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  • Open Access

    ARTICLE

    Leveraging Pre-Trained Word Embedding Models for Fake Review Identification

    Glody Muka1,*, Patrick Mukala1,2,*

    Journal on Artificial Intelligence, Vol.6, pp. 211-223, 2024, DOI:10.32604/jai.2024.049685 - 07 August 2024

    Abstract Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models… More >

  • Open Access

    ARTICLE

    Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

    Waqas Ahmad1, Hikmat Ullah Khan1,2,*, Fawaz Khaled Alarfaj3,*, Saqib Iqbal4, Abdullah Mohammad Alomair3, Naif Almusallam3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3101-3124, 2023, DOI:10.32604/iasc.2023.040614 - 11 September 2023

    Abstract Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative, positive, or neutral while associating them with their identified aspects from the corresponding context. In this regard, prior methodologies widely utilize either word embedding or tree-based representations. Meanwhile, the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss. Generally, word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence. Besides, the tree-based structure conserves the grammatical and logical dependencies of context. In addition,… More >

  • Open Access

    ARTICLE

    Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

    Mohammed Salahat1, Liaqat Ali1, Taher M. Ghazal2,3,*, Haitham M. Alzoubi4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.036019 - 08 June 2023

    Abstract Knowing each other is obligatory in a multi-agent collaborative environment. Collaborators may develop the desired know-how of each other in various aspects such as habits, job roles, status, and behaviors. Among different distinguishing characteristics related to a person, personality traits are an effective predictive tool for an individual’s behavioral pattern. It has been observed that when people are asked to share their details through questionnaires, they intentionally or unintentionally become biased. They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves. Such writings can effectively assess an individual’s personality traits… More >

  • Open Access

    ARTICLE

    Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

    Abdelwahed Motwakel1,*, Najm Alotaibi2, Eatedal Alabdulkreem3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Mohamed K Nour5, Radwa Marzouk6, Mahmoud Othman7

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1249-1266, 2023, DOI:10.32604/csse.2023.034227 - 26 May 2023

    Abstract Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural… More >

  • Open Access

    ARTICLE

    Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

    Samar Elbedwehy1,3,*, T. Medhat2, Taher Hamza3, Mohammed F. Alrahmawy3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3637-3652, 2023, DOI:10.32604/csse.2023.038376 - 03 April 2023

    Abstract One of the issues in Computer Vision is the automatic development of descriptions for images, sometimes known as image captioning. Deep Learning techniques have made significant progress in this area. The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. This paper aims to find optimized models for these two subsystems. For the image feature extraction subsystem, the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image. For the More >

  • Open Access

    ARTICLE

    Neural Machine Translation Models with Attention-Based Dropout Layer

    Huma Israr1,*, Safdar Abbas Khan1, Muhammad Ali Tahir1, Muhammad Khuram Shahzad1, Muneer Ahmad1, Jasni Mohamad Zain2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814 - 31 March 2023

    Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen… More >

  • Open Access

    ARTICLE

    Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

    Badriyya B. Al-onazi1, Abdulkhaleq Q. A. Hassan2, Mohamed K. Nour3, Mesfer Al Duhayyim4,*, Abdullah Mohamed5, Amgad Atta Abdelmageed6, Ishfaq Yaseen6, Gouse Pasha Mohammed6

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2575-2591, 2023, DOI:10.32604/cmc.2023.033531 - 31 March 2023

    Abstract Sentiment Analysis (SA), a Machine Learning (ML) technique, is often applied in the literature. The SA technique is specifically applied to the data collected from social media sites. The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process. In this background, the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets (QPSODL-SAAT). The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic. Initially, the data pre-processing is performed to convert… More >

  • Open Access

    ARTICLE

    A Data Mining Approach to Detecting Bias and Favoritism in Public Procurement

    Yeferson Torres-Berru1,2,*, Vivian F. Lopez-Batista1, Lorena Conde Zhingre3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3501-3516, 2023, DOI:10.32604/iasc.2023.035367 - 15 March 2023

    Abstract In a public procurement process, corruption can occur at each stage, favoring a participant with a previous agreement, which can result in over-pricing and purchases of substandard products, as well as gender discrimination. This paper’s aim is to detect biased purchases using a Spanish Language corpus, analyzing text from the questions and answers registry platform by applicants in a public procurement process in Ecuador. Additionally, gender bias is detected, promoting both men and women to participate under the same conditions. In order to detect gender bias and favoritism towards certain providers by contracting entities, the… More >

  • Open Access

    ARTICLE

    An Intelligent Deep Neural Sentiment Classification Network

    Umamaheswari Ramalingam1,*, Senthil Kumar Murugesan2, Karthikeyan Lakshmanan2, Chidhambararajan Balasubramaniyan3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108 - 05 January 2023

    Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS).… More >

  • Open Access

    ARTICLE

    Translation of English Language into Urdu Language Using LSTM Model

    Sajadul Hassan Kumhar1, Syed Immamul Ansarullah2, Akber Abid Gardezi3, Shafiq Ahmad4, Abdelaty Edrees Sayed4, Muhammad Shafiq5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3899-3912, 2023, DOI:10.32604/cmc.2023.032290 - 31 October 2022

    Abstract English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation… More >

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