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3rd International Conference on Information and Communication Technologies

作者： Homsi, Masun Lutfi, Rania Maria, Carto Rosa Barakat, Ghias Univ Aleppo Fac Sci Aleppo Syria Univ AL Baath Fac Informat Engn Homs Syria Univ Autonoma Madrid Escuela Politecn Super Madrid Spain Fac Arts & Human Sci Damascus Syria

ISBN: (纸本)9781424417513

A new approach for building student model in an Adaptive and intelligent Web-Based Educational System (AIWBES) is introduced. This approach utilizes a hybrid algorithm based on Fuzzy-ART2 neural network and stochastic method called Hidden Markov Model (HMM), in order to evaluate and categorize students' knowledge status in six levels: Excellent, very good, good, fair, weak and very weak;depending on 5 parameters collected through their interactions with the system. The student model is initialized by presenting a pre-test form to students and it is updated dynamically according to their study times and assessment results. Students' knowledge status are modeled through three phases, initialization, training and recall phases. In the initialization phase, input vectors are normalized before they are categorized using unsupervised algorithm Fuzzy-ART2 in 6 clusters representing 6 knowledge status. A HMM is created for each cluster and when new students' parameters are collected, they are introduced to baum-welch re-estimation algorithm to train the 6 HMMs and to maximize the observed sequence that is associated with a particular cluster. Forward algorithm evaluates then the likelihood of this sequence with respect to each of the HMMs and to determine the maximum value, which represents the actual knowledge status of the student. Experiment results show that the proposed approach is capable of categorizing student parameter vectors to their corresponding cluster with good accuracies. The result of such classifications would open new horizons and applications in AIWBES.

关键词： Fuzzy-ART2 Hidden Markov Model Adaptive learning AIWBES Student model baum-welch algorithm

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Automatic Segmentation of Lymph Vessel Wall using Optimal Surface Graph Cut and Hidden Markov Models 37

Automatic Segmentation of Lymph Vessel Wall using Optimal Su...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Jones, Jonathan-Lee Essa, Ehab Xie, Xianghua Swansea Univ Dept Comp Sci Singleton Pk Swansea W Glam Wales

ISBN: (纸本)9781424492701

We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a baum-welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method.

关键词： biomedical optical imaging blood vessels hidden Markov models image segmentation medical image processing baum-welch algorithm Optimal Surface Segmentation automatic segmentation confocal microscopy images hidden Markov Models lymph vessel wall optimal surface graph cut presegmentation transmission probability Biomedical imaging Hidden Markov models Image edge detection Image segmentation Microscopy Three-dimensional displays Yttrium

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A novel approach of Hidden Markov Model for time series forecasting 9

A novel approach of Hidden Markov Model for time series fore...

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9th International Conference on Ubiquitous Information Management and Communication (ACM IMCOM)

作者： Zahari, Azunda Jaafar, Jafreezal Univ Teknol PETRONAS Comp & Informat Sci Perak Malaysia

ISBN: (纸本)9781450333771

In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This paper compares the proposed method with the single HMM and HMM ensemble with neural network. HMM is trained by using forward-backward or baum-welch algorithm and the likelihood value is used to predict future exchange rate price. The forecasting accuracy has been measured according to Root Mean Square Error (RMSE). The statistical performance of all techniques is investigated in testing of EUR/USD exchange rate time series over the period of October 2010 to March 2014. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt HMM-CBR in testing of GBP/USD, GBP/JPY, USD/JPY, and EUR/JPY exchange rate.

关键词： Hidden Markov Model Case Based Reasoning baum-welch algorithm Ibk algorithm

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Short-Term Stochastic Load Forecasting Using Autoregressive Integrated Moving Average Models and Hidden Markov Model

Short-Term Stochastic Load Forecasting Using Autoregressive ...

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International Conference on Information and Communication Technologies (ICICT)

作者： Hermias, Jeffrel P. Teknomo, Kardi Monje, Jose Claro N. Ateneo Manila Univ Sch Sci & Engn Elect Commun & Comp Engn Dept Katipunan Ave Quezon City Philippines Ateneo Manila Univ Sch Sci & Engn Informat Syst & Comp Sci Dept Katipunan Ave Quezon City Philippines

ISBN: (纸本)9781538621868

Load forecasting, particularly short-term load forecasting (STLF) plays a vital role in the economy streaming and tracking of power system. Many stochastic and artificial intelligence techniques haven been used in order to come up with an accurate (less error) short-term load forecast. Here, we introduce a new approach to short-term load forecasting (STLF) using the conventional Hidden Markov Model (HMM) then compare it with Autoregressive Integrated Moving Average (ARIMA) models. Three-dimensional continuous multivariate Gaussian emission probabilities are used in this experiment for HMM. Meanwhile for ARIMA models, different parameters are used for different kinds of dataset. Comparison is done afterwards to the actual load value using MAPE and RMSE.

关键词： Hidden Markov Model HMM Short-term load forecasting STLF ARIMA Autoregressive Integrated Moving Average baum-welch algorithm Machine learning Artificial Intelligence

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A tutorial of techniques for improving standard hidden Markov model algorithms

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Journal of Bioinformatics and Computational Biology 2009年第4期7卷 737-754页

作者： Golod, Daniil Brown, Daniel G. David R. Cheriton School of Computer Science University of Waterloo Waterloo ON N2L 3G1 200 University Avenue West Canada

In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the Viterbi algorithm and the expectation phase of the baum-welch algorithm, and we describe ways to improve their naïve implementations. For the baum-welch algorithm we first present an implementation of the expectation computations using constant space. We then discuss the classical implementation of this calculation and describe ways to reduce its space usage to logarithmic and O(√n), with their respective CPU costs. We also note where each respective algorithm can be parallelized. For the Viterbi algorithm, we describe O(√n) and logarithmic space algorithms which increase CPU use by a factor of two and by a logarithmic factor respectively. We also present two recent heuristics for decreasing space use, which in practice lead to logarithmic space use. Classical version of Viterbi cannot be parallelized by splitting sequence in several subsequences, but we show a parallelization that works if we are willing to pay a significant extra CPU cost. Finally we show a very simple parallelization trick which enables full usage of multiple CPUs/cores under the condition that they share memory. © 2009 Imperial College Press.

关键词： baum-welch algorithm HMM Parallelization Space efficiency Viterbi algorithm

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A Hybrid Feature Extraction Scheme for Off-line English Numeral Recognition

A Hybrid Feature Extraction Scheme for Off-line English Nume...

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International Conference for Convergence of Technology (I2CT)

作者： Prasad, Binod Kumar Sanyal, Goutam Bengal Coll Engn & Technol Dept Elect & Commun Durgapur India Natl Inst Technol Dept Comp Sci & Engn Durgapur India

ISBN: (纸本)9781479937592

This paper aims at presenting a rotation invariant feature extraction scheme to support well known result oriented recognizer HMM. Hybrid feature extraction method consists of features due to moment of inertia (FMI) and projection features. Projection features have been applied in case of digits (2 and 3) and for other numerals FMI is introduced. Any recognition system consists of two major components viz. Feature extraction method and recognizer. This paper uses Hidden Markov Model (HMM) as recognizer to recognize Off-line handwritten English numerals due to its inherent specialities and promising results in automatic speech recognition. Our data-base consists of own collected data from people of different ages and CENPARMI data. The percent recognition accuracy of self collected samples and CENPARMI samples have been found to be 91.7% and 91.2% respectively.

关键词： Numeral recognition HMM features due to moment of inertia (FMI) projection features Viter-bi algorithm baum-welch algorithm

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LPI Emitter Detection Based on Hidden Markov Model 28

LPI Emitter Detection Based on Hidden Markov Model

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Bayindir, Yilmaz Ozkazanc, Yakup ASELSAN AS Radar Elekt Harp & Istihbarat Sistemleri Ankara Turkey Hacettepe Univ Elekt & Elekt Muhendisligi Ankara Turkey

ISBN: (纸本)9781728172064

LPI radar is a special kind of radar which incorporates some features and techniques to avoid being detected. Because of their advantages, FMCW waveform is often used in LPI emitters. In this work, an original method based on hidden Markov model is suggested for the detection of FMCW. The suggested method is composed of learning and decoding layers. As a result of the simulations, it is observed that several simultaneous LPI emitters having low SNR can be detected in a wide dynamic range.

关键词： Electronic Support LPI Radar Frequency Modulated Continuous Wave Hidden Markov Model baum-welch algorithm Viterbi algorithm

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Stock Market Prediction With Hidden Markov Model 2

Stock Market Prediction With Hidden Markov Model

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2nd International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Farshchian, Maryam Jahan, Majid Vafaei Islamic Azad Univ Dept Comp Engn Neyshaboor Sience & Res Branch Neyshaboor Iran Islamic Azad Univ Mashhad Branch Dept Comp Engn Mashhad Iran

ISBN: (纸本)9781467397629

Today, Stock investment is an important part of the economy of the country. Therefore, forecasting changes in the behavior of market has become significantly important to shareholders. In the past years, classic methods often were used to forecast changes in the market behavior, but, in recent years, intelligent methods increasingly have been applied to forecast the behavior of stock market. Using intelligent methods, the accuracy and precision of forecasts have increased to some extent, but further researches still can be carried out to improve the precision of forecasts. The aim of the present research was to develop an appropriate model for increasing the accuracy and precision of forecasting the behavior of Tehran Stock Exchange by using Hidden Markov Model. In this model, normal conditions were supposed for the market and it was also assumed that the changes in the market behavior were independent from factors such as political issues, war and natural disasters including flood, earthquake and fire. First, the model was trained by baum-welch algorithm and after implementing the progressive forecast algorithm for three specific industries in MATLAB, it was found that, in comparison with the conventional methods, accuracy and precision of forecasting had been improved by 2 percent, on average. Moreover, by increasing the number of the days included in a specific interval considered for a certain industry, the precision of training algorithm increased and, consequently, the precision and accuracy of the algorithm increased, too.

关键词： Forecasting the stock market behavior intelligent methods Hidden Markov Model baum-welch algorithm progressive algorithm

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A Hidden Markov Model-based Approach for Weather Prediction in Bangladesh 26

A Hidden Markov Model-based Approach for Weather Prediction ...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Chowdhury, Md. Reasad Zaman Rahman, Mohammad Marufur Tanny, Nawshin Tabassum Bushra, Tasfia Anika Ahsanullah University of Science and Technology Department of CSE Dhaka Bangladesh Daffodil International University Department of CSE Dhaka Bangladesh

ISBN: (纸本)9798350359015

In the context of Bangladesh, a country prone to diverse and often unpredictable weather patterns, reliable weather forecasts are critical for making informed decisions and mitigating the impact of extreme weather events. In this research, we present an innovative approach to predict the average temperature and rainfall of Bangladesh using Hidden Markov Models (HMM). Our HMM-based approach leverages the past weather data as the observation in order to predict future weather patterns. For this study, we used the Bangladesh Weather dataset from Kaggle which contains monthly average temperature and rainfall data from 1901 to 2015. Experimental results show that the proposed HMM model was able to successfully capture the trends of the weather pattern with an MAE of 0.74 and 77.01 for the temperature and rainfall prediction respectively. © 2023 IEEE.

关键词： Bangladesh baum-welch algorithm Expectation-Maximization HMM Weather prediction

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ALIGNTTS: EFFICIENT FEED-FORWARD TEXT-TO-SPEECH SYSTEM WITHOUT EXPLICIT ALIGNMENT

ALIGNTTS: EFFICIENT FEED-FORWARD TEXT-TO-SPEECH SYSTEM WITHO...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeng, Zhen Wang, Jianzong Cheng, Ning Xia, Tian Xiao, Jing Ping An Technol Shenzhen Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781509066315

Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel. AlignTTS is based on a Feed-Forward Transformer which generates mel-spectrum from a sequence of characters, and the duration of each character is determined by a duration predictor. Instead of adopting the attention mechanism in Transformer TTS to align text to mel-spectrum, the alignment loss is presented to consider all possible alignments in training by use of dynamic programming. Experiments on the LJSpeech dataset show that our model achieves not only state-of-the-art performance which outperforms Transformer TTS by 0.03 in mean option score (MOS), but also a high efficiency which is more than 50 times faster than real-time.

关键词： text-to-speech speech synthesis feedforward transformer baum-welch algorithm

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