scholarly journals Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites

2019 ◽  
pp. 290-300
Author(s):  
F. Femila ◽  
S. Janakipriya ◽  
R. B. Nivetha Sruthi ◽  
S. Rohini
Keyword(s):  
Fraud Detection ◽  
Synthetic Dataset ◽  
Time Interval ◽  
Product Reviews ◽  
Aggregation Method ◽  
Product Review ◽  
Global Anomaly ◽  
Effective Product ◽  
Short Time ◽  
Fake Reviews

The percentage of purchasing products by the user has been increased drastically through web. Users even have the facility of sharing their thoughts about the particular product on web in the form of reviews, blogs, comments etc. Many users read review information given on web to take decisions for buying products. Some users may give the reviews for hyping the sale of the product or to decrease the sale. This may confuse the customers who rely on the reviews to buy a product. So, there is a need to find the honest reviews and remove fake reviews that are added by malicious or fraud user. The proposed system comes up with the solution for this problem. Leading events has been used to find the time interval between the reviews. The proposed system mines the active periods such as leading sessions to accurately locate the hierarchical fraud. These leading sessions can be useful for detecting the local anomaly instead of global anomaly of product reviews. After this to analyze the rating, reviews and hierarchy of the product we examine three facts, they are rating based facts, review based facts and hierarchy facts. In addition, we propose an optimization-based aggregation method to integrate all the facts for fraud detection. The evaluations of this optimization are done on synthetic dataset that are collected. The classified and summarized product review information helps web users to understand review contents easily in a short time.

Survey on Review Spam of E-Commerce Sites

2014 ◽  
Vol 631-632 ◽  
pp. 1190-1193
Author(s):  
Sheng Xiu Yang ◽  
Lu Jie Fan
Keyword(s):  
Online Shopping ◽  
Important Determinant ◽  
Spam Detection ◽  
Product Reviews ◽  
Product Review ◽  
Review Spam ◽  
Quality Of Products ◽  
Fake Reviews ◽  
Potential Customers

Online shopping reviews provide valuable information for customers to compare the quality of products, and many other aspects of future purchases. People increasingly rely on information from E-commerce reviews. Product reviews is an important determinant of potential customers’ buying choices. However, spammers are joining this community to try to mislead consumers by writing fake or unfair reviews to confuse the consumers. Fake product review detection makes an attempt to detect fake reviews and remove them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on review spam. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect review spam are introduced and summarized. The trend of review spam detection is concluded finally.

How to Make Effective Product Review Videos

2020 ◽  
Vol 18 (4) ◽  
pp. 73-92
Author(s):  
Tathagata Ghosh
Keyword(s):  
Present Article ◽  
User Generated Content ◽  
Product Reviews ◽  
Technical Quality ◽  
Product Review ◽  
Internet Users ◽  
Effective Product

Extant research on online user-generated content, especially product reviews, has consistently examined the effects of textual reviews on consumers and has ignored an emerging and popular review format such as product review video (PRV) on YouTube. Specifically, no research exists that suggests how to develop an effective PRV. The present article addresses this gap by examining the effects of three important attributes of PRVs – review depth, review frame, and review disposition, on consumers' attitude toward the PRV and their propensity to share it. Technical quality of the video is included as a moderator. A between-subjects experiment was conducted with a sample of Internet users. The findings suggest that PRVs are most effective when the review depth is moderate, the review is comparative in nature, and it highlights product benefits instead of attributes. Technical quality positively moderates these afore-mentioned relationships.

scholarly journals Neuro-Inspired Computing with Spin-VCSELs

2021 ◽  
Vol 11 (9) ◽  
pp. 4232
Author(s):  
Krishan Harkhoe ◽  
Guy Verschaffelt ◽  
Guy Van der Sande
Keyword(s):  
Processing Speed ◽  
Delay Time ◽  
High Speed ◽  
State Of The Art ◽  
Polarization Modulation ◽  
Time Interval ◽  
Computing Technique ◽  
High Modulation ◽  
Short Time ◽  
Modulation Speed

Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals Optimal Perturbations of an Oceanic Vortex Lens

Fluids ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 63 ◽  
Author(s):  
Thomas Meunier ◽  
Claire Ménesguen ◽  
Xavier Carton ◽  
Sylvie Le Gentil ◽  
Richard Schopp
Keyword(s):  
Normal Mode ◽  
Growth Rates ◽  
Time Interval ◽  
Time Intervals ◽  
Horizontal Structure ◽  
Azimuthal Wave ◽  
Wave Numbers ◽  
Non Linear ◽  
Long Time ◽  
Short Time

The stability properties of a vortex lens are studied in the quasi geostrophic (QG) framework using the generalized stability theory. Optimal perturbations are obtained using a tangent linear QG model and its adjoint. Their fine-scale spatial structures are studied in details. Growth rates of optimal perturbations are shown to be extremely sensitive to the time interval of optimization: The most unstable perturbations are found for time intervals of about 3 days, while the growth rates continuously decrease towards the most unstable normal mode, which is reached after about 170 days. The horizontal structure of the optimal perturbations consists of an intense counter-shear spiralling. It is also extremely sensitive to time interval: for short time intervals, the optimal perturbations are made of a broad spectrum of high azimuthal wave numbers. As the time interval increases, only low azimuthal wave numbers are found. The vertical structures of optimal perturbations exhibit strong layering associated with high vertical wave numbers whatever the time interval. However, the latter parameter plays an important role in the width of the vertical spectrum of the perturbation: short time interval perturbations have a narrow vertical spectrum while long time interval perturbations show a broad range of vertical scales. Optimal perturbations were set as initial perturbations of the vortex lens in a fully non linear QG model. It appears that for short time intervals, the perturbations decay after an initial transient growth, while for longer time intervals, the optimal perturbation keeps on growing, quickly leading to a non-linear regime or exciting lower azimuthal modes, consistent with normal mode instability. Very long time intervals simply behave like the most unstable normal mode. The possible impact of optimal perturbations on layering is also discussed.

Estimation of Time-Dependent Turning Fractions at Signalized Intersections

1998 ◽  
Vol 1644 (1) ◽  
pp. 142-149 ◽  
Author(s):  
Gang-Len Chang ◽  
Xianding Tao
Keyword(s):  
Signalized Intersections ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Time Interval ◽  
Short Time Interval ◽  
Simulation Experiments ◽  
Proposed Model ◽  
Short Time ◽  
Estimation Of Time ◽  
Additional Constraints

An effective method for estimating time-varying turning fractions at signalized intersections is described. With the inclusion of approximate intersection delay, the proposed model can account for the impacts of signal setting on the dynamic distribution of intersection flows. To improve the estimation accuracy, the use of preestimated turning fractions from a relatively longer time interval has been proposed to serve as additional constraints for the same estimation but over a short time interval. The results of extensive simulation experiments indicated that the proposed method can yield sufficiently accurate as well as efficient estimation of dynamic turning fractions for signalized intersections.

Micro-Expression Recognition using 3D - CNN

2020 ◽  
pp. 5-13
Author(s):  
Vishal Dubey ◽  
◽  
◽  
◽  
Bhavya Takkar ◽  
...  
Keyword(s):  
Emotional Response ◽  
High Speed ◽  
Actual State ◽  
Time Interval ◽  
Short Time Interval ◽  
Expression Recognition ◽  
Specific Emotion ◽  
3D Cnn ◽  
Short Time ◽  
Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

scholarly journals Inter-rater and inter-device reliability of mechanical thresholds measurement with the Electronic von Frey Anaesthesiometer and the SMALGO in healthy cats

2018 ◽  
Vol 21 (10) ◽  
pp. 979-984 ◽  
Author(s):  
Chiara Adami ◽  
Elena Lardone ◽  
Paolo Monticelli
Keyword(s):  
Small Animal ◽  
Time Interval ◽  
Short Time Interval ◽  
Sensory Thresholds ◽  
Additional Tool ◽  
Bland Altman Method ◽  
Device Reliability ◽  
Short Time ◽  
Mechanical Thresholds ◽  
Level Of Agreement

Objectives The aim of this study was to compare the Electronic von Frey Anaesthesiometer (EVF) and the Small Animal ALGOmeter (SMALGO), used to measure sensory thresholds in 13 healthy cats at both the stifle and the lumbosacral joint, in terms of inter-rater and inter-device reliability. Methods Two independent observers carried out the sets of measurements in a randomised order, with a 45 min interval between them, in each cat. The inter-rater and inter-device reliability were evaluated by calculating the inter-rater correlation coefficient (ICC) for each pair of measurements. The Bland–Altman method was used as an additional tool to assess the level of agreement between the two algometers. Results The mean ± SD sensory thresholds measured with the EVF were 311 ± 116 g and 378 ± 178 g for the stifle and for the lumbosacral junction, respectively, whereas those measured with the SMALGO were 391 ±172 g and 476 ± 172 g. The inter-rater reliability was fair (ICC >0.4) for each pair of measurements except those taken at the level of the stifle with the SMALGO, for which the level of agreement between observers A and B was poor (ICC = 0.01). The inter-device reliability was good (ICC = 0.73; P = 0.001). The repetition of the measurements affected reliability, as the thresholds obtained after the 45 min break were consistently lower than those measured during the first part of the trial ( P = 0.02). Conclusions and relevance The EVF and the SMALGO may be used interchangeably in cats, especially when the area to be tested is the lumbosacral joint. However, when the thresholds are measured at the stifle, the inter-observer reliability is better with the EVF than with the SMALGO. The reliability decreases when the measurements are repeated within a short time interval, suggesting a limited clinical applicability of quantitative sensory testing with both algometers in cats.

Sign in / Sign up

Export Citation Format

Share Document