LEARNING THROUGH PLAYING: DEVELOPMENT OF SOFTWARE ARCHITECT’S SKILLS WITH BUILDING BLOCKS

Software Architecture and Design is a course introduced in the curriculum of Computer Science Bachelor Degree. It is a part of the software engineering body of knowledge to instill good practice in software development. The teaching and learning method of delivering competence in a high level abstraction is challenging in way to obtain knowledge appreciation among students. The experience of deploying learning through playing in a tutorial session encourages students’ engagement, focus and appreciation of the teaching and learning process. The knowledge is delivered, experienced and actively discussed among students to discover more. This paper presents the design and implementation of the learning through playing building blocks for the purpose of understanding software architect’s roles and responsibilities. The analysis based on the observation of the conduct is discussed and lessons learnt are elaborated

An approach to estimate the saving from negotiation based on cost- benefit anaylsis model

This paper presents an approach to estimate the savings from implementing negotiation in requirements elicitation process. The aim of implementing negotiation is to minimize the possibility of introducing defects during the creation of requirements and to decrease later effort required to fix requirements defects. An empirical evaluation study is adopted through a role play experiment to evaluate the benefit of exercising negotiation. The net-gain and the return-oninvestment show positive value which suggest that negotiation activities worth an investment. Based on the return-oninvestment of 197 percent in average, this paper suggests that negotiation is a useful prevention activity to inhibit defects from occurring during the creation of requirements

Content-based feature selection for music genre classification

The most important aspect that one should consider in a content-based analysis study is the feature that represents the information. In music analysis one should know the details of the music contents that can be used to differentiate the songs. The selection of features to represent each music genre is an important step to identify, label, and classify the songs according to the genres. This research investigates, analyzes, and select timbre, rhythm, and pitch-based features to classify music genres. The features that were extracted from the songs consist the singer's voice, the instruments and the melody. The feature selection process focuses on the supervised and unsupervised methods with the reason to select significant generalized and specialized music features. Besides the selection process, two modules of Negative Selection Algorithm; censoring and monitoring are highlighted as well in this work. We then proposed the Modified AIS-based classification algorithm to solve the music genre classification problem. The results from our experiments demonstrate that the features selection process contributes to the proposed modified AIS-based music genre classification performs significantly in classifying the music genres

Offline Handwritten Digit Recognition Using Triangle Geometry Properties

Offline digit handwritten recognition is one of the frequent studies that is being explored nowadays.Most of the digit characters have their own handwriting nature. Recognizing their patterns and types is a challenging task to do.Lately,triangle geometry nature has been adapted to identify the pattern and type of digit handwriting.However,a huge size of generated triangle features and data has caused slow performances and longer processing time.Therefore,in this paper,we proposed an improvement on triangle features by combining the ratio and gradient features respectively in order to overcome the problem.There are four types of datasets used in the experiment which are IFCHDB,HODA,MNIST and BANGLA.In this experiment,the comparison was made based on the training time for each dataset Besides,Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) techniques are used to measure the accuracies for each of datasets in this study

Web-Based Dynamic Similarity Distance Tool

Similarity or distance measures is a well-known method and commonly used for calculating the distance between two samples of a dataset.Basically,the distance between the dataset samples is an important theory in multivariate analysis research.This paper proposes a tool that provides seven common distance methods that can be used in various research area.This tool is a web-based application which can be accessed through the internet browser.The objective of this tool is to introduce a web-based similarity distance application for many analysis and research purposes.Besides,a ranking method based on the Mean Average Precision is also implemented in this tool in order to increase the classification accuracies. This tool can process features that contain numerical values from any type of dataset

A Comparative Study Of Treebased Structure Methods For Handwriting Identification Springer

Handwriting is unique for each individual. Every single word presents the numbers of significant features that can be used for authenticating the author of the writing. The process of identify this significant features is called as feature selection process. Feature selection is an important area in the machine learning, specifically in pattern recognition which is becoming famous among the researchers. Tree-based structure method is one of the feature selection methods which is able to generate a compact subset of non-redundant features and hence improves interpretability and generalization. However its focus is still limited especially in Writer Identification domain. This paper proposes the role of the tree-based structure method performs in Writer Identification. Several methods of the tree-based structure are selected and performed using image dataset from IAM Handwriting Database. The results of each methods of Writer Identification are also analyzed and compared. The most interesting method will be further explored and adapted in future works

Immune Ant Swarm Optimization for Optimum Rough Reducts Generation

Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. It is used in rough reducts calculation for identifying optimally significant attributes set. This paper proposes a hybrid ant swarm optimization algorithm by using immunity to discover better fitness value in optimizing rough reducts set. By integrating PSO with ACO, it will enhance the ability of PSO when updating its local search upon quality solution as the number of generations is increased. Unlike the conventional PSO/ACO algorithm, proposed Immune ant swarm algorithm aims to preserve global search convergence of PSO when reaching the optimum especially under the high dimension situation of optimization with small population size. By combining PSO with ACO algorithms and embedding immune approach, the approach is expected to be able to generate better optimal rough reducts, where PSO algorithm performs the global exploration which can effectively reach the optimal or near optimal solution to increase fitness value as compared to the past research in optimization of attribute reduction. This research is also to enhance the optimization ability by defining a suitable fitness function with immunity process to increase the competency in attribute reduction and has shown improvement of the classification accuracy with its generated reducts in solving NP-Hard problem. The proposed algorithm has shown promising experimental results in obtaining optimal reducts when tested on 12 common benchmark datasets. Result for rough reducts and fitness value performance has been discussed and briefly explored in order to identify the best solution. The experimental analysis on the initial results of IASORR has been proven to offer a better quality algorithm and to maintain PSO’s performance, which are also encouraging in t-test analysis, for most of the tested datasets