Learning Vector Quantization Algorithm Research Articles

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

A selective LVQ algorithm for improving instance reduction techniques and its application for text classification

Instance-Based Learning, such as the k Nearest Neighbor (kNN), offers a straightforward and effective solution for text classification. However, as a lazy learner, kNN’s performance heavily relies on the quality and quantity of training instances, often leading to time and space inefficiencies. This challenge has spurred the development of instance-reduction techniques aimed at retaining essential instances and discarding redundant ones. While such trimming optimizes computational demands, it might adversely affect classification accuracy. This study introduces the novel Selective Learning Vector Quantization (SLVQ) algorithm, specifically designed to enhance the performance of datasets reduced through such techniques. Unlike traditional LVQ algorithms that employ random vector weights (codebook vectors), SLVQ utilizes instances selected by the reduction algorithm as the initial weight vectors. Importantly, as these instances often contain nominal values, SLVQ modifies the distances between these nominal values, rather than modifying the values themselves, aiming to improve their representation of the training set. This approach is crucial because nominal attributes are common in real-world datasets and require effective distance measures, such as the Value Difference Measure (VDM), to handle them properly. Therefore, SLVQ adjusts the VDM distances between nominal values, instead of altering the attribute values of the codebook vectors. Hence, the innovation of the SLVQ approach lies in its integration of instance reduction techniques for selecting initial codebook vectors and its effective handling of nominal attributes. Our experiments, conducted on 17 text classification datasets with four different instance reduction algorithms, confirm SLVQ’s effectiveness. It significantly enhances the kNN’s classification accuracy of reduced datasets. In our empirical study, the SLVQ method improved the performance of these datasets, achieving average classification accuracies of 82.55%, 84.07%, 78.54%, and 83.18%, compared to the average accuracies of 76.25%, 79.62%, 66.54%, and 78.19% achieved by non-fine-tuned datasets, respectively.

Intelligent passive infrared sensor based on learning vector quantization for human detection

Passive Infrared (PIR) Sensors have been used widely in human detection indoors nowadays due to their low cost and range. However, traditional PIR sensors may get fault detection, especially when the human is in a static pose. To overcome this limitation, a Machine Learning (ML)-based PIR sensor is proposed in this work for detection accuracy enhancement. The Learning Vector Quantization (LVQ) approach is used to be easily implemented in the embedded device (which requires a low computational complexity) to provide a real-time response. The experimental scenarios to create the datasets are conducted in two distinct locations for training and testing purposes. In each location, participants performed a series of different activities and left the room unoccupied. Data is collected via a PIR sensor and then wireless transmitted to a computer for training and testing. In the test set, the presence of humans with an accuracy of 89.25 % is obtained using the proposed LVQ algorithm prediction. Finally, the LVQ is implemented on an embedded device based on Xtensa Dual-Core 32-bit LX6 CPU to form an intelligent PIR (iPIR)-based LVQ sensor, this novel iPIR sensor then is evaluated and tested with a remarkable result.

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization

Implementasi Algoritma Learning Vector Quantization Dalam Aplikasi Pengenalan Pola

Along with the development of technology, a lot of technology has emerged and aims to make computers intelligent so that they can mimic the daily work of humans. One of the jobs that is often done is typing documents from a book or article. If the typed document is not too long, it will not cause problems. However, if there are quite a lot of documents to be typed, the operator will need a relatively long time to complete the job. This work can be streamlined by utilizing Artificial Intelligence, namely the Learning Vector Quantization (LVQ) algorithm. LVQ is used because it can learn, calculate distances and separate clusters from the patterns it has learned. This application is implemented using the Microsoft Visual Basic 6 programming language. The result is that the application can segment image characters, and carry out the training process as well as the process of recognizing patterns or shapes of characters, which in turn can recognize each character in a sentence. As well as displaying the steps in the training process and the recognition process, so that it can help learning the LVQ algorithm, especially in character recognition.

Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out

Universities, as providers of academic education services, are required to provide an optimal educational process for students to produce a generation of quality human beings. Student learning success is seen as the success of universities in implementing the higher education process. One of the problems universities face in maintaining the quality of education is student dropout. The high dropout rate in universities can impact accreditation assessments. As a result, it will affect the level of public trust. The number of dropouts in higher education can be minimized from an early age by analyzing the factors that cause student dropouts using data on students who graduated and those who dropped out. This data can be used to determine student dropout patterns by classifying them using the artificial neural network learning vector quantization (LVQ) approach. The data used in this research was 4053, consisting of 3840 graduate student data and 213 dropout student data. This data is considered unbalanced, an unbalanced dataset can cause errors because the model tends to classify the majority class with a high classification and pays less attention to the minority class. So, it is necessary to apply oversampling techniques to overcome this problem. The research results show that the application of the LVQ method to unbalanced data produces an accuracy value of 95.53%, a precision value of 100%, a recall value of 15.02% and an f1-score of 0.26, while the application of the LVQ method to data that has undergone resampling resulting in an accuracy value of 94.66%, a precision value of 92.22%, a recall value of 97.55%, and an f1-score value of 0.95. The LVQ method can be used to classify dropout students with excellent results.

Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%.</p><p> </p></td></tr></tbody></table>

#6868 MACHINE LEARNING-BASED HYPERTENSION DISEASE RISK CLASSIFICATION USING LEARNING VECTOR QUANTIZATION ALGORITHM

Abstract Background and Aims According to the World Health Organization, the disease dubbed as the silent killer of death is the number one cause of death in the world. Hypertension is a disease that does not show certain symptoms. There are about 95% of hypertension cases have no known cause, while the rest are caused by other diseases. Therefore, an early detection system for hypertension is needed. In this research, the Learning Vector Quantization (LVQ) algorithm is used to perform the classification process. Method In this research, the LVQ machine learning algorithm is used. LVQ is an algorithm that is a type of artificial neural network and uses neural computation. The data in this study is hypertension patient data from Sultan Daeng Radja Hospital which is divided into four classes. In the system design stage using the LVQ algorithm, the process in this system is by entering input data in the form of medical records consisting of 12 attributes based on their weights which will then be carried out by the LVQ process to produce classification results. If the classification results have been obtained, the system will stop. After the classification testing process is carried out, the accuracy calculation process is carried out. Results Based on six test scenarios that have been carried out, the system produces recommendations for a value of 0.1 for the learning rate, a value of 0.2 for the learning rate multiplier, the amount of training data of as much as 50%, the maximum epoch value of six, the minimum value of alpha 0.001. From these values, the average accuracy result obtained is 94%. Conclusion Based on the value of accuracy results obtained exceeding 90%, the system can be done quite successfully in detecting hypertension. However, further research is needed to further develop the system.

Leakage detection in water distribution networks using machine-learning strategies

Abstract This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%.

LVQ Treatment for Zero-Shot Learning

In image classification, there are no labeled training instances for some classes, which are therefore called unseen classes or test classes. To classify these classes, zero-shot learning (ZSL) was developed, which typically attempts to learn a mapping from the (visual) feature space to the semantic space in which the classes are represented by a list of semantically meaningful attributes. However, the fact that this mapping is learned without using instances of the test classes affects the performance of ZSL, which is known as the domain shift problem. In this study, we propose to apply the learning vector quantization (LVQ) algorithm in the semantic space once the mapping is determined. First and foremost, this allows us to refine the prototypes of the test classes with respect to the learned mapping, which reduces the effects of the domain shift problem. Secondly, the LVQ algorithm increases the margin of the 1-NN classifier used in ZSL, resulting in better classification. Moreover, for this work, we consider a range of LVQ algorithms, from initial to advanced variants, and applied them to a number of state-of-the-art ZSL methods, then obtained their LVQ extensions. The experiments based on five ZSL benchmark datasets showed that the LVQ-empowered extensions of the ZSL methods are superior to their original counterparts in almost all settings.

Implementation and Development of Learning Vector Quantization Supervised Neural Network

Electricity is vital energy for the sustainability of human activities both as individuals, community groups, and the industrial world. Electricity users are increasing every year, which causes irresponsible users and does not comply with existing rules; the number of users causes the staff to find it challenging to determine whether the power used is appropriate with household needs. This study uses data on 100 electricity users obtained from PT. PLN Rayon Trade is one of the branch offices of PLN in Indonesia. The method used to classify electricity users is the Learning Vector Quantization (LVQ) algorithm using the 4-8-3 architectural model. Several input variables are used, such as the number of bills, the number of hours, the metered rate, and the class. The results obtained an accuracy rate of 72% with a time of 11 minutes 53 seconds. So it can be concluded that the LVQ algorithm with the 4-8-3 architectural model can be used to classify electricity users. However, it is not very good because the accuracy still needs to be improved.

Implementasi Algoritma Learning Vector Quantization Untuk Pengenalan Barcode Barang

Problems in barcode recognition during the barcode identification process. Where when the barcode has noise (damage) then the barcode becomes difficult to recognize. Learning Vector Quantization (LVQ) is a classification method in which each output unit presents a class. LVQ is used for grouping and is also one of the artificial neural networks which is a competitive learning algorithm supervised version of the Kohonen Self-Organizing Map (SOM) algorithm. The purpose of this algorithm is to approach the distribution of vector classes in order to minimize errors in classifying. LVQ learning models are trained significantly to be faster than other algorithms such as the Back Propagation Neural Network. This can summarize or reduce large datasets for a small number of vectors. Based on the results of barcode recognition testing using LVQ algorithm success with training data as much as 4 and conducted calrifikas trial of two data namely: {1,1,1,0} and {1,0,1,1}. Obtained accuracy value generated as much as 90% barcode recognized. The more training data used, LVQ will have a more complete knowledge.

Application Of Learning Vector Quantization Algorithm In Educational Games Learning To Write Aksara Bali

Almost every region in the nusantara has its own regional script, one of which is in Bali the people know the Balinese script as a script commonly used by their ancestors in writing. In order to keep this Balinese script sustainable and not extinct, learning about this script is applied in schools in Balinese language subjects. In addition to applying learning about script in schools, it would be better if learning about Balinese script could be done in more interesting ways and concepts. One of them is by making an educational game for the introduction of Balinese characters. Which in this application the user will provide input in the form of Balinese script characters written on the application, which then the input will go through a preprocessing process then proceed with diagonal feature extraction, then the results of the feature extraction will go through a classification process using the Learning Vector Quantization method. . The result is a web-based application that can recognize Balinese script writing using the Learning Vector Quantization classification method with an accuracy rate of 58.6% and get a good response from each respondent who has tested the application.

Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Abstract Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when the size of data grows large it becomes slower. In this paper, a modified version of LVQ, which is called PDLVQ is proposed to accelerate the traditional version. The proposed scheme aims to avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are used in the experiments. The comparisons have been done between LVQ and PDLVQ in terms of runtime and in result, it turns out that PDLVQ shows better efficiency than LVQ. PDLVQ has achieved up to 37% efficiency in runtime compared to LVQ when the dimensions have increased. Also, the enhanced algorithm (PDLVQ) shows clear enhancement to decrease runtime when the size of dimensions, the number of clusters, or the size of data becomes increased compared with the traditional one which is LVQ.

Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features

The increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle, so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. In order for LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, major axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall and accuracy, it is obtained that the precision value is 85%, recall is 82% and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by a single layer network architecture, this makes the computational process faster and does not burden the computational process.

A NOVEL LEARNING VECTOR QUANTIZATION WITH HAMMING DISTANCE MEASURE FOR EFFECTIVE PREDICTION OF FAKE NEWS

The main aim of the study is to improve the prediction rate in fake news detection. A novel Learning Vector Quantization (LVQ) with hamming distance is proposed for effective prediction of fake news. The sample size is calculated as N=10 for multi group analysis using the G-Power calculator. ISOT dataset is used for the study and the proposed learning vector quantization algorithm is executed and compared with three existing algorithms: Passive Aggressive classifier, LSTM and LS-SVM algorithm.

PENERAPAN METODE LEARNING VECTOR QUANTIZATION PADA PENENTUAN LEVEL BERMAIN TERHADAP GAME EDUKASI SANG SANTRI

Educational games more interesting if given an artificial intelligence. One of the artificial intelligence algorithms that can be applied to this game is Learning Vector Quantization (LVQ). LVQ is an intelligent algorithm that can be implemented in games because LVQ can produce the desired classification. In this study, researcher created an educational game for learning Arabic Vocabulary using the LVQ algorithm. This algorithm is used to determine the level of when a player playing this game. This is done so that a player can complete the game based on his ability to play. This algorithm is applied to the game based on the value of Skill Points and Health Points. Based on the test results, the LVQ algorithm can be applied in Arabic Vocabulary learning games to determine the level of a player's game. The test results show an accuracy value of 80% with 12 accurate data from 15 trial data. Based on these results, the LVQ algorithm can be applied well to Sang Santri game.

Entanglement degree of high-dimensional quantum low-density parity check codes

The important part of quantum information science is quantum error-correction and quantum computation. In thepractical application, some methods of quantum communication, such as the quantum communication based on the atmosphere transmission, require a mix ofmathematics-potentially to solve the problem. The quantum error-correction code is an efficient method to calculate quantum communication. However,since the construction of quantum error-correction codes depends on the degree of quantum entanglement, it is difficult to be widely used in practice.At present, the graph state is a promising solution to reduce the degree of codewords entanglement. But there are still many difficulties in constructingthe high-dimensional graph state. More importantly, the above difficulties can be skillfully solved by the upper and lower bounds of codewords entanglement.Based on the characteristics of the cyclic difference set of stabilizer codes and the combination of U and B of classical low-density parity check (LDPC) codes,a high-dimensional quantum low-density parity check (QLDPC) code is constructed creatively. By calculating the characteristics of the non-$Z$-type generators ofthe new codewords, the minimum number is obtained and the entanglement upper bound of the new code is acquired. And then, the rank of the check matrix of thenew code is calculated as the lower bound of entanglement. When the upper bound and lower bound of entanglement are different, the learning vectorquantization (LVQ) algorithm in machine learning can be used to simultaneously obtain the codewords entanglement and the coding complexity of the code,in which the relationship between them can be derived. In terms of calculating the running speed, compared with the iterative algorithm in the Lagrange multipliermethod, the running speed of LVQ algorithm is increased by $37.68%$. In this paper, a measurement of quantum codewords entanglement is proposed, which supports a helpful role in thedesign of quantum error-correction codes with higher decoding efficiency.

Automatic Radio Map Database Maintenance and Updating Based on Crowdsourced Samples for Indoor Localization

Recently, the crowdsourcing-based fingerprint technique has been promoted as an assuring solution for indoor localization positioning systems to alleviate the burden of conducting a site survey. When reconstructing and updating an indoor localization radio map from crowdsourced data, the following challenges addressed; powers of some Access Point (APs) change, adaptive radio map updating, and inaccurate location Information. In this manuscript, we propose automatic APs Changed Powers Identification and Radio Map database Updating (ACIRMapU) technique based crowdsourcing for indoor localization to handle these challenges. The ACIRMapU consists of APs changed powers detection and identification, fingerprints updating, and online localization positioning algorithms. For each crowdsourcing sample, we first propose a changed APs power detection and identification algorithm. After that, we proposed a fingerprint Learning Vector Quantization (LVQ) algorithm to update indoor radio maps due to changed AP. In the online localization positioning phase, an AP feature selection is performed by the Pearson Correlation Coefficient (PCC) algorithm followed by the Weighted K-nearest Neighbor based on signal Euclidean distance (WKNN-SED) to estimate the target location point. We conduct extensive field measurements and experiments to investigate automatic radio map updating adaptation. Results demonstrate that the proposed ACIRmapU can outperform significant improvements over the state-of-art approaches in the changed APs powered removed, increased, and decreased, whereby the localization errors in updating the radio map have reduced more than 76.86%, 61.83%, and 36.53%, respectively. Meanwhile, achieving average localization accuracy around 0.6325 m, 1.0435 m, and 1.735 m, respectively.

Implementation of Artificial Neural Networks with Learning Vector Quantization (LVQ) Algorithm for Detecting Fingerprint Characteristics

The purpose of implementing Artificial Neural Networks (ANN) with the Learning Vector Quanzition (LVQ) algorithm is to be used as a fingerprint identifier. So with this application can determine who owns the fingerprint in accordance with the database that has. In identifying fingerprints, the Learning Vector Quanzition method is very good, because it can measure the value of a fingerprint image that has been taken either by using a camera or scanner. In identifying fingerprints, it is expected that the fingerprint image input into the database should be from a photo or scanner that has clear and bright results, because this will affect the fingerprint search in the futures.