Monday, January 20, 2020

Journal of Information and Communication Technology (JICT) Vol.19, No.1, January 2020

Ahmad Afif Ahmarofi, Razamin Ramli, Norhaslinda Zainal Abidin, Jastini Mohd Jamil & Izwan Nizal Shaharanee
 
Farhanah Atiqah Norki, Radziah Mohamad & Noraini Ibrahim
 
Md Kamrul Islam, Md Manjur Ahmed, Kamal Zuhairi Zamli & Salman Mehbub
 
Dewi Yanti Liliana, Tarzan Basaruddin, Muhammad Rahmat Widyanto & Imelda Ika Dian Oriza
 
Natesan Sathish Kumar & Krishnan Raja Kumar
 

VARIATION ON THE NUMBER OF HIDDEN NODES THROUGH MULTILAYER PERCEPTRON NETWORKS TO PREDICT THE CYCLE TIME
1Ahmad Afif Ahmarofi, 2Razamin Ramli, 2Norhaslinda Zainal Abidin,2Jastini Mohd Jamil & 2Izwan Nizal Shaharanee
1Fakulti Pengurusan Industri, Universiti Malaysia Pahang, Malaysia
2School of Quantitative Sciences, Universiti Utara Malaysia, Malaysia
aafif@ump.edu.my; razamin@uum.edu.my; nhaslinda@uum.edu.my; jastini@uum.edu.my; nizal@uum.edu.my

Abstract

Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure ofthe MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no ruleof thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line. 
 
Keywords: Artificial neural networks, multilayer perceptron, hidden node, cycle time, production line.

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CONTEXT ONTOLOGY IN MOBILE APPLICATIONS
Farhanah Atiqah Norki, Radziah Mohamad & Noraini Ibrahim 
School of Computing, Universiti Teknologi Malaysia, Malaysia
farhanahatiqah@ymail.com; radziahm@utm.my; noraini_ib@utm.my

Abstract

Mobile applications are expected to receive context input Mobile applications are expected to receive context in put such as location, speech, and network from different context providers. Since context can be considered as knowledge, a formal method is needed to capture this knowledge. There isless work on ontology model that could be reused to model a new context ontology for Android mobile application. Therefore, this study proposed an ontology specifically for Android mobile application, COCCC, to formalize context knowledge present within it. METHONTOLOGY method was used to create COCCC ontology as it offers intermediate representation in the form of concepts. The concepts from the context ontology were extracted from various resources, sorted and categorized based on types and functions for standardization purposes. Survey was given to five domain experts for evaluation of COCCC ontology in terms of its usability. Data from these experts were analyzed and the results have confirmed that the proposed context ontology is usable to Android mobile application developers.
 
Keywords: Context ontology, knowledge representation, mobile application, ontology.

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ENHANCED PRIVACY PROTECTION FROM LOCATION-DEPENDENT ATTACKS IN LOCATION BASED SERVICES USING SPATIAL CLOAKING
Perumal Shanthi Saravanan & Sadhu Ramakrishnan Balasundaram
Department of Computer Applications, National Institute of Technology Tiruchirappalli, India
shanthisaravanan09@gmail.com; blsundar@nitt.edu

Abstract

Use of Internet enabled mobile devices has facilitated the rapid Use of Internet enabled mobile devices has facilitated the rapid development of location-based services (LBS). LBS allow users to access useful information such as the nearest ATM, temple, and so on. Although users enjoy the convenience of LBS, they are being exposed to the risk of location disclosures which could lead to potential abuse of location data. Hence, location privacy protection has recently received considerable attention in LBS. There are numerous techniques presented by various researchers to protect the location-context of users. Location cloaking is an often used technique to protect location-contexts. Most of the existing location cloaking algorithms are only concerned with snapshot user locations and cannot effectively prevent users from location-dependent attacks when user location-contexts are continuously updated. This paper presents a solution to protect users from location-dependent attacks by improving the existing clique based cloaking algorithm. The main idea is to maintain maximum sized cliques required for location cloaking in an undirected graph. Thus, a qualified clique can be quickly identified and used to generate a cloaked region when a new request arrives. In addition, dummy queries are generated to protect users from unusual situations. Through maximum sized cliques and dummy query generation, more user queries get cloaked within a reasonable amount of time, thereby providing cloaked within a reasonable amount of time, thereby providing better privacy protection when using LBS applications. The experimental results showed that the proposed cloaking algorithm out performed existing algorithms such as IClique, OptClique and MMBClique in terms of its cloaking success rate and processing time.
 
Keywords: Location based services, location-dependent attacks, privacy preservation, spatial cloaking.

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AN ONLINE FRAMEWORK FOR CIVIL UNREST PREDICTION USING TWEET STREAM BASED ON TWEET WEIGHT AND EVENT DIFFUSION
1Md Kamrul Islam, 2Md Manjur Ahmed, 2Kamal Zuhairi Zamli & 3Salman Mehbub
1Lorraine Research Laboratory in Computer Science and its Applications, University of Lorraine, France.
2Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Malaysia.
3Department of Computer Science & Engineering, Jessore University of Science & Technology, Bangladesh.
kamrul.islam@loria.fr; manjur@ump.edu.my; kamalz@ump.edu.my; s.a.mehbub@gmail.com
 

Abstract

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient.
 
KeywordsText classification, information diffusion, sentiment analysis, polynomial regression, connected graph.

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HIGH-LEVEL FUZZY LINGUISTIC FEATURES OF FACIAL COMPONENTS IN HUMAN EMOTION RECOGNITION
1Dewi Yanti Liliana, 1Tarzan Basaruddin, 1Muhammad Rahmat Widyanto & 2Imelda Ika Dian Oriza
1Faculty of Computer Science, Universitas Indonesia, Indonesia
2Faculty of Psychology, Universitas Indonesia, Indonesia
dewiyanti.liliana@tik.pnj.ac.id; chan@cs.ui.ac.id; widyanto@cs.ui.ac.id; imelda.ika@ui.ac.id

Abstract

Emotion is an important element in an interaction since it conveys Emotion is an important element in an interaction since it conveys human perception and response of an event. Unlike verbal words that can be manipulated, emotion is brief, spontaneous and provides more honest information. There are several classes of basic primary human emotions that differ from one another. These classes are happy, sad, fearful, surprised, disgusted, and angry. Meanwhile, a psychologist has developed a set of rules to recognize emotions based on facial expressions. This research aims to develop an artificial intelligent model based on psychological knowledge to recognize emotions by analyzing facial expressions. Moreover, the proposed model has defined high-level fuzzy linguistic features of facial components which distinguish it from existing methods that commonly use low-level image features (e.g. color, intensity, histogram, texture). High-level linguistic features (e.g. opened eyes, wrinkled nose) are better at representing human minds than low-level features which are only understood by machines. The model functions by detecting facial points first to locate important facial components; then extracting geometric facial components features which are then applied to a fuzzy facial components features which are then applied to a fuzzy facial components inference system resulting in high-level linguistic facial features. In the last step, the high-level linguistic features are applied to a fuzzy emotion inference system which classifies the input image into its respective emotion class based on psychological rules. Experiments conducted using facial expression dataset gave a high accuracy rate of 98.26% for fuzzy facial components linguistic identification. The proposed model also outperformed other classifiers (Fuzzy C-Means, Fuzzy Inference System, and Support Vector Machine). This intelligent model can contribute in various fields, including psychology, health, and education, especially in helping people with emotional disorders (e.g.Alexithymia, Asperger syndrome, and Autism) to recognize emotions.
 

Keywords: Basic emotion, emotion recognition, facial expression, facial component, fuzzy system, high-level linguistic features.

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A COLLISION AWARE PRIORITY LEVEL MEDIUM ACCESS CONTROL PROTOCOL FOR UNDERWATER ACOUSTIC SENSOR NETWORKS
Natesan Sathish Kumar & Krishnan Raja Kumar
Department of Computer Science and Engineering,Vellore Institute of Technology, India
sathishkumar.n@vit.ac.in; rajakumar.krishnan@vit.ac.in

Abstract

The Underwater Acoustic Sensor Network (UASN) plays a significant role in many application areas like surveillance, security, commercial and industrial applications. In UASN routing, propagation delay and collision are perennial problems due to data transfers from various sensor nodes to the Sink Node (SN) at the same time. In this paper, we propose a Collision Aware Priority Level mechanism based on Medium Access Control protocol (CAPL-MAC) for transferring data from the Sensor Head (SH) to the SN. In the proposed protocol, we use Parallel Competition Scheme (PCS) for high channel utilization and energy saving of battery. In each Competition Cycle (CC), the data packet produced by each SH in a different time slot can joinin CC for data packet transmission in parallel with high channel utilization. In CAPL-MAC, each SH is assigned with a different Priority Level Number (PLN) during every CC. Instead of broadcasting, each SH sends its respective PLN to each SH with the help of the nearest SH to save battery energy. Based on the highest PLN, each SH communicates with SN without collision, and it will also reduce propagation delay as well as improve timing efficiency. Finally, Quality of Service is also improved. We adopt the single-layer approach with the handshaking protocol for communication. We carried out the simulation utilizing Aqua-Sim Network Simulator 2. The simulation results showed that the proposed CAPL-MAC protocol achieved the earlier stated performance rather than by existing protocols suchas Competitive Transmission-MAC and Channel Aware Aloha.
 
Keywords: Underwater acoustic sensor network, medium access control protocol, handshaking protocol, channel aware aloha, quality of service.

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