Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training

Neural network is widely used for image classification problems, and is proven to be effective with high successful rate. However one of its main challenges is the significant amount of time it takes to train the network. The goal of this research is to improve the neural network training algorithms and apply and test them in classification and recognition problems. In this paper, we describe a method of applying Bayesian regularization to improve Levenberg-Marquardt (LM) algorithm and make it better usable in training neural networks. In the experimental part, we qualify the modified LM algorithm using Bayesian regularization and use it to determine an appropriate number of hidden layers in the network to avoid overtraining. The result of the experiment was very encouraging with a 98.8% correct classification when run on test samples

A preliminary study on teaching programming at Malaysian school

This paper presents a study on teaching and learning programming in a Malaysian school.The study attempts to identify all possible relations of student’s background and attitudes towards learning programming.The motivations for this study comes from the fact that introductory programming in Higher Learning Institutes face a high rate of under achievers.Since the feeder to these institutions are schools, it is felt that if the teaching and learning of programming in schools are strengthen the above mentioned problem can too be greatly reduced. This study attempts to find all possible relations that would help in proposing a more effective method of teaching and learning programming in school and at Higher Learning Institution

The evaluation of an embedded system kit as A C programming teaching tool

This paper describes the methodology used in evaluating the effectiveness of an embedded system teaching tool for C programming. Teaching programming is one of the major problems among schools and universities. To overcome this problem, a teaching module and an embedded-system training kit for teaching programming to beginners were developed. The teaching module and kit were then tested on selected groups of school children. Focusing more on the testing phase of the research work, this paper gives a detailed account of the testing process and the evaluation method used. The result shows that the students are interested to learn programming using the embedded system

Applying a new hybrid model of embedded system development methodology on a flood detection system

The embedded system development methodology has not been as well established as the development methodology in software engineering. Involving inter-disciplinary activities in the development of hardware and software need to be further considered when developing an embedded system. This paper presents a new development model for an embedded system by the hybridization of selected development methodologies in software engineering and systems engineering, considering that they are both essential in embedded system. The model is harmonized with embedded system design vital tasks and also non-functional properties following the ISO/IEC 9126 standard.The model is then applied to a flood detection system for verification purposes.With the phases and steps in the new hybrid model for embedded systems development methodology carefully followed, the system was built in a more systematic and structured manner addressing the every needs of an embedded system’s requirements

Comparative Study Using WEKA for Red Blood Cells Classification

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as "anemia". Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

An Investigation of IoT Importance and Viability of Health Records Retrieval using Electronic Tags in Pilgrimage

Healthcare services is one of most important domains in the world. One of most crucial aspects of healthcare services is the need to make accurate healthcare decisions at the right time. Retrieving useful historical health records of patients in real-time is necessary to provide accurate healthcare decisions. Traditional health record systems such as paperbased system require time and effort to collect, manage, and retrieve patients’ records. Electronic health record systems were adopted to allow healthcare staff to retrieve useful health records in real-time and consequently improve and speed up healthcare services. Although EHR is effective to serve patients in their local countries, the implementation of EHR for global purposes is still an issue and EHR is not always applicable for people who travel to other countries. One of the most important purposes for Muslims to travel is the pilgrimage journey to the Kingdom of Saudi Arabia (KSA) to perform religious rites. The millions of pilgrims converging there may need healthcare services and these services should be accomplished accurately in real-time which require electronic-based historical health records approaches. This study aims to investigate the importance and viability of IoT implementati ons to support retrieval of pilgrims’ EHR using electronic tags. A questionnaire with 60 academic staff and interview with five experts from KSA were conducted to address the main aim of this study. The significance of the results shows that EHR supporting tag reading is a promising solution to enhance healthcare services and counter the challenges of EHR implementations in pilgrimage

Chain coding and pre processing stages of handwritten character image file

In this paper detailed descriptions of the algorithms used in the pre-processing and feature extraction phases of an offline handwritten character are discussed. In classifying handwritten characters, the stages prior to the classification phase play a role as major as the classification itself. There are many pre-processing functions and methods that can be used and different research works will use different methods. This paper discusses in detail some of the algorithms used in the pre-processing stages of an offline handwritten character image file. This paper serves as part of the whole research work that aims at recognizing handwritten characters. The whole research presents a hybrid approach of HMM and Fuzzy Logic in the field of handwritten character recognition. Fuzzy Logic is used in the classification phase while HMM is used in the process of extracting features for the preparation of linguistic variables of the fuzzy rules. However, only the preprocessing stages as employed by the research are described here. The pre-processing phase starts from reading in the input file, the process of binarization, reference line estimation and thinning of the character image for further use in the next stage of the feature extraction and recognition process. Each of the pre-processing stages and the chain coding process will be described in detail giving improvised algorithms, and examples of the processes on existing samples from the database shown. Where comparing experiments with other methods is done, the experimental results are given

Extracting features for the linguistic variables of fuzzy rules using hidden Markov model

In classifying handwritten characters, the stages prior to the classification phase play a role as major as the classification itself. This research work will be classifying the characters using a syntactical classification method namely fuzzy logic but will use the statistical method of Hidden Markov Model as an approach in extracting features for the linguistic variables of the fuzzy rule‐based system. In this paper the feature extraction method will be highlighted and detailed. The HMM Model of a variable to be used in the classification system will be discussed. Experimental results from a few sample images show that the proposed technique is both effective and efficient to be used in extracting features for the linguistic variables of fuzzy rules