Suggestion Mining from Opinionated Text of Big Social Media Data

Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.     

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.     

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.     

Magnetohydrodynamic stagnation point flow of third-grade liquid toward variable sheet thickness

Neural Computing and Applications - The current study examines the boundary layer stagnation point flow of third-grade fluid toward a stretching surface with variable thickness. Electrically...     

A theoretical 2D image model for locating 3D targets

To construct a water quality monitoring system, challenging issues need to be addressed regarding the acquisition of target information (e.g. 3D location and occlusion) as well as the behavioural analysis of aquatic organisms. This paper presents a novel 3D information acquisition and location method, by means of an information acquisition platform consisting of a monitoring terminal, frame grabbers, a single camera and a single mirror. Using this platform, we propose a theoretical 2D image model for locating 3D targets and then validate it using data obtained from both real and artificial fish. The proposed model is based on the principles of light refraction, plane mirror imaging, underwater objects and camera imaging as well as the technologies of digital to analog conversion and object segmentation. In contrast with existing methods, our method can accurately reflect 3D information of aquatic organisms, thus providing critical technical support for the development of water quality monitoring systems in the future.     

Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering

This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.     

Data interpretation for spectral sensors with correlated bands

New classes of spectral sensors are emerging that have significant overlap in the band spectral response functions. While conventional sensors such as the Multispectral Thermal Images (MTI) or Landsat may have responses with a few percent overlap between adjacent bands, some of the emerging sensors can have more than 50% correlation among all spectral bands. The traditional geometrical models used to describe spectral data fail when such high levels of correlation exist. In this paper we present a generalized geometrical model that relies on functional analysis. We define a sensor space and a scene space that can be used to characterize the suitability of a sensor for a particular spectral sensing task. We demonstrate that classifiers based on first-order distance and angle metrics fail for sensors with highly correlated bands unless appropriate preprocessing is carried out. We further show that second-order statistical classifiers are largely immune to many of the problems introduced by the correlated band responses.     

Enzymolysis reaction kinetics and thermodynamics of rapeseed protein with sequential dual‐frequency ultrasound pretreatment

We evaluated the effect of sequential dual‐frequency ultrasound (SDFU) pretreatments on rapeseed protein enzymolysis, using alcalase as a model enzyme. Hydrolysed protein concentrations, enzymolysis kinetics and thermodynamic parameters were investigated. The results showed that the hydrolysed rapeseed protein concentration following SDFU pretreatments was higher compared to that of the control for up to 75 min of enzymolysis at various substrate concentrations of 5–25 g L^?1; both control and SDFU pretreatment groups showed first‐order reaction kinetics. Compared to the control, the Michaelis–Menten constant (K_M) value decreased remarkably by 17.61%, while an increase in the binding frequency between enzyme and substrate (K_A) by 10.47% was observed. The thermodynamic parameters, enthalpy, entropy and activation energy were reduced in the SDFU pretreatment group compared to the control by 31.78%, 18.0% and 29.56%, respectively. SDFU pretreatment showed little effect on Gibbs free energy at the various temperatures studied.