scholarly journals A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Haitao Guo ◽  
Hongwei Huo
Keyword(s):  
Hidden Markov ◽  
Regulation Of Gene Expression ◽  
Structure Model ◽  
Regulatory Structure ◽  
Regulatory Modules ◽  
Motif Site ◽  
Benchmark Datasets ◽  
Considerable Impact ◽  
Internal Motif ◽  
Better Than

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

scholarly journals Unsupervised Outlier Detection in Multidimensional Data

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
High Dimensions ◽  
Comprehensive Performance ◽  
Benchmark Datasets ◽  
Distance Vector ◽  
Detection Schemes ◽  
Unsupervised Outlier Detection ◽  
Better Than

Abstract Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. In order to detect the anomalies in a dataset in an unsupervised manner, some novel statistical techniques are proposed in this paper. The proposed techniques are based on statistical methods considering data compactness and other properties. The newly proposed ideas are found efficient in terms of performance, ease of implementation, and computational complexity. Furthermore, two proposed techniques presented in this paper use only a single dimensional distance vector to detect the outliers, so irrespective of the data’s high dimensions, the techniques remain computationally inexpensive and feasible. Comprehensive performance analysis of the proposed anomaly detection schemes is presented in the paper, and the newly proposed schemes are found better than the state-of-the-art methods when tested on several benchmark datasets.

Ant Miner

2020 ◽  
Vol 10 (1) ◽  
pp. 45-59
Author(s):  
Bijaya Kumar Nanda ◽  
Satchidananda Dehuri
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Classification Rule ◽  
Classification Rules ◽  
Rule Mining ◽  
Ant Colonies ◽  
Benchmark Datasets ◽  
Objective Classification ◽  
Single Objective ◽  
Better Than

In data mining the task of extracting classification rules from large data is an important task and is gaining considerable attention. This article presents a novel ant miner for classification rule mining. The ant miner is inspired by researches on the behaviour of real ant colonies, simulated annealing, and some data mining concepts as well as principles. This paper presents a Pittsburgh style approach for single objective classification rule mining. The algorithm is tested on a few benchmark datasets drawn from UCI repository. The experimental outcomes confirm that ant miner-HPB (Hybrid Pittsburgh Style Classification) is significantly better than ant-miner-PB (Pittsburgh Style Classification).

scholarly journals Structural damage identification based on gray cloud rule generator algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881990 ◽  
Author(s):  
Hui-Yong Guo ◽  
He-Fa Yuan ◽  
Qi Huang
Keyword(s):  
Energy Dissipation ◽  
Structural Damage ◽  
Strain Energy ◽  
Damage Identification ◽  
Cloud Model ◽  
Structure Model ◽  
Cloud Droplets ◽  
Modal Strain Energy ◽  
Energy Dissipation Ratio ◽  
Better Than

It is difficult for the traditional methods to identify uncertain damage problems caused by noise. Therefore, a gray cloud rule generator algorithm based on cloud model and modal strain energy is presented to solve the problems. Cloud model can simulate both randomness and fuzziness with fixed parameters. Therefore, it is applicable for the uncertain damage problems. First, modal strain energy and modal strain energy dissipation ratio index are introduced. Then, numerical characteristics of a cloud model are described and some cloud generators are analyzed. Finally, a gray cloud rule is proposed and the gray cloud rule generator algorithm based on the gray cloud rule generator and modal strain energy is developed. The interference of uncertain noise is reduced through a large number of cloud droplets. A two-dimensional truss structure model has been used to verify the effectiveness of the algorithm. The results indicate that the proposed gray cloud rule generator algorithm is applicable to identify the uncertain damage caused by noise, and the identification results of the proposed method are relatively better than those of modal strain energy dissipation ratio index.

Infant Cry Recognition System

2019 ◽  
Vol 11 (1) ◽  
pp. 15-32
Author(s):  
Yosra Abdulaziz Mohammed
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Acoustic Features ◽  
Infant Cry ◽  
Identification Rate ◽  
The One ◽  
Better Than

Cries of infants can be seen as an indicator of pain. It has been proven that crying caused by pain, hunger, fear, stress, etc., show different cry patterns. The work presented here introduces a comparative study between the performance of two different classification techniques implemented in an automatic classification system for identifying two types of infants' cries, pain, and non-pain. The techniques are namely, Continuous Hidden Markov Models (CHMM) and Artificial Neural Networks (ANN). Two different sets of acoustic features were extracted from the cry samples, those are MFCC and LPCC, the feature vectors generated by each were eventually fed into the classification module for the purpose of training and testing. The results of this work showed that the system based on CDHMM have better performance than that based on ANN. CDHMM gives the best identification rate at 96.1%, which is much higher than 79% of ANN whereby in general the system based on MFCC features performed better than the one that utilizes LPCC features.

scholarly journals Effective Representing of Information Network by Variational Autoencoder

2017 ◽  
Author(s):  
Hang Li ◽  
Haozheng Wang ◽  
Zhenglu Yang ◽  
Haochen Liu
Keyword(s):  
State Of The Art ◽  
Vector Model ◽  
Information Network ◽  
Network Representation ◽  
Linear Relationships ◽  
Variational Autoencoder ◽  
Benchmark Datasets ◽  
Text Information ◽  
Complex Features ◽  
Better Than

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study proposes a deep network representation model that seamlessly integrates the text information and structure of a network. Our model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. We also merge the representation learned with a paragraph vector model and that learned with the VAE to obtain the network representation that preserves both structure and text information. We conduct comprehensive empirical experiments on benchmark datasets and find our model performs better than state-of-the-art techniques by a large margin.

scholarly journals A Proposed Hidden Markov Model Method for Dynamic Device Pairing on Internet of Things End-Devices

2021 ◽  
Vol 14 (3) ◽  
pp. 274-285
Author(s):  
Aji Gautama Putrada ◽  
Nur Ghaniaviyanto Ramadhan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Access Point ◽  
Received Signal Strength Indicator ◽  
Model Method ◽  
Iot Platform ◽  
High Level ◽  
Device Pairing ◽  
Better Than

Dynamic device pairing is a context-based zero-interaction method to pair end-devices in an IoT System based on Received Signal Strength Indicator (RSSI) values. But if RSSI detection is done in high level, the accuracy is troublesome due to poor sampling rates. This research proposes the Hidden Markov Model method to increase the performance of dynamic device pairing detection. This research implements an IoT system consisting an Access Point, an IoT End Device, an IoT Platform, and an IoT application and performs a comparison of two different methods to prove the concept. The results show that the precision of dynamic device pairing with HMM is better than without HMM and the value is 83,93%.

scholarly journals Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy

2007 ◽  
Vol 11 (3) ◽  
pp. 319-326 ◽  
Author(s):  
Ruck Thawonmas ◽  
◽  
Ji-Young Ho ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Online Game ◽  
Classification Approach ◽  
Game Players ◽  
Better Than

Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.

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