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Application of Natural Neighbor-based Algorithm on Oversampling SMOTE Algorithms
(2021-04-01)
Srinilta, Chutimet
;
Kanharattanachai, Sivakorn
Classification performance depends highly on data distribution. In real life, data often come imbalanced where one class is found more often than others. SMOTE-based algorithms are usually used to handle the class imbalance problem. One key parameter that algorithms in SMOTE family require is k-the number of nearest neighbors with respect to a certain data point. K that fits the dataset the most gives the optimum performance. This paper proposes an approach to suggest a value of the parameter k using Natural Neighbor algorithm. Datasets are made balanced by four SMOTE-based algorithms-standard SMOTE, Safe-Level-SMOTE, ModifiedSMOTE and Weighted-SMOTE. The F-measure and Recall matrices are used to evaluate classification performance of a Support Vector Machine classifier running against six datasets with different imbalance ratios. The results show that, the average classification performance achieved by the proposed k's is closer to the optimum when compared with the performance given by the default value of k.
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Municipal solid waste segregation with CNN
(2019-07-01)
Srinilta, Chutimet
;
Kanharattanachai, Sivakorn
Pollution from municipal solid waste has been a problem in Thailand for a long time. People generate waste in every minute. Ineffective waste segregation does increase difficulties in solid waste management. The Pollution Control Department of Thailand provides segregation guideline for municipal solid waste. Household wastes should be separated into four types-general waste, compostable waste, recyclable waste and hazardous waste. This paper explored performance of CNN-based waste-type classifiers (VGG-16, ResNet-50, MobileNet V2 and DenseNet-121) in classifying waste types of 9,200 municipal solid waste images. Waste type can be identified directly from waste-type classifier or derived from waste-item class. Derived classifiers outperformed their corresponding direct classifiers in the experiment. The highest waste-type classification accuracy was 94.86% from the derived ResNet-50 classifier.
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Microfluidic magnetic switching valves based on aggregates of magnetic nanoparticles: Effects of aggregate length and nanoparticle sizes
(2017-01-15)
Jiemsakul, Thanakorn
;
Manakasettharn, Supone
;
Kanharattanachai, Sivakorn
;
Wanna, Yongyuth
;
Wangsuya, Sujint
We demonstrate microfluidic switching valves using magnetic nanoparticles blended within the working fluid as an alternative microfluidic flow control in microchannels. Y-shaped microchannels have been fabricated by using a CO<inf>2</inf> laser cutter to pattern microchannels on transparent poly(methyl methacrylate) (PMMA) sheets covered with thermally bonded transparent polyvinyl chloride (PVC) sheets. To examine the performance of the microfluidic magnetic switching valves, an aqueous magnetic nanoparticle suspension was injected into the microchannels by a syringe pump. Neodymium magnets were then employed to attract magnetic nanoparticles and form an aggregate that blocked the microchannels at a required position. We have found that the maximum volumetric flow rate of the syringe pump that the magnetic nanoparticle aggregate can withstand scales with the square of the external magnetic flux density. The viscosity of the fluid exhibits dependent on the aggregate length and the size of the magnetic nanoparticles. This microfluidic switching valve based on aggregates of magnetic nanoparticles has strong potentials as an on-demand flow control, which may help simplifying microfluidic channel designs.

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