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10th International Conference on Advanced Cloud and Big Data (CBD)

作者： Zheng, Xiao Zhang, Qiuyue Tang, Xuan Wang, Xiujun Du, Chunlai Anhui Univ Technol Sch Comp Sci & Technol Maanshan Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Anhui Peoples R China Anhui Engn Res Ctr Intelligent Applicat & Secur I Maanshan Anhui Peoples R China North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China

ISBN: (纸本)9798350309713

With more and more people turning to online medical pre-diagnosis systems, it becomes increasingly important to protect patient privacy and enhance the accuracy and efficiency of diagnosis. That is because the ever rapidly growing medical records not only contain a large amount of private information but are often highly unequally distributed (e.g., the number of cases and the rate of increase of covid-19 can be much higher than that of common diseases). However, existing methods are not capable of simultaneously boosting the intensity of privacy protection, and the accuracy and efficiency of diagnosis. In this paper, we propose an online medical pre-diagnosis scheme based on incremental learning vector quantization (called WL-OMPD) to achieve the two objectives at the same time. Specifically, within WL-OMPD, we design an efficient algorithm, Wasserstein-learning vector quantization (W-LVQ), to smartly compress the original medical records into hypothetic samples. Then, we transmit these compressed data to the cloud instead of the original records to offer a more accurate pre-diagnosis. Extensive evaluations of real medical datasets show that the WL-OMPD scheme can improve the imbalance ratio of the data to a certain extent and then the intensity of privacy protection. These results also demonstrate that WL-OMPD substantially boost the accuracy of the classification model and increase diagnostic efficiency at a lower compression rate.

关键词： Medical data On-line Pre-diagnosis Privacy preserving Big data learning vector quantization Imbalanced data Wasserstein

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Evaluation of LBP-based descriptors for Iris Recognition based on learning vector quantization Classifier under a Multi-core Platform 38

Evaluation of LBP-based descriptors for Iris Recognition bas...

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38th International Conference of the Chilean-Computer-Science-Society (SCCC)

作者： Campos, Hernan Hernandez-Garcia, Ruber Barrientos, Ricardo J. Univ Catolica Maule Lab LITRP Dept DCI Fac Ciencias Ingn Talca Chile

ISBN: (纸本)9781728156132

In recent years, iris recognition techniques have focused the attention on the biometric area. Particularly, the performance of an iris recognition system depends on the representation of the texture of the images. In this sense, Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. This paper aims to evaluate the use of different LBP-based descriptors for iris recognition by using a learning vector quantization Classifier (LVQ). Our study shows the first analysis of the performance of LVQ classifier on different LBP-based descriptors for iris recognition. Besides, the proposed method evaluates the impact of a parallel implementation of the system under a multi-core platform. The evaluation carried out on the CASIA-Iris-Interval database, shows that LVQ classifier is an effective alternative for iris recognition using LBP-based descriptors. The proposed method achieved a recognition rate of 98.36% and obtaining a speed-up of 24.81x.

关键词： Biometrics Iris recognition Local Binary Patterns learning vector quantization

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learning vector quantization with Local Adaptive Weighting for Relevance Determination in Genome-Wide Association Studies

Learning Vector Quantization with Local Adaptive Weighting f...

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International Joint Conference on Neural Networks

作者： Flavia R B Araujo Hansenclever F Bassani Aluizio F R Araujo Center of Informatics Federal University of Pernambuco Cidade Universitária Recife Brasil

ISBN: (纸本)9781467361279

In Genome-Wide Association Studies (GWAS) huge amounts of genetic information are analyzed in order to discover how the observed variations, more specifically, the Single Nucleotide Polymorphisms (SNPs), are related with a certain trait of interest, such as the susceptibility for a disease. However, the high dimensionality observed in the datasets imposes significant challenges for methods that try to identify the relevant SNPs and their interactions. In particular, we emphasize the challenges imposed by the great amount of irrelevant dimensions shadowing information which is object of study. In this work, we present a prototype-based classification method, derived from learning vector quantization (LVQ), in which the relevance of each input dimension is learned independently for each prototype. We validate our method in simulated datasets of GWAS with a significant number of dimensions (20, 50, or 100) in which few of them (from 2 to 5) are relevant. Such dimensions have to be identified. The proposed method presented promising results, showing graceful degradation when the number of irrelevant dimensions increases, in comparison with Multifactor Dimensionality Reduction (MDR), Generalized Relevance learning vector quantization (GRLVQ) and Supervised Relevance Neural Gas (SRNG).

关键词： GWAS SNPs LVQ learning vector quantization Single nucleotide polymorphism Genome-Wide Association Study dimensions vector quantization Dataset

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A Novel learning vector quantization Inference Classifier 1

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9th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Maisen, Chakkraphop Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Chiang Mai Univ Fac Engn Dept Comp Engn Chiang Mai Thailand Chiang Mai Univ Grad Sch Chiang Mai Thailand Chiang Mai Univ Biomed Engn Ctr Chiang Mai Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai Thailand

ISBN: (数字)9783319544724

ISBN: (纸本)9783319544724

One of the popular tools in pattern recognition is a neuro-fuzzy system. Most of the neuro-fuzzy systems are based on a multi-layer perceptrons. In this paper, we incorporate learning vector quantization in a neuro-fuzzy system. The prototype update equation is based on the learning vector quantization while the gradient descent technique is used in the weight update equation. Since weights contain informative information, they are exploited to select a good feature set. There are 8 data sets used in the experiment, i.e., Iris Plants, Wisconsin Breast Cancer (WBC), Pima Indians Diabetes, Wine, Ionosphere, Colon Tumor, Diffuse Large B-Cell Lymphoma (DLBCL), and Glioma Tumor (GLI_85). The results show that our algorithm provides good classification rates on all data sets. It is able to select a good feature set with a small number of features. We compare our results indirectly with the existing algorithms as well. The comparison result shows that our algorithm performs better than those existing ones.

关键词： Neuro-fuzzy classifier learning vector quantization Prototype-based rule Rule extraction Feature selection

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Automatic Essay Grading System with Latent Semantic Analysis and learning vector quantization 17

Automatic Essay Grading System with Latent Semantic Analysis...

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3rd International Conference on Communication and Information Processing (ICCIP)

作者： Ratna, Anak Agung Putri Raharjo, Budi Selamet Purnamasari, Prima Dewi Sanjaya, Randy Univ Indonesia Fac Engn Dept Elect Engn Kampus Baru UI Depok 16424 Indonesia

ISBN: (纸本)9781450353656

Automatic essay grading system called SIMPLE-O (Sistem Penilai Esai Otomatis) that developed by Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia was built using PHP. This system was developed for helping lecturer assessing the examination, especially with essay form. Currently, the SIMPLE-O is still developed using C programming language for implementing more methods in development that can only be done in that language to improve the performance of the system. To increase the accuracy, learning vector quantization (LVQ) algorithm is implemented in the development due to its ability for supervised classification. The number of data samples in LVQ training phase are affecting the essay scoring performance, less data used will lead to decrease in the result accuracy of the validation phase. Moreover, singular value that generated by Frobenius norm and vector angle pre-processing will also affect the scoring accuracy. But, the number of words-per-column when creating the LSA matrix did not have any significant effect. At the end, SIMPLE-O with LVQ has an average accuracy of 53.57%, 41.66% higher than the system that did not use LVQ. This accuracy performance was still low due to the missing of the words similarity feature. In the previous version of SIMPLE-O, this feature can improve the performance of the essay grading system significantly.

关键词： Essay grading latent semantic analysis learning vector quantization SIMPLE-O singular value decomposition

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A new initial codebook algorithm for learning vector quantization

A new initial codebook algorithm for learning vector quantiz...

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IEEE International Conference on Communications (ICC 2007)

作者： Li, Hongsong Xu, Baohua Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China

ISBN: (纸本)9781424403523

In this paper, a new sorting initial codebook algorithm for, learning vector quantization (LVQ) based upon self-organizing feature maps (SOM) has been proposed. The basic idea is that the similar codewords are put together in the initial codebook. To improve the performance of SOM algorithm, we have presented a new winning self-organizing feature maps (WSOM). Experimental results for image VQ show that the average PSNR improvement of this new initial codebook algorithm is 0.7dB compared to the common random sampling algorithm, and the WSOM algorithm has better coding performance than the LBG and SOM algorithm.

关键词： self-organizing feature maps learning vector quantization LBG algorithm image coding

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Application of Unsupervised K Nearest Neighbor (UNN) and learning vector quantization (LVQ) Methods in Predicting Rupiah to Dollar 4

Application of Unsupervised K Nearest Neighbor (UNN) and Lea...

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4th International Conference on Cyber and IT Service Management

作者： Haerani, Elin Apriyanti, Liza Wardhani, Luh Kesuma UIN Sultan Syarif Kasim Informat Fac Sci & Technol Kota Pekanbaru Riau Indonesia UIN Syarif Hidayatullah Informat Fac Sci & Technol South Tangerang Indonesia

ISBN: (纸本)9781467384414

One of the factors in a country's economy is the exchange value of the currency towards another currency. The exchange value of Rupiah towards Dollar (USA) can quickly change depending on the environmental conditions and has a huge impact for the Indonesian Government. In this research, learning vector quantization (LVQ) and Unsupervised K Nearest Network (UNN) was implemented in predicting the currency value towards dollar. The UNN method was used to predict the selling value of the currency, the LVQ method was used to predict the buying value of the currency. The input data that is used is the selling, buying and interest data times series of the currency from the central back of the United States. From the research result and discussions that was made, UNN can achieve the lowest MAPE, which is 1,544% with the amount of data as much as 25 and the LVQ algorithm can accurately achieve a forecast with the amount of data as much as 25 with the learning rate of 0,075. The amount of trained data and the many patterns that exist in one LVQ class method can affect the result of the study and the result of the system.

关键词： Dollar Foreign Exchange Rates learning vector quantization LVQ Rupiah Unsupervised K Nearest Network UNN

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Smell Classification of Wines by the learning vector quantization Method 15

Smell Classification of Wines by the Learning Vector Quantiz...

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30th ACM Symposium on Applied Computing (SAC)

作者： Omatu, Sigeru Yano, Mitsuaki Ikeda, Yoshinori Osaka Inst Technol Asahi Ku 5-16-1 Omiya Osaka 5358585 Japan

ISBN: (纸本)9781450331968

We consider a classification of white wine and red wine by a learning vector quantization method of competitive neural network. First, we measure smell data using metal-oxide semiconductor gas sensors which change smell data into electrical voltages based on oxidation and reduction processes. Two kinds of wines, white wine and red wine, are classified using smell data. Since a smell density of wine is rather thin, we use a bubbling method to make the density level higher. Here, we adopt a mono trap which is a kind of molecular sieves. By this way we obtain smell data of wines of high concentration level. After absorbing process, we take the temperature of a silica tube from a room temperature to 300 degrees Celsius. Using the learning vector quantization method, we classify two kinds of wines. We show that the classification accuracy rate for the white wine is around 97% and that for the red wine is around 83.4%, respectively.

关键词： smell information processing electronic nose wine classification learning vector quantization competitive neural network

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Weighted learning vector quantization to Cost-Sensitive learning

Weighted Learning Vector Quantization to Cost-Sensitive Lear...

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20th International Conference on Artificial Neural Networks

作者： Chen, Ning Ribeiro, Bernardete Vieira, Armando Duarte, Joao Neves, Joao Inst Politecn Porto GECAD Inst Super Engn Porto Oporto Portugal Univ Coimbra CISUC Dept Informat Engn Coimbra Portugal Univ Tecn Lisboa ISEG Sch Econo Lisbon Portugal

ISBN: (纸本)9783642158247

The importance of cost-sensitive learning becomes crucial when the costs of misclassifications are quite different. Many evidences have demonstrated that a cost-sensitive predictive model is more desirable in practical applications than a traditional one without taking the cost into consideration. In this paper, we propose two approaches which incorporate the cost matrix into original learning vector quantization by means of instance weighting. Empirical results show that the proposed algorithms are effective on both binary-class data and multi-class data.

关键词： classification neural network cost-sensitive learning learning vector quantization

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A combined method of optimized learning vector quantization and neuro-fuzzy techniques for predicting unified Parkinson's disease rating scale using vocal features

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METHODSX 2024年 12卷 102553页

作者： Zogaan, Waleed Abdu Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Abosaq, Hamad Alghamdi, Abdullah Jazan Univ Fac Comp Sci & Informat Technol Dept Comp Sci Jazan 45142 Saudi Arabia UCSI Univ UCSI Grad Business Sch Kuala Lumpur 56000 Malaysia Univ Sains Malaysia Ctr Global Sustainabil Studies CGSS George Town 11800 Penang Malaysia Univ Plymouth Fac Hlth Ctr Hlth Technol Plymouth PL4 8AA England Univ Plymouth Fac Hlth Plymouth PL4 8AA England Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar Najran Univ Coll Comp Sci & Informat Syst Informat Syst Dept Najran Saudi Arabia Najran Univ Sci & Engn Res Ctr SERC Najran Saudi Arabia Najran Univ Coll Comp Sci & Informat Syst Comp Sci Dept Najran Saudi Arabia

Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized learning Rate learning vector quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732;UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876;UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented. i. ANFIS and OLVQ1 are combined to predict UPDRS. ii. OLVQ1 is used for PD data segmentation. iii. ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.

关键词： Parkinson's disease Neuro-fuzzy Optimized learning rate Motor-UPDRS Total-UPDRS learning vector quantization

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