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Numerical Analysis and Scientific Computing
Research Collection School Of Computing and Information Systems
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Toward Effective Concept Representation In Decision Support To Improve Patient Safety, Tze-Yun Leong
Toward Effective Concept Representation In Decision Support To Improve Patient Safety, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Patient safety is an emerging, major health care discipline with significance accentuated in the influential Institute of Medicine (IOM) reports in the United States “To Err is Human” and “Crossing the Quality Chasm”. These reports highlighted the danger and prevalence of medical errors and preventable adverse events, explained the three main sources of system-related, human factors-related and cognitive-related errors, and recommended the use of information and decision support technologies to help alleviate the problem. A number of studies and reports from all over the world with similar findings have since followed, culminating in the 55th World Health Assembly Resolution on …
Sequence Alignment Based Analysis Of Player Behavior In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Sequence Alignment Based Analysis Of Player Behavior In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following …
A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun
A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun
Research Collection School Of Computing and Information Systems
This paper presents a structure first image inpainting method based on self-organizing map (SOM). SOM is employed to find the useful structure information of the damaged image. The useful structure information which includes relevant edges of the image is used to simulate the structure information of the lost or damaged area in the image. The structure information is described by distinct or indistinct curves in an image in this paper. The obtained target curves separate the damaged area of the image into several parts. As soon as each part of the damaged image is restored respectively, the damaged image is …
Automobile Exhaust Gas Detection Based On Fuzzy Temperature Compensation System, Zhiyong Wang, Hao Ding, Fufei Hao, Zhaoxia Wang, Zhen Sun, Shujin Li
Automobile Exhaust Gas Detection Based On Fuzzy Temperature Compensation System, Zhiyong Wang, Hao Ding, Fufei Hao, Zhaoxia Wang, Zhen Sun, Shujin Li
Research Collection School Of Computing and Information Systems
A temperature compensation scheme of detecting automobile exhaust gas based on fuzzy logic inference is presented in this paper. The principles of the infrared automobile exhaust gas analyzer and the influence of the environmental temperature on analyzer are discussed. A fuzzy inference system is designed to improve the measurement accuracy of the measurement equipment by reducing the measurement errors caused by environmental temperature. The case studies demonstrate the effectiveness of the proposed method. The fuzzy compensation scheme is promising as demonstrated by the simulation results in this paper.
Would Position Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh
Would Position Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh
Research Collection School Of Computing and Information Systems
On May 6, 2010, the US equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (DJIA) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. Earlier research by Lee, Cheng and Koh (2010) identified the conditions under which a “flash crash” can be triggered by systematic traders running highly similar trading strategies, especially when they are “crowding out” other liquidity providers in the market. The authors contend that the events of May 6, …
Detecting Product Review Spammers Using Rating Behaviors, Ee Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, Hady Wirawan Lauw
Detecting Product Review Spammers Using Rating Behaviors, Ee Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic be- haviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their im- pact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a sub- set of highly suspicious …
Finding Unusual Review Patterns Using Unexpected Rules, Nitin Jindal, Bing Liu, Ee Peng Lim
Finding Unusual Review Patterns Using Unexpected Rules, Nitin Jindal, Bing Liu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com …
Context Modeling For Ranking And Tagging Bursty Features In Text Streams, Xin Zhao, Jing Jiang, Jing He, Xiaoming Li, Hongfei Yan, Dongdong Shan
Context Modeling For Ranking And Tagging Bursty Features In Text Streams, Xin Zhao, Jing Jiang, Jing He, Xiaoming Li, Hongfei Yan, Dongdong Shan
Research Collection School Of Computing and Information Systems
Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty …
Mining Collaboration Patterns From A Large Developer Network, Didi Surian, David Lo, Ee Peng Lim
Mining Collaboration Patterns From A Large Developer Network, Didi Surian, David Lo, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In this study, we extract patterns from a large developer collaborations network extracted from Source Forge. Net at high and low level of details. At the high level of details, we extract various network-level statistics from the network. At the low level of details, we extract topological sub-graph patterns that are frequently seen among collaborating developers. Extracting sub graph patterns from large graphs is a hard NP-complete problem. To address this challenge, we employ a novel combination of graph mining and graph matching by leveraging network-level properties of a developer network. With the approach, we successfully analyze a snapshot of …
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Research Collection School Of Computing and Information Systems
Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework …
Shortest Path Computation On Air Indexes, Georgios Kellaris, Kyriakos Mouratidis
Shortest Path Computation On Air Indexes, Georgios Kellaris, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Shortest path computation is one of the most common queries in location-based services that involve transportation net- works. Motivated by scalability challenges faced in the mo- bile network industry, we propose adopting the wireless broad- cast model for such location-dependent applications. In this model the data are continuously transmitted on the air, while clients listen to the broadcast and process their queries locally. Although spatial problems have been considered in this environment, there exists no study on shortest path queries in road networks. We develop the rst framework to compute shortest paths on the air, and demonstrate the practicality and …
A Biologically-Inspired Cognitive Agent Model Integrating Declarative Knowledge And Reinforcement Learning, Ah-Hwee Tan, Gee-Wah Ng
A Biologically-Inspired Cognitive Agent Model Integrating Declarative Knowledge And Reinforcement Learning, Ah-Hwee Tan, Gee-Wah Ng
Research Collection School Of Computing and Information Systems
The paper proposes a biologically-inspired cognitive agent model, known as FALCON-X, based on an integration of the Adaptive Control of Thought (ACT-R) architecture and a class of self-organizing neural networks called fusion Adaptive Resonance Theory (fusion ART). By replacing the production system of ACT-R by a fusion ART model, FALCON-X integrates high-level deliberative cognitive behaviors and real-time learning abilities, based on biologically plausible neural pathways. We illustrate how FALCON-X, consisting of a core inference area interacting with the associated intentional, declarative, perceptual, motor and critic memory modules, can be used to build virtual robots for battles in a simulated RoboCode …
Team Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Team Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of teams. This study uses performance data of game players and teams in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for task performing teams. The prediction models provide a projection of task performing team's future performance based on the past performance patterns of participating players on the team as well as team characteristics. While the existing game system lacks the ability to predict team-level performance, the prediction models proposed in this study are expected to be …
On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood
On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood
Research Collection School Of Computing and Information Systems
This paper introduces the Deliberation Decision Support System (DDSS). The DDSS obtains heuristically (using a genetic algorithm) solutions of interest for constrained optimization models. This is illustrated, without loss of generality, by generalized assignment problems. The DDSS also provides users with graphical tools that support post-solution deliberation for constrained optimization models. The DDSS and this paper, as befits practical concerns, are focused on deliberation with respect to the constraints of the models being used.
A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang
A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang
Research Collection School Of Computing and Information Systems
In this work, we study the task of personalized tag recommendation in social tagging systems. To reach out to tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for incorporating translations by similar users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such similarity measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with personomy translation methods based …
Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow
Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow
Research Collection School Of Computing and Information Systems
In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive …
Messaging Behavior Modeling In Mobile Social Networks, Byung-Won On, Ee Peng Lim, Jing Jiang, Freddy Tat Chua Chua, Viet-An Nguyen, Loo Nin Teow
Messaging Behavior Modeling In Mobile Social Networks, Byung-Won On, Ee Peng Lim, Jing Jiang, Freddy Tat Chua Chua, Viet-An Nguyen, Loo Nin Teow
Research Collection School Of Computing and Information Systems
Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message and responding to an incoming message. We propose various models for the two behaviors also known as engagingness and responsiveness. Our experiments show that …
Effective Music Tagging Through Advanced Statistical Modeling, Jialie Shen, Meng Wang, Shuicheng Yan, Hwee Hwa Pang, Xian-Sheng Hua
Effective Music Tagging Through Advanced Statistical Modeling, Jialie Shen, Meng Wang, Shuicheng Yan, Hwee Hwa Pang, Xian-Sheng Hua
Research Collection School Of Computing and Information Systems
Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this …
Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang
Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. We …
Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam
Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam
Research Collection School Of Computing and Information Systems
In this highly volatile and fast-paced financial market, traders and managers working in banking and financial organizations must struggle to cope with large and complex data from multi-sources, that move throughout the market at increasingly high speed. The cost of making poor business and investment decisions is very high. This places great demands on data analysts, who are responsible for providing process information, to support the activities of traders and managers. Static reports and traditional business intelligence tools simply cannot keep up with a market that is changing on a second-to-second basis. By the time the traders and bankers have …
Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau
Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper introduces the SoI problem, that of finding nonoptimal solutions of interest for constrained optimization models. SoI problems subsume finding FoIs (feasible solutions of interest), and IoIs (infeasible solutions of interest). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making (discussed in the paper) come into play and for this purpose the SoIs can be of …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Research Collection School Of Computing and Information Systems
Given a set D of trajectories, a query object q, and a query time extent Γ, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D, the set of trajectories that are among the k1 nearest neighbors (NNs) of q within Γ, and meanwhile, have q as one of their k2 NNs. This type of queries is useful in many applications such as decision making, data mining, and pattern recognition, as it considers both the proximity of the trajectories to q and the proximity of q to the trajectories. In this paper, we first formalize MNN search …
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus.
Player Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Richa Sharan, Jaideep Srivastava
Player Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Richa Sharan, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models forgame players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance …
Z-Sky: An Efficient Skyline Query Processing Framework Based On Z-Order, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Huajing Li, Yuan Tian
Z-Sky: An Efficient Skyline Query Processing Framework Based On Z-Order, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng, Huajing Li, Yuan Tian
Research Collection School Of Computing and Information Systems
Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and …
Visualizing And Exploring Evolving Information Networks In Wikipedia, Ee Peng Lim, Agus Trisnajaya Kwee, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Maureen Maureen
Visualizing And Exploring Evolving Information Networks In Wikipedia, Ee Peng Lim, Agus Trisnajaya Kwee, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Maureen Maureen
Research Collection School Of Computing and Information Systems
Information networks in Wikipedia evolve as users collaboratively edit articles that embed the networks. These information networks represent both the structure and content of community’s knowledge and the networks evolve as the knowledge gets updated. By observing the networks evolve and finding their evolving patterns, one can gain higher order knowledge about the networks and conduct longitudinal network analysis to detect events and summarize trends. In this paper, we present SSNetViz+, a visual analytic tool to support visualization and exploration of Wikipedia’s information networks. SSNetViz+ supports time-based network browsing, content browsing and search. Using a terrorism information network as an …
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Research Collection School Of Computing and Information Systems
Spatio-temporal data concerning the movement of individuals over space and time contains latent information on the associations among these individuals. Sources of spatio-temporal data include usage logs of mobile and Internet technologies. This article defines a spatio-temporal event by the co-occurrences among individuals that indicate potential associations among them. Each spatio-temporal event is assigned a weight based on the precision and uniqueness of the event. By aggregating the weights of events relating two individuals, we can determine the strength of association between them. We conduct extensive experimentation to investigate both the efficacy of the proposed model as well as the …