Electroencephalography and Clinical Neurophysiology

The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic and critical review of about one hundred and twenty-two (122) pertinent research works reported in academic journals over 11 years (2007–2018) in the area of stock market prediction using machine learning. The various techniques identified from these reports were clustered into three categories, namely technical, fundamental, and combined analyses. The grouping was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modelling. The results revealed that 66% of documents reviewed were based on technical analysis; whiles 23% and 11% were based on fundamental analysis and combined analyses, respectively. Concerning the number of data source, 89.34% of documents reviewed, used single sources; whiles 8.2% and 2.46% used two and three sources respectively. Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.

A systematic review of fundamental and technical analysis of stock market predictions

Various tools and methods are used in participatory modelling, at different stages of the process and for different purposes. The diversity of tools and methods can create challenges for stakeholders and modelers when selecting the ones most appropriate for their projects. We offer a systematic overview, assessment, and categorization of methods to assist modelers and stakeholders with their choices and decisions. Most available literature provides little justification or information on the reasons for the use of particular methods or tools in a given study. In most of the cases, it seems that the prior experience and skills of the modelers had a dominant effect on the selection of the methods used. While we have not found any real evidence of this approach being wrong, we do think that putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results. Based on expert opinion and a survey of modelers engaged in participatory processes, we offer practical guidelines to improve decisions about method selection at different stages of the participatory modeling process.

/pdf/tools-and-methods-in-participatory-modeling-selecting-the-4o3523pz2i.pdf

Tools and methods in participatory modeling: Selecting the right tool for the job

A machine learning-based intrusion detection for detecting internet of things network attacks

The data mining and its tool has played a vital role in exploring the data from different ware houses. Using data mining tools and analytical technologies we do a quantifiable amount of research to explore new approach for the investment decisions .The market with huge volume of investor with good enough knowledge and have a prediction as well as control over their investments. The stock market some time fails to attract new investor. The reason states that non-aware and also people don’t want to come forward to fall in to the risk. An approach with adequate expertise is designed to help investors to ascertain veiled patterns from the historic data that have feasible predictive ability in their investment decisions. In this paper the NSE – Nifty Midcap50 companies among them top 4 companies having max Midcap value has been selected for analysis. The historical data has a significant role in, helping the investing people to get an overview about the market behavior during the past decade. The stock data for the past five years has been collected and trained using ARIMA model with different parameters. The test criterions like Akaike Information Criterion Bayesian Information Criterion (AICBIC) are applied to predict the accuracy of the model. The performance of the trained model is analyzed and it also tested to find the trend and the market behavior for future forecast.

/pdf/an-effective-time-series-analysis-for-stock-trend-prediction-3tt43lq7yf.pdf

An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50

The field of mathematics plays vital role in various fields. One of the important areas in mathematics is graph theory which is used in structural models. This structural arrangements of various objects or technologies lead to new inventions and modifications in the existing environment for enhancement in those fields. The field graph theory started its journey from the problem of Koinsberg bridge in 1735. This paper gives an overview of the applications of graph theory in heterogeneous fields to some extent but mainly focuses on the computer science applications that uses graph theoretical concepts. Various papers based on graph theory have been studied related to scheduling concepts, computer science applications and an overview has been presented here.

https://www.cs.xu.edu/csci390/12s/IJEST10-02-09-124.pdf

Applications of graph theory in computer science an overview

Diabetes is one of the most feared diseases currently faced by humanity. The disease is due to a poor reaction of the body to insulin: it is an important hormone in our body that converts sugar into energy that is necessary for the proper functioning of a normal life. Diabetic disease has serious complications on our body because it increases the risk of developing kidney disease, heart disease, retinal disease, nerve damage, and blood vessels. In this article, we have proposed a decision tree model: SLDPS (Diabetes Prediction System with Supervised Learning). The data set is collected via IoT sensors. The classification accuracy obtained with this model was improved to 94.63% after the rebalancing of the data set and shows a potential relative to other classification models in the literature.

Prediction of Diabetes Using Internet of Things (IoT) and Decision Trees: SLDPS.

Generally, Magnetic Resonance Imaging (MRI) is utilised in radiology for diagnose the anatomy and the physiological processes of the body. Nowadays, the classification of tumour region play...

Exponential cuckoo search algorithm to Radial Basis Neural Network for automatic classification in MRI images

Cloud computing is one of the new age technologies which has great prominent factor in the development of the enterprises and markets. The major exertion in the cloud computing is related to the resource being allocated. The optimal resource allocation is one which allocates the best suitable cluster resources for the task to execute with consideration of the different parameters, such as time, cost, and scalability, makespan, reliability, availability, throughput, resource utilization and so on. In this paper, a resource allocation optimization method in the cloud computing based on the exponential lion algorithm is proposed. The exponential lion based resource allocation for cloud computing taken into account saves the execution time, run time, and improves the revenue for the cloud provider. The proposed E-Lion based resource allocation approaches are compared with the PSO, SL-PSO, and Lion using the performance measures profit, CPU utilization rate, and memory utilization rate. The simulations of the experiments show that the algorithm in this paper has improved the algorithm performance efficiently with profit maximal profit of 38.74 and minimal CPU and memory utilization rate of 0.00031, and 0.00036 respectively.

Design and development of exponential lion algorithm for optimal allocation of cluster resources in cloud

An integrated cloud service model utilising both public and private cloud services to provide a holistic deployment of the enterprise applications is the need of the hour. Traditional existing applications getting modernised to meet the high business demands to serve end customers, by adapting technology enhancements towards big data and cloud to achieve the futuristic Service Level Agreements (SLA).Some efforts have been made to model with respect to cost and its impacts, yet the distinctive need is to get a holistic picture of the overall modernisation characteristics. In this paper, we propose a proactive cost optimisation model along with a proposed algorithm to solve some of the key issues towards service placement during enterprise modernisation. The proposed cost optimised algorithm is applied to different scenarios on multiple clouds with enterprise modernisation characteristics. Experimental results derived shown that the proposed work effectively optimises the service placement on multiple clouds.

PSPO: a framework for cost-effective service placement optimisation during enterprise modernisation on hybrid clouds

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