基于特征选择和深度信念网络的文本情感分类算法

由于人类语言的复杂性，文本情感分类算法大多都存在因为冗余而造成的词汇量过大的问题。深度信念网络（DBN）通过学习输入语料中的有用信息以及它的几个隐藏层来解决这个问题。然而对于大型应用程序来说，DBN是一个耗时且计算代价昂贵的算法。针对这个问题，提出了一种半监督的情感分类算法，即基于特征选择和深度信念网络的文本情感分类算法（FSDBN）。首先使用特征选择方法（文档频率（DF）、信息增益（IG）、卡方统计（CHI）、互信息（MI））过滤掉一些不相关的特征从而使词汇表的复杂性降低；然后将特征选择的结果输入到DBN中，使得DBN的学习阶段更加高效。将所提算法应用到中文以及维吾尔语中，实验结果表明在酒店评论数据集上，FSDBN在准确率方面比DBN提高了1.6%，在训练时间上比DBN缩短一半。     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

Antioxidant and antimicrobial potential of cajazeira leaves (Spondias mombin) extracts

Cajazeira leaves (Spondias mombin) have their highlighted use as antioxidant and natural antimicrobial, which justifies the objective of this work to evaluate the biological activities of different extracts. In order to evaluate the antioxidant and antimicrobial potentials of the cajazeira leaves, extractions at low pressure and high pressure were performed. The low pressure extractions (PLE) were carried out using Soxhlet (SOX) and tip ultrasound, using different solvents. Supercritical fluid extraction (SFE) was evaluated at temperature of 40?60°C and pressure of 150?300 bar besides extraction with cosolvent. Higher yields were obtained with the use of more polar solvents at LPE. The extracts obtained by SOX with ethanol and others polar solvents presented the best TPC values and antioxidant activity. The extracts at LPE with hexane and ethyl acetate and SFE presented better antimicrobial activity. Through liquid chromatography of high efficiency, it was possible to identify compounds with recognized biological activity, like ellagic acid, gallic acid and catechin.     

Extraction of protein from food waste: An overview of current status and opportunities

The chief intent of this review is to explain the different extraction techniques and efficiencies for the recovery of protein from food waste (FW) sources. Although FW is not a new concept, increasing concerns about chronic hunger, nutritional deficiency, food security, and sustainability have intensified attention on alternative and sustainable sources of protein for food and feed. Initiatives to extract and utilize protein from FW on a commercial scale have been undertaken, mainly in the developed countries, but they remain largely underutilized and generally suited for low-quality products. The current analysis reveals the extraction of protein from FW is a many-sided (complex) issue, and that identifies for a stronger and extensive integration of diverse extraction perspectives, focusing on nutritional quality, yield, and functionality of the isolated protein as a valued recycled ingredient.     

Heat transfer trait simulation of H finned tube in ventilation methane oxidation steam generator for hydrogen production

With the steam obtained by the energy released from ventilation air methane oxidation, the hydrogen production through gasification method is considered more commercial. In order to constrain the steam parameter fluctuation, the shunt honeycomb ceramics are adopted to fill between the heat exchange tubes. However, transient heat transfer characteristics of this kind of heat exchanger have not been fully studied. This paper carried out a numerical simulation study on the transient heat transfer characteristics of H finned tube under periodic reverse-flow conditions. Results show that the existence of shunt honeycomb ceramics enhances the effect of radiation. Gas flow direction reversing destructs the original boundary layer, achieving a sudden rise of the convective heat flux in the new upstream. It takes about 12s to form a new relative stable boundary layer. The apparent heat transfer coefficient achieves a maximum of 108.77 W m⁻² K⁻¹ and an average of 99.1 W m⁻² K⁻¹.     

Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.