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Sri Listia Rosa

Department Of Informatics Engineering, Faculty Of Engineering, Universitas Islam Riau

Author-ID : 364071

Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences Other

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An Immune Based Patient Anomaly Detection using RFID Technology Sri Listia Rosa; Siti Mariyam Shamsuddin; Evizal Evizal
Computer Engineering and Applications Journal Vol 2 No 1 (2013)
Publisher : Universitas Sriwijaya

Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI:Â 10.18495/comengapp.21.121142

An Immune Based Patient Anomaly Detection using RFID Technology Sri Listia Rosa; Siti Mariyam Shamsuddin; Evizal Evizal
Computer Engineering and Applications Journal Vol 2 No 2 (2013)
Publisher : Universitas Sriwijaya

Online product reviews is considered as a major informative resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volume of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve. Â Key words: Opinion, Classification, Machine Learning.

An Immune Based Patient Anomaly Detection using RFID Technology Rosa, Sri Listia; Shamsuddin, Siti Mariyam; Evizal
Computer Engineering and Applications Journal (ComEngApp) Vol. 2 No. 1 (2013)
Publisher : Universitas Sriwijaya

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Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI:Â 10.18495/comengapp.21.121142

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Journal : ComEngApp : Computer Engineering and Applications Journal