crossbody bags cyber monday

TABLE 4 Detailed Information of Public Existing Datasets in the Literature FIGURE 1. D. Rulr-Based Method During the past few decades, a wide range of problems, including face recognition, speech recognition, font recognition, fraud detection, and disease diagnosis, have been addressed by machine learning methods [125]–​[131]. In recent years, machine learning has also been investigated to combat spam, an issue that is expanding to various online applications such as SMS, email, and blogs [132]–​[135]. Below we provided the detection techniques used for detecting fake reviews with their pros and cons. B. Neural Network in Detecting Fake Reviews RNN is utilized for processing sequential data that use internal memory for the input sequence. Theoretically, RNN can save information for long sequences. However, in practice, RNN can only run for a few steps due to exploding gradient or vanishing gradient problems. Consequently, the researchers have developed new models to overcome the limitations of RNN, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM, Stacked LSTM, and LSTM with attention method. We display an RNN algorithm for fake reviews detection with a simple example, as shown in Fig. 4. Firstly, a particular vector using a word embedding technology is defined for each input word. The built-in word vectors are then fed one by one into RNN cells. The RNN cells with the input vector are fed into the next hidden layer. The RNN has the same weight of each input word and shares the parameters between different parts. Lastly, the reviews label can be predicted by the last hidden layer output. In this subsection, we shall sum up the existing RNN techniques in the literature shown in Table 9. Hierarchical Attention Network (HAN) is an algorithm proposed to capture the whole document structure. The HAN model consists of hierarchal structures (word encoder, word attention, sentence encoder, and sentence attention) using Bidirectional GRU [176]. In our work, we set the maximum length to 200, then, Bi-GRU with 100 output dimensions was fed to the attention layer. We utilized the word encoder as input to generate sentence encoder time distributed layer. Lastly, we used an ADAM optimizer with a 0.001 learning rate to optimize our model. Cross domain fake review detection. The cross-domain problem needs to be effectively addressed. The issue of lack of annotation datasets is disappointing in fake review detection. Applying a model trained in the source domain and tested in the target domains is a significant research direction. The existing literature focused only on one domain of fake review detection, so these proposed models failed when training in the domain and tested in other domains. For example, the authors [80] trained the model in the domain and tested the other domain. The experimental results show the performance has significantly dropped compared with the performance in the same domain. More research and investigation are needed for cross-domain fake reviews detection [67], [186]. camera bag cyber mondayvintage burberry bag real or fake

we love to take pictures and show them to the world.