Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features

Fraudulent online sellers often collude with reviewers to garner fake reviews for their products. This act undermines the trust of buyers in product reviews, and potentially reduces the effectiveness of online markets. Being able to accurately detect fake reviews is, therefore, critical. In this study, we investigate several preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to build a fake review detection system. Given the nature of product review data, where the number of fake reviews is far less than that of genuine reviews, we look into the results of each class in detail in addition to the overall results. We recognise from our preliminary analysis that, owing to imbalanced data, there is a high imbalance between the accuracies for different classes (e.g., 1.3% for the fake review class and 99.7% for the genuine review class), despite the overall accuracy looking promising (around 89.7%). We propose two dynamic random sampling techniques that are possible for textual-based featuring methods to solve this class imbalance problem. Our results indicate that both sampling techniques can improve the accuracy of the fake review class—for balanced datasets, the accuracies can be improved to a maximum of 84.5% and 75.6% for random under and over-sampling, respectively. However, the accuracies for genuine reviews decrease to 75% and 58.8% for random under and over-sampling, respectively. We also discover that, for smaller datasets, the Adaptive Boosting ensemble model outperforms other single classifiers; whereas, for larger datasets, the performance improvement from ensemble models is insignificant compared to the best results obtained by single classifiers

Using a hybrid content-based and behaviour-based featuring approach in a parallel environment to detect fake reviews

The financial impact of positive reviews has prompted some fraudulent sellers to generate fake product reviews for either promoting their products or discrediting competing products. Many e-commerce portals have implemented measures to detect such fake reviews, and these measures require excellent detectors to be effective. In this work, we propose 133 unique features from the combination of content and behaviour-based features to detect fake reviews using machine learning classifiers. Preliminary results show that these features can provide good results for all datasets tested. Detailed analysis of the results, however, reveals the existence of class imbalance issues for two of the bigger datasets - there is a high imbalance between the accuracies of different classes (e.g., 7.73% for the fake class and 99.3% for the genuine class using a Multilayer Perceptron classifier). We therefore introduce two sampling methods that can improve the accuracy of the fake review class on balanced datasets. The accuracies can be improved to a maximum of 89% for both random under and oversampling on Convolutional Neural Networks. Additionally, we propose a parallel cross-validation method that can speed up the validation process in a parallel environment

Digital Flow Control of Electroosmotic Pump: Onsager Coefficients and Interfacial Parameters Determination

Electroosmosis and streaming potential are two complementary electrokinetic processes related by the Onsager relation. In particular, an electroosmotic pump (EOP) is potentially useful for a variety of engineering and bio-related applications. By fabricating samples consisting of dry-etched cylindrical pores (50 mu m in length and 3.5 mu m in diameter) on silicon wafers, we demonstrate that the use of digital control via voltage pulses can resolve the flow regulation and stability issues associated with the EOP, so that the intrinsic characteristics of the porous sample medium may be revealed. Through the consistency of the measured electroosmosis (EO) and the streaming potential (SP) coefficients as required by the Onsager relation, we deduce the zeta potential and the surface conductivity, both physical parameters pertaining to the liquid-solid interface. (C) 2011 Elsevier Ltd. All rights reserved

Enhanced photocatalytic performance of TiO2-ZnO hybrid nanostructures

The author\u27s studied the photocatalytic properties of rational designed TiO2-ZnO hybrid nanostructures, which were fabricated by the site-specific deposition of amorphous TiO2 on the tips of ZnO nanorods. Compared with the pure components of ZnO nanorods and amorphous TiO2 nanoparticles, these TiO2-ZnO hybrid nanostructures demonstrated a higher catalytic activity. The strong green emission quenching observed from photoluminescence of TiO2-ZnO hybrid nanostructures implied an enhanced charge transfer/separation process resulting from the novel type II heterostructures with fine interfaces. The catalytic performance of annealing products with different TiO2phase varied with the annealing temperatures. This is attributed to the combinational changes in Egof the TiO2phase, the specific surface area and the quantity of surface hydroxyl groups

Physio-Biochemical and Transcriptomic Features of Arbuscular Mycorrhizal Fungi Relieving Cadmium Stress in Wheat

Arbuscular mycorrhizal fungi (AMF) can improve plant cadmium (Cd) tolerance, but the tolerance mechanism in wheat is not fully understood. This study aimed to examine the physiological properties and transcriptome changes in wheat inoculated with or without Glomus mosseae (GM) under Cd stress (0, 5, and 10 mg·kg−1 CdCl2) to understand its role in wheat Cd tolerance. The results showed that the Cd content in shoots decreased while the Cd accumulation in roots increased under AMF symbiosis compared to the non-inoculation group and that AMF significantly promoted the growth of wheat seedlings and reduced Cd-induced oxidative damage. This alleviative effect of AMF on wheat under Cd stress was mainly attributed to the fact that AMF accelerated the ascorbate-glutathione (AsA-GSH) cycle, promoted the production of GSH and metallothionein (MTs), improved the degradation of methylglyoxal (MG), and induced GRSP (glomalin-related soil protein) secretion. Furthermore, a comparative analysis of the transcriptomes of the symbiotic group and the non-symbiotic group revealed multiple differentially expressed genes (DEGs) in the ‘metal ion transport’, ‘glutathione metabolism’, ‘cysteine and methionine metabolism’, and ‘plant hormone signal transduction’ terms. The expression changes of these DEGs were basically consistent with the changes in physio-biochemical characteristics. Overall, AMF alleviated Cd stress in wheat mainly by promoting immobilization and sequestration of Cd, reducing ROS production and accelerating their scavenging, in which the rapid metabolism of GSH may play an important role

The value of mediastinoscopy in the staging of lung cancer with clinical N2 disease

Background and objective To determine the value of mediastinoscopy in the mediastinal staging of lung cancer with clinical N2 disease. Methods We retrospectively reviewed 87 patients received mediastinoscopy for known or suspected lung cancer, including 83 cervical mediastinoscopies and 4 parasternal mediastinoscopies. All patients were staged clinical N2 for enlarged ipsilateral mediastinal and/or subcarinal lymph nodes (short axis > 1.0 cm)on computed tomography scan. Results Of the 87 patients, 61 cases proved N2 disease on mediastinoscopy. The other 26 mediastinoscopy-negative patients underwent thoracotomy for lung resection and mediastinal lymph node dissection in the same operative session. Final pathologic N staging were consistent for mediastinoscopic sampling and surgical dissection in 24 patients. N2 disease was found in 2 patients (false-negative of mediastinoscopy). The sensitivity, specificity, and accuracy of mediastinoscopy were 96.8%, 100%, and 97.7%, respectively. There was no mortality and only 1 complication(1.1%) for all 87 mediastinoscopic procedures. Conclusion Mediastinoscopy is a highly effective and safe procedure for the mediastinal staging of lung cancer with clinical N2 disease

Physiological and proteomic analyses reveal the important role of arbuscular mycorrhizal fungi on enhancing photosynthesis in wheat under cadmium stress

Arbuscular mycorrhizal fungi (AMF) are important in the phytoremediation of cadmium (Cd). Improving photosynthesis under Cd stress helps to increase crop yields. However, the molecular regulatory mechanisms of AMF on photosynthetic processes in wheat (Triticum aestivum) under Cd stress remain unclear. This study utilized physiological and proteomic analyses to reveal the key processes and related genes of AMF that regulate photosynthesis under Cd stress. The results showed that AMF promoted the accumulation of Cd in the roots of wheat but significantly reduced the content of Cd in the shoots and grains. The photosynthetic rates, stomatal conductance, transpiration rates, chlorophyll content, and accumulation of carbohydrates under Cd stress were increased by AMF symbiosis. Proteomic analysis showed that AMF significantly induced the expression of two enzymes involved in the chlorophyll biosynthetic pathway (coproporphyrinogen oxidase and Mg-protoporphyrin IX chelatase), improved the expression of two proteins related to CO2 assimilation (ribulose-1,5-bisphosphate carboxylase and malic enzyme), and increased the expression of S-adenosylmethionine synthase, which positively regulates abiotic stress. Therefore, AMF may regulate photosynthesis under Cd stress by promoting chlorophyll biosynthesis, carbon assimilation, and S-adenosylmethionine metabolism