The healthcare domain is basically “data rich”, yet tragically not every one of the information are dug which is required for finding concealed examples and successful basic leadership used to find learning in database and for restorative research, especially in heart malady forecast. This article has examined forecast frameworks for heart disease utilizing more number of info attributes. In this article, we proposed an altered calculation for classification with decision trees which furnishes precise outcomes when contrasted and others calculations. The proposed work is planned to show the data mining method in disease forecast frameworks in medicinal space by utilizing avaricious way to deal with select the best attributes. Our investigation demonstrates that among various prediction models neural networks and Gini index prediction models results with most noteworthy precision for heart attack prediction. A portion of the discretization strategies like voting technique are known to deliver more precise decision trees. To improve execution in coronary illness finding, this research work examines the outcomes in the wake of applying a scope of procedures to various sorts of decision trees and accuracy and sensitivity are attained by the execution of elective decision tree methods.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

Resilient fault-tolerant anti-synchronization for stochastic delayed reaction–diffusion neural networks with semi-Markov jump parameters

Blockchain is a relatively young technology. In recent years, blockchain has gained popularity, which keeps growing. Such interest is mainly due to cryptocurrencies, such as Bitcoin and Ethereum. This technology has presented promising prospects of application. With the increasing interest in blockchain from cryptocurrency to smart contracts, there are precedents of attacks on the blockchain platform. This, in turn, encourages consideration of blockchain in terms of security, identifying vulnerabilities and predicting the emergence of new vulnerabilities. That further would allow to find new methods and solutions for blockchain security. In this paper, the known vulnerabilities were considered, as well as the known attacks on blockchain and their successful implementations in recent times.

Review of Blockchain Technology Vulnerabilities and Blockchain-System Attacks

Sequential pattern mining (SPM) is one of the main application areas in the field of online business, e-commerce, bioinformatics, etc. The traditional approaches in SPM are unable to accurately mine the huge volume of data. Therefore, the proposed work employs a sequential mining model based on deep learning to minimize complexity in handling huge data. Application areas such as online retailing, finance, and e-commerce face a dynamic change in data, which results in non-stationary data. Therefore, our proposed work uses discrete wavelet analysis to convert non-stationary data into time series. In the proposed SPM, a reformed hybrid combination of convolutional neural network (CNN) with long short-term memory (LSTM) is designed to find out customer behavior and purchasing patterns in terms of time. CNN is used to find the concerned itemsets (frequent) at the end of the pattern and LSTM for finding the time interval among each pair of successive itemsets. The proposed work mines the sequential pattern from a progressive database that removes the obsolete data. Finally, the accuracy of the proposed work is compared with some traditional algorithms to demonstrate its robustness.

Deep learning-based sequential pattern mining for progressive database

Foreign currency exchange plays a vital role for trading of currency in the financial market. Due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper presents different machine learning techniques like Artificial Neural Network (ANN), Recurrent Neural Network (RNN) to develop prediction model between Nepalese Rupees against three major currencies Euro, Pound Sterling and US dollar. Recurrent Neural Network is a type of neural network that have feedback connections. In this paper, prediction model were based on different RNN architectures, feed forward ANN with back propagation algorithm and then compared the accuracy of each model. Different ANN architecture models like Feed forward neural network, Simple Recurrent Neural Network (SRNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) were used. Input parameters were open, low, high and closing prices for each currency. From this study, we have found that LSTM networks provided better results than SRNN and GRU networks.

Comparison of algorithms in Foreign Exchange Rate Prediction

The object of the study are neural networks. The goal is to understand and develop a recognition system of mathematical formulas and symbols. The article deals with the basic provisions of neural networks, which principle of work is based on functioning neural networks of the human brain. We describe a mathematical representation of the pulse passing through the synaptic connections of neurons and their processing for further action. It describes problems at creation and use of neural networks, which lead to restriction of the scope of information being processed, complication of the structure and the emergence of new tasks to be solved. The article also suggests some ways to resolve these difficulties. Based on the study, the authors review a stepwise algorithm of the neural network, which allows recognizing the symbols and mathematical expressions. The steps of the algorithm are disassembled using specific examples to demonstrate the principle of its operation.

http://ieeexplore.ieee.org/iel7/7898598/7910885/07911596.pdf

A. Sinkov

Papers

Neural networks in data mining