2

Programme

AICS2014

DAY 1 – 15 SEPT 2014

We have shorten the duration of the conference to 1 day (15 September 2014) Indonesian Hour There is no presentation at day 2 (16 September, 2014). There will be a sight-seeing tour around Bandung City on day 2 (16 September ,

2014) - (Free for presenters & participants only). Time: 9.00am – 1.00pm Departure: Golden Flower Hotel Lobby

Lunch are provided on day 2 (16 September , 2014) - (Free for presenters & partic-ipants only).

Registration: 8.00-9.00am

Lunch: Restaurant Lobby Level

Musolla: Level 1

Seminar Room 1 - Tulip 1

Seminar Room 2 - Tulip 2

08.00 –

09.00

09.00-10.30

10.30-11.00

11.00-01.00

01.00pm-

02.00pm

2.30pm –

04.30pm

04.30pm

Registration

Presentation

Session 1

WCIK

Room 1

AICS

Room 2

Morning

Break Coffee/

Tea break

Presentation

Session 2

WCIK

Room 1

AICS

Room 2

Lunch &

Prayer

Presentation

Session 3

WCIK

Room 1

AICS

Room 2

Coffee/Tea

break

3

Reviewers

AICS2014

Professor Dr. Abhay Saxena, Dev Sanskriti Vishwavidyalya, Hardwar, Uttrakhand, INDIA

Assoc. Prof. Dr. Utpal Roy, Siksha-Bhavana, Visva-Bharati(Central University), Santiniketan, INDIA

Assoc. Prof. Dr. Mohd Lazim Abdullah, Universiti Malaysia Terengganu (UMT), MALAYSIA.

Dr. Jitender Grover, Assistant Professor, M.M. University, Sadopur, Ambala (Haryana), INDIA.

Dr. Ho Pei Yee, Universiti Malaya (UM), MALAYSIA.

Dr. Mokmin Basri, Kolej Universiti Islam Antarabangsa Selangor (KUIS), MALAYSIA.

Dr. Serkan Dincer, University of Cukurova, TURKEY.

Mr. Leon Andretti Abdillah, Senior Lecturer, Bina Darma University, INDONESIA

Mr. Khirulnizam Abd Rahman, International Islamic University College Selangor (KUIS), MALAYSIA.

Mr. Mohd Helmy Abd Wahab, Universiti Tun Hussein Onn Malaysia (UTHM), MALAYSIA.

Mr. Olawale Surajudeen Adebayo, International Islamic University Malaysia (UIAM), MALAYSIA.

4

Schedule of Oral Presentation

15 September 2014 (Monday)

Day 1

15 September 2014 (Monday)

Day 1

Session 1

(9.00am-10.30am)

Session 2

(11.00am-1.00pm)

A061 - COLLISION PREDICTION AND AVOIDANCE IN DYNAMIC CROWD SIMULATION Mrs. Iznora Aini Zolkifly, Dr. Abdullah Bade & Dr. Naz-reen Abdullasim

Keynote OPTIMAL CONFIGURATION FOR URBAN VANETS ROUT-

ING USING PARTICLE SWARM OPTIMIZATION

Assoc. Prof. Dr. Zuriati Ahmad Zukarnain

Faculty of Computer Science and Information Technology

University of Putra Malaysia, MALAYSIA

A016 - AUTOMATED SOIL PROFILE GENERATION

METHODS ON THE BASIS OF DMT AND CPT DATA

Dr. Michal Kruk, Dr. Jaroslaw Kurek, Dr. Piotr Bilski &

Dr. Simon Rabarijoely

A060 - AN EXTENSION OF PETRI NETWORK FOR MULTI-AGENT SYSTEM REPRESENTATION P. Sauvage, A. Courtin, P.A Bonneau, K. Chauffou & V. Claisse

A017 - THE SOIL PROFILE ASSESSMENT USING ARTI-

FICIAL INTELLIGENCE METHODS

Dr. Jaroslaw Kurek, Dr. Michal Kruk, Dr. Piotr Bilski,

Dr. Simon Rabarijoely & Dr. Bartosz Swiderski

A057 - HANDLING COVERAGE OF UNPATHABLE AREAS IN A SEARCH AND SECURE PROBLEM USING MULTIPLE RO-BOTS Mr. Saad Shaikh & Dr. Kunwar Faraz Ahmad Khan

A018 - CONCEPTUAL CLUSTERING FOR THE GEOTECHNICAL DATA ANALYSIS

Dr. Piotr Bilski & Dr. Simon Rabarijoely

A068 - FACTORS INFLUENCING USER INVOLVEMENT IN BUSINESS PROCESS MODELLING: A FRAMEWORK

Mrs. Nurzakina Zakaria & Dr. Maryati Mohd. Yusof

A052 - RETROFITTING A CONVENTIONAL LATHE TO A DIGITAL INTELLIGENCE SYSTEM S. B. Mohamed, M. Minhat, M. S. Kasim, M. J. Kadhim, M. A. Sulaiman and R. Besar

A048 - SOFTWARE DEVELOPMENT IN ESTIMATING PA-RAMETERS FOR LOGISTIC AND GOMPERTZ POPULATION GROWTH MODELS ARISING FROM A FISHERY PROBLEM Mr. Agung Pre Ramadhan, Karli Eka Setiawan, Poppy Am-riyati & Asep K. Supriatna

A033 - MODELLING AND APPLICATION OF LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

Miss Norhazlina Suhaimi & Adriana Ismail

A056 - MULTI-ROBOT COVERAGE PATH PLANNING FOR SEARCH AND SECURE PROBLEM IN A POLYGONAL ENVI-RONMENT Mr. Saad Shaikh & Dr. Kunwar Faraz Ahmad Khan

A063 - CRITICAL REVIEW OF SENTIMENT ANALYSIS TECHNIQUES

Mr. Asad Bukhari, Um-e-Ghazia & Dr. Usman Qamar

A004 - The Development of Malay-English Idiom Automated Translation

Mr. Khirulnizam Abd Rahman

5

Schedule of Oral Presentation

15 September 2014 (Monday)

Day 1

Session 3

(2.00PM-4.30PM)

A042 - PERFORMANCE ANALYSIS OF BACHELOR OF COMPUTER SCIENCE STUDENTS USING MULTIPLE

CLASSIFICATION MINING ALGORITHMS

Mr. Azwa Abdul Aziz, Nor Hafieza Ismail & Fadhilah

A062 - MALAYSIA HARD EVALUATION ANALYSIS: ACCURACY AGAINST HYBRID MEASURES FOR CLASSIFICATION

TRAINING

Dr. Mohammad Hossin & Dr. Md Nasir Sulaiman

A049 - PREDICTING PURCHASED POLICY FOR CUSTOMERS IN ALLSTATE PURCHASE PREDICTION CHALLENGE ON

KAGGLE

Mrs. Saba Arslan Shah & Dr. Mehreen Saeed

A031 - BIO-PASSWORD: A NEW USER AUTHENTICATION TECHNIQUE

Soumen Roy, Dr. Utpal Roy, D. D. Sinha

A036 - THE INCORPORATION OF ALGORITHM WITH NEWTON LAW OF COOLING AS A METHODOLOGY TO

ESTIMATE THE TIME OF DEATH OF CORPSE OR CADAVER

Muhammad Che Abdullah, Dr. Noor Maizura Mohamad Noor & Dr. Zuriana Abu Bakar

A026 - METHOD FOR AN KNOWLEDGE BASED KEYWORD GENERATION AND RETRIEVAL

Amru Yusrin Amruddin, Chew Yew Choong & Mary Michael

A071 - SOBI-RO FOR AUTOMATIC REMOVAL OF ELECTROOCULAR ARTIFACTS FROM EEG DATA-BASED MOTOR IMAGERY

Dr. Arjon Turnip & Fajar Budi Utomo

A044 - MACHINE LEARNING BASED TICKET CLASSIFICATION IN ISSUE TRACKING SYSTEMS

Mücahit ALTINTAŞ, A. Cüneyd TANTUĞ

A030 - KERANGKA PEMBANGUNAN SISTEM SOAL JAWAB FAQ INTERAKTIF BERBAHASA MELAYU MENGGUNAKAN PENDEKATAN HYBRID

Suziane Haslinda Suhaimi dan Prof. Dr. Juhana Salim

A019 - A MALAY STEMMER FOR OLD JAWI CHARACTER

Mr. Che Wan Shamsul Bahri, Prof. Dr. Khairuddin Omar, Dr. Mohammad Faidzul Nasrudin, Mohd Zamri Murah & Juhaida Abu Bakar

6

THEME AND TOPICS

International Conference on Artificial Intelligence and Computer Science 2014 (AICS 2014) “Artificial Intelligence and its Application for the Future” The topics of the conference are (not an exhaustive list):

Sub-themes

Natural language processing

Machine Translation

Machine Transliteration

Computer Science

Computer Intelligence

Algorithm and Programming

Software Engineering

Fuzzy logic and soft computing

Software tools for AI

Expert systems

Decision support systems

Automated problem solving

Knowledge discovery

Knowledge representation

Knowledge acquisition

Knowledge-intensive problem solving techniques

Knowledge networks and management

Intelligent information systems

Intelligent web-based business

Intelligent agents

Intelligent networks

Intelligent databases

Intelligent user interface

AI and evolutionary algorithms

Intelligent tutoring systems

Reasoning strategies

Distributed AI algorithms and techniques

Distributed AI systems and architectures

Neural networks and applications

Heuristic searching methods

Languages and programming techniques for AI

Constraint-based reasoning and constraint program-ming

Intelligent information fusion

Search and meta-heuristics

Swarm Optimization

Integration of AI with other technologies

Evaluation of AI tools

Social intelligence (markets and computational socie-ties)

Social impact of AI

Emerging technologies

Applications (including: computer vision, signal processing, pattern recognition, face recognition, finger print recognition, education, emerging ap-plications, …)

Machine Learning

General Machine Learning Theory

Statistical learning theory

Unsupervised and Supervised Learning

Hierarchical learning models

Relational learning models

Meta learning

Stochastic optimization

Simulated annealing

Heuristic optimization techniques

Neural networks

Reinforcement learning

Multi-criteria reinforcement learning

Multiple hypothesis testing

Decision making

Markov chain Monte Carlo (MCMC) methods

Graphical models

Gaussian graphical models

Cross-Entropy method

Ant colony optimization

Time series prediction

Fuzzy logic and learning

Inductive learning and applications

Grammatical inference

General Graph-based Machine Learning Tech-niques

Graph-based semi-supervised learning

Graph clustering

Graph learning based on graph transformations and grammars

Graph learning based on graph matching

Information-theoretical approaches to graphs

Motif search

Network inference

General issues in graph and tree mining

Computational Intelligence

Induction of document grammars

Supervised and unsupervised classification of web data

7

Keynote Speech for AICS2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

Background of the Keynote Speaker Zuriati Ahmad Zukarnain is an Associate Professor at the Faculty of Computer Science and Information Technology, University Putra Malaysia (UPM) Malaysia. She is the head for High Performance Computing Section at Institute for Mathematics and Research (INSPEM), University Putra Malaysia. She received her PhD from the University of Bradford, UK. Her research interests include: Efficient multiparty QKD protocol for classical network and cloud, load balancing in the wireless ad hoc network, quantum processor unit for quantum computer, Authentication Time of IEEE 802.15.4 with Multiple-key Protocol, Intra-domain Mobility Handling Scheme for Wireless Networks, Efficiency and Fairness for new AIMD Algorithms and a Kernel model to improve the computation speedup and workload performance. She has been actively involved as a member of the editorial board for some international peer-reviewed and cited journals. Dr. Zuriati is currently undertaking some national funded projects on QKD protocol for cloud environment as well as routing and load balancing in the wireless ad hoc network.

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“Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

Zuriati Ahmad Zukarnain, Nori M. Al-Kharasani, Shamala K. Subramaniam and Zurina Mohd. Hanapi Department of Communication Technology and Network, Faculty of Computer Science and Information Technology,

University of Putra Malaysia zuriati@upm.edu.my, nori_kh@yahoo.com

Abstract Optimization Link State Routing Protocol (OLSR) is initially designed for ad hoc networks (MANETs). This protocol collects information to build and maintain the route between the source and the destination with minimum cost by periodically exchange Hello and control messages. The aggregate function of communication cost for all nodes computed based on the required performance metrics. The standard configuration of OLSR is insufficient with vehicular ad hoc networks (VANETs) due to its limited Wi-Fi coverage and high mobility that cause rapid changes in its topology. The limitation of the ordinary OLSR could produce the greatest amount of the generated routing overhead which in turn leads to a high degradation in the overall VANETs performance. In this paper, Particle Swarm Optimization is used to find optimal parameters to tune OLSR by aggregating the response performance metrics generated in a realistic VANETs simulation. The simulation results show that OLSR with optimum parameters achieved better performance in urban environment as compared to the ordinary OLSR. Keywords: Optimization Link State Routing Protocol (OLSR), Particle swarm optimization (PSO), vehicular ad hoc networks (VANETs). 1. Introduction Vehicular Ad Hoc Networks (VANETs) is a part of MANETs uses wireless communication to link between themselves and other vehicles. VANETs have especial features that make it easier to differentiate it from various MANETs Al-Sultan, S., et [1]. Optimized Link State Routing (OLSR) protocol represents a novel operation scheme proposed for Mobile Ad Hoc Networks (MANETs) platforms. The features of this protocol are limited in hardware and energy consumption resources, Feng, C., Li, Z., et [2], which adapt packets in the OLSR protocol to travel from the source to the destination node with the best condition. In VANETs, providing QoS features in routing using OLSR protocol is a tedious task, especially in some scenarios like urban environments. Thus, to develop a reliable routing scheme for VANET applications, it is necessary to re-identify

8

Keynote Speech for AICS 2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

the configuration settings for OLSR in order to accommodate with characteristics and requirements of vehicle environment. In OLSR, performance parameters should be tuned to provide high priority to optimize QoS characteristics such as bandwidth capacity, controlled End-to-End delay, and Normalizing Routing Network (NRN) Alwan, H., & Agarwal, A. [3]. Artificial Intelligent (AI) techniques represent a wide range of algorithms as a solution for routing ad-hoc networks. These techniques apply and ensure a varied set of methods to solve different types of problems in the MANET and VANET networks Di Caro, G., et [4]. In OLSR, performance parameters should be tuned to provide high priority to optimize QoS characteristics such as bandwidth capacity, controlled End-to-End delay, and Normalizing Routing Network (NRN) Baras, J. S., & Mehta, H. [5]. Basically, MANET configurations are not suitable in VANETs due to the high mobility which may result in frequent disconnection in addition to the diverse environment for example urban area. These characteristics make a high challenge in developing suitable and efficient routing protocols for Urban VANET environments. The main objective of this paper focuses on tuning OLSR parameters Using Particle Swarm Optimization (PSO) algorithm in such a way to be suitable for VANET. Moreover, we discuss some VANET routing issues, and challenges in an urban environment. 2. Related works Toutouh, J., García-Nieto, et [6], defined an intelligent technic to find a suitable configuration in search space by using the PSO swarm algorithm and simulation procedure in order to obtain the optimum OLSR parameters, which efficiently suitable for VANET urban environments. Yangcheng and Huang Saleem, et in [7] studded in depth, diverse impacts by tuning OLSR parameters timer such as massages interval based on different scenarios. The simulation result has shown that, the value of HELLO intervals is more impact on OLSR routing performance than the value TC intervals. Another fundamental topic proposed by [José García-Nieto, Enrique Alba [8] which represents an optimal automatic tuning in for AODV protocol parameters to support VANET utilizing a metaheuristic method. Simulation results improve the performance of their proposal against other compared routing protocols in terms of efficiency. Gomez, C and Garcia, D in [9] proposed statically method to define and comprehensively study MANET performance after link failures. They analyze the impact of tuning OLSR parameters on QoS metrics such as delay and Packet Delivery Ratio (PDR). End-to-end connectivity can be greatly enhanced by using tuned parameters for OLSR than standard parameter settings. 3. Proposed Framework The automatic optimization of the OLSR parameters configuration is carried out through two phases, namely simulation phase and optimization procedure. Figure 1 depicts the main components of our proposed framework for Modified-OLSR (M-OLSR) routing protocol. Generally, the framework is consisting of optimization algorithm and the evaluation procedure. Particle swarm optimization (PSO) algorithm is used as an intelligent technique to find optimal solutions in an iterative manner. Then, a solution vector of real variables range is produced, and each one represents a new defined OLSR parameter and fitness function. The fitness function in PSO is defined to give a specific weight to the employed performance metrics like delay, Normalized Routing Load (NLR), and PDR. Therefore, based on Equation (1) that describes fitness and weight for OLSR-PSO in [9], in our proposed model we define a new formula for fitness in Equation (2) with weight modulated to improving the performance of Standard OSLR.

9

Keynote Speech for AICS 2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

Figure 1: Optimization framework for M-OLSR configuration Communication cost = 0.5 × NRL+ 0.3 × E2ED - 0.2 × PDR. (1) Cost function = 0.2 × PL + 0.3 × E2ED +0.2 × NRL – 0.3 × PDR / 4. (2) The simulation procedure, NS-2 simulations [10] over a number of defined VANET scenarios has been carried out based on a set of standard OLSR parameters and their restricted range of values defined in RFC 3626 [11] (see Table 1). In this way, the solution vector can automatically be fine-tuned by an optimization technique, with the aim of obtaining efficient OLSR parameter configurations for VANETs.

Table 1: OLSR RFC 3626 Configurations

4. Results and discussion The simulations were carried out by using ns2-2.34 and C++ as the language that we use to develop the proposed framework. We used the module UM-OLSR in [12] that we added to the simulator and evaluate our proposed framework based on it. The parameters description of the simulation model is shown in Table 2. The simulation time is set to 200 Sec with CBR traffic application in 240000 m2 area. The experiments involved 10 simulation scenarios. A number of 10 to 40 nodes are employed in each simulation for network topology.

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Keynote Speech for AICS 2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

Table 2: Simulation Setting

Figure 2 illustrates PDR versus the different number of nodes used in the standard OLSR RFC. OLSR-PSO and our M-OLSR in CBR traffic application. It is seen that M-OLSR outperformed the OLSR PSO in terms of packet delivery. However, standard OLSR performance is better when compared with the other routing protocols. On the other hand; M-OLSR steadily maintains its performance with OLSR RFC. With respect to other compared algorithms, M-OLSR managed to preserve a high rate of PDR thanks to the optimally adjusted parameters.

Figure 2: Packet Delivery Ratio, Data Rate 33Kbps

Figure 3 describes the average delay of OLSR RFC, OLSR-PSO and our M-OLSR over the offered load. In general, the performance evaluation shows that the average delay increases with the incremented number of nodes. OLSR PSO achieved the minimum average delay when compared with other configuration of OLSR protocols. However, the performance of M-OLSR is better when compared to the rest of the protocols.

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Keynote Speech for AICS 2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

Figure 3 Average End-to-End Delay (ms), Data Rate 33kbps In Figure 4, the amount of lost packets is presented over the increased number of nodes. It is obvious that M-OSLR tends to have the least amount of lost packets when compared to its peers especially when more nodes are introduced.

Figure 4: Packet Loss, Data Rate 33kbps

The evaluation of NRL for M-OLSR is shown Figure 5. We can see that M-OLSR maintains a steady minimum value of NLR, which means the network overheads due to network congestion is effectively reduced. Thereby, a lower routing load is achieved by higher deliver ratio.

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Keynote Speech for AICS 2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

Figure 5: Normalized Routing Load, Data Rate 33kbps Overall, we can say that by employing PSO in our M-OLSR framework, we managed to optimize tuning the parameter selection. The essence behind this tuning is to interrelate between the results of the aforementioned performance metrics that is controlled by the fitness function discussed above. 5. Conclusion Throughout this work we presented a novel optimization algorithm, named M-OLSR, to finely tune OLSR parameters using the PSO algorithm in such a way to be applicable for VANET application. The PSO allows to automatically determining the optimal parameters setting to tune OLSR in any realistic VANETs scenario. With the aim to tune OLSR parameters using PSO algorithm, we could achieve outperforming results for our M-OLSR in terms of maintaining a low LNR ratio, lost packets, and end-to-end delay while guaranteeing a high packet delivery when compared with the standard and another developed version of OLSR protocol. References [1] Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., & Zedan, H. (2014). A comprehensive survey on vehicular Ad Hoc network. Journal of network and computer applications, 37, 380-392. [2] Feng, C., Li, Z., Jiang, S., & Zhang, R. (2014). Data Aggregation and Routing Guidance with QoS Guarantee in VANETs. International Journal of Distributed Sensor Networks, 2014. [3] Alwan, H., & Agarwal, A. (2013, January). Multi-objective QoS routing for wireless sensor networks. In Computing, Networking and Communications (ICNC), 2013 International Conference on (pp. 1074-1079). IEEE. [4] Di Caro, G., Ducatelle, F., & Gambardella, L. M. (2005, June). Swarm intelligence for routing in mobile ad hoc networks. In Proceedings of the IEEE Swarm Intelligence Symposium (pp. 76-83). [5] Baras, J. S., & Mehta, H. (2003). A probabilistic emergent routing algorithm for mobile ad hoc networks. In

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Keynote Speech for AICS 2014 “Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization”

WiOpt'03: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. [6] Toutouh, J., García-Nieto, J., & Alba, E. (2012). Intelligent OLSR routing protocol optimization for VANETs. Vehicular Technology, IEEE Transactions on, 61(4), 1884-1894. [7] Huang, Y., Bhatti, S. N., & Parker, D. (2006, September). Tuning olsr. In Personal, Indoor and Mobile Radio Communications, 2006 IEEE 17th International Symposium on (pp. 1-5). IEEE. [8] García-Nieto, J., & Alba, E. (2010). Automatic parameter tuning with metaheuristics of the AODV routing protocol for vehicular ad-hoc networks. In Applications of Evolutionary Computation (pp. 21-30). Springer Berlin Heidelberg. [9] Gomez, C., Garcia, D., & Paradells, J. (2005, June). Improving performance of a real ad-hoc network by tuning OLSR parameters. In Computers and Communications, 2005. ISCC 2005. Proceedings. 10th IEEE Symposium on (pp. 16-21). IEEE. [10] Simulator, N. (1989). ns-2. [11] Clausen, T., & Jaqcquet, P. (2003). Optimized link state routing (OLSR) RFC 3626. IETF Networking Group (October 2003). [12] http:masimum.fif.um.es/?Software:UM-OLSR

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List of Abstract AICS2014 “Artificial Intelligence and its Application for the Future”

A004

THE DEVELOPMENT OF MALAY-ENGLISH IDIOM AUTOMATED TRANSLATION Khirulnizam Abd Rahman

Idioms are part of the multiword expression (MWE). However the meaning of idiomatic expression cannot be defined literally, as the meaning of each word are totally different than the whole phrase. That’s why it provides a different challenge in MWE identification. In this this paper we will briefly discuss several approaches of Idiom detection in English, Germany, Malay and several other languages which appear in several researches in Information Retrieval or Machine Translation. The main part of the discussion is the design and development of the Idiom automated translator. The main objective of the research is to develop a model for a compatible treatment of idioms in Malay-English machine translation. In order to accomplish that first the researcher have devised an algorithm to detect idioms in Malay contemporary text, using the combination of look ahead, look-up table, and hash table searching. The second phase is to devise an algorithm to translate the Malay idioms into English with the help of Google Translate API. Keywords: MVE, Idiom, figurative phrase, Idiom automated detection A016

AUTOMATED SOIL PROFILE GENERATION METHODS ON THE BASIS OF DMT AND CPT DATA Dr. Michal Kruk, Dr. Jaroslaw Kurek, Dr. Piotr Bilski and Dr. Simon Rabarijoely

The paper presents automated methods of the soil profile generation on the basis of DMT and CPT data gathered from the Warsaw University of Life Sciences campus. Knowledge about the structure of the soil is important not only for the researchers, but also for engineers planning foundation o f the new buildings. To properly design the building structure, the detailed information about the type and parameters of the soil must be determined. The traditional approach to this task was drilling boreholes in the ground to obtain soil samples. They could be then analyzed in the laboratory, so the information about the soil types in the test site was known with high precision. Unfortunately, the cost of drilling boreholes is high, therefore numerous attempts to obtain faster and cheaper methods are proposed.Application of geotechnical probes is more convenient, faster and cheaper than boreholes, therefore they supplement the boreholes during the in situ investigations. Based on the measured physical parameters and the diagram (called nomogram) connecting their values with the soil type, the profile can be generated by the human expert. Unfortunately, knowledge stored in nomograms is applicable only to specific geographical location. Using the diagram for soils with different geological history is prone to inaccuracies and identification errors. Therefore data analysis methods are implemented. The main task of this work is to find the automated method to determine the structure of the soil. This method must be equal or even better than human expert to be accepted. In the work we proposed two solutions of this task and compared them with the human experts and with the soil samples obtained from the boreholes. The first one is based on the gradient analysis. It is composed of the Gaussian, average and median filtering and gradient or Laplacian zero-crossing search. The main problem in this method is smoothing the data and removing outliers (measurements significantly different than the neighbouring ones) – if we use bad parameters to filtering, we obtain too little or too many soil layers. The second method is based on the cluster analysis. The main problem in such methods is to find automatically the number of clusters. To do this we compared existing methods with our solution which is based on the gradient analysis. The experiments had two aims. The first one was confrontation the soil profiles generated by the algorithms with the ones generated in the laboratory from the boreholes. In the optimal situation both profiles should be identical. The second one was to confirm the accuracy of the profile by the geotechnical expert. Keywords: soil profiles, soil categorization, soft computing methods, clustering, dilatometer test

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A017

AUTOMATIC ESTIMATION OF THE NUMBER OF SOIL PROFILE LAYERS USING BAYESIAN INFORMATION CRITERION

Dr. Jarosław Kurek, Dr. Michał Kruk, Dr. Piotr Bilski , Dr. Simon Rabarijoely and Bartosz Świderski

In this study, Bayesian Information Criterion algorithm is utilized for the estimation of number of soil profile layers. In order to collect data, several probes are performed by geotechnical specialists in Warsaw University of Life Sciences (WUoLS) campus. Then soil profiles have been manually generated by geotechnical experts. It lets us to compare the results of novel automated method presented in this paper to real soil profile manually generated by geotechnical engineers. The database has been generated based on values derive from a probe CPT applied by geotechnical experts. Examination and accuracy calculation of the proposed method is presented and compared to reference real soil profile obtained by experts group. Keywords: geotechnical probes, soil categorization, BIC, Bayesian Information Criterion, clustering

A018

CONCEPTUAL CLUSTERING FOR THE GEOTECHNICAL DATA ANALYSIS Dr. Piotr Bilski and Dr. Simon Rabarijoely

The paper presents the application of the conceptual clustering algorithm for the geotechnical data analysis. First, the problem of the geotechnical layers identification for the construction engineering purposes is introduced. Then, the idea of the computer-based data analysis and its advantage over the traditional methods is explained. The pro-posed algorithm of conceptual clustering, adjusted for the continuous data processing is presented in detail. Its gen-eral steps, used data structures and the tuning procedure of the approach parameters to obtain the optimal results are explained. After presenting the investigation site and the available data sets (obtained from the geotechnical probes used in the in situ tests) for experiments, the results are discussed and conclusions drawn. The comparison between the reference profiles and the ones obtained thanks to clustering are provided, showing advantages of the proposed approach. The possible applications of the method to create automated soil profiles and calculate geotech-nical indexes are provided as well. Keywords: unsupervised learning, geotechnical investigation, soil profiles A019

A MALAY STEMMER FOR OLD JAWI CHARACTER Che Wan Shamsul Bahri, Khairuddin Omar, Mohammad Faidzul Nasrudin, Mohd Zamri Murah & Juhaida

Abu Bakar

Stemming are very useful in the field of documents and information retrieval. It is also able to reduce the size of dic-tionary. In Malay, there are two types of characters used, either Roman characters (Roman) or Jawi. Most Malay stemmers only covers Rumi derivatives and several modern Jawi. This paper proposes stemmer for Jawi characters using a set of rules in the old Jawi (A set of rules used to stem various forms of old Jawi derivative words). There are 2500 Jawi derivative words consisting of prefix, circumfix, suffix, and infix tested using this stemmer. Experimental results showed that 86.28% of the successful Jawi words stemmed properly. Keywords: Jawi, Stemmer, Stemmer Melayu

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A026

A SYSTEM AND METHOD FOR AN KNOWLEDGE BASED KEYWORD GENERATION AND RETRIEVAL Amru Yusrin Amruddin, Chew Yew Choong and Mary Michael

The World Wide Web (WWW) is a big reservoir of information. Current internet search technology is based on search keywords which return results purely in single typical page based on matching keywords. This makes discover-ing to desire information is a tedious and time-consuming task. This paper explains a method for generation and re-trieval of knowledge based keyword. Methods include accepting a search term from the user and detect the lan-guage. The system then search the WWW using available search engines (Google, Bing and Yahoo), getting the relat-ed keywords from knowledge base (lexical, ontology and wiki services) and comparing the WWW search results with the related keywords. Finally, the system assigning the frequency or relevancy of the keywords and then the system will generate and displaying the search results. These search results then will be displayed into user friendly mashup search engine results page (SERP) user interface via relevant categories like web, images, videos, books, Wikipedia, etc.

Keywords: Search Engine, Mashup, Methodology, World Wide Web, Uniform resource locator

A030

KERANGKA PEMBANGUNAN SISTEM SOAL JAWAB FAQ INTERAKTIF BERBAHASA MELAYU MENGGUNAKAN PENDEKATAN HYBRID

Suziane Haslinda Suhaimi dan Prof. Juhana Salim

Khidmat meja bantuan merupakan perantara organisasi dalam menguruskan pertanyaan dan masalah pengguna. Isu utama kajian adalah untuk mengenal pasti permasalahan yang timbul berkait dengan khidmat meja bantuan sedia ada dan bagaimana laman FAQ berbentuk dinamik berpotensi menjadi alternatif dalam menyelesaikan permasala-han sedia ada. Hasil tinjauan awal dan kajian kes yang dijalankan ke atas Program E-Pembelajaran Sektor Awam, IN-TAN Bukit Kiara menunjukkan, antara masalah yang wujud adalah organisasi kekurangan staf, talian telefon sentiasa sibuk, maklumbalas emel yang lewat dan paparan maklumat pada laman FAQ sangat terhad dan berbentuk statik. Kajian ini mencadangkan kerangka pembangunan sistem soal jawab FAQ interaktif berbahasa Melayu menggunakan pendekatan hybrid yang menggabungkan teknik capaian FAQ statistikal dan teknik berasaskan templat. Kefungsian perkataan sinonim dan kategori soalan turut diaplikasikan bagi memaksimakan ketepatan capaian FAQ. Kerangka pembangunan yang direka bentuk terbahagi kepada tiga bahagian iaitu pemprosesan kueri, capaian jawapan FAQ, dan pengurusan FAQ. Kebolehgunaan sistem diuji dan dinilai dari segi kecekapan, tahap kepuasan pengguna, dan keberkesanan capaian maklumat. Dua kaedah penilaian digunakan iaitu soal selidik pengguna ke atas 30 responden yang dipilih secara rawak di kalangan pengguna Sistem EPSA dan juga temubual ke atas 3 pakar domain menggunakan teknik ‘think aloud’. Analisis data kuantitatif menunjukkan peserta berpuas hati dengan prestasi sis-tem kerana pengguna dapat mencapai jawapan FAQ yang relevan dengan lebih cepat, mudah, dan efisien berband-ing khidmat meja bantuan sedia ada. Penilaian oleh pakar domain juga mengesahkan bahawa kerangka yang dibangunkan memenuhi keperluan dalam membantu pengguna membuat capaian jawapan FAQ yang tepat, jelas, dan relevan dengan soalan yang diajukan pengguna.

Kata kunci: Meja bantuan, Sistem soal jawab, FAQ interaktif, Capaian FAQ, Pemprosesan kueri

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A031

BIO-PASSWORD: A NEW USER AUTHENTICATION TECHNIQUE Soumen Roy, Dr. Utpal Roy and D. D. Sinha

Keystroke Dynamics is a technology to segregate and distinguish people based on their typing rhythms. Here users are not only identified by their corresponding userID and password, but their typing style is also accounted for. Our proposed system uses a password comprising of characters as well as takes into account the typing style of the depressed characters entered. It enhances the security level and can be used to identify an individual too. Recognizing typing style promises a parameter like biometric characteristics that may facilitate non-intrusive, cost-effective and continuous monitoring. But this technique, as is now, suffers from accuracy level, performance and consumes much memory space. Thus, in order to realize this technique in practice a higher level of security and performance together with low cost, less memory consuming version is demanded with an error to an accepted level as may be designed. Hence, it is highly needed to identify the controlling parameters and optimize the accuracy, performance, memory and cost with new algorithms. Among various user authentication techniques knowledge-based, token-based and biometric-based authentication techniques are popular. But each of these techniques has their own merits and demerits. Further it has been established that none of these technique is self sufficient for the security purpose. In this article we have proposed a hybrid approach. We have attempted to hybridize two or more authentication techniques with a view to provide a stronger security to the system. Here, we have fused the password of Knowledge-based user authentication technique with the Keystroke Dynamics, a strong behavioral biometric characteristic. Here fused-password has been considered as a key issue of a security system. In this paper we also suggest some future plans that can be effectively implemented by this technique. Keywords: Keystroke Dynamics, Behavioral biometric, Computer Security, Bio-password, Major Voting Method,

Growing Window, Moving Window, SVM, K-NN, Neural Networks

A033

MODELLING AND APPLICATION OF LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS Norhazlina Suhaimi, Adriana Ismail and Nurul Adyani Ghazali

Data mining involves exploration of large dataset to find valuable information that can aid decision making. The uses of data mining approach to build predictive model for predicting blood glucose level of T2DM patient after receiving treatment in ward. There are two data mining predictive models; logistic regression and artificial neural networks (ANNs). The purpose of this study work was to compare the performance of logistic regression and ANNs models for identifying risk factors that contributing to blood glucose level on T2DM patients. The scope of this study only in-volves one public hospital in Kelantan, Malaysia. 229 patients with T2DM who had received treatment in ward be-tween 2008 and 2012 with ten input variables were selected. The classification accuracy, sensitivity and specificity have been measured to evaluate the performance for both models. For overall dataset, the logistic regression mod-els achieved classification accuracy of 69.9% with a sensitivity of 50% and a specificity of 70.4%. The ANNs model reached classification accuracy 77.3% with sensitivity of 78.4% and specificity of 71.8%. Meanwhile, after partitioning dataset, logistic regression achieved classification accuracy of 71.3% with a sensitivity of 58.3% and specificity of 73.5%; and the ANNs reached classification accuracy of 72.5% with sensitivity of 72.3% and specificity of 75.3%. Hence, the ANNs model for the overall dataset had the highest classification accuracy compared to logistic regres-sion model. Five important independent variables were identified on blood glucose level including diastolic blood pressure, platelet, white blood cell, low density lipoprotein and total cholesterol in ANNs analysis. This study would be able to contribute to the improvement of strategies and planning in hospital in Malaysia. Keywords: T2DM, Logistic Regression, Artificial Neural Networks, Accuracy, Risk Factors

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A036

THE INCORPORATION OF ALGORITHM WITH NEWTON LAW OF COOLING AS A METHODOLOGY TO ESTIMATE THE TIME OF DEATH OF CORPSE OR CADAVER

Muhammmad Che Abdullah, Dr. Noor Maizura Mohamad Dr. Noor and Zuriana Abu Bakar Forensic science had been a large spectrum of science and technology for investigating or establishing facts in crime scene or civil law. It had been used widely to analyze and collecting evidences and help in recognizing process for any cases especially in crime cases. Its goal is to determine the fact and subsequently identify the truth. Beneath of all that, forensic science only work for truth by making sure the examination is complete, the test are perform correctly, the data is interpret deeply and the report is written correctly and easily understood by non-scientist. In fact, in fo-rensic science one of the important things before undergo a deep investigation is to determine the time of death. Time of death is the time elapse since a person died. From the time of death, the time of dead person died can be obtain as well as suggest or get rid of suspects and strengthen or confute an alibi. The time of death determination introduced had been expanded and used in solving many cases in forensic investigation. Unfortunately, all methods now in use to determine the time of death are to a degree which is unreliable and not precise. They usually give vague or dubious answers. A method called Henssge nomogram formula which based on Newton Law of Cooling had been introduced as the most versatile and gives accurate result. Therefore, a fully computerized algorithm will be made based on that method. The algorithm will also be support with decision making for analysis and comparison with other factor that also significant and contribute in estimating the time of death. Thus, any conflict that occurs within the result can be solved and concluded with a better result by providing a report along with analysis that had been made from the data provided through the investigation. Keywords: algorithm, decision making, time of death A042

FIRST SEMESTER COMPUTER SCIENCE STUDENTS’ ACADEMIC PERFORMANCES ANALYSIS BY USING DATA MINING CLASSIFICATION ALGORITHMS

Azwa Abdul Aziz, Nor Hafieza Ismail and Fadhilah Ahmad The research on educational field that involves Data Mining techniques is rapidly increasing. Applying Data Mining techniques in an educational environment are known as Educational Data Mining that aims to discover hidden knowledge and patterns about students’ behaviour. This research aims to develop Students’ Academic Performance prediction models for the first semester Bachelor of Computer Science from Universiti Sultan Zainal Abidin (UniSZA) by using three selected classification methods; Naïve Bayes, Rule Based, and Decision Tree. The comparative analysis is also conducted to discover the best classification model for prediction. From the experiment, the models develop using Rule Based and Decision Tree algorithm shows the best result compared to the model develop from the Naïve Bayes algorithm. Five independent parameters (gender, race, hometown, family income, university entry mode) have been selected to conduct this study. These parameters are chosen based on prior research studies including from social sciences domains. The result discovers the race is a most influence parameter to the students’ perfor-mance followed by family income, gender, university entry mode, and hometown location parameters. The predic-tion model can be used to classify the students so the lecturer can take an early action to improve students’ perfor-mance. Keywords: Educational Data Mining, Classification, Students’ Academic Performance, Naives-Bayes

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A044

MACHINE LEARNING BASED TICKET CLASSIFICATION IN ISSUE TRACKING SYSTEMS Mücahit ALTINTAŞ, A. Cüneyd TANTUĞ

maltintas@itu.edu.tr, tantug@itu.edu.tr Due to the rise of usage of virtual systems, support ticket systems have come into prominence. Addressing the issue tickets to appropriate person or unit in the support team has critical importance in order to provide improved end user satisfaction while ensuring better allotment of support recourses. The assignment of help ticket to appropriate group is still manually performed. Especially at large organizations, the manual assignment is not applicable suffi-ciently. It is time consuming and requires human efforts. There may be mistakes due to human errors. Also resource consumption is carried out ineffectively because of the misaddressing. On the other hand, manual assignment in-creases the response time which result in end user satisfaction deterioration. Multiple-choice systems which provide the user to choose the related categories or unit within defined categories may seem like better, but the systems are not useful because of those users, especially new users which have never used the system before, usually have no idea about the related category or department. Also users do not want to fill long ticket forms which are needed to identify the issue. In this study, an extension to ITS for auto-addressing the issue ticket to the relevant person or unit in support team is proposed.. In this system, bag of word approach, machine learning techniques and other algo-rithms which proven performance in text processing are used. The recommended method provides high quality user support and boosts end-user satisfaction. It reduces manual efforts and human errors while ensuring high service levels and improved end-user satisfaction. Keywords: Issue Tracking System, Automatic Assignment, Ticket Classification A048

SOFTWARE DEVELOPMENT IN ESTIMATING PARAMETERS FOR LOGISTIC AND GOMPERTZ POPULATION GROWTH MODELS ARISING FROM A FISHERY PROBLEM

Agung Pre Ramadhan, Karli Eka Setiawan, Poppy Amriyati and Asep K. Supriatna Maximum Sustainable Yield (MSY) is an important concept in fisheries management. It represents the maximum amount of biomass that are allowed to be taken as a harvest without harming the sustainability of the fisheries. The value of the MSY is a function of the growth parameters of the harvested population. Since the MSY is not stable, knowing the right growth parameters of the population is critical in determining the MSY. Using an inaccurate values of growth parameters is likely detrimental in applying the MSY to the real fisheries. Hence, to reduce the error in calculating these growth parameters we need a good tool or software. In this paper we present a software as a Deci-sion Support System (DSS) to estimate the values of the growth parameters from the yield effort data of a harvested population and to suggest the value of the maximum sustainable yield as a decision to the harvester in managing the population. The software is developed using C# programming language in windows form application. In order to check validity of some computational outputs, we use several software that have a capability to compute a multiple linear regression using Ordinary Least Square (OLS) method. As has been mentioned before, the statistical method used in the software is multiple linear regression. We use two different mathematical models in the software, name-ly Logistic and Gompertz growth models. We discretized the models in terms of yield effort variables and used the resulting equations as the bases for computing the intrinsic growth rate r and the carrying capacity K parameter. Those two models are also used as two selection tools placed in the main menu of the software. To test the software we use a well-known data as an example to estimate the growth parameters. The results are in agreement with the known literatures. Keywords: MSY, Biomass, OLS, Logistic, Gompertz

20

A049

PREDICTING PURCHASED POLICY FOR CUSTOMERS IN ALLSTATE PURCHASE PREDICTION CHALLENGE ON KAGGLE

Saba Arslan Shah and Mehreen Saeed This paper gives an overview of the methodology developed for predicting the purchased policy for a customer in Allstate purchase prediction challenge held by Kaggle (Kaggle). It gives an account of challenges faced during the pro-cess and strategies used to predict the policy choices for customers. The techniques used include logistic regression, naïve Bayes (Mitchell, 1997), SVM (Lin., 2011) , random forest (Breiman, 2001), probability calculation for each policy and its change and a voting mechanism. Effect of previously presented policies is also measured. The dataset pre-sented a challenge since it included feature set in both rows and columns for each of the customer. Furthermore, seven policy options were to be correct as a combination, for a prediction to be deemed accurate. Relationships are also explored between different policy options. Keywords: Purchased policy prediction, Allstate purchase prediction challenge, SVM, Data mining, Random forest A052

RETROFITTING A CONVENTIONAL LATHE TO A DIGITAL INTELLIGENCE SYSTEM M. Minhat, S. B. Mohamed, M. S. Kasim, M. J. Kadhim, M. A. Sulaiman and R. Besar

The development of modern technologies in computer software, hardware, and firmware as well as the integration of these technologies in the industrial lines have proved better and more efficient productions can be machined. In this project, an attempt has been made to equip the conventional lathe machine with Digital Intelligence system (DIS) in order to get more accurate dimensions and precise shape of high end products. The retrofitting process of the conventional lathe machines into semi automatic control lathe machine, demands two key elements, namely, mechanical and electronics parts. In the mechanical part, a design is made to feed the stepper motor to the lead screw. On the other hand, in the electronics part, an electronic circuit containing the motor driver circuit is designed in order to control the motor movement. Keywords: Lathe CNC Machine, Retrofitting, Intelligence System A056

MULTI-ROBOT COVERAGE PATH PLANNING FOR SEARCH AND SECURE PROBLEM IN A POLYGONAL ENVIRONMENT

Saad Shaikh and Dr. Kunwar Faraz Ahmad Khan

In this paper, we study the visibility-based search and secure problem, in which multiple robots move through a simply connected and known polygonal environment such that they guarantee the detection of all contaminants or intruders within, that can move arbitrarily fast. Our objective is to investigate the usefulness of the GRAPH-CLEAR methodology for analytical modeling of this problem and develop a strategy for an uncoordinated search to accom-plish this task, targeting application domains such as securing buildings for surveillance and security purposes. The aim is neither to take care of the actual control system of the robots, nor to handle the actual detection of an intrud-er (through video or image processing); rather introduce an effective coverage path planning strategy to identify the paths the robots are required to follow. To this end, we present an algorithm to compute the strategy required, us-ing the concept of searcher and blocker robots/agents. The algorithm models the given environment in the form of a connectivity graph and then converts it into a tree, calculating the number of robots and the trajectories these ro-bots have to follow in order to secure the complete environment. We show how the strategy devised by this algo-rithm is more time and cost efficient by performing simulations and comparing the results against methods already being used, giving examples of different computed trajectories. For a particular case study, the results of the algo-rithm show an approximately 1/4th increase in time efficiency along with a 1/3rd reduction in the robot resources required. Keywords: search and secure, graph-clear, coverage path planning, searchers and blockers

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A057

HANDLING COVERAGE OF UNPATHABLE AREAS IN A SEARCH AND SECURE PROBLEM USING MULTIPLE ROBOTS

Saad Shaikh and Dr. Kunwar Faraz Ahmad Khan

In this paper, we present a novel approach for the visibility-based search and secure problem, in which multiple ro-bots or agents move through a known polygonal environment such that the environment, besides obstacles, can also contain unpathable areas; i.e. areas which are physically inaccessible to the searching robots but are required to be searched and secured since they are accessible to intruders e.g. lawns etc. Our objective is to identify a search strate-gy that can handle such unpathable areas and secure the complete environment using the concept of searcher and blocker robots/agents. We model the whole environment and all unpathable areas individually, forming a connectivi-ty graph for each area keeping any obstacles inside the areas in consideration. To break the cycles/loops formed within the unpathable areas as well as in the overall environment, all the connectivity graphs are analyzed and the cycle(s) within an unpathable area is broken by placing a static blocker robot on the corner(s) of that respective un-pathable area. A search tree is finalized and the algorithm calculates the number of robots required and the trajecto-ries these robots have to follow in order to totally search the remaining regions in the tree and secure the unpatha-bles as well as the rest of the environment. We show results of the algorithm by performing a simulation on an actu-al map of a targeted environment, depicting the critical paths, positions and trajectories finalized for search of such unpathable areas. Keywords: search and secure, unpathable areas, coverage path planning, searchers and blockers A060

AN EXTENSION OF PETRI NETWORK FOR MULTI-AGENT SYSTEM REPRESENTATION P. Sauvage, A. Courtin, P.A Bonneau, K. Chauffour and V. Claisse

With emergence of multi-agent systems, there is a need to invent new models to represent them. Petri nets are good candidates for representation of interaction and change of state. However, their limitations do not allow them to be relevant for modeling in software engineering development. Here, a new mathematical model incorporating the same graphical conventions and providing backward compatibility with the canonical model has been formalized. A practical application of the model is proposed to present the various features detailed in the analysis. Keywords: Software Engineering, Multi-Agent System, Distributed Systems, Modeling A061

COLLISION PREDICTION AND AVOIDANCE IN DYNAMIC CROWD SIMULATION Iznora Aini Zolkifly, Abdullah Bade and Nazreen Abdullasim

Collision prediction is important to predict the collision between moving object A and objects belong to a complex dynamic environment. It is the forecasting of when and where the intersection of two or more agents will happen. Difficulties to obtain motion prediction, abrupt obstacles and dynamic environments give a challenge for collision prediction. Abrupt obstacles may occur anytime and dynamic changes of the obstacles cause a problem to obtain a prediction of target’s motion. Lack of interactions among agents cause them to interact only in condition where close proximity is detected. Collision prediction is important to develop solutions for real-time path finding to be able to react and readjust to any abrupt obstacles. It is another option to solve interactions handling problem be-tween virtual agents. It is necessary to predict agents’ future position to avoid collision between multiple agents. The prediction methods consider the objects or agent’s behaviors including position, velocity, goal and perception in the dynamic crowd simulation. This paper will discuss the obstacles, predictive collision avoidance algorithms and decision making algorithms in response to avoid the obstacles in dynamic crowd simulation. Keywords: Collision predictions, obstacles, avoidance algorithms, decision making algorithms "

22

A062

HARD EVALUATION ANALYSIS: ACCURACY AGAINST HYBRID MEASURES FOR CLASSIFICATION TRAINING Dr. Mohammad Hossin and Dr. Md Nasir Sulaiman

As reported in literature, the use of accuracy could lead the discrimination process of optimal solution during the classification training under-performing due to its less distinctive and less discriminable value. Furthermore, the ac-curacy also could not perform optimally when confronted with imbalanced class problem. In contrast, the OARP and OAERP measure have shown better classification results as compared to accuracy through several comparisons and analyses. Thus, the interest of this study is to further analyze and observe under which condition one of these measures perform better than the others using hard evaluation analysis. For the evaluation purposes, this paper in-troduces a modified version of hard evaluation analysis. The different between the proposed evaluation method with the existing evaluation methods is, the proposed evaluation method employs a formal guideline through three performance characteristics and systematic procedure when comparing and analyzing the studied measures. In this study, three performance characteristics are value distinctiveness and discriminability, informativeness, and favors towards minority class. From the hard evaluation analysis, this study shows that both OARP and OAERP have distinc-tive and discriminable produced-value as compared to accuracy. However, the OAERP has shown that its produced-value much better than the accuracy and OARP in terms of informativeness, and favors towards minority class. Intui-tively, the OAERP measure is a good discriminator and offer more advantages than accuracy and OARP measures in discriminating the optimal solution for balanced and imbalanced two-class classification problems. Keywords: accuracy, hybrid measure, data classification evaluation, hard evaluation analysis A063

CRITICAL REVIEW OF SENTIMENT ANALYSIS TECHNIQUES Asad Bukhari, Um-e-Ghazia and Dr. Usman Qamar

The innovation of Web 2.0 is increasing day by day and its latest hype is Microblogging. Short messages exchange platforms connect people worldwide in an unprecedented manner by publishing short text updates regarding vari-ous topics. People freely express their views and opinions on microblogging sites that give rise to multiple user senti-ments. In order to precisely determine the mood and nature of user it is essential to analyze the sentiments based on contextual information. Further, sentiment analysis is required for different markets to consider their consumer views and critics to improve their products. We have analyzed different sentiment analysis techniques and algo-rithms in order to highlight their pros and cons. Along with their comparison on basis of Precision, Recall, F-measure, and Accuracy. This study helps to identify the major difficulties in the area of sentiment analysis that are still not re-solved and being considered by the researchers. Keywords: Natural Language Processing, Sentiment Analysis, Critical Review, Microblog

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A068

FACTORS INFLUENCING USER INVOLVEMENT IN BUSINESS PROCESS MODELLING: A FRAMEWORK Nurzakina Zakaria and Dr. Maryati Mohd. Yusof

The process models are one of the most common approaches employed during the early phases of Information Sys-tem Development (ISD). Process Modelling is an approach that visually describes how a business operates by defin-ing the business processes, entities, activities, business opportunities and threats. Most ISD projects fail due to fac-tors which include user and stakeholder involvement issues. Although various perspectives and criteria have been employed by researchers to study the factors that influence user involvement during ISD, reported evidence on in-volvement factors for Business Process Modelling is still limited and requires further study. The current framework is lacking in factors that are related to the process modelling domain. The objective of this paper is to discuss factors affecting user involvement in business process modelling mainly in ISD projects. We proposed a framework for user involvement in business process modelling based on previous models. The framework combines five elements; user, project, process, modeller and interaction. A case study was conducted to validate the proposed framework. The findings show that the framework is applicable in establishing user involvement before and during initial business process modelling implementation to ensure its success. The identified influencing factors includes user intention towards participation, attitude, interest, commit-ment, experience, perceived ease of user participation, project importance, benefits, outcome, visibility, complexity and recognition, process efficiency, process learning and innovation, process clarity, modeller satisfaction and exper-tise, interaction quality and nature. This framework offers guidelines for user involvement prior to the implementa-tion of Business Process Modelling in the future. Keywords: Business process management, Business process modeling, User involvement/ Involvement, Stakeholders A071

SOBI-RO FOR AUTOMATIC REMOVAL OF ELECTROOCULAR ARTIFACTS FROM EEG DATA-BASED MOTOR IMAGERY

Dr. Arjon Turnip and Fajar Budi Utomo

Signals from eye movements and blinks can be orders of magnitude larger than brain-generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG datain amotor im-agery experiment. BBS is a signal-processing methodology that includes independent component analysis (ICA)using second order blind identification with robust orthogonalization (SOBI-RO) is proposed.Simulation results shows that the ocular artifacts are significantly removed and the sources of the brain activity are clearly identified. The identifi-cation performance using signal to distortion ratio value about 68.88% is achieved. Keywords—EEG signal, Ocular Artifact, SOBI-RO

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Notes