Automated Personalized Big Data Model to Promote Traditional Culture with Aesthetic Education

Big data can make significant contributions to the field of aesthetic education in universities. By analyzing large amounts of data, researchers can gain insights into student engagement with artistic content and better understand how students learn and appreciate the arts. Aesthetic education is a field of study that focuses on the cultivation of aesthetic sensibility and appreciation, as well as the development of skills in various forms of artistic expression. Aesthetic education in universities is that it helps to develop students’ emotional intelligence and empathy. Hence, in this paper constructed the automated framework model based on big data is constructed for Aesthetic education in universities. The constructed model is termed the Mamdani Fuzzy Set Optimization (MFsO) for the personalized automated model. The student information associated with aesthetic education in universities is processed with MFsO model. The MFsO model uses the fuzzy set rules for the personalized comments to the students for the promotion of tradition among students. The model uses the Flemingo Optimization model for the computation of the effective features in the big data for the generation of rules. The automated model uses the deep learning architecture model for the data transmission to the students. The comparative analysis stated that the proposed MFsO model performance is effective compared with the conventional techniques for the personalized automated system design

A Robust Cardiovascular Disease Predictor Based on Genetic Feature Selection and Ensemble Learning Classification

Timely detection of heart diseases is crucial for treating cardiac patients prior to the occurrence of any fatality. Automated early detection of these diseases is a necessity in areas where specialized doctors are limited. Deep learning methods provided with a decent set of heart disease data can be used to achieve this. This article proposes a robust heart disease prediction strategy using genetic algorithms and ensemble deep learning techniques. The efficiency of genetic algorithms is utilized to select more significant features from a high-dimensional dataset, combined with deep learning techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), and Radial Basis Function (RBF), to achieve the goal. The boosting algorithm, Logit Boost, is made use of as a meta-learning classifier for predicting heart disease. The Cleveland heart disease dataset found in the UCI repository yields an overall accuracy of 99.66%, which is higher than many of the most efficient approaches now in existence

Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Background: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters

Water Resources Decision Making Under Uncertainty

Uncertainty is in part about variability in relation to the physical characteristics of water resources systems. But uncertainty is also about ambiguity (Simonovic, 2009). Both variability and ambiguity are associated with a lack of clarity because of the behaviour of all system components, a lack of data, a lack of detail, a lack of structure to consider water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. Climate change, addressed in this research project (CFCAS, 2008), is another important source of uncertainty that contributes to the variability in the input variables for water resources management. This report presents a set of examples that illustrate (a) probabilistic and (b) fuzzy set approaches for solving various water resources management problems. The main goal of this report is to demonstrate how information provided to water resources decision makers can be improved by using the tools that incorporate risk and uncertainty. The uncertainty associated with water resources decision making problems is quantified using probabilistic and fuzzy set approaches. A set of selected examples are presented to illustrate the application of probabilistic and fuzzy simulation, optimization, and multi-objective analysis to water resources design, planning and operations. Selected examples include dike design, sewer pipe design, optimal operations of a single purpose reservoir, and planning of a multi-purpose reservoir system. Demonstrated probabilistic and fuzzy tools can be easily adapted to many other water resources decision making problems.https://ir.lib.uwo.ca/wrrr/1035/thumbnail.jp