Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceives customers, inflicts losses, and tarnishes the reputation of companies. The present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the reviewer. The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency (TF-IDF) approach for extracting features and their representation. For detection and classification, n-grams of review texts were inputted into the constructed models to be classified as fake or truthful. However, the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website. The classification results of these experiments showed that naïve Bayes (NB), support vector machine (SVM), adaptive boosting (AB), and random forest (RF) received 88%, 93%, 94%, and 95%, respectively, based on testing accuracy and tje F1-score. The obtained results were compared with existing works that used the same dataset, and the proposed methods outperformed the comparable methods in terms of accuracy.

Online product reviews play a major role in the success or failure of an E-commerce business. Before procuring products or services, the shoppers usually go through the online reviews posted by previous customers to get recommendations of the details of products and make purchasing decisions. Nevertheless, it is possible to enhance or hamper specific E-business products by posting fake reviews, which can be written by persons called fraudsters. These reviews can cause financial loss to E-commerce businesses and misguide consumers to take the wrong decision to search for alternative products. Thus, developing a fake review detection system is ultimately required for E-commerce business. The proposed methodology has used four standard fake review datasets of multidomains include hotels, restaurants, Yelp, and Amazon. Further, preprocessing methods such as stopword removal, punctuation removal, and tokenization have performed as well as padding sequence method for making the input sequence has fixed length during training, validation, and testing the model. As this methodology uses different sizes of datasets, various input word-embedding matrices of n-gram features of the review’s text are developed and created with help of word-embedding layer that is one component of the proposed model. Convolutional and max-pooling layers of the CNN technique are implemented for dimensionality reduction and feature extraction, respectively. Based on gate mechanisms, the LSTM layer is combined with the CNN technique for learning and handling the contextual information of n-gram features of the review’s text. Finally, a sigmoid activation function as the last layer of the proposed model receives the input sequences from the previous layer and performs binary classification task of review text into fake or truthful. In this paper, the proposed CNN-LSTM model was evaluated in two types of experiments, in-domain and cross-domain experiments. For an in-domain experiment, the model is applied on each dataset individually, while in the case of a cross-domain experiment, all datasets are gathered and put into a single data frame and evaluated entirely. The testing results of the model in-domain experiment datasets were 77%, 85%, 86%, and 87% in the terms of accuracy for restaurant, hotel, Yelp, and Amazon datasets, respectively. Concerning the cross-domain experiment, the proposed model has attained 89% accuracy. Furthermore, comparative analysis of the results of in-domain experiments with existing approaches has been done based on accuracy metric and, it is observed that the proposed model outperformed the compared methods.

Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comprehending patterns of suicidal ideation represent a challenging task. Therefore, it is essential to develop a machine learning system for automated early detection of suicidal ideation or any abrupt changes in a user’s behavior by analyzing his or her posts on social media. In this paper, we propose a methodology based on experimental research for building a suicidal ideation detection system using publicly available Reddit datasets, word-embedding approaches, such as TF-IDF and Word2Vec, for text representation, and hybrid deep learning and machine learning algorithms for classification. A convolutional neural network and Bidirectional long short-term memory (CNN–BiLSTM) model and the machine learning XGBoost model were used to classify social posts as suicidal or non-suicidal using textual and LIWC-22-based features by conducting two experiments. To assess the models’ performance, we used the standard metrics of accuracy, precision, recall, and F1-scores. A comparison of the test results showed that when using textual features, the CNN–BiLSTM model outperformed the XGBoost model, achieving 95% suicidal ideation detection accuracy, compared with the latter’s 91.5% accuracy. Conversely, when using LIWC features, XGBoost showed better performance than CNN–BiLSTM.

The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole.

Most consumers rely on online reviews when deciding to purchase e-commerce services or products. Unfortunately, the main problem of these reviews, which is not completely tackled, is the existence of deceptive reviews. The novelty of the proposed system is the application of opinion mining on consumers’ reviews to help businesses and organizations continually improve their market strategies and obtain an in-depth analysis of the consumers’ opinions regarding their products and brands. In this paper, the long short-term memory (LSTM) and deep learning convolutional neural network integrated with LSTM (CNN-LSTM) models were used for sentiment analysis of reviews in the e-commerce domain. The system was tested and evaluated by using real-time data that included reviews of cameras, laptops, mobile phones, tablets, televisions, and video surveillance products from the Amazon website. Data preprocessing steps, such as lowercase processing, stopword removal, punctuation removal, and tokenization, were used for data cleaning. The clean data were processed with the LSTM and CNN-LSTM models for the detection and classification of the consumers’ sentiment into positive or negative. The LSTM and CNN-LSTM algorithms achieved an accuracy of 94% and 91%, respectively. We conclude that the deep learning techniques applied here provide optimal results for the classification of the customers’ sentiment toward the products.

Solar power is an excellent alternative power source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. Solar radiation (SR) can be predicted with great precision, and it may be possible to drastically minimize the impact cost associated with the development of solar energy. To successfully implement solar power, all projects using solar energy must have access to reliable sun radiation data. However, the deployment, administration, and performance of photovoltaic or thermal systems may be severely impacted by the lack of access to and the ambiguity of this data. Methods for estimating and predicting solar radiation can help solve these problems. Prediction techniques can be put to use in the real world to, for example, keep the power grid functioning smoothly and ensure that the supply of electricity exactly matches the demand at all times. Recently developed forecasting methods include the deep learning convolutional neural networks combined with long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination of meteorological data, along with the CNN-LSTM methods, in order to design and train the most accurate SR forecasting artificial neural network model possible. Weather data was collected from a NASA meteorological station that included details such as the current temperature, the relative humidity, and the speed of the wind. This research revealed that SR is highly correlated with both temperature and radiation. Furthermore, the findings demonstrated that the CNN-LSTM algorithm outperformed the other algorithm-trained models, as evidenced by the performance score of the respective models, with a maximum coefficient determination (R²) > 95% and a minimum mean square error (MSE) of 0.000987 at the testing step. In comparison with different existing artificial intelligence models, the CNN-LSTM model outperformed the other models. These scenarios demonstrated that a basic implementation of CNN-LSTM can be used to supplement conventional methods for predicting SR, provide possibilities to monitor radiation at a low cost, and encourage the adoption of data-driven management.

Cyberbullying is characterized by deliberate and sustained peer aggression, as well as a power differential between the victim and the perpetrators or abusers. Cyberbullying can have a variety of consequences for victims, including mental health problems, poor academic performance, a tendency to drop out of work, and even suicidal thoughts. The main objective of this study was to develop a cyberbullying detection system (CDS) to uncover hateful and abusive behaviour on social media platforms. Two experiments were carried out to train and test the proposed system with binary and multiclass cyberbullying classification datasets. Hybrid deep learning architecture consisting of convolutional neural networks integrated with bidirectional long short-term memory networks (CNN-BiLSTM) and single BiLSTM models were compared in terms of their ability to classify social media posts into several bullying types related to gender, religion, ethnicity, age, aggression, and non-cyberbullying. Both classifiers showed promising performance in the binary classification dataset (aggressive or non-aggressive bullying), with a detection accuracy of 94%. For the multiclass dataset, BiLSTM outperformed the combined CNN-BiLSTM classifier, achieving an accuracy of 99%. A comparison of our method to the existing method on the multiclass classification dataset revealed that our method performed better in detecting online bullying.

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