Recognizing Patterns of Music Signals to Songs Classification Using Modified AIS-Based Classifier

Human capabilities of recognizing different type of music and grouping them into categories of genre are so remarkable that experts in music can perform such classification using their hearing senses and logical judgment. For decades now, the scientific community were involved in research to automate the human process of recognizing genre of songs. These efforts would normally imitate the human method of recognizing the music by considering every essential component of the songs from artist voice, melody of the music through to the type of instruments used. As a result, various approaches or mechanisms are introduced and developed to automate the classification process. The results of these studies so far have been remarkable yet can still be improved. The aim of this research is to investigate Artificial Immune System (AIS) domain by focusing on the modified AIS-based classifier to solve this problem where the focuses are the censoring and monitoring modules. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed classifier and WEKA application is discussed

Computationally Inexpensive Sequential Forward Floating Selection for Acquiring Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. This paper proposes an improved Sequential Forward Floating Selection method besides the exploration of significant features for invarianceness of authorship from global shape features by using various wrapper feature selection methods. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

THE EFFECTIVENESS OF SIMULATION EDUCATION FOR UNDERGRADUATE STUDENTS IN SOFTWARE ENGINEERING AREA

Simulation in education is a replacement model for real world experience. This approach is seen effective to give undergraduate students the opportunity to apply their theoretical knowledge and practice their skills learnt in laboratory. Simulation education is designed to emphasize outcome based education or known as OBE to bridge the gap between theory and practice. Implementing simulation model to represent real world practice in industry, undergraduate students are trained to deal with real problems in the right atmosphere. The effort eventually prepares the students to face a real working environment when they are graduated from the university. This paper discusses the curriculum designed for simulation education applied for undergraduate students in university, its implementation and the analysis of the outcome product. A specific case of Universiti Teknikal Malaysia Melaka is presented here

Learning through practice via role-playing: Lessons learnt

Software engineering is the establishment and use of sound engineering principles in order to obtain economically software that is reliable and works efficiently on real machine. Sound software engineering closely related with socio-technical activity that depends on several human issues which are communication, collaboration, motivation, work environment, team harmony, engagement, training and education. These issues affect everything for students to fully understand software engineering and be prepared for software development careers. Therefore courses offered in the university must also consider the sociological and communication aspects, often called the socio-technical aspects. One popular method is to use role-playing exercises. Role-playing is a less technologically elaborate form of simulation for learning interpersonal skills and is analogous to rehearsal. It is particularly helpful when students are having difficulties to relate lessons learnt in the university to the applicability of the knowledge in the real implementation. This is because many students view software engineering as meaningless bureaucracy and have little interest in the knowledge delivered in the lecture hall. This scenario impedes the expansion of current knowledge and inhibits the possibility of knowledge exploration to solve range of industry problems. Simply lecturing about software engineering will never engage students or convince them that software engineering has value. Given this student bias, the goal of teaching software engineering often becomes convincing students that it has value. To achieve this, students need to experience firsthand the sociological and communication difficulties associated with developing software systems. In this paper, we argue that in teaching software engineering we must cover two essential things; delivery of knowledge and skills required in the software engineering domain in a form of lecture and hands-on practice to experience the value of the knowledge and skills learnt. We report on our experiences gained in deploying role-playing in master degree program. Role-playing is used as pedagogical tool to give students a greater appreciation of the range of issues and problems associated with software engineering in real settings. We believe that the lessons learnt from this exercise will be valuable for those interested in advancing software engineering education and training

Requirements Negotiation: Does Consensus Reduce Software Development Cost?

The requirements engineering activities within a software project are known to be critical to the successful production of a correctly functioning system. This is particularly so when considering the varying views of multiple stakeholders. One promising approach for improving the outcome is to introduce formal negotiation. Negotiation is beneficial to identify and to resolve conflicts between stakeholders. Consensus achieved through negotiation represents all key stakeholders’ perspectives and perceptions regarding the system to be developed. 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. This paper answers the question of whether consensus gives positive significant impact to the software project as a whole or not. It presents an approach to estimate the savings from implementing negotiation in the requirements elicitation process. An empirical evaluation study is adopted through a role play experiment to evaluate the benefit of exercising negotiation. The net gain and the return on investment show positive values which suggest that negotiation activities are worth an investment. Based on a return on investment of 197 percent on average, this paper suggests that negotiation is a useful prevention activity to inhibit defects from occurring during the requirements creation process

Selecting Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. It relates to invarianceness of authorship where invarianceness between features for intraclass (same writer) is lower than inter-class (different writer). This paper discusses and reports the exploration of significant features for invarianceness of authorship from global shape features by using feature selection technique. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

A Compact Optical Fiber Based Gas Sensing System for Carbon Dioxide Monitoring

This paper presents an approach to develop an optical fiber based gas leak sensor that can be used to detect and quantify carbon dioxide gas. The developed sensor system operates based on the principles of Infrared (IR) absorption spectroscopy using the dual channel and double wavelength method. Therefore, it is possible to eliminate the interference signal from the outside environment by using this method. The design of the Carbon Dioxide (CO2) gas sensor system consists of a Broadband light source, two IR detectors with different Narrow Band Pass (NBP) filters for Active and Reference channel, chalcogenide infrared (CIR) fibers and a compact gas detection cell. The output of the sensor system is captured using in-house LabVIEW program with Data Acquisition (DAQ) capabilities and displayed in real time. Experimental results show that the developed optical fiber based gas sensor system is capable to detect and quantify concentrations of carbon dioxide gas for leak detection and also environmental monitoring applications

Dual Channel Double Wavelength Method in Optical Fiber Based Gas Sensor for Carbon Dioxide Detection

A novel double wavelength strategy in optical fiberbased sensor for detection of carbon dioxide (CO2) gas is presented. The detection strategy works based on an open-path direct absorption spectroscopy in the mid-infrared (mid-IR) wavelength range. The sensor system comprises of a broadband filament emitter acting as the Infrared (IR) light source, Chalcogenide Infrared (CIR) Optical Fibers, two Pyroelectric detectors which been fitted built- in with a limited bandpass CO2 channel and a reference channel. The sensor additionally uses calcium fluoride (CaF2) limit bandpass (NBP) channel for recognition of CO2 gas without cross-sensitivity to different gasses exist in the surrounding environment. The correlation amongst test and computed sensor yields are additionally exhibited

A Bio-Inspired Music Genre Classification Framework using Modified AIS-Based Classifier

For decades now, scientific community are involved in various works to automate the human process of recognizing different types of music using different elements for example different instruments used. These efforts would imitate the human method of recognizing the music by considering every essential component of the songs from artist voice, melody of the music through to the type of instruments used. Various approaches or mechanisms are introduced and developed to automate the classification process since then. The results of these studies so far have been remarkable yet can still be improved. The aim of this research is to investigate Artificial Immune System (AIS) domain by focusing on the modified AIS-based classifier to solve this problem where the focuses are the censoring and monitoring modules. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed classifier and WEKA application is discussed. Almost 20 to 30 percent of classification accuracies are increased in this study

A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis

Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual’s handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual’s writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework 386 S.F. Pratama et al. and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally