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Full-Text Articles in Entire DC Network
Adaptive Credit Scoring With Analytic Hierarchy Process, Kwang Yong Koh, Murphy Choy, Michelle L. F. Cheong
Adaptive Credit Scoring With Analytic Hierarchy Process, Kwang Yong Koh, Murphy Choy, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
Credit risk assessment for consumers has been a cornerstone of risk management in financial institutions and constitutes a component of the three pillars of Basel II. Traditionally, the concept of 5 ‘C’s was widely adopted by financial institutions as the key basis for credit risk assessment for loan applications by prospective borrowers. With the evolution of the credit risk management practices, more quantitative methods such as credit scorecards have been developed, which is implemented through the use of logistic regression, decision trees and neural networks. However, such approaches proved to be inadequate with the validity and effectiveness of the approaches …
A New Unpredictability-Based Rfid Privacy Model, Anjia Yang, Yunhui Zhuang, Duncan S. Wong, Guomin Yang
A New Unpredictability-Based Rfid Privacy Model, Anjia Yang, Yunhui Zhuang, Duncan S. Wong, Guomin Yang
Research Collection School Of Computing and Information Systems
Ind-privacy and unp-privacy, later refined to unp∗-privacy, are two different classes of privacy models for RFID authentication protocols. These models have captured the major anonymity and untraceability related attacks regarding RFID authentication protocols with privacy, and existing work indicates that unp∗-privacy seems to be a stronger notion when compared with ind-privacy. In this paper, we continue studying the RFID privacy models, and there are two folds regarding our results. First of all, we describe a new traceability attack and show that schemes proven secure in unp∗-privacy may not be secure against this new and practical type of traceability attacks. We …
Hunts: A Trajectory Recommendation System For Effective And Efficient Hunting Of Taxi Passengers, Ye Ding, Siyuan Liu, Jiansu Pu, Lionel Ni
Hunts: A Trajectory Recommendation System For Effective And Efficient Hunting Of Taxi Passengers, Ye Ding, Siyuan Liu, Jiansu Pu, Lionel Ni
Research Collection School Of Computing and Information Systems
Nowadays, there are many taxis traversing around the city searching for available passengers, but their hunts of passengers are not always efficient. To the dynamics of traffic and biased passenger distributions, current offline recommendations based on place of interests may not work well. In this paper, we define a new problem, global-optimal trajectory retrieving (GOTR), as finding a connected trajectory of high profit and high probability to pick up a passenger within a given time period in real-time. To tackle this challenging problem, we present a system, called HUNTS, based on the knowledge from both historical and online GPS data …
Collaborative Urban Logistics – Synchronizing The Last Mile, Robert De Souza, Mark Goh, Hoong Chuin Lau, Wee-Siong Ng, Puay-Siew Tan
Collaborative Urban Logistics – Synchronizing The Last Mile, Robert De Souza, Mark Goh, Hoong Chuin Lau, Wee-Siong Ng, Puay-Siew Tan
Research Collection School Of Computing and Information Systems
The synchronized last mile logistics concept seeks to address, through coordinated collaboration, several challenges that hinderreliability, cost efficiency, effective resource planning, scheduling and utilization; and increasingly, sustainability objectives.Subsequently, the meeting of service level and contractual commitments are competitively impacted with any loss of efficiency.These challenges, against a backdrop of Singapore, can essentially be addressed in selected industry sectors through a betterunderstanding of logistics structures; innovative supply chain designs and coordination of services, operations and processescoupled with concerted policies and supply chain strategies.
Introducing Programmers To Pair Programming: A Controlled Experiment, A. S. M. Sajeev, Subhajit Datta
Introducing Programmers To Pair Programming: A Controlled Experiment, A. S. M. Sajeev, Subhajit Datta
Research Collection School Of Computing and Information Systems
Pair programming is a key characteristic of the Extreme Programming (XP) method. Through a controlled experiment we investigate pair programming behaviour of programmers without prior experience in XP. The factors investigated are: (a) characteristics of pair programming that are less favored (b) perceptions of team effectiveness and how they relate to product quality, and (c) whether it is better to train a pair by giving routine tasks first or by giving complex tasks first. Our results show that: (a) the least liked aspects of pair programming were having to share the screen, keyboard and mouse, and having to switch between …
Real Time Event Detection In Twitter, Xun Wang, Feida Zhu, Jing Jiang, Sujian Li
Real Time Event Detection In Twitter, Xun Wang, Feida Zhu, Jing Jiang, Sujian Li
Research Collection School Of Computing and Information Systems
Event detection has been an important task for a long time. When it comes to Twitter, new problems are presented. Twitter data is a huge temporal data flow with much noise and various kinds of topics. Traditional sophisticated methods with a high computational complexity aren’t designed to handle such data flow efficiently. In this paper, we propose a mixture Gaussian model for bursty word extraction in Twitter and then employ a novel time-dependent HDP model for new topic detection. Our model can grasp new events, the location and the time an event becomes bursty promptly and accurately. Experiments show the …
Cameo: A Middleware For Mobile Advertisement Delivery, Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Krishna Balan, Srinivasan Seshan
Cameo: A Middleware For Mobile Advertisement Delivery, Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Krishna Balan, Srinivasan Seshan
Research Collection School Of Computing and Information Systems
Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and far from ideal. In particular, as we show, applications which use mobile advertising are capable of using significant amounts of a mobile users' critical resources without being controlled or held accountable. This paper seeks to redress this situation by enabling advertisement supported applications to become significantly more "user-friendly". To this end, we present the design and implementation of CAMEO, a …
Architectural Control And Value Migration In Layered Ecosystems: The Case Of Open-Source Cloud Management Platforms, Richard Tee, C. Jason Woodard
Architectural Control And Value Migration In Layered Ecosystems: The Case Of Open-Source Cloud Management Platforms, Richard Tee, C. Jason Woodard
Research Collection School Of Computing and Information Systems
Our paper focuses on strategic decision making in layered business ecosystems, highlighting the role of cross-layer interactions in shaping choices about product design and platform governance. Based on evidence from the cloud computing ecosystem, we analyze how concerns about architectural control and expectations regarding future value migration influence the design of product interfaces and the degree of openness to external contributions. We draw on qualitative longitudinal data to trace the development of two open-source platforms for managing cloudbased computing resources. We focus in particular on the emergence of a layered "stack" in which these platforms must compete with both vertically …
Cugar: A Model For Open Innovation In Science And Technology Parks, Arcot Desai Narasimhalu
Cugar: A Model For Open Innovation In Science And Technology Parks, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
This paper reviews key elements of a Science or Technology Park in the context of open innovation. Insights into and recommendations on key issues related to intellectual property, licensing and venture capital that would be of interest to any Science Park are presented later.
Approximate Inference In Collective Graphical Models, Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich
Approximate Inference In Collective Graphical Models, Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich
Research Collection School Of Computing and Information Systems
We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical models, exact inference in CGMs is NP-hard even for tree-structured models. We then develop a tractable convex approximation to the NP-hard MAP inference problem in CGMs, and show how to use MAP inference for approximate marginal inference within the EM framework. We demonstrate empirically that these approximation techniques can reduce the computational cost of inference by two orders …
Enforcing Secure And Privacy-Preserving Information Brokering In Distributed Information Sharing, Fengjun Li, Bo Luo, Peng Liu, Dongwon Lee, Chao-Hsien Chu
Enforcing Secure And Privacy-Preserving Information Brokering In Distributed Information Sharing, Fengjun Li, Bo Luo, Peng Liu, Dongwon Lee, Chao-Hsien Chu
Research Collection School Of Computing and Information Systems
Today’s organizations raise an increasing need for information sharing via on-demand access. Information brokering systems (IBSs) have been proposed to connect large-scale loosely federated data sources via a brokering overlay, in which the brokers make routing decisions to direct client queries to the requested data servers. Many existing IBSs assume that brokers are trusted and thus only adopt server-side access control for data confidentiality. However, privacy of data location and data consumer can still be inferred from metadata (such as query and access control rules) exchanged within the IBS, but little attention has been put on its protection. In this …
Main-Stream Media Behaviour Analysis On Twitter: A Case Study On Uk General Election, Zhongyu Wei, Yulan He, Wei Gao, Binyang Li, Lanjun Zhou, Kam-Fai Wong
Main-Stream Media Behaviour Analysis On Twitter: A Case Study On Uk General Election, Zhongyu Wei, Yulan He, Wei Gao, Binyang Li, Lanjun Zhou, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the …
Analyzing The Friendliness Of Exchanges In An Online Software Developer Community, Brendan Cleary, Margaret-Anne Storey, Carlos Gómez, Leif Singer, Christoph Treude
Analyzing The Friendliness Of Exchanges In An Online Software Developer Community, Brendan Cleary, Margaret-Anne Storey, Carlos Gómez, Leif Singer, Christoph Treude
Research Collection School Of Computing and Information Systems
Many online communities struggle with conflicts — e.g. between newcomers and elders — at some point. In July 2012, the Stack Exchange organization attempted to assess the overall “niceness” of the Stack Overflow community by rating the “friendliness” of 7,000 comments made on the site over a 4 year period. We performed a deeper examination of the comment dataset published by Stack Exchange. We find a high degree of comment repetition in the Stack Overflow database and suggest some simple heuristics that may help in automatically identifying unfriendly comments, providing managers of developer communities with simple means that could counter …
Blogging Developer Knowledge: Motivations, Challenges, And Future Directions, Chris Parnin, Christoph Treude, Margaret-Anne Storey
Blogging Developer Knowledge: Motivations, Challenges, And Future Directions, Chris Parnin, Christoph Treude, Margaret-Anne Storey
Research Collection School Of Computing and Information Systems
Why do software developers place so much effort into writing public blog posts about their knowledge, experiences, and opinions on software development? What are the benefits, problems, and tools needed-what can the research community do to help? In this paper, we describe a research agenda aimed at understanding the motivations and issues of software development blogging. We interviewed developers as well as mined and analyzed their blog posts. For this initial study, we selected developers from various backgrounds: IDE plugin development, mobile development, and web development. We found that developers used blogging for a variety of functions such as documentation, …
Enabling Generative, Emergent Artificial Culture, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
Enabling Generative, Emergent Artificial Culture, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
Research Collection School Of Computing and Information Systems
Despite the demand for culturally placed agent models, an adequate simulation approach to the relationship between group-cultural and individual-psychological qualities, including culture emergence, is just appearing. It could be argued that we are at the beginning of a domain forming process, a dawn of generative, emergent artificial culture. In this context we discuss current limitations and argue e.g. that too far reaching agent simplicity within Agent Based Modeling limits the emergence of realistic cultural-conventional level and we advocate psychologically rich models of culture forming mechanisms. We propose an approach to cultural phenomena modeling based on the interaction of habitual, affective …
Tower Of Babel: A Crowdsourcing Game Building Sentiment Lexicons For Resource-Scarce Languages, Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue. Moon
Tower Of Babel: A Crowdsourcing Game Building Sentiment Lexicons For Resource-Scarce Languages, Yoonsung Hong, Haewoon Kwak, Youngmin Baek, Sue. Moon
Research Collection School Of Computing and Information Systems
With the growing amount of textual data produced by online social media today, the demands for sentiment analysis are also rapidly increasing; and, this is true for worldwide. However, non-English languages often lack sentiment lexicons, a core resource in performing sentiment analysis. Our solution, Tower of Babel (ToB), is a language-independent sentiment-lexicon-generating crowdsourcing game. We conducted an experiment with 135 participants to explore the difference between our solution and a conventional manual annotation method. We evaluated ToB in terms of effectiveness, efficiency, and satisfactions. Based on the result of the evaluation, we conclude that sentiment classification via ToB is accurate, …
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
Research Collection School Of Computing and Information Systems
The robustness of a network is determined by how well its vertices are connected to one another so as to keep the network strong and sustainable. As the network evolves its robustness changes and may reveal events as well as periodic trend patterns that affect the interactions among users in the network. In this paper, we develop R-energy as a new measure of network robustness based on the spectral analysis of normalized Laplacian matrix. R-energy can cope with disconnected networks, and is efficient to compute with a time complexity of O (jV j + jEj) where V and E are …
Designing Leakage-Resilient Password Entry On Touchscreen Mobile Devices, Qiang Yan, Jin Han, Yingjiu Li, Jianying Zhou, Robert H. Deng
Designing Leakage-Resilient Password Entry On Touchscreen Mobile Devices, Qiang Yan, Jin Han, Yingjiu Li, Jianying Zhou, Robert H. Deng
Research Collection School Of Computing and Information Systems
Touchscreen mobile devices are becoming commodities as the wide adoption of pervasive computing. These devices allow users to access various services at anytime and anywhere. In order to prevent unauthorized access to these services, passwords have been pervasively used in user authentication. However, password-based authentication has intrinsic weakness in password leakage. This threat could be more serious on mobile devices, as mobile devices are widely used in public places. Most prior research on improving leakage resilience of password entry focuses on desktop computers, where specific restrictions on mobile devices such as small screen size are usually not addressed. Meanwhile, additional …
Anonymous Authentication Of Visitors For Mobile Crowd Sensing At Amusement Parks, Divyan Konidala, Robert H. Deng, Yingjiu Li, Hoong Chuin Lau, Stephen Fienberg
Anonymous Authentication Of Visitors For Mobile Crowd Sensing At Amusement Parks, Divyan Konidala, Robert H. Deng, Yingjiu Li, Hoong Chuin Lau, Stephen Fienberg
Research Collection School Of Computing and Information Systems
In this paper we focus on authentication and privacy aspects of an application scenario that utilizes mobile crowd sensing for the benefit of amusement park operators and their visitors. The scenario involves a mobile app that gathers visitors’ demographic details, preferences, and current location coordinates, and sends them to the park’s sever for various analyses. These analyses assist the park operators to efficiently deploy their resources, estimate waiting times and queue lengths, and understand the behavior of individual visitors and groups. The app server also offers visitors optimal recommendations on routes and attractions for an improved dynamic experience and minimized …
Behind The Magical Numbers: Hierarchical Chunking And The Human Working Memory Capacity, Guoqi Li, Ning Ning, Kiruthika Ramanathan, Wei He, Li Pan, Luping Shi
Behind The Magical Numbers: Hierarchical Chunking And The Human Working Memory Capacity, Guoqi Li, Ning Ning, Kiruthika Ramanathan, Wei He, Li Pan, Luping Shi
Research Collection School Of Computing and Information Systems
To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition. With the assumption of the existence of an upper bound to the inhibitory weights in neurobiological networks, it is shown that chunking increases the number of memorized items in working memory from the "magical number 7" to 16 items. The optimal number of chunks and the number of the memorized items in each chunk are the "magical number 4".
Efficient Extraction Of Json Information In Sas Using The Scanover Function, Murphy Choy, Kyong Jin Shim
Efficient Extraction Of Json Information In Sas Using The Scanover Function, Murphy Choy, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
JSON, otherwise known as JavaScript Object Notation, is a popular data interchange format which provides a humanreadable format. It is language independent and can be read easily in a variety of computer languages. With the riseof Twitter and other unstructured data, there has been a move to incorporate such data as a way of disseminatinginformation. Twitter currently provides a simple API for users to extract tweets using JSON format. While SAS doesnot currently have a direct way of reading JSON, the SCANOVER function in SAS data step provides user with asimple and effective approach to getting JSON information into SAS …
Disclosing Climate Change Patterns Using An Adaptive Markov Chain Pattern Detection Method, Zhaoxia Wang, Gary Lee, Hoong Maeng Chan, Reuben Li, Xiuju Fu, Rick Goh, Pauline A. W. Poh Kim, Martin L. Hibberd, Hoong Chor Chin
Disclosing Climate Change Patterns Using An Adaptive Markov Chain Pattern Detection Method, Zhaoxia Wang, Gary Lee, Hoong Maeng Chan, Reuben Li, Xiuju Fu, Rick Goh, Pauline A. W. Poh Kim, Martin L. Hibberd, Hoong Chor Chin
Research Collection School Of Computing and Information Systems
This paper proposes an adaptive Markov chain pattern detection (AMCPD) method for disclosing the climate change patterns of Singapore through meteorological data mining. Meteorological variables, including daily mean temperature, mean dew point temperature, mean visibility, mean wind speed, maximum sustained wind speed, maximum temperature and minimum temperature are simultaneously considered for identifying climate change patterns in this study. The results depict various weather patterns from 1962 to 2011 in Singapore, based on the records of the Changi Meteorological Station. Different scenarios with varied cluster thresholds are employed for testing the sensitivity of the proposed method. The robustness of the proposed …
Your Love Is Public Now: Questioning The Use Of Personal Information In Authentication, Payas Gupta, Swapna Gottipati, Jing Jiang, Debin Gao
Your Love Is Public Now: Questioning The Use Of Personal Information In Authentication, Payas Gupta, Swapna Gottipati, Jing Jiang, Debin Gao
Research Collection School Of Computing and Information Systems
Most social networking platforms protect user's private information by limiting access to it to a small group of members, typically friends of the user, while allowing (virtually) everyone's access to the user's public data. In this paper, we exploit public data available on Facebook to infer users' undisclosed interests on their profile pages. In particular, we infer their undisclosed interests from the public data fetched using Graph APIs provided by Facebook. We demonstrate that simply liking a Facebook page does not corroborate that the user is interested in the page. Instead, we perform sentiment-oriented mining on various attributes of a …
Cost-Sensitive Double Updating Online Learning And Its Application To Online Anomaly Detection, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Double Updating Online Learning And Its Application To Online Anomaly Detection, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Although both cost-sensitive classification and online learning have been well studied separately in data mining and machine learning, there was very few comprehensive study of cost-sensitive online classification in literature. In this paper, we formally investigate this problem by directly optimizing cost-sensitive measures for an online classification task. As the first comprehensive study, we propose the Cost-Sensitive Double Updating Online Learning (CSDUOL) algorithms, which explores a recent double updating technique to tackle the online optimization task of cost-sensitive classification by maximizing the weighted sum or minimizing the weighted misclassification cost. We theoretically analyze the cost-sensitive measure bounds of the proposed …
Fans: Face Annotation By Searching Large-Scale Web Facial Images, Steven Hoi, Dayong Wang, I Yeu Cheng, Elmer Lin, Jianke Zhu, Ying He, Chunyan Miao
Fans: Face Annotation By Searching Large-Scale Web Facial Images, Steven Hoi, Dayong Wang, I Yeu Cheng, Elmer Lin, Jianke Zhu, Ying He, Chunyan Miao
Research Collection School Of Computing and Information Systems
Auto face annotation is an important technique for many real-world applications, such as online photo album management, new video summarization, and so on. It aims to automatically detect human faces from a photo image and further name the faces with the corresponding human names. Recently, mining web facial images on the internet has emerged as a promising paradigm towards auto face annotation. In this paper, we present a demonstration system of search-based face annotation: FANS - Face ANnotation by Searching large-scale web facial images. Given a query facial image for annotation, we first retrieve a short list of the most …
Why Individuals Seek Diverse Opinions (Or Why They Don't), Jisun An, Daniele Quercia, Jon Crowcroft
Why Individuals Seek Diverse Opinions (Or Why They Don't), Jisun An, Daniele Quercia, Jon Crowcroft
Research Collection School Of Computing and Information Systems
Fact checking has been hard enough to do in traditional settings, but, as news consumption is moving on the Internet and sources multiply, it is almost unmanageable. To solve this problem, researchers have created applications that expose people to diverse opinions and, as a result, expose them to balanced information. The wisdom of this solution is, however, placed in doubt by this paper. Survey responses of 60 individuals in the UK and South Korea and in-depth structured interviews of 10 respondents suggest that exposure to diverse opinions would not always work. That is partly because not all individuals equally value …
Dynamic Synthesis Of Local Time Requirement For Service Composition, Tian Huat Tan, Étienne André, Jun Sun, Yang Liu, Jin Song Dong, Manman Chen
Dynamic Synthesis Of Local Time Requirement For Service Composition, Tian Huat Tan, Étienne André, Jun Sun, Yang Liu, Jin Song Dong, Manman Chen
Research Collection School Of Computing and Information Systems
Service composition makes use of existing servicebased applications as components to achieve a business goal. In time critical business environments, the response time of a service is crucial, which is also reflected as a clause in service level agreements (SLAs) between service providers and service users. To allow the composite service to fulfill the response time requirement as promised, it is important to find a feasible set of component services, such that their response time could collectively allow the satisfaction of the response time of the composite service. In this work, we propose a fully automated approach to synthesize the …
Tag Recommendation In Software Information Sites, Xin Xia, David Lo, Xinyu Wang, Bo Zhou
Tag Recommendation In Software Information Sites, Xin Xia, David Lo, Xinyu Wang, Bo Zhou
Research Collection School Of Computing and Information Systems
Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance and test processes as software information sites. It is common to see tags in software information sites and many sites allow users to tag various objects with their own words. Users increasingly use tags to describe the most important features of their posted contents or projects. In this paper, we propose TagCombine, an automatic …
Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau
Distributed Gibbs: A Memory-Bounded Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this paper, we introduce a new sampling-based DCOP algorithm called Distributed Gibbs, whose memory requirements per agent is linear in the number of agents in the problem. Additionally, we show …
Demand Forecasting Using A Growth Model And Negative Binomial Regression Framework, Cally Yeru Ong, Murphy Choy, Michelle L. F. Cheong
Demand Forecasting Using A Growth Model And Negative Binomial Regression Framework, Cally Yeru Ong, Murphy Choy, Michelle L. F. Cheong
Research Collection School Of Computing and Information Systems
In this paper, we look at demand forecasting by using a growth model and negative binomial regression framework. Using cumulative sales, we model the sales data for different wristwatch brands and relate it to their sales and growth characteristics. We apply clustering to determine the distinctive characteristics of each individual cluster. Four different growth models are applied to the clusters to find the most suitable growth model to be used. After determining the appropriate growth model to be applied, we then forecast the sales by applying the model to new products being launched in the market and continue to monitor …