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Advanced Machine Learning for Big Data Analytics in Natural Language Processing

Submission Deadline: 28 February 2023 (closed)

Guest Editors

Professor Jemal H. Abawajy, Deakin University, Australia
Assistant Professor Haruna Chiroma, University of Hafr Al-Batin, Saudi Arabia


Machine learning has proven effective, robust, and efficient in solving clustering, forecasting, classification, association rules, etc. The emergence of unexpected very large-scale data being generated at very high velocity from various sources such as the web, sensors, social media, mobile phones, etc. mostly in natural language have led the world to the era of big data. The data being generated is characterized by variety – that is the data in different forms: structured, semi-structured, and unstructured. Such kind of data set can be effectively handled by the flexibility characteristic of the advanced machine learning methodologies because it gives the algorithms the capability to effectively handle different types of data – text, audio, video, and images. Big data poses a new challenge to machine learning algorithms. Despite the progress recorded by advanced machine learning algorithms such as deep learning architectures in the big data analytics for natural language, the progress is still in its early stage beseeching for novel machine learning-based analytical methodologies. Big data analytics require novel new analytical approaches to process the big data mostly in natural language to make it valuable for informing decision making.


This special issue aimed to address the challenges of advanced machine learning approaches within the context of big data analytics for natural language processing and serve as a platform for dissemination as well as sharing of the latest scientific contributions from machine learning methodologies for big data in natural language processing.


The special issue welcomes submissions of high-quality original works, review/survey articles, extended papers with at least 70% new materials, and theories that described significant scientific contributions on the aspect of advanced machine learning for big data analytics in natural language processing on the following topics of interest but not limited to Cyberbullying, multilingual, fake news, emotions, hate speech, sarcasm, stylometry, etc. 


Deep Learning; Natural Language Processing; Big Data Analytic; Intelligent Algorithms; Parallel Computing

Published Papers

  • Open Access


    Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

    Mohammed Abdalsalam, Chunlin Li, Abdelghani Dahou, Natalia Kryvinska
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1427-1467, 2024, DOI:10.32604/cmes.2023.029911
    (This article belongs to this Special Issue: Advanced Machine Learning for Big Data Analytics in Natural Language Processing)
    Abstract One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database… More >

  • Open Access


    A Deep Learning Approach to Mesh Segmentation

    Abubakar Sulaiman Gezawa, Qicong Wang, Haruna Chiroma, Yunqi Lei
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1745-1763, 2023, DOI:10.32604/cmes.2022.021351
    (This article belongs to this Special Issue: Advanced Machine Learning for Big Data Analytics in Natural Language Processing)
    Abstract In the shape analysis community, decomposing a 3D shape into meaningful parts has become a topic of interest. 3D model segmentation is largely used in tasks such as shape deformation, shape partial matching, skeleton extraction, shape correspondence, shape annotation and texture mapping. Numerous approaches have attempted to provide better segmentation solutions; however, the majority of the previous techniques used handcrafted features, which are usually focused on a particular attribute of 3D objects and so are difficult to generalize. In this paper, we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually… More >

    Graphic Abstract

    A Deep Learning Approach to Mesh Segmentation

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