Figure - available from: Information Systems and e-Business Management
This content is subject to copyright. Terms and conditions apply.
The overall framework of the intelligent search engine system

The overall framework of the intelligent search engine system

Source publication
Article
Full-text available
Out of commercial interests, merchants will hire professional writers to write good reviews for their products or write bad reviews for competitors, which has a serious adverse impact on the ecological development of e-commerce platforms. This article uses the feature set of product reviews as an entry point, and uses a deep-confidence network algo...

Similar publications

Article
Full-text available
The timbre and volume of a single tone are among its fundamental characteristics. The single-tone detection technology is the key to the foundation of MNFR (musical note feature recognition), which is built on the fundamental feature extraction of single tones. A MNFR method based on LSTM (long short-term memory) is proposed because traditional met...
Article
Full-text available
Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to t...

Citations

... In its study of spam reviews, [39], the authors also applied LSTM to investigate the semantic features of the reviews. ...
... Multi-dimensional features combined with LSTM and Capsule Network for detection [38] LSTM Unsupervised learning-based LSTM model effectively distinguishes real reviews from spam [39] LSTM, CNN, Deep Belief Network LSTM and CNN used for semantic and discrete feature detection, DBN for credibility [40] Self Attention-based Self Attention-based approach achieves high performance in detecting spam reviews • RBF kernels : ...
Article
Full-text available
Online reviews are important information that customers seek when deciding to buy products or services. Also, organizations benefit from these reviews as essential feedback for their products or services. Such information required reliability, especially during the Covid-19 pandemic which showed a massive increase in online reviews due to quarantine and sitting at home. Not only the number of reviews was boosted but also the context and preferences during the pandemic. Therefore, spam reviewers reflect on these changes and improve their deception technique. Spam reviews usually consist of misleading, fake, or fraudulent reviews that tend to deceive customers for the purpose of making money or causing harm to other competitors. Hence, this work presents a Weighted Support Vector Machine (WSVM) and Harris Hawks Optimization (HHO) for spam review detection. The HHO works as an algorithm for optimizing hyperparameters and feature weighting. Three different language corpora have been used as datasets, namely English, Spanish, and Arabic in order to solve the multilingual problem in spam reviews. Moreover, pre-trained word embedding (BERT) has been applied alongside three-word representation methods (NGram-3, TFIDF, and One-hot encoding). Four experiments have been conducted, each focused on solving and demonstrating different aspects. In all experiments, the proposed approach showed excellent results compared with other state-of-the-art algorithms. In other words, the WSVM-HHO achieved an accuracy of 88.163%, 71.913%, 89.565%, and 84.270%, for English, Spanish, Arabic, and Multilingual datasets, respectively. Further, a deep analysis has been conducted to investigate the context of reviews before and after the COVID-19 situation. In addition, it has been generated to create a new dataset with statistical features and merge its previous textual features for improving detection performance.
... Literature [10] used the multiscale characteristics of wavelet decomposition to embed a self-similar watermark into the first and second level detail coefficients of wavelet decomposition, respectively. A method of embedding watermarks in the wavelet coefficients of visual importance was proposed [11]. e algorithm searched for the coefficients of visual importance and embedded watermarks successively. ...
Article
Full-text available
According to the characteristics of traditional clothing, clothing identification is studied, and clothing identification and clothing culture learning are effectively combined to find a new method for the inheritance of national culture and strive to make contributions to the inheritance of national culture. According to the requirement of the traditional garment identification watermark monitoring system, a self-synchronous digital watermarking algorithm is designed and implemented. Watermark is embedded in the time domain, and feature information is extracted from traditional clothing by means of mean filtering and replaced by watermark to achieve the purpose of embedding information. Blind detection is realized without the participation of the original image. The difference between the traditional costume embedded with watermark and the original traditional costume is almost imperceptible. It can effectively resist synchronous attacks including clipping and time shifting, showing good robustness. Imperceptibility and robustness can be adjusted freely by embedding strength. The HOG + SVM algorithm is applied to minority clothing classification and recognition. By comparing different classifiers, it is concluded that the classifier trained by the support vector machine algorithm has the best classification effect on ethnic clothing. In order to improve the classification effect, the classical algorithm of color, texture, and shape feature extraction was combined with SVM to conduct experiments on the clothing database collected and sorted out in Yunnan ethnic minority communities, and finally, we verified that the HOG feature combined with the SVM classification algorithm achieved good results in the classification of ethnic clothing.
... However, it is difficult to extract features from the images that can characterize the shelf state because the warehouse images collected in real application scenarios are affected by the type of goods, shelf color, lighting changes, and shooting angles. The unsupervised learning algorithms are: principal component analysis (PCA) (Assia et al. 2018), traditional clustering methods (Hedjam et al. 2021), selforganizing mapping (SOM) (Chen and Huang 2020), deep confidence networks (DBN) (Zhou and Zhang 2021), deep Boltzmann machines (DRBM) (Tao 2021), self-encoder (AE) (Tang et al. 2021), and generative adversarial networks (GAN) (Huang et al. 2015). The first three unsupervised learning algorithms mentioned above have the same drawbacks as traditional supervised classification algorithms and are therefore not applicable to library status detection. ...
Article
Full-text available
Growing international trade requires more flexible warehouse management to match it. In order to achieve more effective warehouse management efficiency, a shelf status–detection method based on deep learning is proposed. Firstly, the image acquisition of a multi-level shelf containing multiple bays is performed under different time and lighting conditions. Due to the difference in image characteristics between the bottom shelf on the ground and the upper shelf on the non-ground level, the collected images were divided into two groups: floor images and shelf images; and the warehouse status recognition was performed on the two groups separately. The two sets of images are cropped and center projection transformed separately to obtain the region of interest. On this basis, the improved residual network model is used to construct different depot detection models for the two sets of images, respectively, and the above algorithm is verified by actual measurements. In this paper, 102,614 images of 3246 depots with different states of non-ground layer, and 27,903 images of ground layer are collected. They are divided into training set and test set according to the ratio of 4:1, and the accuracy of training set is 99.6%, and the accuracy of test set is 99.3%. The experimental outcomes provide a theoretical method and technical support for the intelligent warehouse system management.
... The experimental results reveal that their framework can distinguish spam from real reviews efficiently. Zhou and Zhang [141] also employed the LSTM to study the semantic features of spam reviews. The authors used Deep Belief Network to detect the credibility of product reviews and CNN for discrete features extraction. ...
Article
Full-text available
During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.
... In this example, true positive means forecasting customers with poor real credit as poor credit customers. While FN is an abbreviation for False Negative, it refers to predicting customers with poor credit in the sports industry (Zhou and Zhang 2021). Similarly, FP and TN are abbreviations for False Positive and True Negative, which predict the real sports industry as low credit and sports industry, respectively. ...
Article
Full-text available
The rapid development and advancement in Internet technology have provided a common interface to different sporting companies to use the Internet as a platform to develop business models for the sports industry. The number of players and supporting staff increases with the passage of time and they generate data with a much higher speed, resulting in a large volume of sports-related data. The analysis and forecasting of such a huge quantities of data is a great task that cannot be solved using standard methods. To handle this issue, we designed and developed an intelligent deep learning (DL) model based on the concept of stacked self-coding network, which uses a DL framework for decision making at the backend. The proposed model can learn and select the embedding of original features in the high dimensional, sparse, and noisy big data environment of the sports industry. The dataset used for the analysis and implementation purpose consists of a total of 2,053,500 samples, which is further divided into six sample sets by introducing random noise of different intensities to the test sample set. The performance of the proposed model in risk assessment and feature embedding learning is also described from a visual analysis perspective. Up to a point, the interpretability of DL technology in the sports business is also investigated and examined. The experimental results of credit risk assessment attained via the proposed model are compared with the traditional method, which verifies the effectiveness and significance of the proposed model. In terms of noise decrease, the exploratory comes approximately appears that the proposed model encompasses a higher disturbance resistance capability towards huge information in a loud environment, as well as for the input information with noise obstructions.
... Compared with previous methods, deep learning has been greatly improved in the fields of sound, image and pattern recognition [8]. is is an integral part of learning multiple features, and each level learns the form of feature expression. In recent years, Deep Belief Networks have been widely used and achieved very good results [9]. In order to better understand the following chapters, this chapter provides the basic principles of BP neural network and convinced network. ...
Article
Full-text available
It is of great significance to accurately predict the operation of the system economy, analyze the gains and losses of macrocontrol policies, evaluate the operation quality of the economic system, and correctly formulate the future development plan and strategy. This paper introduces the deep belief network, which has attracted much attention in the field of deep learning in recent years, into the research of system economic operation and management. This method solves the problems of slow training and learning speed, easy to fall into local minima and insufficient generalization of BP artificial neural network in the research of system economic operation and management. Taking the consumer price index and total import and export volume of F Province as the research object, the experiment proves that DBN has better application in system economic operation and management than BP neural network and vector autoregressive analysis. This paper analyzes and compares the modeling performance of DBN, BP neural network, and VaR method from many aspects, such as prediction accuracy, training convergence speed, and pretraining with or without samples. Relevant empirical results show that DBN has better economic prediction performance than BP neural network and ver. On the other hand, DBN can effectively use nonstandard samples to pretrain network weight parameters. Therefore, DBN is a better operation and management modeling means of economic system, with excellent practicability and application, and is expected to be popularized and applied in the field of economic forecasting.
... Over the last 40 years, the development of language recognition has evolved, and the technology has matured, resulting in a mainstream approach to language identification using parallel Gaussian mixture models [9]. The Mel-Frequency Cepstral Coefficient (MFCC) feature, which is commonly used in language recognition systems today, is susceptible to noise contamination, and its noise immunity is weak as each frame usually contains only 20-30 ms of speech signal [10]. For another feature extraction method, Shifted Delta Cepstra (SDC) [11], although it is a great improvement over MFCC parameters, the parameters of SDC are artificially set, making it the parameters of SDC are artificially set, which makes it universally applicable to all speech data [12]. ...
... The calculation of the new association is actually an error-driven process, in which the annotation result is compared with the correct answer, causing the trustworthiness of the annotated association to increase and the skepticism to decrease for the correct effect, and conversely, causing the trustworthiness of the annotated association to decrease and the skepticism to increase for the incorrect effect. For skepticism, a wrongly chosen central word will cause the skepticism between it and the surrounding words to rise, thus speeding up the rise of the total skepticism in the labeled nodes and allowing the wrong choice of the presumed central word to be exposed as soon as possible [10]. ...
Article
Full-text available
Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to the insufficient amount of information on each frame of the signal. At the same time, SDC feature extraction methods commonly used today require artificial settings in parameter selection, which increases the uncertainty of recognition results. The method of identifying English text chunks by association evaluation of central word extensions identifies English text chunks from a different perspective. It has the following features: (i) each phase is considered as a cluster with the central word as the core, and the internal composition pattern of each phrase is fully considered; (ii) the results are dynamically evaluated using association and confidence. The results show that the proposed method can achieve higher recognition rate than traditional feature extraction methods. The recognition rate is faster, and the F -measure value of English block recognition reaches 94.05%, which is comparable to the best results so far.