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Journal articles on the topic 'Hidden Markov support vector machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Sloin, Alba, and David Burshtein. "Support Vector Machine Training for Improved Hidden Markov Modeling." IEEE Transactions on Signal Processing 56, no. 1 (2008): 172–88. http://dx.doi.org/10.1109/tsp.2007.906741.

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ZAKI, NAZAR M., SAFAAI DERIS, and ROSLI M. ILLIAS. "FEATURES EXTRACTION FOR PROTEIN HOMOLOGY DETECTION USING HIDDEN MARKOV MODELS COMBINING SCORES." International Journal of Computational Intelligence and Applications 04, no. 01 (2004): 1–12. http://dx.doi.org/10.1142/s1469026804001161.

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Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher scores that are assigned to the sequence and then use support vector machine in conjunction with the fisher scores for protein homologies detection. In this paper, we revisit the idea of using a discriminative approach, and in particular support vector machines for protein homologies detection. However, in place of the Fisher scoring method, we present a new Hidden Markov Model Combining Scores approach. Six scoring algorithms are combined as a way of extracting features from a protein sequence. Experiments show that our method, improves on previous methods for homologies detection of protein domains.

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LIU, Guanjun. "Fault diagnosis approach based on hidden Markov model and support vector machine." Chinese Journal of Mechanical Engineering (English Edition) 20, no. 05 (2007): 92. http://dx.doi.org/10.3901/cjme.2007.05.092.

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Kumar, Manish, Ruchi Verma, and Gajendra P. S. Raghava. "Prediction of Mitochondrial Proteins Using Support Vector Machine and Hidden Markov Model." Journal of Biological Chemistry 281, no. 9 (2005): 5357–63. http://dx.doi.org/10.1074/jbc.m511061200.

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S, Omprakash. "Coronary artery disease prediction using hidden Markov model based support vector machine." Indian Journal of Science and Technology 13, no. 17 (2020): 1703–13. http://dx.doi.org/10.17485/ijst/v13i17.20.

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Sloukia, Fatima Ezzahra, Rajae Bouarfa, Hicham Medromi, and Mohammed Wahbi. "Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine." International Journal of Network Security & Its Applications 5, no. 3 (2013): 85–97. http://dx.doi.org/10.5121/ijnsa.2013.5308.

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Xia, Tian, and Xuemin Chen. "A Discrete Hidden Markov Model for SMS Spam Detection." Applied Sciences 10, no. 14 (2020): 5011. http://dx.doi.org/10.3390/app10145011.

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

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Wissel, Tobias, Tim Pfeiffer, Robert Frysch, et al. "Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography." Journal of Neural Engineering 10, no. 5 (2013): 056020. http://dx.doi.org/10.1088/1741-2560/10/5/056020.

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Liu, Bin, Bingquan Liu, Fule Liu, and Xiaolong Wang. "Protein Binding Site Prediction by Combining Hidden Markov Support Vector Machine and Profile-Based Propensities." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/464093.

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Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.

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Firdaniza, Firdaniza, and Jondri Jondri. "Prediksi Trend Pergerakan Harga Saham dengan Hidden Markov Model (HMM) dan Support Vector Machine (SVM)." Jurnal Matematika Integratif 10, no. 1 (2020): 19. http://dx.doi.org/10.24198/jmi.v10.n1.10181.19-24.

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Jain, Ruchi, and Nasser S. Abouzakhar. "A Comparative Study of Hidden Markov Model and Support Vector Machine in Anomaly Intrusion Detection." Journal of Internet Technology and Secured Transaction 2, no. 3/4 (2013): 176–84. http://dx.doi.org/10.20533/jitst.2046.3723.2013.0023.

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Suharjito, Suharjito, and Fitra Bayu Adinugraha. "Hand Motion Gesture for Human-Computer Interaction Using Support Vector Machine and Hidden Markov Model." International Review on Computers and Software (IRECOS) 11, no. 5 (2016): 374. http://dx.doi.org/10.15866/irecos.v11i5.8641.

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Hoettinger, Hannes, Franziska Mally, and Anton Sabo. "Activity Recognition in Surfing - A Comparative Study between Hidden Markov Model and Support Vector Machine." Procedia Engineering 147 (2016): 912–17. http://dx.doi.org/10.1016/j.proeng.2016.06.279.

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Prasad, Binod Kumar, and Goutam Sanyal. "Multiple Hidden Markov Model Post Processed with Support Vector Machine to Recognize English Handwritten Numerals." International Journal on Artificial Intelligence Tools 27, no. 05 (2018): 1850019. http://dx.doi.org/10.1142/s0218213018500197.

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This paper presents rotation and size invariant English numerals recognition system with, competitive recognition rate. The novelty of this paper is the introduction of two unique methods of feature extraction namely Pixel Moment of Inertia (PMI) and Delta Distance Coding (DDC). The proposed Multiple Hidden Markov Model (MHMM) is a two tier model to neutralize the effect of two very frequent writing styles of numerals ‘4’ and ‘7’ on their recognition rates. The novelty of PMI is that it finds moment of all the pixels of a specified zone about the central pixel and not about geometrical centroid of image area. In this paper, PMI has been observed to have an upper hand over centroidal MI. DDC is a new technique of curvature coding, based on distance from a reference level and is similar to the logic behind Delta modulation scheme in Digital Communications. Thus, the current paper correlates two digital domains namely, Digital Image Processing and Digital Communications. Support Vector Machine differentiates two close output classes obtained from classification with MHMM. The overall recognition accuracy rate of 99.17% has been achieved based on MNIST database.

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Mohanambal, M., and Dr P. Vishnu Vardhan. "Wavelet based Extraction of Features from EEG Signals and Classification of Novel Emotion Recognition Using SVM and HMM Classifier and to Measure its Accuracy." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 727–32. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21102.

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Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

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Yaseri, Abbas, and Seyed Mahmoud Anisheh. "A Novel Paper Currency Recognition using Fourier Mellin Transform, Hidden Markov Model and Support Vector Machine." International Journal of Computer Applications 61, no. 7 (2013): 17–22. http://dx.doi.org/10.5120/9939-3997.

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Sun, Lu, Yang Li, Han Du, Peipei Liang, and Fushun Nian. "Fault Diagnosis Method of Low Noise Amplifier Based on Support Vector Machine and Hidden Markov Model." Journal of Electronic Testing 37, no. 2 (2021): 215–23. http://dx.doi.org/10.1007/s10836-021-05938-0.

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Tallapragada, V. V. S., and E. G. Rajan. "Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden Markov model." IET Image Processing 6, no. 6 (2012): 661–67. http://dx.doi.org/10.1049/iet-ipr.2011.0249.

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Khorasani, Abed, Mohammad Reza Daliri, and Mohammad Pooyan. "Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model." Biomedical Engineering / Biomedizinische Technik 61, no. 1 (2016): 119–26. http://dx.doi.org/10.1515/bmt-2014-0089.

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Abstract Amyotrophic lateral sclerosis (ALS) is a common disease among neurological disorders that can change the pattern of gait in human. One of the effective methods for recognition and analysis of gait patterns in ALS patients is utilizing stride interval time series. With proper preprocessing for removing unwanted artifacts from the raw stride interval times and then extracting meaningful features from these data, the factorial hidden Markov model (FHMM) was used to distinguish ALS patients from healthy subjects. The results of classification accuracy evaluated using the leave-one-out (LOO) cross-validation algorithm showed that the FHMM method provides better recognition of ALS and healthy subjects compared to standard HMM. Moreover, comparing our method with a state-of-the art method named least square support vector machine (LS-SVM) showed the efficiency of the FHMM in distinguishing ALS subjects from healthy ones.

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Li, Xiang, Xuan Jing Shen, Ying Da Lv, and Hai Peng Chen. "Visual Saliency and Extended Hidden Markov Model Based Approach for Image Splicing Detection." Applied Mechanics and Materials 385-386 (August 2013): 1466–69. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1466.

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In order to improve the detection accuracy of spliced images, a new blind detection based on visual saliency was proposed in this paper. Firstly, create the edge conspicuous map by an improved OSF-based method, and extract fixations by visual attention model. Then locate those fixations on conspicuous edges by conspicuous edge positioning method. Accordingly, key feature fragments can be captured. Secondly, extract Extended Hidden Markov Model features, and reduce their dimension by SVM-RFE. Finally, support vector machine was exploited to classify the authentic and spliced images. The experimental results showed that, when testing on the Columbia image splicing detection dataset, the detection accuracy of the proposed method was 96.68%.

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Miao, Qiang, Hong-Zhong Huang, and Xianfeng Fan. "A comparison study of support vector machines and hidden Markov models in machinery condition monitoring." Journal of Mechanical Science and Technology 21, no. 4 (2007): 607–15. http://dx.doi.org/10.1007/bf03026965.

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Xiao, Zi Ming, Yu Long Shi, Yong Xue, Feng Hu, and Yu Chuan Wu. "Research on the Taxonomy of Activity Recognition Based on Inertial Sensors." Advanced Materials Research 823 (October 2013): 107–10. http://dx.doi.org/10.4028/www.scientific.net/amr.823.107.

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This paper introduces some techniques on classifying human activities with inertial sensors and point out a number of characteristics of classification algorithm. The goal of human activity recognition is to automatically analyze ongoing activities from people who wear inertial sensor. Initially, we provide introduce information about the activity recognition, such as the way of acquisition, sensors used and the steps of activity recognition using machine learning algorithm. Next, we focus on the classification techniques together with a detailed taxonomy, and the classification techniques implemented and compared in this study are: Decision Tree Algorithm (DTA), Bayesian Decision Making (BDM), Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Hidden Markov Model (HMM)[. Finally, we make a summarize about it investigate the directions for future research.

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Liu, Li, Dashi Luo, Ming Liu, Jun Zhong, Ye Wei, and Letian Sun. "A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/987189.

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Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that theF-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

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Zhao, ShiJie, Toshihiko Sasama, Takao Kawamura, and Kazunori Sugahara. "Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs." International Journal of New Media Technology 4, no. 2 (2017): 94–98. http://dx.doi.org/10.31937/ijnmt.v4i2.786.

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We propose a human behavior detect method based on our development system of multifunctional outlet. This is a low-power sensor network system that can recognize human behavior without any wearable devices. In order to detect human regular daily behaviors, we setup various sensors in rooms and use them to record daily lives. In this paper we present a monitoring method of unusual behaviors, and it also can be used for healthcare and so on. We use Hidden Markov Model(HMM), and set two series HMM input to recognize irregular movement from daily lives, One is time sequential sensor data blocks whose sensor values are binarized and splitted by its response. And the other is time sequential labels using Support Vector Machine (SVM). In experiments, our developed sensor network system logged 34days data. HMM learns data of the first 34days that include only usual daily behaviors as training data, and then evaluates the last 8 days that include unusual behaviors. Index Terms—multifunctional outlet system; behavior detection; hidden markov model; sensor network; support vector machine. REFERENCES [1] T.Sasama, S.Iwasaki, and T.Okamoto, “Sensor Data Classification for Indoor Situation Using the Multifunctional Outlet”, The Institute of Electrinical Engineers of Japan, vol.134(7),2014,pp.949-995 [2] M.Anjali Manikannan, R.Jayarajan, “Wireless Sensor Netwrork For Lonely Elderly Perple Wellness”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, vol. 3, 2015, pp.41-45 [3] Nagender Kumar Suryadevara, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE SENSORS JOURNAL, VOL. 12, NO. 6, JUNE 2012, pp. 1965-1972. [4] iTec Co., safety confirmation system: Mimamorou, http://www.minamoro.biz/. [6] Alexander Schliep's group for bioinformatics, The General Hidden Markov Model library(GHMM), http://ghmm.sourceforge.net/. [7] Jr Joe H.Ward, Joumal of the American Statistical Association, vol58(301), 1963, pp236-244 [5] SOLXYZ Co., status monitoring system:Ima-Irumo, http://www.imairumo.com/.

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Sun, Shuming, Juan Chen, and Jian Sun. "Traffic congestion prediction based on GPS trajectory data." International Journal of Distributed Sensor Networks 15, no. 5 (2019): 155014771984744. http://dx.doi.org/10.1177/1550147719847440.

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Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridge regression are used to perform congestion level prediction. According to the experimental results, deep learning models obtain higher accuracy in traffic congestion prediction compared with conventional machine learning models.

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Yu, W. H., H. Hovik, and T. Chen. "A hidden Markov support vector machine framework incorporating profile geometry learning for identifying microbial RNA in tiling array data." Bioinformatics 26, no. 11 (2010): 1423–30. http://dx.doi.org/10.1093/bioinformatics/btq162.

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Shen, Zhehan, Taigang Liu, and Ting Xu. "Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles." Computational and Mathematical Methods in Medicine 2021 (August 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/5770981.

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Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.

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Dawa, Tenzin, and N. Vijayalakshmi. "A Comparative Review on Different Methods of Face Recognition." Oriental journal of computer science and technology 10, no. 1 (2017): 227–31. http://dx.doi.org/10.13005/ojcst/10.01.31.

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Face Recognition is a biometric system which can be used to identify or verify a person from digital image by using the facial features that are unique to each other. There are many techniques which can be used in a face recognition system. In this paper we review some of the algorithms and compare them to see which technique is better compared to one another. Techniques that are compared in this technique are Non-negative matrix factorization (NMF) with Support Vector Machine (SVM), Partial Least Squares (PLS) with Hidden Markov Model (HMM) and Local Ternary Pattern (LTP) with Booth’s Algorithm.

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Wu, Xiao Juan, Bao Feng Zhang, and Xiao Long Zhang. "Intermittent Fault Diagnosis Method of Power System Based on HMM-SVM Characteristics." Applied Mechanics and Materials 63-64 (June 2011): 850–54. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.850.

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For avoiding false-alarm and diagnosing intermittent fault, based on the relationship analysis between intermittent fault and false-alarm, the intermittent fault diagnosis model of power system based on HMM (Hidden Markov Model)-SVM (Support Vector Machine) characteristics was constructed in this paper. By means of this model, the intermittent fault diagnosis procedure of power system based on HMM-SVM characteristics was built. Finally, the intermittent fault diagnosis simulation system of power system based on HMM-SVM characteristics was demonstrated and validated by QUEST software. The result showed that the proposed method was available, and can provided guidance for avoiding false-alarm and diagnosing intermittent fault, and support for stable performance in power system.

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Yabuki, Y., T. Muramatsu, T. Hirokawa, H. Mukai, and M. Suwa. "GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model." Nucleic Acids Research 33, Web Server (2005): W148—W153. http://dx.doi.org/10.1093/nar/gki495.

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Yan, Ren Wu, and Jian Min Dai. "Fault Diagnosis of Power Electronic Circuit Based on Hybrid Intelligent Method." Applied Mechanics and Materials 651-653 (September 2014): 1074–77. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1074.

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Because of the problems of fault diagnosis in the power electric circuit and the merit of FCM is efficient in clustering and the merit of hidden Markov model (HMM) that have the ability to deal with continuous dynamic signals and the merit of support vector machine (SVM) with perfect classifying ability, With the features extracted from the circuit, based on the trained FCM algorithm, HMM was used to calculate the matching degree among the unknown signal and the circuit’s states, which formed the features for SVM to diagnosis. Double-bridge 12-pulse rectifier is used as a example to verifiy the effectiveness of the method. The experimental results show that the proposed method has a good correct rate.

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Liang, Jiuzhen, Wei Song, and Mei Wang. "Stock Price Prediction Based on Procedural Neural Networks." Advances in Artificial Neural Systems 2011 (June 15, 2011): 1–11. http://dx.doi.org/10.1155/2011/814769.

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We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two different structures of procedural neural networks are constructed for modeling multidimensional time series problems. Learning algorithms for training the models and sustained improvement of learning are presented and discussed. Experiments on Yahoo stock market of the past decade years are implemented, and simulation results are compared by PNN, BNN, HMM, and SVM.

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Al-Momani, Orobah, and Khaled M. Gharaibeh. "Effect of Wireless Channels on Detection and Classification of Asthma Attacks in Wireless Remote Health Monitoring Systems." International Journal of Telemedicine and Applications 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/816369.

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This paper aims to study the performance of support vector machine (SVM) classification in detecting asthma attacks in a wireless remote monitoring scenario. The effect of wireless channels on decision making of the SVM classifier is studied in order to determine the channel conditions under which transmission is not recommended from a clinical point of view. The simulation results show that the performance of the SVM classification algorithm in detecting asthma attacks is highly influenced by the mobility of the user where Doppler effects are manifested. The results also show that SVM classifiers outperform other methods used for classification of cough signals such as the hidden markov model (HMM) based classifier specially when wireless channel impairments are considered.

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Zhang, Chun Liang, Sheng Li, and Xia Yue. "State Monitoring for Centrifugal Pump of PWR Based on HMM and SVM." Advanced Materials Research 97-101 (March 2010): 3233–38. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3233.

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The centrifugal pump of pressurized water reactor (PWR) in nuclear power plant is characterized by its complicated system, small accumulated data and fault samples. HMM has a strong ability to deal with time series modeling for dynamic process, while SVM has excellent generalization ability to solve the learning problems with small samples. This paper develops a state monitoring system based on the hybrid HMM/SVM model. The wavelet analysis techniques are used to extract features and the Hidden Markov Model (HMM) and Support Vector Machine (SVM) are used as the basic modeling and identification tools. The identification results of centrifugal pump show that the hybrid HMM/SVM system is effective and available for the state monitoring of the centrifugal pump of PWR in nuclear power plan.

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Li, Sheng, Chun Liang Zhang, and Liang Bin Hu. "Study of Data Fusion Method for Fault Diagnosis Based on FDR Feature Selection Algorithm and HMM/SVM Model." Advanced Materials Research 591-593 (November 2012): 2046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2046.

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To effectively avoid the loss of useful information, in this paper, we extract feature information from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then for the multi-dimensional feature extraction is prone to the problem of “dimension disaster”, introduce the principles of FDR in data mining to determine the classification ability of each individual feature, and introduce the cross correlation coefficient to solve the problem that dealing with individual feature neglects the interrelationship between the features, and construct a new feature level data fusion algorithm. Finally, According to the characteristics of the HMM (Hidden Markov model), SVM (Support Vector Machine) and its hybrid model, we construct a new decision-level data fusion model.

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Abidine, M'hamed Bilal, and Belkacem Fergani. "Activity Recognition From Smartphone Data Using WSVM-HMM Classification." International Journal of E-Health and Medical Communications 12, no. 6 (2021): 1–20. http://dx.doi.org/10.4018/ijehmc.20211101.oa11.

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A lot of real-life mobile sensing applications are becoming available nowadays. The traditional approach for activity recognition employs machine learning algorithms to learn from collected data from smartphpne and induce a model. The model generation is usually performed offline on a server system and later deployed to the phone for activity recognition. In this paper, we propose a new hybrid classification model to perform automatic recognition of activities using built-in embedded sensors present in smartphones. The proposed method uses a trick to classify the ongoing activity by combining Weighted Support Vector Machines (WSVM) model and Hidden Markov Model (HMM) model. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our proposed method outperforms the state-of-the-art on a large benchmark dataset.

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Mi, Shou Fang, and Ling Hua Li. "Study on Techniques of Hand Gesture Recognition." Applied Mechanics and Materials 241-244 (December 2012): 1664–67. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1664.

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This paper describes the study of techniques used in hand gesture recognition in sign language interpretation. The study is discussed from two aspects: the process of hand gesture recognition and the techniques of feature extraction in hand gesture recognition. The techniques of feature extraction in hand gesture recognition are grouped into five categories: Hidden Markov Model (HMM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Dynamic Bayesian Network (DBN), and Dynamic Time Warping (DTW). The main ideas and the application of each technique are described in detail.

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M’hamed Abidine, Bilal, Belkacem Fergani, Mourad Oussalah, and Lamya Fergani. "A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data." Kybernetes 43, no. 8 (2014): 1150–64. http://dx.doi.org/10.1108/k-07-2014-0138.

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Purpose – The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues. Design/methodology/approach – In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem. Findings – The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors. Originality/value – Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.

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Li, Feng, Zhonghua Yu, Xuanwei Shen, and Hao Zhang. "Status recognition for fused deposition modeling manufactured parts based on acoustic emission." E3S Web of Conferences 95 (2019): 01005. http://dx.doi.org/10.1051/e3sconf/20199501005.

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Fused deposition modelling (FDM), as one technology of additive manufacturing, fabricates parts always with curl and looseness defects which restrict its development to a great extent. In this paper, a method based on acoustic emission (AE) was proposed to recognise the status of the manufactured part in FDM process. Experiments were carried out to acquire the AE signal when the printing part was respectively in normal, looseness and curl state. The ensemble empirical mode decomposition (EEMD) was employed to the process of feature extraction and both the methods of Hidden-semi Markov model (HSMM) and support vector machine(SVM) were applied to recognise the three states of the normal, looseness and curl. The results reveal that the combination of EEMD and HSMM makes it more accurate to recognize these three states.

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Ben Ayed, Yassine. "A New SVM Kernel for Keyword Spotting Using Confidence Measures." International Journal on Artificial Intelligence Tools 24, no. 03 (2015): 1550010. http://dx.doi.org/10.1142/s0218213015500104.

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In this paper, we propose an alternative keyword spotting method relying on confidence measures and support vector machines. Confidence measures are computed from phone information provided by a Hidden Markov Model based speech recognizer. We use three kinds of techniques, i.e., arithmetic, geometric and harmonic means to compute a confidence measure for each word. The acceptance/rejection decision of a word is based on the confidence vector processed by the SVM classifier for which we propose a new Beta kernel. The performance of the proposed SVM classifier is compared with spotting methods based on some confidence means. Experimental results presented in this paper show that the proposed SVM classifier method improves the performances of the keyword spotting system.

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Ajani, Taiwo Samuel, Agbotiname Lucky Imoize, and Aderemi A. Atayero. "An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications." Sensors 21, no. 13 (2021): 4412. http://dx.doi.org/10.3390/s21134412.

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Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

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Prima, B., and M. Bouhorma. "USING TRANSFER LEARNING FOR MALWARE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 343–49. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-343-2020.

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Abstract. In this paper, we propose a malware classification framework using transfer learning based on existing Deep Learning models that have been pre-trained on massive image datasets. In recent years there has been a significant increase in the number and variety of malwares, which amplifies the need to improve automatic detection and classification of the malwares. Nowadays, neural network methodology has reached a level that may exceed the limits of previous machine learning methods, such as Hidden Markov Models and Support Vector Machines (SVM). As a result, convolutional neural networks (CNNs) have shown superior performance compared to traditional learning techniques, specifically in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture for malware classification. The malicious binary files are represented as grayscale images and a deep neural network is trained by freezing the pre-trained VGG16 layers on the ImageNet dataset and adapting the last fully connected layer to the malware family classification. Our evaluation results show that our approach is able to achieve an average of 98% accuracy for the MALIMG dataset.

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Tian, Yan Ping, Xiao Hui Ye, and Ming Yin. "Electronic Equipment Combination Fault Prediction Technology Research Based on LSSVM-HMM." Applied Mechanics and Materials 687-691 (November 2014): 978–83. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.978.

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In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.

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Li, Ling Hua, and Ji Fang Du. "Visual Based Hand Gesture Recognition Systems." Applied Mechanics and Materials 263-266 (December 2012): 2422–25. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2422.

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This paper describes the techniques used in visual based hand gesture recognition systems. The study is discussed from three aspects: the two categories, the five components, and the methods of feature extraction of visual based hand gesture recognition systems. The two categories are 3D model based systems and appearance model based systems. The five components are image sequences capture, pre-processing, hand regions detection, feature extraction and gesture classification. The methods of feature extraction are Hidden Markov Model (HMM), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). The main ideas of each technique are described in detail.

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Liu, Da, Yanan Wei, Shuxia Yang, and Zhitao Guan. "Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/648101.

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A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM) is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM), USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM), generalized regression neural networks (GRNN), day-ahead modeling, and self-organized map (SOM) similar days modeling.

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Sang, Xiuzhi, Wanyue Xiao, Huiwen Zheng, Yang Yang, and Taigang Liu. "HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection." Computational and Mathematical Methods in Medicine 2020 (March 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/1384749.

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Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto- and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

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Atmarani, Luh Ria, Made Sudarma, and IA Dwi Giriantari. "Sistem Opinion Mining dengan Metode Pos Tagging dan SVM Untuk Ekstraksi Data Opini Publik pada Layanan JKBM." Majalah Ilmiah Teknologi Elektro 16, no. 1 (2016): 91. http://dx.doi.org/10.24843/mite.1601.13.

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Analisis sentimen atau opinion mining dapat digunakan untuk mengekstrak opini dari baris baris teks menjadi suatu informasi. Salah satu metode yang digunakan adalah Hidden Markov Model (HMM). HMM digunakan untuk memberikan kelas kata secara gramatikal pada suatu kalimat. Setelah kelas kata dapat ditentukan selanjutnya menentukan aturan dengan menggunakan rule based. Dengan menggunakan rule based suatu kalimat dapat ditentukan termasuk opini atau bukan. Penerapan metode Support Vector Machine digunakan untuk mengklasifikasikan opini ke dalam opini positif dan negatif. Data yang digunakan adalah data pada penangan keluhan dan pada opini online pada Unit Pelayanan Teknis Jaminan Kesehatan Bali Mandara Provinsi Bali. Hasil proses opinion mining akan diuji menggunakan metode precission, recall dan akurasi. Hasil penelitian menunjukkan presentase nilai precission, recall dan akurasi memiliki rata rata presentase sebesar 89 persen. Ini menunjukkan metode pos tagging dan SVM mampu mengklasifikasikan kalimat kedalam opini dan menentukan kalimat ke dalam opini positif dan negatifDOI: 10.24843/MITE.1601.13

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Liu, Jia, Chun Liang Zhang, Jian Li, Sen Li, and Yue Hua Xiong. "The Remote Fault Intelligent Diagnosis System Based on B/S Structure." Advanced Materials Research 328-330 (September 2011): 1067–71. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.1067.

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The feasibility and superiority of the remote fault diagnosis system based on B/S structure is analyzed in this paper. The B/S structure is introduced and compared with C/S structure briefly. The paper summarize frame and main function module of the remote fault diagnosis system and introduce its key technology, such as data acquisition technology, data transmission technology between server and client, intelligent diagnosis technology, database technology etc. The hybrid model of support vector machine (SVM) and hidden markov models(HMM) is used as a intelligent diagnosis method of the system, and a new design which could improve the integrity and privacy of the system database data is applied. According to the diagnostic results to all kinds of simulated faults in the Bently vibration test bed, it shows the system is not only stable, reliable and high accuracy, but also has a certain application value to engineering.

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Zhou, Deyu, and Yulan He. "Semi-Supervised Learning of Statistical Models for Natural Language Understanding." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/121650.

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Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved inF-measure.

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