Browsing by Author "Zhang, Ji"

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An Intelligent Recommender System based on Short-term Risk Prediction for Heart Disease Patients
(2015)
Lafta, Raid
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Zhang, Ji
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Tao, Xiaohui
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Li, Yan
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Tseng, Vincent S.
In this paper, an intelligent recommender system is developed, which uses an innovative time series prediction algorithm to provide recommendations to heart disease patients in the tele-health environment. Based on analytics of each patient\'s medical tests in records, the system provides the patient with decision support for necessity of medical tests. The experimental results show that the proposed system yields satisfactory accuracy in recommendations. The system also offers a promising way for saving the workload for patients and healthcare practitioners in conducting daily medical tests. The research will help reduce the workload and cost in healthcare and help the healthcare industry transform from the traditional scenario to more a personalized paradigm in a tele-health environment.
FrauDetector(+): An Incremental Graph-Mining Approach for Efficient Fraudulent Phone Call Detection
(2018-10-01)
Ying, Josh Jia-Ching
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Zhang, Ji
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Huang, Che-Wei
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Chen, Kuan-Ta
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Tseng, Vincent S.
In recent years, telecommunication fraud has become more rampant internationally with the development of modern technology and global communication. Because of rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with big data issues in real-world implementations. Although our previous work, FrauDetector, addressed this problem and achieved some promising results, it can be further enhanced because it focuses only on fraud detection accuracy, whereas the efficiency and scalability are not top priorities. Other known approaches for fraudulent call number detection suffer from long training times or cannot accurately detect fraudulent phone calls in real time. However, the learning process of FrauDetector is too time-consuming to support real-world application. Although we have attempted to accelerate the the learning process of FrauDetector by parallelization, the parallelized learning process, namely PFrauDetector, still cannot afford the computing cost. In this article, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely FrauDetectoe , which can automatically label fraudulent phone numbers with a "fraud" tag a crucial prerequisite for distinguishing fraudulent phone call numbers from nonfraudulent ones. FratiDetectoe initially generates smaller, more manageable subnetworks from original graph and performs a parallelized weighted HITS algorithm for a significant speed increase in the graph learning module. It adopts a novel aggregation approach to generate a trust (or experience) value for each phone number (or user) based on their respective local values. After the initial procedure, we can incrementally update the trust (or experience) value for each phone number (or user) while a new fraud phone number is identified. An efficient fraud-centric hash structure is constructed to support fast real-time detection of fraudulent phone numbers in the detection module. We conduct a comprehensive experimental study based on real datasets collected through an antifraud mobile application called Whoscall. The results demonstrate a significantly improved efficiency of our approach compared with FrauDetector as well as superior performance against other major classifier-based methods.
PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection
(2016)
Ying, Josh Jia-Ching
;
Zhang, Ji
;
Huang, Che-Wei
;
Chen, Kuan-Ta
;
Tseng, Vincent S.
In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly-efficient parallelized graph-mining-based fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a "fraud" tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.