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Hidden Markov Model [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
The process involves modeling methodology, implementation choices, and dynamic tuning.  ...  Hidden Markov Model The best thing about the future is that it comes one day at a time.  ...  Parameter Estimation Block You can use GMM to represent feature distributions in a workload phase prediction system, in which individual component densities model an underlying set of latent classes.  ... 
doi:10.1007/978-1-4302-5990-9_5 fatcat:gmc74ou7zbdi3itneyxyriopne

A review on stochastic approach for dynamic power management in wireless sensor networks

Anuradha Pughat, Vidushi Sharma
2015 Human-Centric Computing and Information Sciences  
Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states  ...  This review paper classifies different dynamic power management techniques and focuses on stochastic modeling scheme which dynamically manage wireless sensor node operations in order to minimize its power  ...  Acknowledgement The author's would like to thank Gautam Buddha University for providing workplace, support and resources. Received: 29 July 2014 Accepted: 26 January 2015  ... 
doi:10.1186/s13673-015-0021-6 fatcat:apd5wmwckfe3fa5mjn67wwkdce

Control Theoretic Approach to Platform Optimization using HMM [chapter]

Rahul Khanna, Huaping Liu, Mariette Aw
2011 Hidden Markov Models, Theory and Applications  
Control Theoretic Approach to Platform Optimization using HMM 14 www.intechopen.com 292 Hidden Markov Models, Theory and Applications www.intechopen.com the pattern in the sequence of events to predict  ...  System development tends to be a complex process that competes for performance in the presence of design constraints.  ...  Hidden Markov Models, Theory and Applications www.intechopen.com Hidden Markov Models, Theory and Applications  ... 
doi:10.5772/15038 fatcat:angt6jtebbh3tjv5fa5eorn7tu

Storage workload modelling by hidden Markov models: Application to Flash memory

P.G. Harrison, S.K. Harrison, N.M. Patel, S. Zertal
2012 Performance evaluation (Print)  
A workload analysis technique is presented that processes data from operation type traces and creates a Hidden Markov Model (HMM) to represent the workload that generated those traces.  ...  It can also be used to estimate directly the transition probabilities and rates of a Markov modulated arrival process, for use as input to an analytical performance model of Flash memory.  ...  Rationale and modelling methodology We develop a methodology for representing workloads concisely as a Markov modulated process -a hidden Markov model -that is suitable both as a portable and flexible  ... 
doi:10.1016/j.peva.2011.07.022 fatcat:kajuwsbagncj5l2wb2d4hr4jlq

Storage Workload Modelling by Hidden Markov Models: Application to FLASH Memory [article]

P. G. Harrison, S. K. Harrison, N. M. Patel, S. Zertal
2012 arXiv   pre-print
A workload analysis technique is presented that processes data from operation type traces and creates a Hidden Markov Model (HMM) to represent the workload that generated those traces.  ...  It can also be used to estimate directly the transition probabilities and rates of a Markov modulated arrival process, for use as input to an analytical performance model of Flash memory.  ...  Rationale and modelling methodology We develop a methodology for representing workloads concisely as a Markov modulated process -a hidden Markov model -that is suitable both as a portable and flexible  ... 
arXiv:1209.3315v1 fatcat:22nr2lfey5fmzpdhj2lniayd64

Latency-based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacentres

Yao Lu, Lu Liu, John Panneerselvam, Xiaojun Zhai, Xiang Sun, Nick Antonopoulos
2019 IEEE Transactions on Sustainable Computing  
But prediction analytics of Cloud ds suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads.  ...  Experiments conducted on real-world Cloud datasets exhibit that the proposed model exhibits better prediction accuracy, outperforming traditional Hidden Markov Model, Naïve Bayes Classifier and our earlier  ...  The prediction efficiencies of the proposed K-RVBLPNN prediction model are compared with Hidden Markov Model (HMM), Naïve Bayes Classifier (NBC), and our previously proposed prediction model RVLBPNN [  ... 
doi:10.1109/tsusc.2019.2905728 fatcat:3jnpkglajzfd7amx5w7zajqd3u

A Neural Network Model for Driver's Lane-Changing Trajectory Prediction in Urban Traffic Flow

Chenxi Ding, Wuhong Wang, Xiao Wang, Martin Baumann
2013 Mathematical Problems in Engineering  
BP neural network model and Elman Network model in terms of the training time and accuracy.  ...  In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the colleagues from Transportation Research Center of Beijing University of Technology, which have improved the paper.  ... 
doi:10.1155/2013/967358 fatcat:bur5rqrx4bfirg3npjvxdkp3a4

DTHMM ExaLB: discrete-time hidden Markov model for load balancing in distributed exascale computing environment

Ulphat Bakhishoff, Ehsan Mousavi Khaneghah, Araz R. Aliev, Amirhossein Reyhani Showkatabadi, Stefania Tomasiello
2020 Cogent Engineering  
In this article, a Discrete-Time Hidden Markov Models (DT HMM)-based mathematical model is proposed for Load Balancing to be able to predict the influences of Dynamic and Interactive events on the system  ...  In this article, dynamic and interactive events, which violate the function and activity of the burden distribution manager based on the Discrete-Time Hidden Markov Models, were analyzed.  ...  Funding The authors received no direct funding for this research. Author details  ... 
doi:10.1080/23311916.2020.1743404 fatcat:bt5xeicvczcinigz2qasguddl4

Optimizing Maintenance Planning in the Production Industry Using the Markovian Approach

B Kareem, HA Owolabi
2012 The Journal of Engineering Research  
Markov chains, transition matrices, decision processes, and dynamic programming models were formulated for the decision problem related to maintenance operations of a cable production company.  ...  Preventive and corrective maintenance data based on workloads and costs, were collected from the company and utilized in this study.  ...  Rabinar (1989) applied hidden Markov models in the area of speech recognition.  ... 
doi:10.24200/tjer.vol9iss2pp46-63 fatcat:7b5uceuaxrft7fbg5ywpmkegrm

Automatic Generation of Workload Profiles Using Unsupervised Learning Pipelines

David Buchaca Prats, Josep Lluis Berral, David Carrera
2018 IEEE Transactions on Network and Service Management  
This low dimensional and time-aware vector can be passed through clustering methods, from simplistic ones like k-means to more complex ones like those based on Hidden Markov Models (HMM).  ...  We use these methods to find phases of similar behaviour in the workloads.  ...  input vectors into n h dimensional vectors, taking the time dimension into account, and feeding them to Hidden Markov Models.  ... 
doi:10.1109/tnsm.2017.2786047 fatcat:idlfldnoxnbufjnu2si4dljj4q

Un modèle de trafic adaptéà la volatilité de charge d'un service de vidéo à la demande: Identification, validation et application à la gestion dynamique de ressources [article]

Shubhabrata Roy , Paulo Goncalves
2012 arXiv   pre-print
Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations  ...  We show how good can our model fit to real workload traces with respect to the stationary case in terms of steady-state probability and autocorrelation structure.  ...  Figure 3 - 3 Markov chain diagram representing the evolution of the Current viewers (i) and Past Viewers (r) populations with a Hidden Markov Model.  ... 
arXiv:1209.5158v2 fatcat:pwh72ar2bfhrfbirysdt5hdkai

Review of Existing Data Mining Techniques Used For Weather Prediction

Sameer Kaul
2017 International Journal for Research in Applied Science and Engineering Technology  
Paper discusses the significant properties that are vital for data mining techniques to be incorporated in predictive model of weather forecasting.  ...  This paper will provide a detailed overview of popularly used data mining techniques and gives an insight on the utility of data mining techniques in the weather prediction models.  ...  prediction model using the K-means clustering with the Hidden Markov Model for data extraction of the weather condition observations; and the proposed technique was performed on JAVA technology as shown  ... 
doi:10.22214/ijraset.2017.11353 fatcat:eeumempnv5fgbdmfvpnapcc7ca

Machine Learning in Action: Examples [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
More sophisticated models can facilitate decision support systems, using hierarchies of domains and respective domain-specific models.  ...  These computing models synthesize the knowledge embedded in the unstructured data and learn domain-specific trends and attributes.  ...  The process involves modeling methodology, implementation choices, and dynamic tuning.  ... 
doi:10.1007/978-1-4302-5990-9_11 fatcat:mdmgulxgljginjs7jdwjdxg7qe

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

K. Bhargavi, B. Sathish Babu
2019 Cybernetics and Information Technologies  
The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering  ...  of resources and so on.  ...  Identify the uncertainty in the jobs and resources by representing their states in the form of Partially Observable Markov Decision Process model (POMDP) and Hidden Markov Model (HMM) model.  ... 
doi:10.2478/cait-2019-0028 fatcat:xnkaaf32iffofcyfjo7bw24b3u

Human-machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net

Chen, Liu, Chen
2019 Applied Sciences  
Given the operation logs from data recording hardware, a Hidden Markov model on top of a human cognitive model was trained to capture a production line worker's sequential faults.  ...  This study proposes a cognition-adaptive approach for the administrative control of human-machine safety interaction through Internet of Things (IoT) data.  ...  PN is a dynamic modeling tool and can capture unplanned failures and their sequence, thereby predicting the quality and reliability impact in a dynamic manner [14] .  ... 
doi:10.3390/app9235066 fatcat:tcpt4ph6y5hpdjimruixaotnzm
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