Self-organizing Map Artificial Neural Network Research Articles

Quantification of toxic metals in chicken egg and chicken feed via SOM-artificial neural network.

Poultry products such as meat and eggs are rich sources of proteins, vitamins, and minerals. It is a good indicator of healthy food. Keeping in view, the present study is designed to evaluate the prevalence of toxic heavy metals (lead, nickel, cadmium, and chromium) in chicken eggs and feed. For this purpose, five samples of egg and feed were collected from five different commercial markets in Skardu City. Each sample was prepared using the wet digestion method and analyzed using a flame atomic absorption spectrophotometer. The results showed that lead, nickel, and chromium were present in varying amounts in the feed and egg, with nickel being the most concentrated metal, followed by lead and chromium in egg samples, while the feed samples showed the highest concentration of chromium followed by lead and nickel. However, concentrations of selected heavy metals except cadmium were all above the permissible limit of the World Health Organization. The self-organizing map-artificial neural network is employed for the identification of patterns of heavy metals in chicken feed and egg samples. The lower left neurons of the maps showed higher heavy metal concentrations found in samples taken from Bazar, whereas the rest of the samples showed varied concentrations. A comparison of feed and egg concentrations showed that nickel concentration was lower in feed samples than in egg samples. The lead concentration decreased in eggs except in the Krasmathang feed sample. Chromium concentration presented a negative correlation due to the extremely high concentration found in the Bazar feed sample.

New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM

In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.

Tracking the Connection Between Brazilian Agricultural Diversity and Native Vegetation Change by a Machine Learning Approach

In Brazil, agribusiness has a considerable role in the country's GDP. Because of this, the State needs territorial planning to minimize the impacts on natural resources, especially in the Pantanal and Amazon biomes, where agribusiness has expanded. The lower the agricultural diversification, the lower the pattern of land use homogeneity, generally associated with agribusiness, especially when it occupies large areas with more technological productive units. This paper investigates the relationship between spatial diversification patterns and the dynamics of native vegetation in Brazil. We propose a feature engineering and clustering approach for 5570 Brazilian municipalities between 1999 and 2018. It was based on the unsupervised artificial neural network Self-Organizing Map (SOM) to divide the municipalities into homogeneous groups of agricultural products diversity trends. The results were compared with the change in vegetation area using data from the national land use-mapping project called Mapbiomas. The analysis allowed the identification of three different regimes of modification in native vegetation, particularly related to municipalities in Brazil's Midwest and North regions, indicating substantial changes in the Cerrado and Amazon biomes.

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

In this paper, a new feature extraction method is presented based on spectro-temporal representation of speech signal for phoneme classification. In the proposed method, an artificial neural network approach is used to cluster spectro-temporal domain. Self-organizing map artificial neural network (SOM) was applied to clustering of features space. Scale, rate and frequency were used as spatial information of each point and the magnitude component was used as similarity attribute in clustering algorithm. Three mechanisms were considered to select attributes in spectro-temporal features space. Spatial information of clusters, the magnitude component of samples in spectro-temporal domain and the average of the amplitude components of each cluster points were considered as secondary features. The proposed features vectors were used for phonemes classification. The results demonstrate that a significant improvement is obtained in classification rate of different sets of phonemes in comparison to previous clustering-based methods. The obtained results of new features indicate the system error is compensated in all vowels and consonants subsets in compare to weighted K-means clustering.

Self-organizing map artificial neural networks and sequential Gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils

The application of multivariate geostatistical and statistical methods remain valuable tools for environmental pollution assessment. In particular, stochastic simulation techniques like sequential Gaussian simulation (SGS) and the self-organizing map artificial neural networks (SeOM-ANNs) have facilitated the understanding of the spatial distribution of potentially toxic elements (PTEs) in polluted soils. However, there is a dearth of literature on the application of SGS and SeOM-ANN in mapping potentially toxic elements (PTE) in heavily polluted mining and smelter affected floodplain soils. This study shows the applicability SGS and SeOM–ANN which is a powerful visualization tool for the categorization of PTEs [Cadmium (Cd), Arsenic (As), Antimony (Sb), Lead (Pb) and Zinc (Zn)] levels together with selected soil properties [oxidizable carbon (Cox) and soil reaction (pH_H2O)] in one of the most polluted mining floodplain soils in Europe. A k-means algorithm was used to classify distinct clusters which were visually unclear based on the SeOM–ANN Neighbor distance plot (U-Matrix). The k-means resulted in 5 distinct clusters. Cluster 1 to 5 based on SeOM–ANN for all PTEs revealed an increase in concentration levels in the same order (1–5) while for soil properties the trend was not clear. The soils were successfully assessed based on different intensity level combinations and k-means clustering results efficiently mapped into a spatial distribution map. High concentration levels of the PTEs were noticed in the northern parts of the study area based on the conditional Gaussian simulations (CGSs) generated through SGS, while low levels were prominent in the southwestern parts. The hotspot areas were comparable with the k-means spatial distribution maps. It is recommended that special attention be paid to the identified hotspots for possible remediation. This study further demonstrates the usefulness of geostatistics and advanced statistical methods in site-specific planning and implementation of remediation measures for polluted mining floodplain soils.

Self organizing map neural networks approach for lithologic interpretation of nuclear and electrical well logs in basaltic environment, Southern Syria

An approach based on self organizing map (SOM) artificial neural networks is proposed herewith oriented towards interpreting nuclear and electrical well logging data. The well logging measurements of Kodana well in Southern Syria have been interpreted by applying the proposed approach. Lithological cross-section model of the basaltic environment has been derived and four different kinds of basalt have been consequently distinguished. The four basalts are hard massive basalt, hard basalt, pyroclastic basalt and the alteration basalt products- clay. The results obtained by SOM artificial neural networks are in a good agreement with the previous published results obtained by other different techniques. The SOM approach is practiced successfully in the case study of the Kodana well logging data, and can be therefore recommended as a suitable and effective approach for handling huge well logging data with higher number of variables required for lithological discrimination purposes.

Artificial neural network for ecological-economic zoning as a tool for spatial planning

Abstract: The objective of this work was to analyze social and environmental information through an artificial neural network-self-organizing map (ANN-SOM), in order to provide subsidy to ecological-economic zoning (EEZ) as a tool to reduce the subjectivity of the process. The study area comprises 16 municipalities in the northeast of the state of Pará, Brazil, representative of the agricultural development in the state. Data processing involved three steps: preparation of the data in a geographic information system (GIS) environment; mathematical processing (ANN-SOM) of the data; and visualization and interpretation of the processing results, allowing the spatial planning of northeastern Pará. The results comprised 13 classes, regrouped according to behavioral similarity criteria into four categories, which represent the main areas of sustainability proposed for the state of Pará, according to existing EEZ. The proposed methodology allows individualizing areas in the region that EEZ had not defined, mainly due to the greater possibility of combining and integrating a large number of physical, social, and economic variables through the SOM.

Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment

Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation study in river sediments revealed that exchangeable, reducible and oxidizable fractions were dominant in all the studied metals (Cr, Ni, Cu, Zn, Cd, Pb) except Mn and Fe. High pollution load index (1.64–3.89) recommends urgent need of mitigation measures. Self-organizing Map-Artificial Neural Network (SOM-ANN) was applied to the data set for the prediction of major point sources of pollution in the river Ganga.

Biosom: gene synonym analysis by self-organizing map.

There are several guidelines for gene nomenclature, but they are not always applied to the names of newly identified genes. The lack of standardization in naming genes generates inconsistent databases with errors such as genes with the same function and different names, genes with different functions and the same name, and use of an abbreviated name. This paper presents a methodology for predicting synonyms in a given gene nomenclature, thereby detecting and minimizing naming redundancy and inconsistency and facilitating the annotation of new genes and data mining in public databases. To identify gene synonyms, i.e., gene ambiguity, the methodology proposed begins by grouping genes according to their names using a Kohonen self-organizing map artificial neural network. Afterwards, it identifies the groups generated employing the Matrix-U technique. The employment of such techniques allows one to infer the synonyms of genes, to predict probable hypothetical gene names and to point out possible errors in a database record. Many mistakes related to gene nomenclature were detected in this research, demonstrating the importance of predicting synonyms. The methodology developed is applicable for describing hypothetical, putative and other types of genes without a known function. Moreover, it can also indicate a possible function for genes after grouping them.

Application of artificial neural network (ANN)–self-organizing map (SOM) for the categorization of water, soil and sediment quality in petrochemical regions

► The physico-chemical properties and heavy metals in environmental materials from the Niger Delta area were assessed. ► The SOM was used as a powerful visualization tool to identify trends in the dataset. ► Preliminary diagnoses of the quality of locational environmental materials are effectively carried out using the SOM algorithm to develop the c-planes . ► Areas with high concentrations of pollutants were easily identified from the c-planes . ► These stations from the Niger Delta should be classified as high priority sites. The utilization of mathematical and computational tools for pollutant assessment frameworks has become increasingly valuable due to the capability to interpret integrated variable measurements. Artificial neural networks (ANNs) are considered as dependable and inexpensive techniques for data interpretation and prediction. The self-organizing map (SOM) is an unsupervised ANN used for data training to classify and effectively recognize patterns embedded in the input data space. Application of SOM–ANN is useful for recognizing spatial patterns in contaminated zones by integrating chemical, physical, ecotoxicological and toxicokinetic variables in the identification of pollution sources and similarities in the quality of the samples. Water ( n = 11), soil ( n = 38) and sediment ( n = 54) samples from four areas in the Niger Delta (Nigeria) were classified based on their chemical, toxicological and physical variables applying the SOM. The results obtained in this study provided valuable assessment using the SOM visualization capabilities and highlighted zones of priority that might require additional investigations and also provide productive pathway for effective decision making and remedial actions.

An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network

The database of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery in databases (KDD) appears as a possible solution to find out such relations aiming at converting information into knowledge. However, not all data presented in the bases are useful to a KDD. Usually, data are processed before being presented to a KDD aiming at reducing the amount of data and also at selecting more relevant data to be used by the system. This work consists in the use of Rough Sets Theory, in order to pre-processing data to be presented to Self-Organizing Map neural network (Hybrid Architecture) for clusters analysis. Experiments' results evidence the better performance using the Hybrid Architecture than Self-Organizing Map. The paper also presents all phases of the KDD process.

Self-organizing map artificial neural network application in multidimensional soil data analysis

Because of the complex nonlinear relationships between soil variables and their multivariable aspects, classical analytic, deterministic, or linear statistical methods are unreliable and cause difficulty to present or visualize the results. Using intelligent techniques, which have ability to analyze the multidimensional soil data with an intricate visualization technique, is crucial for nutrient and water management in soil, consequently, for sustainable agriculture and groundwater management. In this study, first, the Kohonen self-organizing feature maps (KSOFM) neural network was applied to analyze the effects of soil physical properties on soil chemical/hydraulic processes, and to diagnose the inter-relationships of the multivariable soil data in vadose zone. The inter-relationships among the soil variables were extracted and interpreted using the pattern analysis visualized in component planes. Then K-means clustering algorithm was used to determine the optimal number of clusters by using the Silhouette clustering validity index, resulting in six clusters or groups for soil variables. In conclusion, the KSOFM technique is an effective tool for analyzing and diagnosing the dynamics in soil and extracting information from the multidimensional soil data. These results suggest that this technique has a potential to monitor and diagnose not only soil physical/chemical/hydraulic processes, but also soil morphological and microbiological processes.

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

The application of mathematical tools in initial steps of sediment quality assessment frameworks can be useful to provide an integrated interpretation of multiple measured variables. This study reveals that the Self-Organizing Map (SOM) artificial neural network can be an effective tool for the integration of multiple physical, chemical and ecotoxicological variables in order to classify different sites under study according to their similar sediment quality. Sediment samples from 40 sites of 3 estuaries of Cantabria (Spain) were classified with respect to 13 physical, chemical and toxicological variables using the SOM. Results obtained with the SOM, when compared to those of traditional multivariate statistical techniques commonly used in the field of sediment quality (principal component analysis (PCA) and hierarchical cluster analysis (HCA)), provided a more useful classification for further assessment steps. Especially, the powerful visualization tools of the SOM, which offer more information and in an easier way than HCA and PCA, facilitate the task of establishing an order of priority between the distinguished groups of sites depending on their need for further investigations or remediation actions in subsequent management steps.

A Relational Frame and Artificial Neural Network Approach to Computer-Interactive Mathematics

Fifteen participants unfamiliar with mathematical operations relative to reflections and vertical and horizontal shifts were exposed to an introductory lecture regarding the fundamentals of the rectangular coordinate system and the relationship between formulas and their graphed analogues. The lecture was followed immediately by computer-assisted instructions and matching-to-sample procedures in which participants were exposed to computer-posted rules regarding the relationship between particular types of formulas and their respective graphs. After participants demonstrated mutual entailment on formula-to-graph and graph-to-formula functions, they were assessed for 36 novel relations on complex variations of the original training formulas and graphs. In Experiment 1, 5 of 15 participants demonstrated perfect or near perfect performance on all novel relationships. Experiment 2 was directed at the remaining 10 participants who failed to correctly identify all mathematical relationships assessed in Experiment 1. The error patterns for these 10 participants were classified with the help of an artificial neural network self-organizing map (SOM). Training in Experiment 2 was directed exclusively at the types of errors identified by the SOM. Following remedial training, all participants demonstrated a substantial reduction in errors compared to their performance in Experiment 1. Derived transfer of stimulus control using mathematical relations is discussed.