TITLE IN ALL CAPITAL LETTERS

FULL NAME

BACHELOR OF CHEMICAL ENGINEERING (ENVIRONMENT) WITH HONOURS

UNIVERSITI TEKNOLOGI MARA

2017

TITLE IN ALL CAPITAL LETTERS

By

YOUR NAME IN ALL CAPITAL LETTERS

This report is submitted in partial fulfillment of the requirements

needed for the award of

Bachelor of Chemical Engineering (Environment) with Honours

FACULTY OF CHEMICAL ENGINEERING

UNIVERSITI TEKNOLOGI MARA

JAN 2017

AUTHOR’S DECLARATION

I declare that the work in the thesis was carried out in accordance with the regulation of Universiti Teknologi MARA. It is original and is the results of my own, unless otherwise indicated or acknowledge as reference work.

I, hereby acknowledge that I have been supplied with the Academic Rules and Regulations, Universiti Teknologi MARA, regulating the conduct of my study and research.

Signed : ………………………………

Date : ……………………………...

<<Name>>

Student ID : 20141xxxx

SUPERVISOR’S CERTIFICATION

We declared that we read this thesis and in our point of view this thesis is qualified in terms of scope and quality for the purpose of awarding the Bachelor of Chemical Engineering (Environment) with Honours.

Signed : ……………………

Date : ……………………

Main Supervisor

Dr. Nor Aida Zubir

Faculty of Chemical Engineering

Universiti Teknologi MARA

Cawangan Pulau Pinang

13500 Permatang Pauh

Pulau Pinang

Signed : ……………………

Date : ……………………

Co-Supervisor

Nur Fadzeelah Abu Kassim

Faculty of Chemical Engineering

Universiti Teknologi MARA

Cawangan Pulau Pinang

13500 Permatang Pauh

Pulau Pinang

COORDINATOR’S CERTIFICATION

Accepted :

Signed : ……………………

Date : ……………………

Coordinator

Noorzalila Muhammad Niza

Faculty of Chemical Engineering

Universiti Teknologi MARA

Cawangan Pulau Pinang

13500 Permatang Pauh

Pulau Pinang

ACKNOWLEDGEMENT

[Enter your acknowledgment here, single spacing]

TABLE OF CONTENTS

TITLE	PAGE

AUTHOR’S DECLARATION	i

SUPERVISOR’S CERTIFICATION	ii

COORDINATOR’S CERTIFICATION	iii

ACKNOWLEDGEMENT	iv

TABLE OF CONTENTS	v

LIST OF TABLES	ix

LIST OF FIGURES	x

LIST OF PLATES	xi

LIST OF SYMBOLS	xii

LIST OF ABBREVIATIONS	xiii

ABSTRACT	xiv

CHAPTER 1 INTRODUCTION	1

1.1	INTRODUCTION	1

1.2	AIR POLLUTION IN MALAYSIA	2

1.3	PROBLEM STATEMENT	2

1.4	OBJECTIVES	3

1.5	SCOPE OF RESEARCH	3

1.6	THESIS LAYOUT	4

CHAPTER 2 LITERATURE REVIEW	6

2.1	LITERATURE REVIEW	6

2.2	SOURCES OF PARTICULATE MATTER	7

2.2.1	Motor Vehicles	7

2.2.1 (a)	Heading 4 with (a)	8

2.2.2	Industry / Power Plants	9

2.2.3	Open Burning / Trans-Boundary	10

2.3	CHARACTERISTICS OF PARTICULATE MATTER	11

2.4	EFFECT OF PM10 ON HUMANS	13

2.5	WEATHER INFLUENCE	13

2.5.1	Wind Speed	13

2.5.2	Temperature and Sunlight	13

2.5.3	Relative Humidity	13

2.6	REGRESSION MODELS	13

2.6.1	Multiple Linear Regression	13

2.6.2	Robust Regression	13

2.6.3	Quantile Regression	13

2.7	HYBRID MODEL	13

2.7.1	Principal Component Analysis	13

2.8	CONCLUSION	14

CHAPTER 3 RESEARCH METHODOLOGY	15

3.1	INTRODUCTION	15

3.2	STUDY AREA	15

3.3	MONITORING RECORD ACQUISITIONS	15

3.4	PARAMETERS SELECTION	15

3.5	MONITORING RECORD SCREENING	15

3.6	DESCRIPTIVE STATISTICS	15

3.7	Box and Whisker Plot	15

3.8	One Way Analyses of Variance	15

3.9	MONITORING RECORD MANAGEMENT	16

3.10	REGRESSION MODELS	16

3.10.1	Multiple Linear Regression Models	16

3.10.2	Robust Regression Models	16

3.10.3	Quantile Regression Models	16

3.11	ARTIFICIAL NEURAL NETWORK MODELS	16

3.11.1	Feedforward Models	16

3.11.2	General Regression Neural Network	16

3.11.2 (a)	Test	16

3.12	PRINCIPAL COMPONENT ANALYSIS	16

CHAPTER 4 RESULTS AND DISCUSSION	18

4.1	INTRODUCTION	18

4.2	CHARACTERISTIC OF MONITORING RECORD	19

4.2.1	Descriptive Statistics	20

4.2.2	Box and Whisker Plot	20

4.2.3	One Way Analyses of Variance (ANOVA)	20

4.3	REGRESSION MODELS	20

4.3.1	Multiple Linear Regression Model	20

4.3.2	Robust Regression Models	20

4.3.3	Quantile Regression Models	20

4.4	ARTIFICIAL NEURAL NETWORK MODEL	20

4.4.1	Feedforward Backpropagation Models	20

4.4.2	General Regression Neural Network Models	20

4.5	APPLICATION OF HYBRID MODELS	21

4.5.1	Principal Component Analysis	21

4.5.2	Principal Component Analysis and Multiple Linear Regression	21

4.5.3	Principal Component Analysis and Robust Regression	21

4.6	VERIFICATION OF MODELS	21

4.7	DETERMINING THE MOST SUITABLE MODEL	21

4.8	DEVELOPING A NEW PREDICTIVE TOOL FOR FUTURE PM10	21

4.9	CONCENTRATIONS PREDICTION IN MALAYSIA	21

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS	22

5.1	CONCLUSION	22

5.2	LIMITATION AND FUTURE WORK	22

LIST OF REFERENCES	23

APPENDICES	24

LIST OF TABLES

PAGE

Table 1.1 : Type Caption	3

Table 1.2 : Type Caption	4

Table 1.3 : Type Caption	4

Table 1.4 : Type Caption	4

Table 2.1 : Type Caption	8

Table 2.2 : Type Caption	12

LIST OF FIGURES

PAGE

Figure 1.1 : Type Caption	4

Figure 1.2 : Type Caption	4

Figure 1.3 : Type Caption	5

Figure 2.1: Type Caption	7

Figure 2.2: Type Caption	10

Figure 2.3: Type Caption	11

LIST OF PLATES

PAGE

Plate 2.1: Use makro	7

Plate 2.2: Type caption	8

LIST OF SYMBOLS

Symbols

Sort symbols according to alphabetical order

LIST OF ABBREVIATIONS

Sort abbreviations according to alphabetical order

ABSTRACT

Lorem ipsum dolor sit amet, consectetaur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum Et harumd und lookum like Greek to me, dereud facilis est er expedit distinct. Nam liber te conscient to factor tum poen legum odioque civiuda. Et tam neque pecun modut est neque nonor et imper ned libidig met, consectetur adipiscing elit, sed ut labore et dolore magna aliquam makes one wonder who would ever read this stuff? Bis nostrud exercitation ullam mmodo

INTRODUCTION

INTRODUCTION

One of the biggest challenges in Software Engineering involves accurate estimation of software development effort and cost. Significant over and underestimates can be very expensive for the company and can destroy their reputation. Hence, estimation is a critical step in the development process. Accurate estimates must be provided as early as possible during the project’s life cycle to determine project feasibility and for effective resource allocation. A low effort estimate may put the software development team under excessive pressure that may compromise the quality of the software product. In contrast, if the effort estimate is too high, it may lead to wastage of the resources and over allocation of the personnel. Similarly, an accurate cost estimate must be provided as early as possible during the project’s life cycle. A very high cost estimate can lead customers to cancel the projects and cost underestimates may result in losses for the organization.

However, uncertainty that exists early in the project’s life cycle affects the accuracy of cost and effort estimates. The work style, project organization, ability, interest, experience, training and other employee characteristics affect these estimates.

AIR POLLUTION IN MALAYSIA

The above statistics clearly show that there is still a need for an improvement in the software development process. Effort and Cost estimation represent a critical step at the beginning of the development process. Therefore, there is still a motivation for research in this field to improve the estimation models and to obtain more accurate predictions for cost, effort and personnel.

PROBLEM STATEMENT

“Research on software effort and cost estimation models started more than 20 years ago”. However, general advice about which cost model fits to a certain organization could not be drawn. The mystery associated with software estimation is that it is essential to accurately estimate the effort and time required for a new software project but it is quite impossible to precisely predict the effort required in advance. Some of the major challenges faced during estimation process are listed:

“Estimates are often done in a hurry because they need to be presented as early as possible to customers”. Also, the estimation often has to be done without clear specifications and with ever changing requirements during the development process. In accordance with such changes, cost and time plans have to be adapted as well.

“There are difficulties in determining tailored cost factors, which have an influence on the predictions for a certain project in advance”

The rapid pace of change in Information Technology also poses a problem when trying to develop a reliable cost model. New programming languages, development tools and strategies are being introduced. Their influence on cost estimates has to be investigated as well.

OBJECTIVES

The project has the following objectives:

To analyze various effort and cost estimation models like SLIM, COCOMO, Function Points etc.

Analyze the functionalities of the WEKA Data Mining software tool.

Conduct Data Mining using WEKA and other tools to discover useful knowledge from a vast data set of 2,027 completed software projects provided by the International Software Benchmarking Standards Group (ISBSG). Apply Data Mining to make effort and cost estimates for the software development projects in the future.

Perform Data Mining using various classifiers like Linear Regression, Decision Trees, Neural Networks, Bayesian Networks etc and the Clustering techniques available with the WEKA tool. Interpretation and Evaluation of the results obtained with different classifiers to judge which specific classifier is the most suitable for effort and cost estimation of a particular software development project.

SCOPE OF RESEARCH

The project aims to explore the Data Mining techniques for effort and cost estimation in Software Engineering. Over the past years, organizations have generated large volumes of digital data in the form of files and databases. Data Mining refers to the discovery of new information in terms of patterns or rules from vast volumes of data. Data Mining may be defined.

Table 1.1 : Type Caption

Table 1.2 : Type Caption

Table 1.3 : Type Caption

Table 1.4 : Type Caption

THESIS LAYOUT

This dissertation report has been organized into 6 chapters, each of which consists of several sections and sub-sections. A broad overview of each of these 6 chapters is presented below:

Figure 1.1 : Type Caption

Figure 1.2 : Type Caption

Figure 1.3 : Type Caption

LITERATURE REVIEW

LITERATURE REVIEW

Algorithmic models attempt to represent the relationship between effort and one or more project characteristics. The main cost driver used in such a model is usually taken to be some notion of the size of the software, for example the number of lines of source code, so that a very simple example model might be of the form:

Effort = * Size (where is the productivity constant)

Often these models have one main input, normally a measure of product size and various secondary adjustment factors called the cost drivers.

Figure 2.1: Type Caption

SOURCES OF PARTICULATE MATTER

Motor Vehicles

The intermediate version of the COCOMO model allows the basic estimate to be adjusted by 15 cost drivers that are intended as refinements to the estimate to take account of local project characteristics. The cost drivers are spread across four categories (product, computer, personnel and project) and are assigned ratings (six ratings from Very Low to Extra High) depending on the extent to which they affect the project under consideration. The basic effort estimate is then multiplied by each of these factors to determine the adjusted estimate value.

Plate 2.1: Use makro

Plate 2.2: Type caption

Table 2.1 : Type Caption

Heading 4 with (a)

Video provides a powerful way to help you prove your point. When you click Online Video, you can paste in the embed code for the video you want to add. You can also type a keyword to search online for the video that best fits your document.

To make your document look professionally produced, Word provides header, footer, cover page, and text box designs that complement each other. For example, you can add a matching cover page, header, and sidebar. Click Insert and then choose the elements you want from the different galleries.

Themes and styles also help keep your document coordinated. When you click Design and choose a new Theme, the pictures, charts, and SmartArt graphics change to match your new theme. When you apply styles, your headings change to match the new theme.

Save time in Word with new buttons that show up where you need them. To change the way a picture fits in your document, click it and a button for layout options appears next to it. When you work on a table, click where you want to add a row or a column, and then click the plus sign.

Reading is easier, too, in the new Reading view. You can collapse parts of the document and focus on the text you want. If you need to stop reading before you reach the end, Word remembers where you left off - even on another device.

Industry / Power Plants

At stage 1 when the project is using prototypes for high risk issues including user interfaces, software and system interaction, COCOMO 2.0 estimates size in terms of Object Points. At stage 2, the designer considers whether to use different architectures and notions of operation. There is not sufficient information to support very precise effort and duration estimates. For stage 2, COCOMO 2.0 uses Function Point as a size measure. By stage 3, development has started and far more information is available. This stage matches the original COCOMO model, where sizing can be done with regards to lines of code and many factors can be estimated with a certain degree of comfort.

One major positive aspect is that COCOMO 2.0 allows size to be expressed in terms of the available data such as Object and Function Points. This removes some of the guess work involved in the estimation of the lines of code.

Figure 2.2: Type Caption

Open Burning / Trans-Boundary

They also assessed the stability of COCOMO for a test project of 14,000 lines of code and found that poor choice of development mode was more dangerous than a misestimate of size and further, a misestimate of just one of the 15 cost drivers was potentially as dangerous as either of the above. Criticism of the cost drivers is also made: a) There is evidence that some of the cost drivers are not independent, b) Some of the cost drivers may not be relevant in all environments and c) It is difficult to make sure that estimators evaluate the cost drivers in the way they were intended.

Another problem associated with COCOMO is the need for subjective estimates of size and productivity drivers. The risk here is that poor input estimates will lead to misleading effort projections although Boehm does recommend that the inputs be re-evaluated as more information becomes available.

Figure 2.3: Type Caption

CHARACTERISTICS OF PARTICULATE MATTER

The individual functions identified from the specification are weighted according to their complexity and the sum of the weighted function types becomes the Unadjusted Function Point count (UFP) for the system. Albrecht originally provided textual guidelines on how to rate the complexity of function types. Recently a more objective approach has been devised that relates complexity to the number of file, record and data element types referenced by the function type.

Table 2.2 : Type Caption

EFFECT OF PM10 ON HUMANS

WEATHER INFLUENCE

Wind Speed

Temperature and Sunlight

Relative Humidity

REGRESSION MODELS

Multiple Linear Regression

Robust Regression

Quantile Regression

HYBRID MODEL

Principal Component Analysis

CONCLUSION

RESEARCH METHODOLOGY

INTRODUCTION

STUDY AREA

MONITORING RECORD ACQUISITIONS

PARAMETERS SELECTION

MONITORING RECORD SCREENING

DESCRIPTIVE STATISTICS

Box and Whisker Plot

One Way Analyses of Variance

MONITORING RECORD MANAGEMENT

REGRESSION MODELS

Multiple Linear Regression Models

Robust Regression Models

Quantile Regression Models

ARTIFICIAL NEURAL NETWORK MODELS

Feedforward Models

General Regression Neural Network

Test

PRINCIPAL COMPONENT ANALYSIS

RESULTS AND DISCUSSION

INTRODUCTION

Video provides a powerful way to help you prove your point. When you click Online Video, you can paste in the embed code for the video you want to add. You can also type a keyword to search online for the video that best fits your document.

To make your document look professionally produced, Word provides header, footer, cover page, and text box designs that complement each other. For example, you can add a matching cover page, header, and sidebar. Click Insert and then choose the elements you want from the different galleries.

Themes and styles also help keep your document coordinated. When you click Design and choose a new Theme, the pictures, charts, and SmartArt graphics change to match your new theme. When you apply styles, your headings change to match the new theme.

Save time in Word with new buttons that show up where you need them. To change the way a picture fits in your document, click it and a button for layout options appears next to it. When you work on a table, click where you want to add a row or a column, and then click the plus sign.

Reading is easier, too, in the new Reading view. You can collapse parts of the document and focus on the text you want. If you need to stop reading before you reach the end, Word remembers where you left off - even on another device.

CHARACTERISTIC OF MONITORING RECORD

Video provides a powerful way to help you prove your point. When you click Online Video, you can paste in the embed code for the video you want to add. You can also type a keyword to search online for the video that best fits your document.

To make your document look professionally produced, Word provides header, footer, cover page, and text box designs that complement each other. For example, you can add a matching cover page, header, and sidebar. Click Insert and then choose the elements you want from the different galleries.

Themes and styles also help keep your document coordinated. When you click Design and choose a new Theme, the pictures, charts, and SmartArt graphics change to match your new theme. When you apply styles, your headings change to match the new theme.

Save time in Word with new buttons that show up where you need them. To change the way a picture fits in your document, click it and a button for layout options appears next to it. When you work on a table, click where you want to add a row or a column, and then click the plus sign.

Reading is easier, too, in the new Reading view. You can collapse parts of the document and focus on the text you want. If you need to stop reading before you reach the end, Word remembers where you left off - even on another device.

Descriptive Statistics

Box and Whisker Plot

One Way Analyses of Variance (ANOVA)

REGRESSION MODELS

Multiple Linear Regression Model

Robust Regression Models

Quantile Regression Models

ARTIFICIAL NEURAL NETWORK MODEL

Feedforward Backpropagation Models

General Regression Neural Network Models

APPLICATION OF HYBRID MODELS

Principal Component Analysis

Principal Component Analysis and Multiple Linear Regression

Principal Component Analysis and Robust Regression

VERIFICATION OF MODELS

DETERMINING THE MOST SUITABLE MODEL

DEVELOPING A NEW PREDICTIVE TOOL FOR FUTURE PM10

CONCENTRATIONS PREDICTION IN MALAYSIA

CONCLUSIONS AND RECOMMENDATIONS

CONCLUSION

LIMITATION AND FUTURE WORK

LIST OF REFERENCES

APPENDICES