SOM Algorithm Research Articles

Evaluation and application of local fiscal science and technology expenditure efficiency in the Yangtze River Delta urban agglomeration based on AHP-TOPSIS and SOM clustering

This study selected 18 indicators from four dimensions: the scale of regional fiscal science and technology expenditure, the social impact of science and technology, the efficiency of science and technology expenditure, and the environment for scientific and technological innovation, and constructed an evaluation index system for the efficiency of local fiscal science and technology expenditure in the Yangtze River Delta urban agglomeration. The AHP-TOPSIS model was used to determine the weights of evaluation indicators, and the efficiency ranking of fiscal science and technology expenditures in various cities of the Yangtze River Delta urban agglomeration was quantified. Furthermore, using the SOM algorithm for clustering analysis, the level division of innovation capability in the Yangtze River Delta urban agglomeration was clarified. This study provides a scientific tool and method for evaluating the efficiency of local financial science and technology expenditure in the Yangtze River Delta urban agglomeration, and provides solid theoretical support and practical guidance for promoting the coordinated development of regional science and technology innovation.

Fault Warning Technology of Comprehensive Energy System Based on Digital Twins

The traditional energy industry has many problems, such as huge losses and non-renewability. With the development of informatization and digitalization, integrated smart energy systems have become a current research hotspot. At present, the research on integrated smart energy is still in the theoretical stage, and the fault warning part for integrated smart energy is still very scarce. Therefore, this article proposes a comprehensive energy failure warning technology based on digital twins. Firstly, the comprehensive energy system based on digital twins is divided into five layers, and the SOM algorithm is used to analyze the system’s status data for fault warning. Finally, the superiority of the proposed method was verified through experiments.

VoomSOM: voom-based Self-Organizing Maps for Clustering RNASequencing Data

Background: Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several clustering approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed. Objective: In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering algorithms such as kmeans, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering. Methods: We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm values to get the codebook used in the downstream analysis. Next, we calculate the weighted distance matrices using the sample quality weights obtained from voom transformation and codebooks from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical clustering algorithms to cluster samples. Results: The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similarly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in overdispersed RNA-seq data. Conclusion: The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.

Retracted: Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis.

[This retracts the article DOI: 10.1155/2022/7057437.].

Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis.

The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users' academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users' explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users' academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.

Development of a New Means to Improve the Performance of Self-Organizing Maps

In machine learning, a Self-Organizing map plays a significant role in finding hidden patterns or intrinsic structures in data. In this study, a new modified expression is arrived at to update the radius of the neighbourhood of BMU in SOM. Further, a new approach is introduced to find the eligible nodes for an update in SOM. We have also incorporated the previous work, such as new initialization algorithms to find the initial weight vectors, a method to place the weight vectors in each node of the grid and a method to identify the number of clusters to improve the performance of the proposed SOM algorithm. The proposed SOM performance in terms of Quantization error (QE), Convergence time (CT) and Modified Semantic Relevance Index (MSRI) are compared with conventional SOM for both class label and non-class label datasets. In addition to the above measure, Classification Accuracy (CA) is also used to evaluate the performance against class label datasets. The proposed SOM algorithm shows better performance in all cases.

Urban Cultural Industry Management System and Public Economic Based on Improved Algorithm.

Social reliability and sustainable financial development in metropolises have become urgent problems to be solved. Due to its solid importance and imagination, urban cultural industry management and public economy have gradually become the focus of consideration in all fields of society. Organized and coordinated improvement of the urban economy and society and imaginative undertakings have also become the focus of review at all levels of academia and government. This article aims to study and apply the improved algorithm to the urban cultural industry management system and public economic analysis. It proposed the improved algorithm and related methods, the correlation between urban cultural industry management and economy, the improved particle swarm algorithm and the SOM algorithm, and finally, applied the improved SOM algorithm to evaluate the regional economy of a city. The experimental results showed that the contribution rate of the economic evaluation level of the eight economic evaluation indicators to the economic development level of each county (district and city) in the city was different, of which, the cumulative contribution rate of the first four main components reached 83%.

Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain

With the fast growth of AI and Internet of Things (IoT) technology, many agricultural product sales businesses and logistics sectors have started to concentrate on agricultural product distribution information operations. The requirements for the delivery service time are very high due to the features of perishable and highly easy dehydration of fresh agricultural goods. To preserve the freshness and quality of agricultural goods, the logistics and distribution process must be completed as rapidly as possible via appropriate low temperature control and the use of IoT technology. IoT technology will surely bring about the intelligent operation in the circulation of agricultural products. With the decentralized management of the Fabric blockchain, the investment and maintenance costs of the agricultural IoT will be reduced, which will help to improve the intelligence and scale of the agricultural IoT. Aiming at the specific problems, the path optimization problem in the process of agricultural product distribution is brought out. This paper completes the following work: (1) the traditional agricultural product distribution process is roughly described, and the shortcomings and problems of the traditional mode are explored and studied. On this basis, the agricultural product circulation mode under the IoT and neural network technology is introduced. (2) The TSP problem is defined, then some algorithms commonly used to solve the TSP problem are introduced, and then the theory and method of the SOM neural network and the basic principle of the ORC_SOM algorithm are introduced in detail. (3) Through a large number of experiments, the results prove the validity of the algorithm in this paper and the rationality of the theory.

Two weighted c-medoids batch SOM algorithms for dissimilarity data

This paper proposes two new batch SOM algorithms for dissimilarity data, namely RBSOM-CWMdd and RBSOM-ACWMdd, both designed to give a crisp partition aiming to preserve the topological properties of the data on the map. RBSOM-CWMdd is a batch SOM algorithm for dissimilarity data where each cluster representative is a set of weighted objects whose cardinality is fixed, being the same for all clusters. These weights are computed according to each object relevance to the referred cluster. Likewise, RBSOM-ACWMdd is a batch SOM algorithm for dissimilarity data where each cluster representative is a vector of weighted objects selected according to its relevance to the referred cluster. Therefore, the dimensionality of the cluster representatives self adapt to the particular dataset analysed, change at each algorithm iteration and can differ from one cluster to another. Experiments with 12 datasets from the UCI machine learning repository regarding the metrics of Normalized Mutual Information, Topological error, and Silhouette Coefficient showed that the proposed methods improved, respectively, the traditional set-medoids and multi-medoids SOM methods with a competitive temporal complexity. In addition, it was performed an application study on Ecoli dataset where the proposed RBSOM-ACWMdd algorithms produced a better mapping from a clustering point of view.

Penentuan Pencemaran Air Menggunakan Metode Self Organizing Maps (SOM)

Water pollution is the entry of a component or substance into water, which causes water quality to decrease. The decrease in water quality causes the water to be unfit and affects the community's health. Therefore, an application for determining the water quality level building using the SOM algorithm with several data input parameters: Fluoride, Hardness, and Nitrite. These parameters use for data clustering in determining clean and polluted water. The SOM algorithm arranges SOM neurons based on the data input values ​​in a SOM cluster. For testing the SOM method on 114 data, the Silhouette Coefficient value was used to find the best number of clusters. Silhouette Coefficient will evaluate clustering the proximity of the distance between data. The test was carried out 27 times with variations of the experiment with the learning rate starting from 0.1 to 0.9 and the number of clusters from 2 to 4 to get the best Silhouette Coefficient value. The result of clustering the best silhouette coefficient value obtained is 0.7276473444141 with 3 clusters and the learning rate of 0.2.

Market segmentation in tourism destination sector using self-organizing maps and its validation with structural equation modeling / Segmentação de mercado no setor de destino turístico usando mapas auto-organizados e sua validação com modelagem de equações estruturais

Nowadays, the tourism industry is growing up and competing relatively high, so how to make marketing strategies has been an essential issue for your market. A common problem for marketing strategists is how to appropriately segment the market and select segment-specific marketing strategies. Current segmentation methodologies and clustering techniques have evolved to include the use of artificial neural networks to the segmentation of markets. This article presents an approach that combines the Self-Organizing Map of Kohonen (SOM) with the technique Structural Equation Modeling (SEM) in the application of a segmentation issue. A satisfaction model is used with SEM to measure customers' habits and attitudes, in Brazilian tourist destination also looking at the effects of moderating segments. These market maps not only help the managers to see fully visualized clusters of market but also reveal mutual correlations between different customers’ characteristic variables. SOM algorithm and visualizing technique were implemented in MATLAB environment to produce market maps of data set.

Two Phases Anomaly Detection Based on Clustering and Visualization for Plastic Injection Molding Data

The importance of smart factory is increasing. Among them, the importance of sensor data-based anomaly detection research is increasing. Accordingly, this paper presents a framework by using sensor data of plastic injection machine and deep learning models to perform anomaly detection and cluster secured outliers. This study aimed to be a practical solution and proposal for factories that want to introduce smartfactory system. According to this goal, three main contributions are assumed. The three main contributions are as follows. First, assuming a situation suitable for the manufacturing site, and appropriate approach was used. Second, it is possible to secure outliers by applying Auto Label using with pseudo labeling technique. Third, the decision maker can identify the potential cause of the defect. These three advantages can be found in the Two Phases Anomaly Detection system architecture. The artificial intelligence model used as a classifier in the pseudo labeling technique was LSTM and Accuracy showed high reliability of more than 90%. And through the SOM algorithm, clustering visualization of defective data is shown.

Accurate medical information recommendation system based on big data analysis

In order to solve the problems of low recommendation accuracy and long response time in traditional medical information recommendation system, a medical information accurate-recommendation system based on big data analysis is proposed. The system is designed as medical information data acquisition module, medical information storage module and medical information accurate-recommendation module. In the medical information data acquisition module, crawler technology is used to obtain medical information data, and association rule algorithm is used to mine the medical information data. In the medical information storage module, personalised configuration is set. In the medical information accurate-recommendation module, the user interest model is quantified by vector space method, and BP algorithm and SOM algorithm are introduced to complete the accuracy of medical information recommend. The experimental results show that: the highest accuracy rate of medical information recommendation is 98.8%, and the shortest retrieval response time is 20 ms.

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

The self-organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and time-demanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

Environmental and social risk factors in association with spatial clustering of childhood cancer incidence

BACKGROUND AND AIM: Childhood cancer incidence is known to vary by age, sex, and race/ethnicity, but evidence is limited regarding external risk factors. We aim to identify harmful combinations of air pollutants and other environmental and social risk factors in association with the incidence of childhood cancer based on 2003-2017 data from the Georgia Cancer Registry. METHODS: We calculated the standardized incidence ratios (SIR) of Central Nervous System (CNS) tumors, leukemia and lymphomas based on age, gender and ethnic composition in each of the 159 counties in Georgia, USA. County-level information on eight carcinogenic air pollutants (benzene, trichloroethylene, ethylene-oxide, formaldehyde, pentachlorophenol, styrene, tetrachloroethylene, 1,3-butadiene), socioeconomic status, tobacco smoking, alcohol drinking and obesity were derived from US EPA and other public data sources. We applied an unsupervised learning tool (self-organizing map, SOM) to identify pertinent types of multi-exposure combinations. Spatial Bayesian Poisson models (Leroux-CAR) were fit with indicators for each multi-exposure category as exposure and SIR of childhood cancers as outcomes. RESULTS:Based on county-level information of external risk factors, the SOM algorithm divided the state of Georgia into 5 distinct clusters (clusters 4-5 comprising metropolitan/urban areas). Mean SIR ranged from 0.8 (rural-cluster-1) to 1.1 (urban-cluster-5) for leukemia, 1.0 (rural-cluster-3) to 1.4 (rural-cluster-1) for lymphomas and 1.5 (rural-cluster-1) to 1.8 (urban-cluster-4) for CNS tumors. Children living in urban counties characterized by higher air pollution concentrations and proportions of alcohol drinking (urban-cluster-4) had higher SIR of childhood leukemia and lymphomas than children living in rural-cluster-1 (risk ratio [95%-credible intervals]: 1.3 [1.0,1.6]) and rural-cluster-3 (1.2 [1.0, 1.4]), respectively. We found no significant associations for spatial variation of CNS tumors. CONCLUSIONS:We found spatial clustering of childhood cancer in counties with higher air pollution concentrations and larger proportions of alcohol drinking. More research is needed to identify the causal risk factors for these associations. KEYWORDS: Air pollution, Cancer and cancer precursors, Children's environmental health, Socio-economic factors, Multi-pollutant, Environmental epidemiology

Topic analysis of studies on total quality management and business excellence: an update on research from 2010 to 2019

The research reviews conducted by Lo and Chai ([2012]. Quantitative analysis of quality management literature published in total quality management and business excellence (1996–2010). Total Quality Management & Business Excellence, 23(5-6), 629–651. doi:10.1080/14783363.2012.669553) and Dereli et al. ([2011]. An analysis of the papers published in total quality management & business excellence from 1995 through 2008. Total Quality Management & Business Excellence, 22(3), 373–386. doi:10.1080/14783363.2010.532337) were based on TQM&BE, and together they covered the period between 1995 and 2010. The limitation of these two studies is that the study period ended in 2010. Accordingly, this study extends those previous studies by reviewing papers published between 2010 and 2019. TQM&BE is a suitable object for analysis because it is one of the top journals cited in the quality management field and plays a key role in the quality and scientific knowledge of TQM. This study demonstrated the feasibility of using the (self-organizing map) SOM algorithm to build a topic map. The SOM technique is one of the most popular artificial neural networks (ANNs). Based on this map, six topic areas were identified. In addition, the new identified themes after 2010 include the BE framework, and innovation, learning, team, and design. The results also showed that research on TQM&BE has undergone a shift in focus from a dominance of TQM to service quality, (International Organization for Standardization) ISO, and innovation and learning. Finally, the results showed that the SOM can be considered as a technique for developing topic maps for academic research reviews.

Self-organizing mappings on the flag manifold with applications to hyper-spectral image data analysis

A flag is a nested sequence of vector spaces. The type of the flag encodes the sequence of dimensions of the vector spaces making up the flag. A flag manifold is a manifold whose points parameterize all flags of a fixed type in a fixed vector space. This paper provides the mathematical framework necessary for implementing self-organizing mappings on flag manifolds. Flags arise implicitly in many data analysis contexts including wavelet, Fourier, and singular value decompositions. The proposed geometric framework in this paper enables the computation of distances between flags, the computation of geodesics between flags, and the ability to move one flag a prescribed distance in the direction of another flag. Using these operations as building blocks, we implement the SOM algorithm on a flag manifold. The basic algorithm is applied to the problem of parameterizing a set of flags of a fixed type.

Automatic repair of pollution attacks in SDN topology network based on SOM algorithm

When using the current method to recover the vulnerabilities caused by pollution attacks in SDN network, there are some problems, such as low coverage, low accuracy of vulnerability detection results and low repair efficiency. To this end, an automatic repair method for topology pollution attacks in SDN network based on SOM algorithm is proposed. This paper analyses the security vulnerabilities in SDN architecture and SDN network, and extracts the attack flow characteristics of topology pollution attacks in SDN network. The extracted attack flow features are input into SOM algorithm to detect and identify vulnerabilities caused by pollution attacks. Combining the theory of vertex concavity and convexity and geometric triangle, the detected vulnerabilities are repaired to realise the automatic repair of topology pollution attacks in SDN network. The experimental results show that the proposed method has high coverage, high accuracy of vulnerability detection and high efficiency of vulnerability repair.

Cancel-for-Any-Reason Insurance Recommendation Using Customer Transaction-Based Clustering

In the travel insurance industry, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cancel-for-any-reason</i> insurance, also known as a cancellation protection service (CPS), is a recent attempt to strike a balance between customer satisfaction and service provider (SP) profits. However, some exceptional circumstances, particularly the COVID-19 pandemic, have led to a dramatic decrease in SP revenues, especially for non-refundable tickets purchased early with CPS. This paper begins by presenting a risk group segmentation of customers in an online ticket reservation system. Then, a CPS fee is recommended depending on the different customer risk groups provided by the cluster segmentation via different clustering algorithms such as centroid-based K-means, hierarchical agglomerative, DBSCAN, and artificial neural network-based SOM algorithms. According to the implemented cluster metrics, which include the Silhouette index, Davies-Bouldin index, Entropy index, and DBCV index, the SOM algorithm presents the most appropriate result. After predicting the new customer cluster, a CPS fee will be calculated with the proposed adaptive CPS method based on the cluster segmentation weights. Determining the weight of each cluster is related to the total CPS revenue threshold for all clusters defined by the SP. Therefore, to avoid a loss for SPs, the total CPS revenue will be kept constant with the threshold that the SP has been adjusted. The experimental results based on real-world data show that the risk group segmentation of customers helps to maintain a balance between CPS fees and SP profits. Finally, according to the calculated weights, the proposed model pegs the SP gain/loss variation with a 0.00012 exchange ratio.

Automatic repair of pollution attacks in SDN topology network based on SOM algorithm

Automatic repair of pollution attacks in SDN topology network based on SOM algorithm