The design of a neural network compiler

Computer simulation is a flexible and economical way for rapid prototyping and concept evaluation with Neural Network (NN) models. Increasing research on NNs has led to the development of several simulation programs. Not all simulations have the same scope. Some simulations allow only a fixed network model and some are more general. Designing a simulation program for general purpose NN models has become a current trend nowadays because of its flexibility and efficiency. A proper programming language specifically for NN models is preferred since the existing high-level languages such as C are for NN designers from a strong computer background. The program translations for NN languages come from combinations which are either interpreter and/or compiler. There are also various styles of programming languages such as a procedural, functional, descriptive and object-oriented. The main focus of this thesis is to study the feasibility of using a compiler method for the development of a general-purpose simulator - NEUCOMP that compiles the program written as a list of mathematical specifications of the particular NN model and translates it into a chosen target program. The language supported by NEUCOMP is based on a procedural style. Information regarding the list of mathematical statements required by the NN models are written in the program. The mathematical statements used are represented by scalar, vector and matrix assignments. NEUCOMP translates these expressions into actual program loops. NEUCOMP enables compilation of a simulation program written in the NEUCOMP language for any NN model, contains graphical facilities such as portraying the NN architecture and displaying a graph of the result during training and finally to have a program that can run on a parallel shared memory multi-processor system

Single Slice Grouping Mechanism for Recognition of Cursive Handwritten Courtesy Amounts of Malaysian Bank Cheques

Mechanism to group single slice for recognition involves the process of cutting vertically across an image slice by slice, group every slice at a certain width and tested for recognition using a trained Neural network. The image contains cursive handwritten courtesy Amounts of Malaysian bank cheques. A three layer neural Network architecture with the new error function of Backpropagation learning algorithm is used. This approach yields good recognition results with faster convergence rates

NEUCOMP2 - parallel neural network compiler

A parallel neural network compiler (NEUCOMP2) for a shared-memory parallel machine has been implemented by introducing parallelism in NEUCOMP. The parallel routine detects the program loops of the sequential version generated by NEUCOMP, undergoing analysis of the data dependences and transforms it into a parallel version. Experiments were carried out to study the performance of the NEUCOMP2 programs for the backpropagation network. NEUCOMP2 was developed and run on the Sequent Balance 8000 computer system at Parallel Algorithm Research Centre, U.K

Parallel simulation of character recognition problems using NEUCOMP2

NEUCOMP2 is a parallel Neural Network Compiler for a shared-memory parallel machine. It compiles a program written as a list of mathematical specifications of Neural Network (NN) models and then translates it into a chosen target program which contains parallel codes. Performance results for character recognition problems on popular NN models are presented. The models are the backpropagation, Kohonen, Counterpropagation and ART1 network models. NEUCOMP2 was developed and run on the SEQUENT Balance 8000 computer system at PARC

Pemikiran reflektif: meneroka amalan pemikiran siswa pendidik

Pelbagai cabaran dalam dunia pendidikan masa kini memerlukan guru dan pelajar yang sentiasa berfikir secara reflektif dalam menangani masalah yang dihadapi. Melahirkan pelajar yang mempunyai pemikiran reflektif memerlukan guru yang mengamalkan pemikiran reflektif. Bagi memenuhi tuntutan tersebut, program latihan perguruan memberi tumpuan utama kepada komponen amalan refleksi dalam melatih dan memperkembangkan pemikiran reflektif siswa pendidik. Sehubungan dengan itu, satu kajian telah dijalankan bertujuan untuk melihat amalan pemikiran reflektif siswa pendidik berdasarkan teori pemikiran reflektif Mezirow. Responden kajian terdiri daripada siswa pendidik yang mengikuti Program Ijazah Sarjana Muda Perguruan, Institut Pendidikan Guru Kampus Pendidikan Teknik. Kajian kuantitatif ini menggunakan soal selidik dengan skala likert lima-poin. Hasil dapatan kajian mendapati majoriti amalan pemikiran reflektif siswa pendidik adalah pada tahap yang praktikal. Ini menunjukkan bahawa amalan pemikiran reflektif mereka telah dapat diperkembangkan melalui kursus yang mereka ikuti

Rangkaian Neural Genetik Aplikasi dalam Pengecaman Aksara Jawi

Objektif asas bagi Algoritma Genetik (atau ringkasnya AG) ialah melihat proses evolusi asli dalam bentuk satu versi perisian. Ia sering digunakan untuk masalah pengoptimuman. Dalam proses ini suatu populasi boleh berkembang biak, ditot atau diklon, dan mati dalam beberapa saat. Perubahan ini berlaku secara berterusan. Kini, AG telah dikembangkan konsepnya ke dalam Rangkaian Neural (atau ringkasnya RN). Kertas ini membicarakan konsep atau proses evolusi yang digunakan didalam R

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

In evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x-, y- and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, Naïve Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition

An integrative gene selection with association analysis for microarray data classification

The rising interest in integrative approach has shifted gene selection from purely data-centric to incorporating additional biological knowledge. Integrative gene selection is viewed as a promising approach in microarray data classification that took into consideration the complex relationships among genes. However, in most of the existing methods, the selection of genes is still based on expression values alone and biological knowledge is integrated at the end of analysis to verify experimental results or to gain biological insights. Thus, this paper proposed an integrative gene selection based on filter method and association analysis for selecting genes that are not only differentially expressed but also informative for classification. Association analysis is employed to integrate microarray data with multiple types of biological knowledge simultaneously, and to identify groups of genes that are frequently co-occurred in target samples. It has been tested on four cancer-related datasets, and two types of biological knowledge are incorporated, namely Gene Ontology (GO) and KEGG Pathways (KEGG). The experimental results show that the recommended GO based models, KEGG based models, and GO-KEGG based models outperformed the expression-only models by attaining better classification accuracies with lesser number of genes. The performance of the integrative models verified the efficiency and scalability of association analysis in mining microarray data

Internet of Things (IoT) enabled water monitoring system

Water is always a crucial part of everyday life. Due to global environmental situation, water management and conservation is vital for human survival. In recent times, there were huge needs of consumer based humanitarian projects that could be rapidly developed using Internet of Things (IoT) technology. In this paper, we propose an IoT based water monitoring system that measures water level in real-time. Our prototype is based on idea that the level of the water can be very important parameter when it comes to the flood occurrences especially in disaster prone areas. A water level sensor is used to detect the desired parameter, and if the water level reaches the parameter, the signal will be feed in realtime to social network like Twitter. A cloud server was configured as data repository. The measurement of the water levels are displayed in remote dashboard

Improved method of classification algorithms for crime prediction

The growing availability of information technologies has enabled law enforcement agencies to collect detailed data about various crimes. Classification is the procedure of finding a model (or function) that depicts and distinguishes data classes or notions, with the end goal of having the ability to utilize the model to predict the crime labels. In this research classification is applied to crime dataset to predict the 'crime category' for diverse states of the United States of America (USA). The crime data set utilized within this research is real in nature, it was gathered from socio-economic data from 1990 US census. Law enforcement data from 1990 US LEMAS survey, and from the 1995 FBI UCR. This paper compares two different classification algorithms namely - Naïve Bayesian and Back Propagation (BP) for predicting 'Crime Category' for distinctive states in USA. The result from the analysis demonstrated that Naïve Bayesian calculation out performed BP calculation and attained the accuracy of 90.2207% for group 1 and 94.0822% for group 2. This clearly indicates that Naïve Bayesian calculation is supportive for prediction in diverse states in USA