A hybrid approach for exploring the spatiotemporal patterns of precipitation in Sudan: Insights from neural network clustering and Fourier-wavelet transform analysis

Background: Cluster analysis is a data reduction technique in rows of the data matrix. This technique is widely used in engineering, biology, society, pattern recognition, and image processing. Objective: In this paper, self organized map (SOM) using the artificial neural network and different statistical techniques of cluster analysis are used on Population data of 33 districts of Rajasthan with 9 variables for comparison purpose. Methods: The goal of this work is to identify the most suitable technique for clustering the data by using the artificial neural network and different statistical clustering techniques. We received all patents regarding artificial neural network and k-means cluster method. Conclusion: The k-means cluster analysis is found as good as Neural Network cluster analysis, whereas Hierarchical cluster analysis and two steps cluster analysis provide some variation from the neural network cluster analysis.

ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Selskyy P, Vakulenko D, Televiak A, Veresiuk T. On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering. Family Medicine & Primary Care Review. 2018;20(2):171-175. doi:10.5114/fmpcr.2018.76463. APA Selskyy, P., Vakulenko, D., Televiak, A., & Veresiuk, T. (2018). On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering. Family Medicine & Primary Care Review, 20(2), 171-175. https://doi.org/10.5114/fmpcr.2018.76463 Chicago Selskyy, Petro, Dmytro Vakulenko, Anatolii Televiak, and Taras Veresiuk. 2018. "On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering". Family Medicine & Primary Care Review 20 (2): 171-175. doi:10.5114/fmpcr.2018.76463. Harvard Selskyy, P., Vakulenko, D., Televiak, A., and Veresiuk, T. (2018). On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering. Family Medicine & Primary Care Review, 20(2), pp.171-175. https://doi.org/10.5114/fmpcr.2018.76463 MLA Selskyy, Petro et al. "On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering." Family Medicine & Primary Care Review, vol. 20, no. 2, 2018, pp. 171-175. doi:10.5114/fmpcr.2018.76463. Vancouver Selskyy P, Vakulenko D, Televiak A, Veresiuk T. On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering. Family Medicine & Primary Care Review. 2018;20(2):171-175. doi:10.5114/fmpcr.2018.76463.

A comparison of neural network clustering (NNC) and hierarchical clustering (HC) is conducted to assess computing dominance of two machine learning (ML) methods for classifying a populous data of large number of variables into clusters. An accurate clustering disposition is imperative to investigate assembly-influence of predictors on a system over a course of time. Moreover, categorically designated representation of variables can assist in scaling down a wide data without loss of essential system knowledge. For NNC, a self-organizing map (SOM)-training was used on a local aqua system to learn distribution and topology of variables in an input space. Ternary features of SOM; sample hits, neighbouring weight distances and weight planes were investigated to institute an optical inference of system’s structural attributes. For HC, constitutional partitioning of the data was executed through a coupled dissimilarity-linkage matrix operation. The validation of this approach was established through a higher value of cophenetic coefficient. Additionally, an HC-feature of stem-division was used to determine cluster boundaries. SOM visuals reported two locations’ samples for remarkable concentration analogy and presence of 4 extremely out of range concentration parameter from among 16 samples. NNC analysis also demonstrated that singular conduct of 18 independent components over a period of time can be comparably inquired through aggregate influence of 6 clusters containing these components. However, a precise number of 7 clusters was retrieved through HC analysis for segmentation of the system. Composing elements of each cluster were also distinctly provided. It is concluded that simultaneous categorization of system’s predictors (water components) and inputs (locations) through NNC and HC is valid to the precision probability of 0.8, as compared to data segmentation conducted with either of the methods exclusively. It is also established that cluster genesis through combined HC’s linkage and dissimilarity algorithms and NNC is more reliable than individual optical assessment of NNC, where varying a map size in SOM will alter the association of inputs’ weights to neurons, providing a new consolidation of clusters.

The objective of this paper is to propose a new neural network-based approach to bankruptcy prediction problem, named HYNEN (hybrid neural network-driven reasoning) model which is based on two types of neural networks: unsupervised and supervised neural network. Accordingly, it consists of two stages: 1) clustering neural network (CNN) stage, and 2) output neural network (ONN) stage. CNN categorizes input sample into an appropriate cluster, which is identical to finding a relevant rule to be fired in knowledge base. Then in the ONN stage, ONNs are built based on information about the clusters derived from CNN stage, and used to make a final decision: bankrupt or non-bankrupt. CNN uses two types of unsupervised neural network models for pattern clustering, the self-organizing map and learning vector quantization, and then learns the clusters in a supervised manner. ONN utilizes a supervised neural network. We performed comparative experiments with Korean bankruptcy data using HYNEN, MDA (Multivariate Discriminant Analysis), and ACLS (Analog Concept Learning System) conventional neural network approach. >

A comparison between neural network clustering (NNC), hierarchical clustering (HC) and k-means clustering (KMC) is performed to evaluate the computational superiority of these three machine learning (ML) techniques for organizing large datasets into clusters. For NNC, a self-organizing map (SOM) training was applied to a collection of wavefront sensor reconstructions, decomposed in terms of 15 Zernike coefficients, characterizing the optical aberrations of the phase front transmitted by fluidic lenses. In order to understand the distribution and structure of the 15 Zernike variables within an input space, SOM-neighboring weight distances, SOM-sample hits, SOM-weight positions and SOM-weight planes were analyzed to form a visual interpretation of the system's structural properties. In the case of HC, the data were partitioned using a combined dissimilarity-linkage matrix computation. The accuracy of this method was confirmed by a high cophenetic correlation coefficient value (c = 0.9651). Additionally, a maximum number of clusters was established by setting an inconsistency cutoff of 0.8, yielding a total of 7 clusters, while reducing the inconsistency cutoff to 0.7 resulted in 13 clusters for system segmentation. In addition, a KMC approach was employed to establish a quantitative measure of clustering segmentation efficiency, obtaining a silhouette average value of 0.905 for data segmentation into K = 5 non-overlapping clusters. On the other hand, the NNC analysis revealed that the 15 variables could be characterized through the collective influence of 8 clusters. It was established that the formation of clusters through the combined linkage and dissimilarity algorithms of HC alongside KMC is a more dependable clustering solution than separate assessment via NNC or HC, where altering the SOM size or inconsistency cutoff can lead to completely new clustering configurations.

As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD.

At present, the field of natural language will also introduce in-depth learning, using the concept of word vector, so that the neural network can also complete the work in the field of statistics. It can be said that the neural network has begun to show its advantages in the field of natural language processing. In this paper, the author analyzes the multimedia English course based on fuzzy statistics and neural network clustering. Different factors were classified, and scores were classified according to the number of characteristics of different categories. It can be seen that with the popularization of the Internet, MOOC teaching meets the requirements of the current college English curriculum, is a breakthrough in the traditional teaching mode, improves students’ participation, and enables students to learn independently. It not only conforms to the characteristics of College students, but also improves their learning effect. In the automatic scoring stage, the quantitative text features are extracted by the feature extractor in the pre-processing stage, and then the weights of network connections obtained in the training stage are used to score the weights comprehensively. This model can better reflect students’ autonomous learning ability and language application ability.

The prediction of essential biological features based on a given protein sequence is a challenging task in computational biology. To limit the amount of in vitro verification, the prediction of essential biological activities gives the opportunity to detect so far unknown sequences with similar properties. Besides the application within the identification of proteins being involved in tumorigenesis, other functional classes of proteins can be predicted. The prediction accuracy depends on the selected machine learning approach and even more on the composition of the descriptor set used. A computational approach based on feedforward neural networks was applied for the prediction of small GTPases. Consequently, this was realized by taking secondary structure and hydrophobicity information as a preprocessing architecture and thus, as descriptors for the neural networks. We developed a neural network cluster, which consists of a filter network and four subfamily networks. The filter network was trained to identify small GTPases and the subfamily networks were trained to assign a small GTPase to one of the subfamilies. The accuracy of the prediction, whether a given sequence represents a small GTPase is very high (98.25%). The classifications of the subfamily networks yield comparable accuracy. The high prediction accuracy of the neural network cluster developed, gives the opportunity to suggest the use of hydrophobicity and secondary structure prediction in combination with a neural network cluster, as a promising method for the prediction of essential biological activities.

Impedance-based structural health monitoring technique is performed by measuring the variation of the electromechanical impedance of the structure caused by the presence of damage. The impedance signals are collected from patches of piezoelectric material bonded on the surface of the structure (or embedded). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric is used to quantify the damage. Distinguishing damage groups from a universe containing different types of damage is a major challenge in structural health monitoring. There are several types of failures that can occur in a given structure, such as cracks, fissures, loss of mechanical components (e.g., rivets), corrosion, and wear. It is important to characterize each type of damage from the impedance signals considered. In the present paper, probabilistic neural network and fuzzy cluster analysis methods are used for identification, localization, and classification of two types of damage, namely, cracks and rivet losses. The results show that probabilistic neural network and fuzzy cluster analysis methods are useful for identification, localization, and classification of these types of damage.

We present a neural network clustering approach to the analysis of dynamic contrast-enhanced magnetic resonance imaging (MRI) mammography time-series. In contrast to conventional extraction of a few voxel-based perfusion parameters, neural network clustering does not discard information contained in the complete signal dynamics time-series data. We performed exploratory data analysis in patients with breast lesions classified as indeterminate from clinical findings and conventional X-ray mammography. Minimal free energy vector quantization provided a self-organized segmentation of voxels w.r.t. fine-grained differences of signal amplitude and dynamics, thus identifying the lesions from surrounding tissue and enabling a subclassification within the lesions with regard to regions characterized by different MRI signal time-courses. We conclude that neural network clustering can provide a useful extension to the conventional visual inspection of interactively defined regions-of-interest. Thus, it can contribute to the diagnosis of indeterminate breast lesions by non-invasive imaging.KeywordsBreast LesionMinimal Free EnergyMagnetic Resonance Imaging SignalNeural Network AnalysisDeterministic AnnealingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A numerical investigation into the behavior of populations of interacting neural networks is presented. A number of neural networks with randomly generated weights interact with each other and share their knowledge about the same phenomenon through reducing their output differences. Results of this study show that all the interacting neural networks reside in the same state of knowledge, here defined as the state of common knowledge of the population, although their final vectors of connection weights would not necessarily be the same. Also segregation may take place at some stage during their interactions where clusters of neural networks with similar knowledge are formed. However these temporary clusters attract each other and dissolve into one dominant cluster representing the state of common knowledge of the population. Also it was observed that the final knowledge of the population is generally completely different from the average of the initial knowledge of the population. The neural networks used in this study have been three layered perceptrons of similar architecture and activation functions. Populations of 2, 20, 50 and 99 neural networks have been studied. No general conclusions about the subject can be drawn at this stage of the studies.

A novel BiGRU multi-step wind power forecasting approach based on multi-label integration random forest feature selection and neural network clustering

Customized application-specific processors called ASIPs are becoming commonplace in contemporary embedded system designs. Neural networks are an interesting application for which an ASIP can be tailored to increase performance, lower power consumption and/or increase throughput. Here, both the bidirectional associative memory and hopfield auto-associative memory networks are run through an automated instruction-set identification algorithm to identify and select custom instruction candidates suitable for neural network applications. Clusters of neural networks are highly parallel, and therefore it is interesting to consider a homogeneous multiprocessor composed of ASIPs. The two legacy neural network applications showed a 18-120% improvement with the automatic hardware/software partitioning for a uniprocessor ASIP. However, due to pointers and function calling which did not resolve to hardware, the acceleration was concentrated in the network initialization part of the code.

The field of off-line optical character recognition (OCR) has been a topic of intensive research for many years (Bozinovic, 1989; Bunke, 2003; Plamondon, 2000; Toselli, 2004). One of the first steps in the classical architecture of a text recognizer is preprocessing, where noise reduction and normalization take place. Many systems do not require a binarization step, so the images are maintained in gray-level quality. Document enhancement not only influences the overall performance of OCR systems, but it can also significantly improve document readability for human readers. In many cases, the noise of document images is heterogeneous, and a technique fitted for one type of noise may not be valid for the overall set of documents. One possible solution to this problem is to use several filters or techniques and to provide a classifier to select the appropriate one. Neural networks have been used for document enhancement (see (Egmont-Petersen, 2002) for a review of image processing with neural networks). One advantage of neural network filters for image enhancement and denoising is that a different neural filter can be automatically trained for each type of noise. This work proposes the clustering of neural network filters to avoid having to label training data and to reduce the number of filters needed by the enhancement system. An agglomerative hierarchical clustering algorithm of supervised classifiers is proposed to do this. The technique has been applied to filter out the background noise from an office (coffee stains and footprints on documents, folded sheets with degraded printed text, etc.).

For a comprehensive assessment of the management decisions quality, it is necessary to take into account heterogeneous information presented both in numerical form and in natural language expressions. The effective occurs the use of data mining including neural network clustering and fuzzy set theory. The article presents our approach to the use of these methods for evaluating risks and the management decisions quality in Russian higher education on the example of the implementation of the most ambitious Project 5-100 for it. On the example, the expediency of the neural network clustering to assess the possibility of achieving the goals of any such large-scale project has been proved. Clustering the information database used for the analysis, makes it possible to carry out an objective selection of candidate universities-candidates for the right to receive state subsidies, as well as to adjust the composition of the Project participants. Another methods of intellectual analysis – the construction of a complex of fuzzy inference systems, – confirmed the possibility of a quantitative fi evaluating of the project based on the expert verbal estimates of the project. At the same time, the neural network clustering initially illustrated the unattainability of the Project 5-100 goals. The use of a complex of fuzzy inference systems confirmed this statement by the very low quantitative final assessment of the project on the basis of verbal expert opinions.