Self-organizing Map Neural Network Research Articles

Study on Pixel Entanglement Theory for Imagery Classification

Classification of remotely sensed images (RSIs) is prerequisite for further applications. Of the existing RSI classification algorithms, the self-organizing mapping (SOM) neural network is considered as one of the most effective unsupervised classification algorithms. However, the traditional SOM only considers the Euclidean distance between neurons, which cannot reflect the correlation characteristics of neurons. For this reason, a radical algorithm, called “pixel entanglement (PE) through self-organizing pixel entanglement neural network (SOPENN)” is proposed in this article. First, the pixels in an RSI are considered as called “quantum pixels,” and all of the pixels in a RSI are considered as called “quantum pixel array”; then PE coefficient is proposed to mine the quantum entanglement relationship of quantum pixels on the array space; finally, a self-organizing neural network (SONN) is established to stimulate the PE behavior between quantum pixels. The theory mentioned above is applied in RSI classification to verify the performance of the SOPENN through four study areas. The experimental results demonstrate that: 1) the classification accuracy of SOPENN model proposed in this article averagely increases 8.42% when compared with the traditional unsupervised classification methods, Iterative Selforganizing Data Analysis (ISODATA), K-means, and SOM; and Kappa coefficient (KC) averagely increases 0.14; and 2) the classification accuracy and KC from the proposed SOPENN model reached the same level when compared with the supervised classification (support vector machine (SVM) model) in four study areas.

Multi-Behavior RFM Model Based on Improved SOM Neural Network Algorithm for Customer Segmentation

Previous research on RFM (recency, frequency, and monetary value) models focused on only one type of user behavior data, i.e., the purchase behavior, without considering the interactions between users and items, such as clicking, favorite, and adding to cart. In this study, we propose a novel solution for deconstructing the multiple behaviors of consumers in a specific period and performing customer segmentation in an application promotion system called multi-behavior RFM ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MB-RFM</i> ) based on the self-organizing map (SOM) algorithm. First, using the R, F, and M values, we analyzed the weight relationship between multiple behaviors of users and items using the superiority chart and entropy value methods. Each behavior ascribed to a customer was considered to be a part of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MB-RFM</i> model values, which were then used to classify customers using an improved SOM neural network. Subsequently, various promotion strategies were developed according to customer categories that can help application vendors in improving their application utilization and implementing targeted promotion strategies. To prove the effectiveness of the proposed method in sparse datasets, two real-world datasets were used to perform experiments, whose results demonstrated that the classification performance of our method was significantly more accurate.

Developing test color samples to compute color fidelity of light sources for printing matter

Based on the clustering optimization of test color samples and a psychophysical experiment, the objective and subjective color fidelity of light sources for printing matter is evaluated, and an improved evaluation method thus has been proposed. Firstly, for representing the output characteristics of printing press, the International Color Consortium standard color target samples (ICC SCTS), which is measured in the process of color management, is used as a large color sample set. In a 6D spectral color space proposed in this paper, the optimized color sample set (OCSS) is obtained by Self Organizing Maps Neural Networks (SOMNN) clustering algorithm from ICC SCTS. Taking OCSS, ICC SCTS, and standard color sample set (SCSS) as the test color sample set, three objective color fidelity indexes (CFIs) CIE-Ra, Ra,2012, and CIE-Rf of 1202 light sources are calculated. The correlation metrics of the CFIs show that the OCSS highly improved the objective accuracy of color fidelity evaluation for printing matters. Secondly, in the psychophysical experiment, 20 observers have evaluated the visual color difference of the OCSS under the illumination of nine pairs of test and reference light sources. The subjective CFIs are calculated by using the visual color difference of OCSS obtained from the psychophysical experiment. In order to improve the subjective and objective consistency of CFIs, a polynomial modified model for objective color difference of OCSS is proposed. By the optimization of test color samples and the modification of color difference calculation, the method developed in this paper can be effectively and conveniently applied to the subjective and objective evaluation of light source for printing matters.

A novel method for frequency feature extraction of ship radiated noise based on variational mode decomposition, double coupled Duffing chaotic oscillator and multivariate multiscale dispersion entropy

The processing of ship radiated noise is a research hotspot in the field of military and marine resource exploration. Frequency characteristic is an important characteristic of ship radiated noise. Ship radiated noise consists of line spectrum and continuous spectrum, and the frequency features are mainly reflected by line spectrum. Accurate extraction of line spectrum frequency feature is of great significance to realize ship target classification and recognition. This paper proposes a novel method for frequency feature extraction of ship radiated noise based on variational mode decomposition (VMD), double coupled Duffing chaotic oscillator (DCDCO) and multivariate multiscale dispersion entropy (mvMDE), named VMD-DCDCO-mvMDE. For the first time, DCDCO and mvMDE are applied to the feature extraction of ship radiated noise. Firstly, ship radiated noise is decomposed into a series of intrinsic mode functions (IMFs) by VMD. The decomposition layers K of VMD are determined according to the decomposition results of EMD. Secondly, adjust γand let d = 2 to leave DCDCO in critical chaotic state and detect the frequency of low frequency IMF. The mvMDE of the output signal of chaotic oscillator is calculated in the frequency range of DCDCO in the large scale periodic state, and the accurate frequency is determined by the minimum value of mvMDE. Finally, the extracted frequency feature parameters are input into the self-organizing mapping (SOM) neural network to classify and recognize different ship targets. In the analog signal experiment, the maximum frequency error of VMD-DCDCO-MVMDE extraction is only 0.01%, while the minimum error of the current method is 0.33%. The proposed method can extract frequency feature more accurately. In the measured signal experiment, the results show that the correct rate of the proposed method can reach 100% for the classification and recognition of the three groups of ship radiated noise, while the correct rate of the current method is the highest at 84%. The proposed method can effectively extract the line spectrum frequency feature of ship radiated noise, and the classification accuracy is higher, and the recognition effect is better.

Where to Revitalize, and How? A Rural Typology Zoning for China

Under China’s Rural Revitalization program, it is essential to accurately determine the prospects of revival or decay for the villages alongside specific goals and paths, which existing literature lacks a systematic coverage. Based on rural typology theories, this paper proposes an analytical framework to determine the type of village revitalization from the perspective of factor endowments. Utilizing five groups of 45 indicators characterizing the natural, socio-economic, and cultural endowments of villages, this paper applies the Self-Organizing Mapping neural network to cluster 2,388,579 natural villages in 48,322 townships across the nation into the four basic types of rural revitalization as directed by China’s Strategic Plan for Rural Revitalization (2018–2022): (1) Agglomerative Promotion, (2) Suburban Annexation, (3) Special Endowment-based Development, and (4) Out-migration and Relocation. The results of cluster analysis are spatially visualized to form a national rural revitalization zoning map at the township level, the first attempt to our knowledge. We conclude the paper with discussions on the revitalization paths of the various types of villages, particularly the seemingly gloomy prospect of 2/3 of the villages falling into the fourth category, and ways to interpret the deterministic nature of the conclusion. The paper expands the understanding of rural typology to a national scale with both innovative categorization processes and strong linkages to revitalization practices.

High‐Performance Single‐Crystal Diamond Detector for Accurate Pulse Shape Discrimination Based on Self‐Organizing Map Neural Networks

Single-Crystal Diamond DetectorsA high-performance single-crystal diamond (SCD) detector (4.5 × 4.5 × 0.3 mm3) is demonstrated in article number 2100138 by Chun-Zhi Zhou, Yi-Yun Zhang, and co-workers. Enabled by a machine learning method based on self-organizing map neural networks as a proof of concept, the SCD detector has demonstrated its potential for accurate pulse shape discrimination, which can effectively distinguish particle radiations in harsh and complex radiation conditions.

Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model

Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.

Recurrence analysis of coefficient of friction oscillations in DLC-coated and non-coated Ti6Al4V titanium alloy

Though numerous analytical techniques are used to assess performance of surface-modified metals, identification of dominant wear modes, as well as transitions between them, is challenging. Nevertheless, the works devoted to analysis the COF oscillations are still scarce. In this paper, Recurrence Quantification Analysis, Principal Component Analysis and Self-Organizing Map neural network are proposed as tools for analysis of COF time series acquired during tribological tests of the non-coated and DLC-coated Ti6Al4V. The proposed approach can be used to recognize the dominant wear modes present during friction and the transitions between them, as well as to spot the frictional damage of the protective film.

Line Segment-Based Clustering Approach With Self-Organizing Maps

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.

Deep Self-Organizing Map Neural Networks for Plantar Pressure Image Segmentation Employing Marr-Hildreth Features

Using the plantar pressure imaging analysis method to realize the optimization design of shoe last is still relatively preliminary. The analysis and utilization of imaging data still have problems such as single processing, incomplete information acquisition, and poor processing model robustness. A deep self-organizing map neural network based on Marr-Hildreth filter (dSOM-wh) is developed in this research. The structure and learning algorithms were optimized by learning vector quantization (LVQ) and count propagation (CP). As a kind of Marr-Hildreth filter, Laplacian of Gaussian (LoG) was developed for the preprocessing. The proposed method performed high effectiveness in accuracy (AC) (92.88%), sensitive (SE) (0.8941), and f-measurement (F1) (0.8720) by comparing with ANN, CNN, SegNet, ResNet, and pre-trained inception-v neural networks. The classification-based plantar pressure biomedical functional zoning technologies have potential applications in the comfort shoe production industry.

A drawer-type abdominal window with an acrylic/resin coverslip enables long-term intravital fluorescence/photoacoustic imaging of the liver

Abstract The liver has a unique vascular structure and regional immunosuppressive characteristics closely linked to the occurrence and development of diseases. There are no long-term, large-field, and high-quality imaging methods to simultaneously obtain the structure of blood vessels and movement activities of immune cells in abdominal organs in vivo. This research developed a drawer-type abdominal window with an acrylic/resin coverslip named DAWarc and applied it to the intravital fluorescence/photoacoustic imaging of the liver for over 10 days. The liver lobe was inserted into the drawer holder of the DAWarc to physically fix the liver, which decreased the imaging artifacts. The acrylic/resin material used as the coverslip has a high compatibility for fluorescence/photoacoustic imaging. Through intravital fluorescence/photoacoustic imaging, information on the structure of hepatic lobules, spatial distribution of nanopomegranate labeled Kupffer cells (KCs), the movement behavior of invariant natural killer T cells, and morphology of KCs were obtained. We also used a self-organizing map neural network to detect tumor metastases in the photoacoustic images automatically. Conclusively, the DAWarc model provided a powerful tool for intravital fluorescence/photoacoustic imaging of the liver; it helped us to better understand the structure of hepatic lobules and the distribution and function of immune cells during the occurrence and development of liver diseases.

Efficiency of the t-distribution stochastic neighbor embedding technique for detailed visualization and modeling interactions between agricultural soil quality indicators

Dimensionality reduction is important for revealing important details that may be useful in decision-making. Although different dimensionality reduction methods have been applied in several soil-based studies, Kohonen self-organizing map neural network (KSOM-NN) has attracted significant attention from researchers because of the quality of data visualization and interpretation. However, there is a dearth of studies that compare KSOM-NN and other robust data reduction techniques such as the t -distribution stochastic neighbor embedding ( t -SNE) method to improve visualization and interpretation of the relationships between soil quality indicators in agricultural soil. This study compares the above-mentioned methods for characterizing soil quality indicators including particle size distribution, soil organic matter (SOM), cation exchange capacity (CEC), soil reaction (pH), electrical conductivity (EC), zinc (Zn), iron (Fe), manganese (Mn), potassium (K) and phosphorus (P) in agricultural dryland. There were strongly positive associations identified between some of the variables studied for example, clay/Fe (r = 0.95), clay/SOM (r = 0.79) and Mn/Zn (r = 0.90) based on the correlation matrix output. According to the KSOM-NN, the best map size was a 4 by 7 with Quantization error (QE) = 0.108, Topographic error (TE) = 0.875 and Kaski-Lagus error (K-LE) = 9.104. This map only yielded 2 main clusters. As for the t -SNE, applying various perplexity values (i.e. 5, 6, 7, 8, 9 and 10) enabled better visualization of the soil quality indicators as observed from the cluster formations than the KSOM-NN. Ultimately, the t -SNE was considered a better and promising method for assessing the interactions of soil quality indicators and has the potential to appropriately visualise as well as improve the interpretability of soil results by identifying essential features and similarities. The results of this study have good implications for agricultural soil management and decision-making. Moreover, the results are preliminary findings that may be used in the future to verify detailed aspects of soil biogeochemistry within the study area. • t -SNE outperformed KSOM-NN in soil properties dimensionality reduction and visualization. • The t -SNE method is rarely used for data reduction and visualization in soil-based research. • More detailed clusters of the soil quality indicators were observed while using the t -SNE compared to KSOM-NN. • The t -SNE is recommended for future applications in soil quality characterization research.

A Quantitative Quality Assessment Framework for Continuous Raw EEG Data

This paper describes the process of capturing an expert’s knowledge in the form of human understandable rules and then inserting them into a self-organizing map neural network. The Dynamic Cell Structure (DCS) is a form of self-organizing map used for many purposes, including classification and prediction. The DCS is a topology-preserving network that starts with no pre-structure, but assumes a structure once trained. This paper explores applying the DCS to analyse a social science data set that contains some of the many factors that may influence the mortality rate of a region.

Identification of geographical origins of Cordyceps based on data of amino acids with self-organizing map neural network

In this study, data of amino acids of Cordyceps samples from Qinghai and Tibet was analyzed with self-organizing map neural network. A model of XY-Fused network was established with the content of 8 major amino acids and total amino acids for the identification of geographical origins of Cordyceps from Qinghai and Tibet. It had the prediction accuracy of 83.3% for the test set. In addition, data mining indicated that methionine was a special kind of amino acid in Cordyceps which could serve as a marker to identify its geographical origins. On this basis, the content ratio of methionine to total amino acids was proposed to be a quantifiable indicator to distinguish Cordyceps from Qinghai and Tibet.

Research on Innovation Non-Equilibrium of Chinese Urban Agglomeration Based on SOM Neural Network

Different indicators, such as the number of patent applications, the number of grants, and the patent conversion rate, were proposed in this study to analyze the issue of innovation imbalance within and between urban agglomerations from a new perspective. First, a preliminary analysis of the current state of innovation and development of China’s nine urban agglomerations was conducted. Then the Theil index, widely used in equilibrium research, was employed to measure the overall innovation gap of China’s urban agglomerations. The study innovatively used the self-organizing feature map to identify the correlation characteristics of the innovation and development within China’s urban agglomerations and visualize them through Geographic Information Science. The research findings show that the hierarchical differentiation of the innovation and development of China’s urban agglomerations is becoming increasingly clear, and that the imbalance in regional innovation development is pronounced. The imbalance in innovation development within urban agglomerations is more significant than the imbalance in innovation development among urban agglomerations. The analysis indicated that a possible cause is the crowding effect and administrative standard effect of the central city. The key to addressing this problem is promoting innovative and coordinated development between regions.

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.

Clustering of basketball players using self-organizing map neural networks.

Clustering players based on their abilities, a new perspective and an important opportunity to meet needs that in the light of traditional talent identification and player science, which is held periodically and there is not enough time for them to appear. Early recognition of these abilities is a factor influencing the success of sports teams. Artificial neural network (ANN) is a new method of modelling and prediction. The aim of this study was to cluster basketball players based on their individual abilities. For this purpose, Self-Organizing Map (SOM) Neural Networks were used. The data set used by 3000 NBA players for 2011 until 2018 is from the Basketball-Reference site. Each player is assigned 30 attributes to reduce them using the PCA method and the features for each player were reduced to 12 samples. In order to implement a SOM of features and functions in MATLAB software 65% of the data were used as the network training phase and the remaining 35% were used to the test phase. 12 players’ features as network input and output 9 clusters resulting from the combination of features. After simulation using SOM, accuracy parameter with the help of this system were obtained above 95%. The result of the study showed that the performance of the SOM in clustering basketball players was higher than the K-Means algorithm. The network implemented in this article has a faster speed in the training process and generalizability than similar cases.

Recognition of Fault State of RV Reducer Based on self-organizing feature map Neural Network

In order to accurately evaluate the working state of RV reducer, a fault identification method based on the fault identification model established by Self-Organizing Feature Map (SOM) Neural Network is proposed. Firstly, the data measured by the RV reducer test platform are analyzed by wavelet to obtain the wavelet coefficient. Then, combined with the efficiency data of RV reducer, the mean square frequency, center of gravity frequency and frequency variance of the two groups of data are calculated after Fourier transform and power spectrum analysis. After optimization, several eigenvalues are obtained. The eigenvalues are input into the competitive neural network and SOM neural network to establish the fault identification model. Finally, the results of the fault identification model established by the competitive neural network and SOM neural network are compared. The prediction results show that the fault identification model established by SOM neural network can effectively determine the working state of RV reducer.

DISCOVERING HIDDEN CLUSTER STRUCTURES IN CITIZEN COMPLAINT CALL VIA SOM AND ASSOCIATION RULE TECHNIQUE

Significant revolution in different organizations chief’s point of view toward customer treating and the level of product presentation or services resulted in redefining the structure of these organizations based on this point of view. The municipal services are very important as well. The strategy of “CRM” which was so successful in the private sector and has been applying as “CiRM” in the public sector of developed countries could be very useful for this achievement. The main goal of citizen management is realizing the citizen’s needs and demands, improving communication through connection with citizens and optimizing it to increase the level of their satisfaction. The government agencies do it based on their idea and point of view cause the citizen are valuable assets in the planning of services and reduction of costs. This study proposes a combined data mining method to discover hidden knowledge in call citizen compliant of the municipality of Tehran. A Self-organizing map neural network was used to identifying and classifying citizen needs based on RFM analysis. It also classified citizen needs into three majors. the result of classification and clustering of SOM has created a new feature to profiled call’s customer to identify temporal-spatial patterns of problems by using an association rule with the Apriori algorithm. The results of this idea demonstrate that accordance of citizens call compliant in a different area and discovering hidden knowledge can facilitate the performance of human recourse in improving services to citizens.

Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network

Recommendation systems are information filtering tools that present items to users based on their preferences and behavior, for example, suggestions about scientific papers or music a user might like. Based on what we said and with the development of computer science that has started to take an interest in big data and how it is used to discover user interest, we have found a lot of research going on in the area of recommendation and there are powerful systems available. In the unsupervised learning domain, this paper introduces a novel method for creating a hybrid recommender framework that combines Collaborative Filtering with Content Based Approach and Self-Organizing Map neural network technique. By testing our system on a subset of the Movies Database, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and precision, as well as improving the efficiency of the traditional Collaborative Filtering methodology.