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  • Open Access

    ARTICLE

    A Smart English Text Zero-Watermarking Approach Based on Third-Level Order and Word Mechanism of Markov Model

    Fahd N. Al-Wesabi1, 2, *

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1137-1156, 2020, DOI:10.32604/cmc.2020.011151

    Abstract Text information is principally dependent on the natural languages. Therefore, improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter. Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet. In this paper, an intelligent text Zero-Watermarking approach SETZWMWMM (Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model) has been proposed for the content authentication and tampering detection of English text contents. The SETZWMWMM approach embeds and detects the watermark… More >

  • Open Access

    ARTICLE

    Accurate Location Prediction of Social‐Users Using mHMM

    Ahsan Hussain, Bettahally N. Keshavamurthy, Ravi Prasad K. Jagannath

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 473-486, 2019, DOI:10.31209/2018.11007092

    Abstract Prediction space of distinct check-in locations in Location-Based Social Networks is a challenge. In this paper, a thorough analysis of Foursquare Check-ins is done. Based on previous check-in sequences, next location of social-users is accurately predicted using multinomial-Hidden Markov Model (mHMM) with Steady-State probabilities. This information benefits security-agencies in tracking suspects and restaurant-owners to predict their customers’ arrivals at different venues on given days. Higher accuracy and Steady-State venuepopularities obtained for location-prediction using the proposed method, outperform various other baseline methods. More >

  • Open Access

    ARTICLE

    Modified Viterbi Scoring for HMM‐Based Speech Recognition

    Jihyuck Joa, Han‐Gyu Kimb, In‐Cheol Parka, Bang Chul Jungc, Hoyoung Yooc

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 351-358, 2019, DOI:10.31209/2019.100000096

    Abstract A modified Viterbi scoring procedure is presented in this paper based on Dijkstra’s shortest-path algorithm. In HMM-based speech recognition systems, the Viterbi scoring plays a significant role in finding the best matching model, but its computational complexity is linearly proportional to the number of reference models and their states. Therefore, the complexity is serious in implementing a high-speed speech recognition system. In the proposed method, the Viterbi scoring is translated into the searching of a minimum path, and the shortest-path algorithm is exploited to decrease the computational complexity while preventing the recognition accuracy from deteriorating. In addition, a two-phase comparison… More >

  • Open Access

    ARTICLE

    Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model

    Junbiao Liu1, 6, Duanpo Wu2, 3, Zimeng Wang2, Xinyu Jin1, *, Fang Dong4, Lurong Jiang5, Chenyi Cai6

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 401-426, 2020, DOI:10.32604/cmes.2020.08731

    Abstract In the field of medical informatics, sleep staging is a challenging and timeconsuming task undertaken by sleep experts. According to the new standard of the American Academy of Sleep Medicine (AASM), the stages of sleep are divided into wakefulness (W), rapid eye movement (REM) and non-rapid eye movement (NREM) which includes three sleep stages (N1, N2 and N3) that describe the depth of sleep. This study aims to establish an automatic sleep staging algorithm based on the improved weighted random forest (WRF) and Hidden Markov Model (HMM) using only the features extracted from double-channel EEG signals. The WRF classification model… More >

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