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[IEEE 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Changsha, China (2018.9.21-2018.9.23)] 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Tracking Comparison of P&O and INC Based MPPTs under Varying Weather Conditions
摘要： The paper discusses the application of data science and business analytics in the context of optoelectronics, focusing on modeling and simulation techniques for photovoltaic systems. It presents a novel approach to improving efficiency and performance through advanced data analysis methods.
关键词： data science,business analytics,optoelectronics,photovoltaic systems,simulation,modeling
更新于2025-09-23 15:23:52
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[IEEE 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Changsha, China (2018.9.21-2018.9.23)] 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Detection of Diabetic Retinopathy Images Using a Fully Convolutional Neural Network
摘要： The paper discusses the development and application of a convolutional neural network (CNN) model for digital image processing in the context of data science and business analytics. It focuses on improving the accuracy and efficiency of image classification tasks.
关键词： Image Classification,Digital Image Processing,Business Analytics,Data Science,Convolutional Neural Network
更新于2025-09-23 15:22:29
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Structure-Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach
摘要： A study workflow that utilizes several data science methods to apply on polymer materials databases is introduced to reveal correlations among their properties, structural information, or molecular descriptors. The data science methods used in this pipeline include unsupervised machine learning (ML) method self-organizing mapping (SOM) and polymer molecular descriptor generator, both of which have been tailored to fit the polymer materials study. To demonstrate how this pipeline can be applied in this context, we used it on an organic photovoltaic (OPV) donor polymer database to investigate which properties or structural factors positively correlate with the power conversion efficiency (PCE) of OPV materials. This led us to discover that among the studied 8 properties and 11 molecular descriptors, only the photon energy loss (Eloss) and the number of fluorine atoms (nF) show strong positive correlations with PCE values, which is consistent with other verified studies. We also discovered that research trends can also be statistically visualized using our method. In our case study, we found that most of the studied OPV donor materials in the database have branched side chains and typically 7 to 12 non-Hydrogen atoms, and high PCE materials usually have 6 to 9 aromatics rings as well. These results proved that the data science pipeline proposed in this study provides a fast and effective way to obtain research insights for polymer materials.
关键词： self-organizing mapping,data science,molecular descriptors,polymer materials,organic photovoltaic,power conversion efficiency
更新于2025-09-23 15:21:01
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Economical crowdsourcing for camera trap image classification
摘要： Camera trapping is widely used to monitor mammalian wildlife but creates large image datasets that must be classified. In response, there is a trend towards crowdsourcing image classification. For high-profile studies of charismatic faunas, many classifications can be obtained per image, enabling consensus assessments of the image contents. For more local-scale or less charismatic communities, however, demand may outstrip the supply of crowdsourced classifications. Here, we consider MammalWeb, a local-scale project in North East England, which involves citizen scientists in both the capture and classification of sequences of camera trap images. We show that, for our global pool of image sequences, the probability of correct classification exceeds 99% with about nine concordant crowdsourced classifications per sequence. However, there is high variation among species. For highly recognizable species, species-specific consensus algorithms could be even more efficient; for difficult to spot or easily confused taxa, expert classifications might be preferable. We show that two types of incorrect classifications – misidentification of species and overlooking the presence of animals – have different impacts on the confidence of consensus classifications, depending on the true species pictured. Our results have implications for data capture and classification in increasingly numerous, local-scale citizen science projects. The species-specific nature of our findings suggests that the performance of crowdsourcing projects is likely to be highly sensitive to the local fauna and context. The generality of consensus algorithms will, thus, be an important consideration for ecologists interested in harnessing the power of the crowd to assist with camera trapping studies.
关键词： citizen science,crowdsourcing,Camera traps,data science,data classification,MammalWeb
更新于2025-09-10 09:29:36