Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

CA : A Cancer Journal for Clinicians

Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Creating shareholder value

Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms

Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

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A Neuro-fuzzy approach for user behaviour classification and prediction

Ever since the pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China, it has been recognized as a global threat and several studies have been carried out nationally and globally to predict the outbreak with varying levels of dependability and accuracy. Also, the mobility restrictions have had a widespread impact on people’s behavior such as fear of using public transportation (traveling with unknown passengers in the closed area). Securing an appropriate level of safety during the pandemic situation is a highly problematic issue that resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The autoregressive integrated moving average (ARIMA) machine learning model is used to develop the best model for twenty-one worst-affected states of India and six worst-hit countries of the world including India. The best ARIMA models are used for predicting the daily-confirmed cases for 90 days future values of six worst-hit countries of the world and six high incidence states of India. The goodness-of-fit measures for the model achieved 85% MAPE for all the countries and all states of India. The above computational analysis will be able to throw some light on the planning and management of healthcare systems and infrastructure.

/pdf/bifm-big-data-driven-intelligent-forecasting-model-for-covid-5croerpgtf.pdf

BIFM: Big-Data Driven Intelligent Forecasting Model for COVID-19

Intelligent Computing on Time-Series Data Analysis and Prediction of COVID-19 Pandemics

The architectural framework of Fog and edge computing reveals that the network components which lie between the cloud and devices computes application oriented operations. In this paper, an in-depth review of fog and mist computing in the area of health care informatics is analyzed, classified, and discussed various applications cited in the literature. For that purpose, applications are classified into different categories and a list of application-oriented tasks that can be handled by fog and edge computing are enlisted. It is further added that on which layer of the network system such fog and edge computing tasks can be computed and trade-offs with respect to requirements relevant to healthcare are provided. The review undertaken in this paper focuses on three important areas: firstly, the enormous amount of computing tasks of healthcare system can take mileage of these two computing principles; secondly, the limitation of wireless devices can be overcome by having higher network tiers which can execute tasks and aggregate the data; and thirdly, privacy concerns and dependability prevent computation tasks to completely move to the cloud. Another area which has been considered in the study is how Edge and Fog computing can make the security algorithms more efficient. The findings of the study provide evidence of the need for a logical and consistent approach towards fog and mist computing in healthcare system.

/pdf/edge-and-fog-computing-in-healthcare-a-review-11sre97a1a.pdf

Sujata Dash

Papers

Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms

A Neuro-fuzzy approach for user behaviour classification and prediction

BIFM: Big-Data Driven Intelligent Forecasting Model for COVID-19

Intelligent Computing on Time-Series Data Analysis and Prediction of COVID-19 Pandemics

Edge and Fog Computing in Healthcare – A Review