- 标题
- 摘要
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- 实验方案
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Comparison of Thin-Film-Transistor and Photonic Crystal Protein Sensors
摘要： Sarcasm is a sophisticated form of irony widely used in social networks and microblogging websites. It is usually used to convey implicit information within the message a person transmits. Sarcasm might be used for different purposes, such as criticism or mockery. However, it is hard even for humans to recognize. Therefore, recognizing sarcastic statements can be very useful to improve automatic sentiment analysis of data collected from microblogging websites or social networks. Sentiment Analysis refers to the identification and aggregation of attitudes and opinions expressed by Internet users toward a specific topic. In this paper, we propose a pattern-based approach to detect sarcasm on Twitter. We propose four sets of features that cover the different types of sarcasm we defined. We use those to classify tweets as sarcastic and non-sarcastic. Our proposed approach reaches an accuracy of 83.1% with a precision equal to 91.1%. We also study the importance of each of the proposed sets of features and evaluate its added value to the classification. In particular, we emphasize the importance of pattern-based features for the detection of sarcastic statements.
关键词： Twitter,sarcasm detection,sentiment analysis,machine learning
更新于2025-09-19 17:13:59
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Resonant Production of an Ultrarelativistic Electron-Positron Pair by a Gamma Quantum in the Field of a Nucleus and a Laser Wave
摘要： When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, ?rst through statistical analysis, we ?nd that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we veri?ed the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classi?ers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance.
关键词： imbalance problem,attribute selection,phone changing prediction,machine learning,Mobile big data
更新于2025-09-19 17:13:59
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Study on estimating quantum discord by neural network with prior knowledge
摘要： Machine learning has achieved success in many areas because of its powerful ?tting ability, so we hope it can help us to solve some signi?cant physical quantitative problems, such as quantum correlation. In this research, we will use neural networks to predict the value of quantum discord. Quantum discord is a measure of quantum correlation which is de?ned as the difference between quantum mutual information and classical correlation for a bipartite system. Since the de?nition contains an optimization term, it makes analytically solving hard. For some special cases and small systems, such as two-qubit systems and some X-states, the explicit solutions have been calculated. However, for general cases, we still know very little. Therefore, we study the feasibility of estimating quantum discord by machine learning method on two-qubit systems. In order to get an interpretable and high-performance model, we modify the ordinary neural network by introducing some prior knowledge which comes from the analysis about quantum discord. Our results show that prior knowledge actually improves the performance of neural network.
关键词： Neural network,Quantum discord,Machine learning
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - A Benchmark and Validation of Bifacial PV Irradiance Models
摘要： Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol levels are associated with risk factors for various diseases and are related to anthropometric measures. However, controversy remains regarding the best anthropometric indicators of the HDL and LDL cholesterol levels. The objectives of this study were to identify the best predictors of HDL and LDL cholesterol using statistical analyses and two machine learning algorithms and to compare the predictive power of combined anthropometric measures in Korean adults. A total of 13 014 subjects participated in this study. The anthropometric measures were assessed with binary logistic regression (LR) to evaluate statistically significant differences between the subjects with normal and high LDL cholesterol levels and between the subjects with normal and low HDL cholesterol levels. LR and the naive Bayes algorithm (NB), which provides more reasonable and reliable results, were used in the analyses of the predictive power of individual and combined measures. The best predictor of HDL was the rib to hip ratio (p = <0.0001; odds ratio (OR) = 1.895; area under curve (AUC) = 0.681) in women and the waist to hip ratio (WHR) (p =< 0.0001; OR = 1.624; AUC = 0.633) in men. In women, the strongest indicator of LDL was age (p = <0.0001; OR = 1.662; AUC by NB = 0.653; AUC by LR = 0.636). Among the anthropometric measures, the body mass index (BMI), WHR, forehead to waist ratio, forehead to rib ratio, and forehead to chest ratio were the strongest predictors of LDL; these measures had similar predictive powers. The strongest predictor in men was BMI (p = <0.0001; OR = 1.369; AUC by NB = 0.594; AUC by LR = 0.595). The predictive power of almost all individual anthropometric measures was higher for HDL than for LDL, and the predictive power for both HDL and LDL in women was higher than for men. A combination of anthropometric measures slightly improved the predictive power for both HDL and LDL cholesterol. The best indicator for HDL and LDL might differ according to the type of cholesterol and the gender. In women, but not men, age was the variable that strongly predicted HDL and LDL cholesterol levels. Our findings provide new information for the development of better initial screening tools for HDL and LDL cholesterol.
关键词： low-density lipoproteins (LDLs),classification,machine learning,predictor,data mining,statistical data analysis,Anthropometry,high-density lipoproteins (HDLs)
更新于2025-09-19 17:13:59
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Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput <i>Ab Initio</i> Calculations and Machine Learning
摘要： Traditional trial-and-error methods are obstacles for large-scale searching of new optoelectronic materials. Here, we introduce a method combining high-throughput ab initio calculations and machine-learning approaches to predict two-dimensional octahedral oxyhalides with improved optoelectronic properties. We develop an effective machine-learning model based on an expansive data set generated from density functional calculations including the geometric and electronic properties of 300 two-dimensional octahedral oxyhalides. Our model accelerates the screening of potential optoelectronic materials of 5000 two-dimensional octahedral oxyhalides. The distorted stacked octahedral factors proposed in our model play essential roles in the machine-learning prediction. Several potential two-dimensional optoelectronic octahedral oxyhalides with moderate band gaps, high electron mobilities, and ultrahigh absorbance coefficients are successfully hypothesized.
关键词： band gaps,optoelectronic materials,two-dimensional octahedral oxyhalides,absorbance coefficients,electron mobilities,high-throughput ab initio calculations,machine learning
更新于2025-09-19 17:13:59
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Underwater laser micro-milling of fine-grained aluminium and the process modelling by machine learning
摘要： Nanosecond-pulsed laser ablation is often accompanied by adverse thermal effects such as oxidation, debris recast and burr formation. To reduce these effects, in this paper, the authors present the underwater laser milling process using RSA-905 fine-grained aluminium as the target material for the first time. The results show that channels up to 200 μm in width, 700 μm depth and bottom roughness around 1 μm Ra could be fabricated with reduced thermal effects. By conducting multi- and single-factor experiments, empirical models relating the laser processing parameters to the key dimensions of channels were derived using artificial neural network (ANN) algorithm and polynomial regression (PR), and the models’ accuracies were evaluated. Based on the models, the cross-section profile of a channel subject to a given set of processing parameters can be predicted. The process can serve as a pre-treatment technique of mechanical milling such that the tool life will be extended and the profile of a desired feature can be precisely defined.
关键词： Regression analysis,Channel fabrication,Machine learning,Underwater laser machining,Burr-free
更新于2025-09-19 17:13:59
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Microscopy and Analysis || Automatic Interpretation of Melanocytic Images in Confocal Laser Scanning Microscopy
摘要： The frequency of melanoma doubles every 20 years. The early detection of malignant changes augments the therapy success. Confocal laser scanning microscopy (CLSM) enables the noninvasive examination of skin tissue. To diminish the need for training and to improve diagnostic accuracy, computer-aided diagnostic systems are required. Two approaches are presented: a multiresolution analysis and an approach based on deep layer convolutional neural networks. For the diagnosis of the CLSM views, architectural structures such as micro-anatomic structures and cell nests are used as guidelines by the dermatologists. Features based on the wavelet transform enable an exploration of architectural structures at different spatial scales. The subjective diagnostic criteria are objectively reproduced. A tree-based machine-learning algorithm captures the decision structure explicitly and the decision steps are used as diagnostic rules. Deep layer neural networks require no a priori domain knowledge. They are capable of learning their own discriminatory features through the direct analysis of image data. However, deep layer neural networks require large amounts of processing power to learn. Therefore, modern neural network training is performed using graphics cards, which typically possess many hundreds of small, modestly powerful cores that calculate massively in parallel. Readers will learn how to apply multiresolution analysis and modern deep learning neural network techniques to medical image analysis problems.
关键词： convolutional neural networks,skin lesions,multiresolution image analysis,computer-aided diagnosis,confocal laser scanning microscopy,machine learning
更新于2025-09-19 17:13:59
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Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies
摘要： Knowledge of the seed vigor status of individual wheat kernels could provide scientific evidence for the screening of excellent germplasm and the breeding of seedlings. Although many factors collaborate to reduce or render seed vigor, many methods have been employed to detect individual kernel vigor. This study aims to demonstrate the feasibility for using near-infrared (NIR) spectroscopy to detect individual wheat seed vigor and determine suitable machine learning classification models. For this study, 1152 wheat kernel samples were selected, and five-sixths of the portion was treated by artificial aging (AA). All seeds spectra were acquired using a single-seed near-infrared system covering the spectral range of 1200–2400 nm. After NIR spectral collection, all kernels underwent a germination test to confirm their vigor. The spectral data from kernels within 3 germination days, 5 germination days and the non-germination kernels were further used for the development of three-category classification models. After pretreatment by using Savitzky-Golay (SG) second derivative-method and standard normal variate (SNV) correction, the high-dimension spectral data were smoothed, and then were reduced to select most effective wavelengths by two spectral dimensional reduction algorithms: principal component analysis (PCA) and successive projections algorithm (SPA). Four machine learning methodologies, support vector machine (SVM), extreme learning machine (ELM), random forest (RF) and adaptive boosting (AdaBoost) were combined with the two spectral dimensional reduction algorithms to build eight models to discriminate and predict each wheat kernel’s vigor. The results demonstrated that the eight three-category machine learning classification models developed with the two spectral dimensional reduction algorithms provided comparable results for individual wheat kernel vigor. The accuracies of the eight models were higher than 84.0%, and PCA-ELM and SPA-RF models afforded the two highest classification accuracies at 88.9% and 88.5%, respectively. The macro-average F1 of these two models were at the same level of 0.887, which means these two models had almost the same ability to assess kernel’s vigor. This study could serve as a major step towards the development of a fast and non-destructive high-throughput NIR-based sorting system of individual wheat kernel vigor determination for plant breeders, wheat quality inspectors, wheat processors, etc.
关键词： Multiple classification,Machine learning,Near-infrared spectroscopy,Multivariate data analysis,Wheat seed vigor
更新于2025-09-19 17:13:59
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Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells
摘要： Perovskite solar cells (PSCs) have recently received considerable attention due to the high energy conversion efficiency achieved within a few years of their inception. However, a machine learning (ML) approach to guide the development of high-performing PSCs is still lacking. In this paper ML is used to optimize material composition, develop design strategies, and predict the performance of PSCs. The ML models are developed using 333 data points selected from about 2000 peer reviewed publications. These models guide the design of new perovskite materials and the development of high-performing solar cells. Based on ML guidance, new perovskite compositions are experimentally synthesized to test the practicability of the model. The ML model also shows its ability to predict underlying physical phenomena as well as the performance of PSCs. The PSC model matches well with the theoretical prediction by the Shockley and Queisser limit, which is almost impossible for a human to find from an ensemble of data points. Moreover, strategies for developing high-performing PSCs with different bandgaps are also derived from the model. These findings show that ML is very promising not only for predicting the performance, but also for providing a deeper understanding of the physical phenomena associated with the PSCs.
关键词： perovskite solar cells,machine learning,perovskite materials,bandgap prediction
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - A Linear Relation behind Outstanding Crystalline Silicon Solar Cells
摘要： When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, ?rst through statistical analysis, we ?nd that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we veri?ed the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classi?ers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance.
关键词： imbalance problem,attribute selection,phone changing prediction,machine learning,Mobile big data
更新于2025-09-19 17:13:59