Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

We developed and implemented an intelligent control system to be used in an extractive distillation column that produces anhydrous ethanol using ethylene glycol as solvent. The concept of artificial neural networks (ANN) was used to predict new setpoints after disturbances, and proved to be a fast and feasible solution. The developed control system receives data from temperature, flowrate and composition measurements of the azeotrope feed, and the ANN estimates the new set-points of the controllers to maintain 99.5 mol% of ethanol at the top and less than 0.1mol% at the bottom; feed composition was also estimated using an ANN. All ANN were trained to provide output data corresponding to an optimized operating condition. The results showed that the intelligent control system can predict a new operating condition for any disturbance in the column feed and presented superior performance when compared with the control system without ANN.

Objective: Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) are two broadly used for rule generation process in data mining. ANN have been widely used for pattern extraction from medical databases. Methods: Heart disease is one of the leading death cause disease in all over the world in the present situation. A large number of research works have been carried out for diagnosing heart failures using data mining techniques. As the number of heart disease features increasing along with a number of instances, the traditional approaches such as PSO, ANN and BPSO are fail to predict the exact disease causing features. Findings: In this research work, an improved ANN approach model called Optimized Artificial Neural Network (OANN) was implemented on the medical data sets. In this paper, an Optimized Particle Swarm Optimization (PSO) technique is used for disease dimension reduction. Filter based Artificial Neural Network is used for classifying the disease type as positive or negative based on the disease features. Applications/Improvements: The performance of the proposed algorithm is analyzed by using the traditional approaches of Performance plot, Regression, ROC Value and Confusion Matrix. It is proved that the performance of the whole ANN network is optimized after the inclusion of proposed PSO for Feature Reduction.

Artificial Neural Networks (ANNs) represents the overall interconnection of the systems together with numeric weighting that can be tuned based on experience, system Inputs, Processing and Outputs. Moreover, the real advantage of ANNs is the ability to solve complex system problems such as one which are found within the Transportation Infrastructure Systems. Artificial Neural Networks (ANNs) for Transportation Infrastructure System must incorporate system engineering techniques that will be sustainable for future years and maintained at acceptable levels. Accordingly, this paper will introduce the concept of Artificial Neural Networks (ANNs) and its core functions for the optimization of Transportation Infrastructure Systems in particular the maintenance processes.

The aim of this paper is twofold: Firstly, to provide introductory knowledge to the reader who has little or no knowledge of machine learning with examples of applications in clinical and biomedical domains, and secondly, to compare and contrast the concept of Artificial Neural Network (ANN) and the Qur’anic concept of intellect (aql) in the Qur’an. Learning algorithm can generally be categorised into supervised and unsupervised learning. To better understand the machine learning concept, hypothetical data of glaucoma cases are presented. ANN is then selected as an example of supervised learning and the underlying principles in ANN are presented with general audience in mind with an attempt to relate the mechanism employed in the algorithm with Qur’anic verses containing the verbs derived from aql. The applications of machine learning in clinical and biomedical domains are briefly demonstrated based on the author’s own research and most recent examples available from University of California, Irvine Machine Learning Repository. Selected verses which indicate motivation to use the intellect in positive manners and rebuke to those who do not activate the intellect are presented. The evidence found from the verses suggests that ANN shares similar learning process to achieve belief (iman) by analysing the similitudes (amsal) introduced to the algorithm.

CNC lathe is one of the best machining techniques which provides us with better accuracy and precision. Considering speed, feed and depth of cut as inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as the factors those affect the quality, machining time and cost of machining. Design of experiments (DOE) would be carried out in order to minimize the number of experiments. In the later stages application of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) would be used for the Optimization in the advanced manufacturing considering CNC lathe. The obtained output would be minimized (for surface roughness) and maximized (for MRR) using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO). The combination of various input parameters for the same would be identified and a comparison would be drawn with the various above methods.

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer. The network was trained by a particle swarm optimization (PSO) algorithm. Nanofluid concentration, Reynolds number, and Prandtl number are input for the ANN and the nanofluid Nusselt number is its output. There exists an excellent agreement between the ANN predicted values and experimental data. The average and maximum differences between experimental data and those predicted by ANN are about 0.8 and 5.6 %, respectively. It was also found that ANN predicts the Nusselt number of nanofluids more accurately than the previously proposed correlation.

Diophantine equation is an important concept while studying number theory. But there is as such no standard method or procedure to find the solution of the Diophantine equation. In this article, the numerical solution of Diophantine equation has been discussed using the concept of Artificial Neural Network (ANN). A four layer ANN architecture has been constructed for solving Diophantine eqution. Detail ANN procedure with few example problems and their network architectures have been addressed. Convergence plots and/or convergence tables for different example problems have also been included to validate the proposed method.

Nowadays, the number of attacker increasing fast due to the current technologies. Most companies or an organization use Intrusion Detection System (IDS) to protect their network system. Many researchers suggest different ways to improve the IDS such using optimization techniques. Artificial Intelligence (AI) methods also proposed in IDS to attained high accuracy of detection for example; artificial neural network, particle swarm optimization and genetic algorithm. Artificial neural network (ANN) and Particle Swarm Optimization (PSO) used in this paper to equate the method and performances in IDS environment. The ANN output value will be compared with the result where ANN supported by PSO to produce higher accurate value. KDD CUP ’99 Dataset used as the benchmark of IDS and will be simulated in MATLAB Simulink 2013. 200 datasets used consists of attacks and normal activities as an input. In this paper, DoS attack which are Smurf and Neptune attacks selected for detection.

Artificial neural network based modelling of desiccant wheel

Object identification plays an important role in various applications ranging from robotics to computer vision. Artificial Neural Network is being used for in various pattern recognition applications due to the advantage of ability as well as adaptability to learn. This paper presents identification of object independent to size, position and orientation using the concept of Artificial Neural Network and moments. The image of the object is taken with the help of the tactile sensing system. The paper describes the complete hardware and software of the system for the object identification with the help of Artificial Neural Network.

The rapid pace of technological progress has led to an increasing growth in the volume of digital data circulating on servers and on the web. This has contributed to the birth of the concept of Big Data. Simply put, this concept refers to the huge amount of information on the Internet; yet it also reveals the heterogeneity and complexity of such data. Therefore, analyzing these data, especially unstructured data, has become important since they can be used in many areas such as company management, health, smart city. In order to analyze these data, novel efficient tools are required as the current ones are not effective enough. This paper surveys the most frequently used tools and platforms for Big Data analysis with due emphasis on Machine Learning-based models. The results of this study provide in-depth knowledge of Big Data analytics applications related to machine learning that can contribute to the innovation and development of big data analytics platforms. Moreover, it helps to choose the right tools to ensure the best performance for designing an analytics system.

Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

Taking a byte out of big data

Big Data is a set of data with large volume, a various and complex structure and their size is beyond of limit which can be grasped, managed and processed with usual softwares in a reasonable time. Data volume in the world is increasing day to day. Traditional database systems are not able to save and analyze so much data volume. Because the internet is growing, Big Data volume is growing too. Organizations have become very flexible and open. New data types will make new challenges. One of the key challenges in Big Data is security. According to the increasing volume and variety of Big Data, security challenges will grow too. From the other side, software infrastructures like Hadoop make it possible for extenders to process in thousands of nodes parallel. In this paper, a review will be done on Big Data and security challenges and a method will be offered for improving the efficiency of security and access time to data blocks in Big Data.

This research proposes the use of a classifier built using the artificial neural network (ANN) technique for determining teacher engagement based on the Indonesian Teacher Engagement Index (ITEI). ANN classifier will be built in the form of a website. This study uses the concept of artificial intelligence and Artificial neural networks. This research produces Website Workflow and The Classifier Process in Website. In the future the website will become a portal for that can be used to determine the level of engagement of a teacher.