Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. One of the unexplored components of human...
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ISBN:
(纸本)9781450371797
Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. One of the unexplored components of human communication found online in written form is an emotional expression. However, despite the concreteness of the online expressions in written form, it remains unpredictable which kinds of emotions will be expressed in individual messages of Twitter users. To investigate this, we perform an investigation on observing emotions unfolding in a consecutive sequence of tweets for a particular user based on his/her past history. In this paper, we propose a method on given a set of tweets related with some events (identified by the usage of a hashtag), determines how those sentiments will be distributed on behalf of a person within a conversation. We present the Hidden Markov Model (HMM) to understand the nature of emotion dynamics in Twitter messages.
This paper demonstrates how Hidden Markov Model (HMM) approach is used potentially as a tool for predicting the next concepts visited by students in an Adaptive and Intelligent Web-Based Educational System (AIWBES) fo...
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This paper demonstrates how Hidden Markov Model (HMM) approach is used potentially as a tool for predicting the next concepts visited by students in an Adaptive and Intelligent Web-Based Educational System (AIWBES) for teaching English as Foreign Language (EFL). This tool helps teachers to provide their students with appropriate assistance during the learning process in a timely manner. The prediction process is achieved by following three phases, Initialization phase, adjustment phase and prediction phase. The experiment results are encouraging and serve to show the promise of HMM in AIWBESs and they show accuracy in the next action prediction reaching up to 92%.
The ubiquitous deployment of Fourth-generation (4G) and Fifth-generation (5G) networks, while expanding user connectivity over a larger geographic area, presents significant challenges for mobile network management. T...
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This paper deals with the algorithms of training hidden Markov models on sequences with missing observations. The method of imputation using Viterbi algorithm and the method of mar-ginalization of missing observations...
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ISBN:
(纸本)9781509040704
This paper deals with the algorithms of training hidden Markov models on sequences with missing observations. The method of imputation using Viterbi algorithm and the method of mar-ginalization of missing observations are studied. These two methods are compared to the standard methods of dealing with missing data: imputation of gaps using the mode (the most frequent value) of nearest observations and gluing of observable parts of the sequence. The studied methods appeared to be more effective than the standard ones. Although the marginalization method proved to be a bit more effective than the method of imputation using Viterbi algorithm.
In recent times, the power system has expanded in an unprecedented way owing to technological innovations and geographical dimensions. For ensuring proper operation, monitoring and control, a system called Wide Area M...
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ISBN:
(纸本)9781479974566
In recent times, the power system has expanded in an unprecedented way owing to technological innovations and geographical dimensions. For ensuring proper operation, monitoring and control, a system called Wide Area Measurement System (WAMS) has evolved. Phasor Measurement Unit (PMU) is one of the vital components of WAMS. Any failure in PMU has catastrophic consequences on WAMS. Thus reliability analysis of PMU is of utmost importance. Hidden Markov model (HMM) is one of the important techniques for evaluating reliability. However, while evaluating the reliability of PMU using HMM, the HMM parameters such as state transition probability matrix (A), observation probability matrix (B) and initial state probability (π) are either assumed or have been derived from related research papers. In this paper, a baum-welch algorithm has been used to re-estimate these HMM parameters of the seven modules of PMU to best fit the observations.
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