Several Data mining techniques have been developed to enhance the prediction accuracy and analyze several events in Coronary Heart Disease (CHD). One among them was Extended Dynamic Bayesian Network (EDBN) which integrates temporal abstractions with DBN. Then EDBN was extended as Optimized Semi parametric Extended Dynamic Bayesian Network (OSEDBN) to handle Complex temporal abstractions in irregular interval time series data. The deep learning network is generated the various time points in the next level to improve the analysis and prediction of CHD. In this paper, Optimized Semi parametric Extended Deep Dynamic Bayesian Network (OSEDDBN) is proposed by integrating deep learning architecture with OSEDBN to improve the ability of extracting more important data and support complex structures from various types of input sources. Additionally the Fuzzy Analytic Hierarchy Process (FAHP) approach is used to compute the global weights for the attributes based on their individual contribution. The global weights of the attributes obtained by FAHP are utilized for training OSEDDBN to further improve the prediction of Coronary Heart Disease (CHD) risks. The performance of EDBN, OSEDBN, OSEDDBN, and OSEDDBN-FAHP are evaluated in terms of Precision, Recall and FMeasure.
Objectives: This work focuses on creating targeted content-specific topicbased clusters. They can help users to discover the topics in a set of documents information more efficiently. Methods/Statistical analysis: The Non-negative Matrix Factorization (NMF) based models learn topics by directly decomposing the term-document matrix, which is a bag-of-word matrix representation of a text corpus, into two low-rank factor matrices namely Word-Topic feature Matrix(WTOM) and Document-Topic feature Matrix(DTOM). Topic clusters and Document clusters are extracted from obtained features matrices. This method does not require any statistical distribution and probability. Experiments were carried out on a subset of BBC sport Corpus. Findings: The experimental results indicate that the accuracy of TONMF clusters was observed as 100 percent. Novelty/Applications: NMF often fails to improve the given clustering result as the number of parameters increases linearly with the size of the corpus. The computational complexity of the TOPNMF is better than exact decomposition like Singular Value Decomposition (SVD).
Anomaly detection is a significant problem that has been researched within various research areas and application domains. Many anomaly detection methods have been particularly examined for certain application domains, as others are more standard. This present study describes an anomaly detection technique for unsupervised data sets accurately reduce the data from a kernel Eigen space performing a batch re-computation. For each anomaly behavior activities is to identify the key factors, which are used by the methods to differentiate between normal and abnormal actions. This present study provides a best and brief understanding of the techniques belonging to each anomaly and kernel mapping category. Further, for each grouping, to identify the improvements and drawbacks of the techniques in that category. It also provides a discussion on the computational complexity of the techniques since it is an important issue in real application domains hope that this survey will provide a good understanding of the many directions in which research has been done on this topic
Accurate vessel segmentation in retinal images plays a vital role for retinopathy diagnosis and analysis. The presence of very thin vessels in low image contrast, on the other hand makes the segmentation task difficult. In the proposed method retinal vessels are segmented using multiscale Fully Convolved Convolutional Neural Network (FCCNN) architecture. The proposed architecture is trained for pixel classification to cope with the varying width and direction of the vessel structure in the retina. Green channel extraction gives better contrast difference between vessels and background. The skeletonization process is done which prevents the change in structure, thus the vasculature remains unchanged. In addition, an improved class balanced cross entropy loss function is included to avoid misclassification and imbalance problem. The proposed method is verified on public retinal vessel segmentation database (DRIVE). The accuracy of DRIVE FCCNN after 90 epochs was attained as 92.73% and the loss was attained as 0.0632. The experimental results show that segmentation results and accuracy obtained for FCNN are greater compared to the other architecture.
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