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  • Open Access

    ARTICLE

    Decision making in anomalous aortic origin of a coronary artery

    Carlos M. Mery1,2

    Congenital Heart Disease, Vol.12, No.5, pp. 630-632, 2017, DOI:10.1111/chd.12493

    Abstract Anomalous aortic origin of a coronary artery (AAOCA) is the second most common cause of sudden cardiac death (SCD) in young athletes in United States. The exact pathophysiological mechanisms of SCD are unknown. There is lack of long-term outcome data on repaired and unrepaired AAOCA and our current risk stratification scheme for these patients is suboptimal. These patients are evaluated in a nonuniform manner across institutions in United States, and even by different providers residing in the same institution. The main objective of this article is to use what is known and unknown about this More >

  • Open Access

    ARTICLE

    Condition Monitoring of Roller Bearing by K-Star Classifier and K-Nearest Neighborhood Classifier Using Sound Signal.

    Rahul Kumar Sharma*1, V. Sugumaran1, Hemantha Kumar2, Amarnath M3

    Structural Durability & Health Monitoring, Vol.11, No.1, pp. 1-16, 2017, DOI:10.3970/sdhm.2017.012.001

    Abstract Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone)… More >

  • Open Access

    ARTICLE

    A Fuzzy Approach for an IoT-based Automated Employee Performance Appraisal

    Jaideep Kaur1, Kamaljit Kaur2

    CMC-Computers, Materials & Continua, Vol.53, No.1, pp. 23-36, 2017, DOI:10.3970/cmc.2017.053.024

    Abstract The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and other wireless devices is capable of sensing the activities being carried around Industrial environment so as to automate industrial processes. In almost every industry, employee performance appraisal is done manually which may lead to favoritisms. This paper proposes a framework to perform automatic employee performance appraisal based on data sensed from IoT. The framework classifies raw IoT data into three activities (Positive, Negative, Neutral), co-locates employee and activity in order to calculate employee implication and then performs cognitive decision making using fuzzy logic. From More >

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