Efficient Neural Network-Based Methodology for the Design of Multiple Classifiers

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010

We have been working on the application of Machining Learning in Metabolomics, Drug-Drug Interaction Discovery and Human Gait Recognition [1-5], profiling large data sets. Extraction of vital information about 1) plant metabolomics that can improve the environment and food quality, 2) in vitro neuronal network behavior patterns for various drugs that can be used to characterize drugs for brain deceases and 3) human gait patterns that can reveal various diseases were among these applications. We have demonstrated with considerable success in using unsupervised clustering techniques to analyze genetic and metabolomic data. This includes analysis of drought resistance in wheat [4] and microbial metagenomes [5].We introduced two methods: Near Unsupervised Learning (NUL) and Sub-sample Error Graphs (SEGs) [5] to analyze large amount of data. Self Organizing Map (SOM), which is one of the widely used Unsupervised Neural Networks, has been used as a data-mining tool due to its ability to map high dimensional data into a two dimensional feature map, which is expected to be topology preserving allowing users to visually identify clusters by their topological relationships in terms of their proximity on the map. The Growing Self Organizing Maps (GSOM) further allows the map size to be determined by the algorithm, which relies upon a user set parameter called Spread Factor (SF) [6]. The wide availability of GPUs for affordable prices allows faster comparison of various SOMs with different maps sizes taking away some of the advantages GSOM claimed. Further development of GSOM into a Dynamic SOM Tree exploits the possibility of varying SF to obtain multiple GSOMs from a small number of compact clusters to a large number of sparse clusters [7]. NUL methods can be applied on GSOM using a small number of labels that should be available for every class. This is however not realistic in some applications where the number of classes cannot be explicitly known. We propose a new method call Deep Near Unsupervised Learning (D-NUL), where Dynamic SOM tree is used instead of GSOM and the number of classes are not assumed to be known. The implementation of Dynamic SOM tree methods with varying SF on GPUs will make the computation with D-NUL possible for many problems in big data analytics.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 190087, “Unsupervised Statistical Learning With Integrated Pattern-Based Geostatistical Simulation,” by Q. Li and R. Aguilera, SPE, University of Calgary, prepared for the 2018 SPE Western Regional Meeting, Garden Grove, California, USA, 22–27 April. The paper has not been peer reviewed. This paper presents a new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms. The algorithm presented is a neural topology-preserving pattern-based geostatistical simulation algorithm that integrates the self-organizing map (SOM) concept and its updated version—growing self-organizing map (GSOM)—with an unsupervised competitive learning structure. Introduction In oil and gas reservoir modeling, any model construction faces challenges of limited data to some extent. The heuristic behind all geostatistical techniques is the implicit existence of statistical relationships among available data. “Data” here is a broad term; it could be discrete points, such as porosity or permeability at certain locations, but it also could be training images (TIs), which are used in this work. Using TIs as input data originated with multiple-point geostatistics. The aim was to overcome the limitations of using traditional two-point statistical variograms to describe geological continuity, especially in the case of curvilinear structures, which are quite common in nature, such as in fracture networks and geological fluvial structures. The authors write that geostatistics could benefit from the machine-learning or statistical-learning areas. Machine-learning tasks can be divided into two protocols, supervised learning and unsupervised learning. The difference depends on whether the input data have correct labels or not. For the investigation considered in this paper, after retrieving image patches from TIs, machine-learning algorithms were used to cluster those image patches into different classifications. If the correct clusters to which the image patches belong is known beforehand, the data can be said to have correct labels. Further, a large amount of these labeled data could be used for a model to learn. Supervised learning occurs when the model is finely tuned by guidance of these correct labels using an error-correction process. Unsupervised learning, on the other hand, occurs when neither how many clusters exist nor the correct clusters to which the image patches belong is known. Thus, without correct labels for guidance, this problem is identified as an unsupervised-learning problem. This is the first major drawback of applying machine-learning algorithms to geostatistical simulations. Two other important issues are encountered when performing pattern-based geostatistical simulations. The first is related to the large number of image patches. This large image-patch database contains high pattern redundancy, which could make pattern similarity comparison inefficient. The second issue is the high dimensionality of each image patch, which could typically be described by low-dimensional nonlinear-structure manifolds. To model and visualize these high-dimensional data, a nonlinearity dimensionality reduction should be sought.

Enhancing the content and structure of a web site is a very important task which can help to maintain people visiting a web site and gain new visits (or customers). Web mining area helps to enhance a web site organization and contents using data mining algorithms. In particular we may perform Web Mining using a Self Organizing Feature Map (SOFM or SOM) it is always needed an analysis phase by experts. To help analysts to perform this phase after SOFMs' training, many post-processing techniques have been developed (component planes, labels, etc.); however, none of these techniques are useful when working in web mining for off-line enhancements of a web site. In this paper an algorithm called Reverse Cluster Analysis (RCA) will be provided. It aims to identify important web pages based on a self organizing feature map (SOFM) when performing web text mining (WTM) and web usage mining (WUM). We successfully applied this technique in a real web site to show its effectiveness. We have extended previous work performing a comparison with another unsupervised technique, administrators survey and an extended survey.

With an onset of social network amount of data is increasing day by day. In order to analyze the data and extract useful information, the data mining technology can be used. Clustering is one of the popular method of data mining. Clustering can be used for visualizing and analyzing of data. We are discussing Kohonen SOM. We are using neural networks, as a data mining tool which provides statistical observation and layout from big data-sets. We determine how Self-Organizing Kohonen Maps, can be efficiently used for data mining purposes. The Self Organizing Map (SOM) unsupervised learning is an effective computational tool in data mining processes. Self-Organizing Maps (SOMs) used to visualize social network dataset. We used Self-Organizing Map for clustering and analyzing high-dimensional and complex social network datasets. This paper also visualizes SOM neighbor connection, SOM neighbor weight distance, SOM weight position. We perform self organizing map algorithm for social network dataset in matlab.

Pattern recognition of volcanic tremor data on Mt. Etna (Italy) with KKAnalysis—A software program for unsupervised classification

Numerical models provide a way to evaluate groundwater systems, but determining the hydrostratigraphic units (HSUs) used in constructing these models remains subjective, nonunique, and uncertain. A three-step machine-learning approach is proposed in which fusion, estimation, and clustering operations are performed on different data sets to arrive at HSUs at different scales. In step one, data fusion is performed by training a self-organizing map (SOM) with sparse borehole hydrogeologic (lithology, hydraulic conductivity, aqueous field parameters, dissolved constituents) and geophysical (gamma, spontaneous potential, and resistivity) measurements. Estimation is handled by iterative least-squares minimization of the SOM quantization and topographical errors. Application of the Davies-Bouldin criteria to k-means clustering of SOM nodes is used to determine the number and location of discontinuous borehole HSUs with low lateral density (based on borehole spacing at 100 s m) and high vertical density (based on cm-scale logging). In step two, a scaling network is trained using the estimated borehole HSUs, airborne electromagnetic measurements, and numerically inverted resistivity profiles. In step three, independent airborne electromagnetic measurements are applied to the scaling network, and the estimation performed to arrive at a set of continuous HSUs with high lateral density (based on sounding locations at meter (m) spacing) and medium vertical density (based on m-layer modeled structure). Performance metrics are used to evaluate each step of the approach. Efficacy of the proposed approach is demonstrated to map local-to-regional scale HSUs using hydrogeophysical data collected at a heterogeneous surficial aquifer in northwestern Nebraska, USA.

The modern electric power grid has become highly integrated in order to increase reliability of power transmission from the generating units to end consumers. This integrated nature and its upgrade toward an intelligent smart grid make the power grid vulnerable when facing cyber or physical attacks as well as intentional attacks. Therefore, determining the most vulnerable components (e.g., buses or generators) is critically important for power grid defense. In this paper, a new definition of load is proposed by taking power flow into consideration in comparison with the load definition based on degree or network connectivity. Unsupervised learning techniques (e.g., K-means algorithm and self-organizing map (SOM)) are introduced to cluster the nodes (i.e., buses) in IEEE-39 bus and IEEE-57 bus benchmarks. Then most vulnerable node in each cluster is determined based on their load information to form initial victim set. We use percentage of failure (PoF) to compare the performance of clustering based approach and traditional load based approach during cascading failure process. With the simulation results, the unsupervised learning (clustering based) approaches are more efficient in finding the most vulnerable nodes and our proposed definition of load is relatively useful in studying power grid security.

The increasing availability of cutting-edge location-acquisition technologies such as GPS devices, has led to the generation of huge datasets of spatial trajectories. These trajectories store important information regarding the movement of people, vehicles, robots, animals, users of social networks, etc. Many research initiatives have applied data mining techniques in order to extract useful knowledge from this data.  An important, and yet complicated, pre-processing step in mining patterns from trajectory data, is the identification of the Regions of Interest (RoI) that have been collectively navigated by a set of trajectories. The RoI’s are being manually and subjectively pre-defined by a group of experts as popular regions, regardless of the actual behaviour of the moving objects. This research emphasizes the usefulness of applying an unsupervised machine learning technique, namely Self Organizing Map (SOM), in order to identify the RoI’s associated with a trajectory dataset depending on the moving objects’ behaviour. The research experiments were conducted using 180 thousand of the trajectories generated by 442 taxis running in the city of Porto, in Portugal, and they demonstrate the ability of SOM in identifying the RoI’s and interesting taxi trips within the city.

Concrete pavement joint evaluation involves a number of assessment criteria, such as deflection near the joint, load transfer efficiency of the dowels and severity of voids under the slab. Although there are well defined thresholds for each of these parameters, often there arises a situation where each of the considered parameters lends contradictory assessment that leads to a considerable subjectivity in the evaluation process. A Self-Organizing Map (SOM), an unsupervised learning procedure in artificial neural network, is utilised for the first time to map the joint condition of concrete pavements from Falling Weight Deflectometer (FWD) deflection bowls. A novel methodology is proposed for labelling the network, whereby pavement engineering expertise can be directly used in a SOM for consistent deflection data classification in joint evaluation. The effectiveness of the trained network is demonstrated by using joint assessment parameters; namely, load transfer efficiency (LTE), void intercepts and absolute deflection. The joints were classified as good, marginal or poor. For the three parameters based SOM classification, an accuracy of 65-70% was obtained; this improves to 87.5% when the SOM was trained with 2-parameters (LTE and absolute deflection). However, when the SOM was tested with the data classified as 'good', accuracy improves to around 90%. Therefore, a SOM can be a powerful supplementary tool for a consistent and non-subjective evaluation of concrete pavement joints.

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (14.4 Hz); whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

Many intelligent transportation systems (ITS) applications require accurate prediction of traffic parameters. Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. However, these studies focus on highways, or a few road segments. We propose a robust and scalable method using n-SVR to tackle the problem of speed prediction of a large heterogenous road network. The traditional performance measures such as mean absolute percentage error (MAPE) and root mean square error (RMSE) provide little insight into spatial and temporal characteristics of prediction methods for a large network. This inadequacy can be a serious hurdle in effective implementation of prediction models for route guidance, congestion avoidance, dynamic traffic assignment and other ITS applications. We propose unsupervised learning techniques by employing k-means clustering, principal component analysis (PCA), and self organizing maps (SOM) to overcome this insufficiency. We establish the effectiveness of the developed methods by evaluation of spatial and temporal characteristics of prediction performance of the proposed variable window n-SVR method.

Measuring brain activity through Electroencephalogram (EEG) analysis for eye state prediction has attracted attention from machine learning researchers. There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. The tradeoff between the accuracy and computation time of these methods in performing the analysis is an important issue that is rarely investigated in the previous research. This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs). The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction.

Mangroves are indispensable in the coastal ecosystem and are depleting due to man-made and natural causes. Attempts are being made to mitigate the impact on mangroves, and transplantation is considered a strategy for mangrove regeneration. However, it is essential to identify suitable regions and select mangrove species compatible with these regions to achieve maximum benefit from transplantation. Mangrove transplantation is increasingly adopted as a restoration strategy, yet its success hinges on selecting species adapted to site-specific environmental conditions. This study utilized an unsupervised machine learning (ML) approach, a Self-Organizing Map (SOM) model, to predict mangrove species distribution using readily available data, requiring minimal labor, time, and cost. The data used in this study were gathered from the literature and included climatic, hydrological, and solar insolation variables. An SOM model with an 11 × 8 neuron grid was developed to cluster species based on their ecological preferences. Model accuracy was assessed through Quantization error (0.0003) and Topographic error (0.0548), confirming strong predictive performance and topological preservation. The SOM model effectively predicts mangrove species across climatic zones, capturing their zonal preferences based on input variables. This approach offers a practical decision-support tool for restoration practitioners, improving site–species matching and thereby increasing transplantation success rates. Future refinement could be achieved by integrating additional environmental parameters such as soil salinity, tidal dynamics, and seasonal microclimatic variations to better capture temporal shifts in species distribution. Hence, findings highlight the potential of ML to guide cost-effective, evidence-based mangrove conservation and ecological restoration strategies.