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  • Open Access


    Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

    Fahd N. Al-Wesabi1,2,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3701-3719, 2021, DOI:10.32604/cmc.2021.015785

    Abstract Due to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue. The main challenges faced by researchers are authentication, integrity verification, and tampering detection of the digital contents. In this paper, text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents. The proposed approach embeds and detects the watermark logically without altering the original English text document. Based on hidden Markov model (HMM), the fourth level order of the word mechanism is used to analyze… More >

  • Open Access


    Improving Language Translation Using the Hidden Markov Model

    Yunpeng Chang1, Xiaoliang Wang1,*, Meihua Xue1, Yuzhen Liu1, Frank Jiang2

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3921-3931, 2021, DOI:10.32604/cmc.2021.012304

    Abstract Translation software has become an important tool for communication between different languages. People’s requirements for translation are higher and higher, mainly reflected in people’s desire for barrier free cultural exchange. With a large corpus, the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units. Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation, ignoring context. To support the ongoing improvement of translation methods built upon deep learning, we propose a translation algorithm based on the Hidden Markov… More >

  • Open Access


    An Accurate Persian Part-of-Speech Tagger

    Morteza Okhovvat1,∗, Mohsen Sharifi2,†, Behrouz Minaei Bidgoli2,‡

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 423-430, 2020, DOI:10.32604/csse.2020.35.423

    Abstract The processing of any natural language requires that the grammatical properties of every word in that language are tagged by a part of speech (POS) tagger. To present a more accurate POS tagger for the Persian language, we propose an improved and accurate tagger called IAoM that supports properties of text to speech systems such as Lexical Stress Search, Homograph words Disambiguation, Break Phrase Detection, and main aspects of Persian morphology. IAoM uses Maximum Likelihood Estimation (MLE) to determine the tags of unknown words. In addition, it uses a few defined rules for the sake of achieving high accuracy. For… More >

  • Open Access


    A Phoneme-Based Approach for Eliminating Out-of-vocabulary Problem Turkish Speech Recognition Using Hidden Markov Model

    Erdem Yavuz1,∗, Vedat Topuz2

    Computer Systems Science and Engineering, Vol.33, No.6, pp. 429-445, 2018, DOI:10.32604/csse.2018.33.429

    Abstract Since Turkish is a morphologically productive language, it is almost impossible for a word-based recognition system to be realized to completely model Turkish language. Due to the fact that it is difficult for the system to recognize words not introduced to it in a word-based recognition system, recognition success rate drops considerably caused by out-of-vocabulary words. In this study, a speaker-dependent, phoneme-based word recognition system has been designed and implemented for Turkish Language to overcome the problem. An algorithm for finding phoneme-boundaries has been devised in order to segment the word into its phonemes. After the segmentation of words into… More >

  • Open Access


    Intelligent Speech Communication Using Double Humanoid Robots

    Li-Hong Juang1,*, Yi-Hua Zhao2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 291-301, 2020, DOI:10.31209/2020.100000164

    Abstract Speech recognition is one of the most convenient forms of human beings engaging in the exchanging of information. In this research, we want to make robots understand human language and communicate with each other through the human language, and to realize man–machine interactive and humanoid– robot interactive. Therefore, this research mainly studies NAO robots’ speech recognition and humanoid communication between double -humanoid robots. This paper introduces the future direction and application prospect of speech recognition as well as its basic method and knowledge of speech recognition fields. This research also proposes the application of the most advanced method—establishment of the… More >

  • Open Access


    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


    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


    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


    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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