamental programming concepts. It is very important to cater this problem from the beginning before writing the source codes. This article reviews current studies on the designed environments for problem solving and the possibility to propose a new architecture for computational problem solving utilizing multi-agent technology

A multi-agent model for information processing in computational problem solving

Problem solving is vital in the computer programming course. It is the earliest topic that is emphasized and more time is allocated to teach the topic. Problem solving requires the problem understanding knowledge that novice students usually lacks. In order to assist novice students in computational problem solving, a multi-agent model is designed.The proposed model is different from existing model in terms of the unique architecture that utilizes agents for information processing, specifically to extract, transform and generate information. Five agents are designed for this purpose namely the GUI, PAC, IPO, Flowchart and Algorithm agents. The model is tested with three different kinds of problem statement and produced correct results

Face authentication system based on FDA and ANN.

Face authentication systems (FAS) are still in their infancy and many types of algorithms and techniques have been proposed to improve the ability of these systems. Artificial Neural Networks (ANN) have been commonly used as the classifiers for FAS whereas Fisher's Discrimination Analysis (FDA) has been used widely as the feature extractor. However, many current FAS still experiencing low accuracy rates using these techniques due to factors such as illumination, orientation and other disturbance. The purpose of this paper is to investigate the application of photometric normalization, linear subspace feature extraction, and ANN classification in enhancing FAS, and to build and evaluate the performance of the proposed FAS based on this approaches. We similarly used the popular ANN classification, namely Multi-Layer Perceptrons (MLP) as the classifier for our FAS as it has proven to be simple for implementation. meanwhile, we proposed linear subspace feature extraction techniques based on FDA to reduce the dimentionality of the face image. In addition, the photometric normalization techniques based on Histogram Equalization and Homomorphic Filtering are used to improve the appearance of the face. The effect of different combinations of the photometric normalization techniques on the performance of the proposed FAS was studied and the effectiveness of these techniques was highlighted. The results of the proposed FAS were compared among Eigenface and Fisherface FAS. It was discovered that using AT&T datasets, the proposed FAS solution outperformed the FAS based on Eigenface and Fisherface in term of False Acceptance and False Rejection rate. Furthermore, the experimental results demonstrated that MLP was able to produce better classification model that can satisfy the model authentication tests with significant advantages over Euclidean Distance, and Normalized Correlation classifier

A model of a mathematics editor using intelligent agent technology

To master the mathematics subject, a lot of exercises need to be done. Mathematical problem solving requires writing mathematics equations and symbols to simplify them to get the answer. A step by step guidance is important to make sure that no mistakes occur. This paper presents a study of existing mathematics editor and proposed a web-based model of a mathematics editor using intelligent agent technology based on the Belief, Desires, Intention (BDI) model. The feature to guide the user step by step is incorporated in the proposed model

Object-oriented programming semantics education based on intelligent agents

Comprehending Object-Oriented Programming (OOP) concepts is a difficult task especially for novice students. This usually happens during the transition form learning fundamental concepts to object-oriented (OO) concepts. When given an OO problem to solve, novices find it hard to relate with objects. If novices can view the world based on real objects, this can help them solve their problem of comprehending the OO concepts. In this paper, we propose to design an agent model to understand the semantics of OO Java source codes. The agents are designed based on the Belief-Desire-Intention (BDI) architecture. Three agents namely GUI agent, semantic agent and novice agents are constructed. The GUI agent is controlled by the user to provide source codes examples. The semantic agent submits the source codes to the novice agent and explains the semantic or meaning of the source codes to the novice agent. The process involves source codes comparison technique. This model provides an OOP semantics knowledge representation based on intelligent agents

Computational intelligence approaches for student/tutor modelling: a review

The intelligent tutoring system (ITS) is an educational software system that provides personalized and adaptive tutoring to students based on their needs, profiles and preferences. The tutor model and student model are two dependent components of any ITS system. The goal of any ITS system is to help the students to achieve maximum learning gain and improve their engagements to the systems by capturing the student's interests through the system's adaptive behavior. In other words an ITS system is always developed with the aim of providing an immediate and efficient solution to student's learning problems. In recent years a lot of work has been devoted to improving student and tutor models in order enhance the teaching and learning activities within the ITS systems. The aim of this paper is to investigate the most recent state of art in the development of these two vital components of the intelligent tutoring systems

An agent-based adaptive e-content and e-learning architecture design and implementation

Individual students have different approaches towards learning because of different background knowledge, learning styles and preferences. Therefore, it is difficult for instructors to understand their student best learning approach. Furthermore, web application based on multi-agents for adaptive E-Content has been proposed to assist student individualized learning content in order to enhance their learning outcome.Existing systems normally utilize the main techniques of programming scripts and hierarchical course structure to support adaptive Electronic-Learning (E-Learning) course authoring for diverse category of students. These systems need instructor to obligate significant technical skills, and additionally to employ theories of learning styles, which are challenging requirements. To facilitate instructor to contribute in authoring adaptive E-Learning courses, we have designed web application architecture for administrator, assessor/instructor, and student. Three agents namely the exam agent, message agent and E-Content agent have been created to assist instructor and student. We designed the proposed architecture to be implemented for an online adaptive E-Content and E-Learning system. In addition, we conducted user studies to evaluate the effectiveness of the system

An agent-based model of muscle contraction process as a bio-robotic process

This paper introduces a new computational methodology to model muscle contraction process as a bio-robotic process using agent technology. In this work, we have focused on muscle myosin nanomotor as the driven motor of muscles and introduced the nanomotor as a physical intelligent agent. Then, the mechanism of the nanomotor was specified using subsumption architecture of agent technology and modeled with the Finite State Machine (FSM) diagram of Unified Modeling Language (UML). The proposed agent-based FSM model of the mechanism of muscle myosin nanomotor illustrated the internal intelligent and autonomous decision-making process of the nanomotor as a robot mechanism. In order to verify the proposed agent-based FSM model of the mechanism of the nanomotor, we developed its mathematical definitions (its Deterministic Finite Automaton (DFA) and grammar) and compared them with the natural behavior of the nanomotor inside the muscle cells. The comparison results indicated that the mechanism of muscle myosin nanomotor could be defined as a robot mechanism with its inputs, internal decision-making process, and outputs. As muscle contraction process is a set of the mechanisms of muscle myosin nanomotors, our proposed agent-based model of the mechanism of the nanomotor can introduce muscle contraction process as a general bio-robotic process