In an Ambient Assisted Living project the activities of the user will be analyzed. The raw data is from a motion detector. Through data processing, the huge amount of dynamic raw data was translated to state data. Wit...
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In an Ambient Assisted Living project the activities of the user will be analyzed. The raw data is from a motion detector. Through data processing, the huge amount of dynamic raw data was translated to state data. With hidden Markov model, forward algorithm to analyze these state data, the daily activity model of the user was built. Thirdly by comparing the model with observed activity sequences, and finding out the similarities between them, defined the best adapt routine in the model. Furthermore, an activity routine net was built and used to compare with the hidden Markov model.
A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using da...
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A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992-2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using forward and Viterbi algorithms, respectively. Baum-Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012-2013 and 2013-2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days.
Background: G-quadruplexes are four-stranded structures formed in guanine-rich nucleotide sequences. Several functional roles of DNA G-quadruplexes have so far been investigated, where their putative functional roles ...
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Background: G-quadruplexes are four-stranded structures formed in guanine-rich nucleotide sequences. Several functional roles of DNA G-quadruplexes have so far been investigated, where their putative functional roles during DNA replication and transcription have been suggested. A necessary condition for G-quadruplex formation is the presence of four regions of tandem guanines called G-runs and three nucleotide subsequences called loops that connect G-runs. A simple computational way to detect potential G-quadruplex regions in a given genomic sequence is pattern matching with regular expression. Although many putative G-quadruplex motifs can be found in most genomes by the regular expression-based approach, the majority of these sequences are unlikely to form G-quadruplexes because they are unstable as compared with canonical double helix structures. Results: Here we present elaborate computational models for representing DNA G-quadruplex motifs using hidden Markov models (HMMs). Use of HMMs enables us to evaluate G-quadruplex motifs quantitatively by a probabilistic measure. In addition, the parameters of HMMs can be trained by using experimentally verified data. Computational experiments in discriminating between positive and negative G-quadruplex sequences as well as reducing putative G-quadruplexes in the human genome were carried out, indicating that HMM-based models can discern bona fide G-quadruplex structures well and one of them has the possibility of reducing false positive G-quadruplexes predicted by existing regular expression-based methods. Furthermore, our results show that one of our models can be specialized to detect G-quadruplex sequences whose functional roles are expected to be involved in DNA transcription. Conclusions: The HMM-based method along with the conventional pattern matching approach can contribute to reducing costly and laborious wet-lab experiments to perform functional analysis on a given set of potential G-quadruplexes of interest.
To observe and analyze person's daily activities, and build the activities model is an important task in an intelligent environment In an Ambient Assisted Living (AAL) project we get sensor data from a motion dete...
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To observe and analyze person's daily activities, and build the activities model is an important task in an intelligent environment In an Ambient Assisted Living (AAL) project we get sensor data from a motion detector. At first we translate and reduce the raw data to state data. Secondly using hidden Markov model, forward algorithm, and Viterbi algorithm to analyze the data and build the person's daily activity model. Comparing individual observation with the build model to find out best and worst (abnormal) activities.
We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. Pieces in the database are represented as hidden Markov models (HMMs). The query is treated as an observat...
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ISBN:
(纸本)9781581135138
We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. Pieces in the database are represented as hidden Markov models (HMMs). The query is treated as an observation sequence and a piece is judged similar to the query if its HMM has a high likelihood of generating the query. The top pieces are returned to the user in rank-order. This paper reports the basic approach for the construction of the target database of themes, encoding and transcription of user queries, and the results of initial experimentation with a small set of sung queries.
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%.
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