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  • Open Access

    ARTICLE

    An Optimized English Text Watermarking Method Based on Natural Language Processing Techniques

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1519-1536, 2021, DOI:10.32604/cmc.2021.018202

    Abstract In this paper, the text analysis-based approach RTADZWA (Reliable Text Analysis and Digital Zero-Watermarking Approach) has been proposed for transferring and receiving authentic English text via the internet. Second level order of alphanumeric mechanism of hidden Markov model has been used in RTADZWA approach as a natural language processing to analyze the English text and extracts the features of the interrelationship between contexts of the text and utilizes the extracted features as watermark information and then validates it later with attacked English text to detect any tampering occurred on it. Text analysis and text zero-watermarking… More >

  • Open Access

    ARTICLE

    A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning

    Dejia Shi1, Hanzhong Zheng2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 161-181, 2021, DOI:10.32604/cmes.2021.014917

    Abstract ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN).… More >

  • Open Access

    ARTICLE

    Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

    Shaheen Khatoon1,*, Majed A. Alshamari1, Amna Asif1, Md Maruf Hasan1, Sherif Abdou2, Khaled Mostafa Elsayed3, Mohsen Rashwan4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3079-3100, 2021, DOI:10.32604/cmc.2021.017371

    Abstract Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely… More >

  • Open Access

    ARTICLE

    Machine Learning Approach for COVID-19 Detection on Twitter

    Samina Amin1,*, M. Irfan Uddin1, Heyam H. Al-Baity2, M. Ali Zeb1, M. Abrar Khan1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2231-2247, 2021, DOI:10.32604/cmc.2021.016896

    Abstract Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in… More >

  • Open Access

    ARTICLE

    Enhancement of Sentiment Analysis Using Clause and Discourse Connectives

    Kumari Sheeja Saraswathy, Sobha Lalitha Devi*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1983-1999, 2021, DOI:10.32604/cmc.2021.015661

    Abstract The sentiment of a text depends on the clausal structure of the sentence and the connectives’ discourse arguments. In this work, the clause boundary, discourse argument, and syntactic and semantic information of the sentence are used to assign the text’s sentiment. The clause boundaries identify the span of the text, and the discourse connectives identify the arguments. Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence, a deeper-level semantic analysis is required for the correct analysis of sentiments. Hence, in this study, explicit connectives in Malayalam are considered to More >

  • Open Access

    ARTICLE

    Number Entities Recognition in Multiple Rounds of Dialogue Systems

    Shan Zhang1, Bin Cao1, Yueshen Xu2,*, Jing Fan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 309-323, 2021, DOI:10.32604/cmes.2021.014802

    Abstract As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named… More >

  • Open Access

    ARTICLE

    Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

    Mohammed Lafi1,*, Bilal Hawashin2, Shadi AlZu’ bi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 99-116, 2021, DOI:10.32604/cmes.2021.013026

    Abstract Most software systems have different stakeholders with a variety of concerns. The process of collecting requirements from a large number of stakeholders is vital but challenging. We propose an efficient, automatic approach to collecting requirements from different stakeholders’ responses to a specific question. We use natural language processing techniques to get the stakeholder response that represents most other stakeholders’ responses. This study improves existing practices in three ways: Firstly, it reduces the human effort needed to collect the requirements; secondly, it reduces the time required to carry out this task with a large number of More >

  • Open Access

    ARTICLE

    Sentiment Analysis for Arabic Social Media News Polarity

    Adnan A. Hnaif1,*, Emran Kanan2, Tarek Kanan1

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 107-119, 2021, DOI:10.32604/iasc.2021.015939

    Abstract In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate More >

  • Open Access

    ARTICLE

    Time-Aware PolarisX: Auto-Growing Knowledge Graph

    Yeon-Sun Ahn, Ok-Ran Jeong*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2695-2708, 2021, DOI:10.32604/cmc.2021.015636

    Abstract A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge. Research is being actively conducted to cover a wide variety of knowledge, as it can be applied to applications that help humans. However, existing researches are constructing knowledge graphs without the time information that knowledge implies. Knowledge stored without time information becomes outdated over time, and in the future, the possibility of knowledge being false or meaningful changes is excluded. As a result, they can’t reflect information that changes dynamically, and they can’t… More >

  • Open Access

    ARTICLE

    COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

    Saleh Albahli1, Ahmad Algsham1, Shamsulhaq Aeraj1, Muath Alsaeed1, Muath Alrashed1, Hafiz Tayyab Rauf2,*, Muhammad Arif3, Mazin Abed Mohammed4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1613-1627, 2021, DOI:10.32604/cmc.2021.014265

    Abstract Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback. The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms. This phenomenon caused a state of panic among people. Different studies were conducted to stop the spread of fake news to help people cope with the situation. In this paper, a semantic analysis of three levels (negative, neutral, and positive) is used to gauge… More >

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