Hidden Markov Algorithm Research Articles

Aspect Based Opinion Mining & Sentiment Analysis

Opinion mining is a relatively new field that refers to the practice of collecting feedback in the form of online reviews and ratings left by users on various topics. Researchers are now able to monitor the states of consciousness of individuals in real-time because to this development. Just lately, a number of research papers for sentiment analysis were implemented, each of which was based on a unique categorization and ranking procedure. However, the amount of time necessary for the newline performing class has not decreased in any way. Sentiment Sensitivity newline word list SST was provided as a solution to the problem of function mismatch in the go-domain sentiment class across the source area and the target domain; however, achieving improved accuracy and identifying distributional similarities of words became less effective as time went on. Hidden Markov’s persistent development may be seen at the beginning. Cosine In order to achieve more effective and clean pre-processing, a method that is conceptually quite similar to HM-CPCS has been devised. The HM-CPCS methodology, which has recently been suggested, makes use of the POS tagger, a variant of which is based on the Hidden Markov algorithm. Evaluations are created using data from a wide variety of different domains. Similar to a newline, the tags that come before and after it compute the possibility of transitions and the existence of the term newline among the tags in order to increase capability. This is done in order to improve capability.

Cultivation of Students’ Independent Learning Ability in Teaching Chinese as a Foreign Language Based on the CDIO Model of Guiding Data Structures and Hidden Markov Algorithms

The systematic training of metacognitive strategies in teaching Chinese as a foreign language, cultivating learners’ autonomous learning ability, and improving the effectiveness of teaching Chinese as a foreign language is of great significance for realizing the overall goal of teaching Chinese as a foreign language. Therefore, this paper designs a model based on CDIO to guide the teaching of data structures and algorithms, which emphasizes students’ hands-on ability, advocates learning in use, students’ autonomous learning, and teamwork. Taking massive online open courses(MOOC ) and small private online courses ( SPOC) learning data as a sample, hidden Markov algorithm and data mining technology are used to establish a student learning behavior evaluation model to evaluate students’ learning behavior in real-time. Meanwhile, teachers adjust the teaching content according to the evaluation results and enhance students’ learning performance and improve teaching quality.

Research on Industry Prosperity Index Forecast based on Hidden Markov Model

The research is mainly based on the electricity consumption information of 800 key energy-consuming enterprises. By cleaning and sorting the data, high-frequency structured electricity consumption data is obtained, which is based on the production theory in economics. Using complex network models and Hidden Markov Algorithm constructs an industry prosperity index based on power operation data by industry, so as to objectively reflects the operation of various industries and plays a predictive and early warning role in economy.

Path control of panoramic visual recognition for intelligent robots based-edge computing

The application of service-oriented intelligent robots in hospitals solves the problems of manual service such as delivery of medicines and orders from the medical aids. However, there is a problem of robot path planning, which leads to poor service. Taking the self-control robot with visual recognition capability in medical institutions as the research background, this paper proposes an image edge detection algorithm based on three-dimensional features. First, reconstruct the three-dimensional image through visual recognition, and calculate the three-dimensional feature map of the scene in the hospital. Secondly, the edge computing algorithm is used to realize the route area diversion, and the best route area is planned in the 3D feature map to realize the delivery of medicines and other items in the complex environment of the hospital, so as to achieve the purpose of improving the technical level of medical auxiliary equipment. Finally, the image restoration technology can improve the image clarity of the robot vision system and the robot automation level, and it is also helpful for the promotion of robot automatic path planning and control technology. The experimental results show that, compared with the hidden Markov algorithm, the method in this paper is significantly better than the hidden Markov algorithm, which meets the needs of medical institutions for intelligent robots.

Smart plant project scheduling optimization based on hidden Markov algorithm

ABSTRACT In order to enhance the reasonableness of project scheduling optimization process, this paper puts forward a smart plant project scheduling optimization method based on hidden Markov algorithm. First, it introduces a kind of new method to cope with conflict. It refers to that once the conflict appears in the scheduling scheme, conduct status transfer aiming at the time schedule of the conflict instead of re-refining the starting time of the project to get another feasible time schedule, and enhance the calculation efficiency of the algorithm. Second, it takes the active and passive project scheduling problems as single and comprehensive problems for formulation, wherein, the target function includes a benchmark schedule cost and project response cost, and it finds out an optimal active and passive comprehensive project scheduling strategy; finally, it conducts modeling on the above project scheduling optimization problem by aid of Markov decision-making process, designs a kind of solution based on the dynamic planning of random graph. The experiment result shows that the method is effective in the convergence precision and calculation efficiency, and it demonstrates the reasonableness of the scheduling scheme by the means of Gantt chart.

A resource management method based on organizational behavior theory and hidden Markov algorithm

In order to improve the level of human resource management in modern enterprises, a human resource management method in modern enterprises based on organizational behavior theory and hidden Markov algorithm is proposed. First of all, according to characteristics of human resources training, human resources are analyzed from the aspects of psychological capital and incentive theory in the organizational behavior theory to maximize the advantages of human resources, which is of great significance. Secondly, targeted at data partitioning and unknown model quantities in discovery process of the hidden model, the hidden Markov algorithm using Dirichlet process and non-parametric bayesian factors are analyzed for data flow partitioning of human resource management in modern enterprises and model discovery; with psychological capital and incentive theory in organizational behavior theory as the starting point, role of organizational behavior theory in human resources training is explored in this Thesis.

Estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood algorithm.

The effective population size is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator , and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of to several million, hence allowing the estimation of larger . Finally, we demonstrate how this algorithm can cope with nonconstant scenarios and be used as a likelihood-ratio test to test for the equality of throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article.

Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model

Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.

Algorithmic description of hippocampal granule cell dendritic morphology

Recent efforts in computational neuroanatomy have aimed at accurately reproducing all relevant statistical details of dendritic morphology with stochastic models based on local rules and parameters measured from real neurons. Here we present a solution of this problem for dentate gyrus granule cells based on a hidden Markov algorithm. The correctness of the model is supported by the statistical agreement between distributions of emergent parameters measured from population of traced and virtual neurons. The algorithm relies on two local hidden variables, one of which might be associated with dendritic microtubules, and another may represent the time of development.