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[IEEE 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Bangkok, Thailand (2018.10.21-2018.10.24)] 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Optimal PCA-EOC-KNN Model for Detection of NS1 from Salivary SERS Spectra
摘要： Non Structural Protein 1 (NS1) has recently been known as an alternative biomarker for diseases caused by flavivirus. It has been clinically acknowledged for early detection of dengue infection, since NS1 presence in blood can be as early as the first day of infection. Surface Enhanced Raman Spectroscopy (SERS) is an improvement to Raman spectroscopy, which amplifies the intensity of Raman scattering so to be usable. This also enables SERS to detect molecular structure up to a single molecule. As such, it is favorable amongst researchers investigating disease biomarker. Algorithm k-nearest neighbor (kNN) is a strategy to classify an unknown based on learning data, nearest to the class. Our work here intends to determine the optimal nearest neighbor number, distance rule and classifier rule for PCA-EOC-KNN model for automated detection of NS1 fingerprint from SERS spectra of adulterated saliva. Results show that PCA-EOC-KNN classifier performs with accuracy, precision, sensitivity and specificity above 90%, using Consensus classifier rule, Euclidean or Correlation or Cosine distance rule and k-value of 1, 3 and 5.
关键词： k-Nearest Neighbour (kNN),Nonstructural Protein 1 (NS1),Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29
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[IEEE 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) - Coimbatore, India (2019.2.20-2019.2.22)] 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) - Improved Fault Detection and Location Scheme for Photovoltaic Systems
摘要： Photovoltaic is encountering a quick innovation development since a decade ago. Yet, strange conditions, for example, shortcomings, low irradiance and so forth it influence the yield of PV framework. To enhance the execution of and productivity of PV framework, it is important to create enhanced blame location procedures. This paper for the most part centres around recognition conspire for LL and LG blames in the PV cluster. Such blames stay undetected under irradiance conditions, especially, when a most extreme power point following calculation is in administration. In the event that these shortcomings are undetected, there is extensively loss of yield of PV framework, in the event that these issues are not recognized for longer time, it might harm the board and conceivably cause fire dangers. The exhibited blame identification conspire utilizes Multi-Resolution Signal Decomposition (MSD) procedure and two machine learning calculations to be specific Fuzzy Logic and K-Nearest Neighbor (KNN) to group the blame and decide its area. Reenactment results confirm the exactness, unwavering quality and versatility of the exhibited plan.
关键词： K-Nearest Neighbour (KNN) algorithm,Fuzzy logic,MSD,Machine Learning algorithm,Fault detection
更新于2025-09-11 14:15:04