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  • Open Access

    ARTICLE

    Regulation Relatedness Map Creation Method with Latent Semantic Analysis

    Mehmet Murat Huyut1,*, Batuhan Kocaoğlu2, Ünzile Meram3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2093-2107, 2022, DOI:10.32604/cmc.2022.024190

    Abstract Regulatory authorities create a lot of legislation that must be followed. These create complex compliance requirements and time-consuming processes to find regulatory non-compliance. While the regulations establish rules in the relevant areas, recommendations and best practices for compliance are not generally mentioned. Best practices are often used to find a solution to this problem. There are numerous governance, management, and security frameworks in Information Technology (IT) area to guide businesses to run their processes at a much more mature level. Best practice maps can used to map another best practice, and users can adapt themselves by the help of this… More >

  • Open Access

    ARTICLE

    Personality Detection Using Context Based Emotions in Cognitive Agents

    Nouh Sabri Elmitwally1,2, Asma Kanwal3,4, Sagheer Abbas3, Muhammad A. Khan5, Muhammad Adnan Khan6,*, Munir Ahmad3, Saad Alanazi1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4947-4964, 2022, DOI:10.32604/cmc.2022.021104

    Abstract Detection of personality using emotions is a research domain in artificial intelligence. At present, some agents can keep the human’s profile for interaction and adapts themselves according to their preferences. However, the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject. The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior. In our daily life, humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input. This paper presents a conceptual personality model… More >

  • Open Access

    ARTICLE

    A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

    Jari Jussila1, Eman Alkhammash2,*, Norah Saleh Alghamdi3, Prashanth Madhala4, Mohammad Ayoub Khan5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1845-1856, 2022, DOI:10.32604/cmc.2022.017284

    Abstract Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’ stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and arousal to elicit customer insights.… More >

  • Open Access

    ARTICLE

    Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

    Nadia Tabassum1, Tahir Alyas2, Muhammad Hamid3,*, Muhammad Saleem4, Saadia Malik5, Zain Ali2, Umer Farooq2

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 175-186, 2021, DOI:10.32604/iasc.2021.018998

    Abstract Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English… More >

  • Open Access

    ARTICLE

    Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

    Jaiteg Singh1, Tanya Gera1, Farman Ali2, Deepak Thakur1, Karamjeet Singh3, Kyung-sup Kwak4,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2655-2670, 2021, DOI:10.32604/cmc.2021.014504

    Abstract Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never… More >

  • Open Access

    ARTICLE

    Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study

    Belal Abdullah Hezam Murshed1,∗, Hasib Daowd Esmail Al-ariki2,†, Suresha Mallappa3,‡

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 495-512, 2020, DOI:10.32604/csse.2020.35.495

    Abstract This paper conducts a comprehensive review of various word and sentence semantic similarity techniques proposed in the literature. Corpus-based, Knowledge-based, and Feature-based are categorized under word semantic similarity techniques. String and set-based, Word Order-based Similarity, POSbased, Syntactic dependency-based are categorized as sentence semantic similarity techniques. Using these techniques, we propose a model for computing the overall accuracy of the twitter dataset. The proposed model has been tested on the following four measures: Atish’s measure, Li’s measure, Mihalcea’s measure with path similarity, and Mihalcea’s measure with Wu and Palmer’s (WuP) similarity. Finally, we evaluate the proposed method on three real-world twitter… More >

  • Open Access

    ARTICLE

    Attention-Aware Network with Latent Semantic Analysis for Clothing Invariant Gait Recognition

    Hefei Ling1, Jia Wu1, Ping Li1,*, Jialie Shen2

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1041-1054, 2019, DOI:10.32604/cmc.2019.05605

    Abstract Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from… More >

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