Bibliometric of Feature Selection Using Optimization Techniques in Healthcare using Scopus and Web of Science Databases

Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is built using the feature selection algorithm. The solitary motive of the research paper analysis is to comprehend the reach and utility of optimization algorithms such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO) in the field of Health care. The aim is to bring efficiency and maximum optimization in the health care sector using the vast information that is already available related to these fields. With the help of data sets that are available in the health care analysis, our focus is to extract the most important features using optimization techniques and work on different algorithms so as to get the most optimized result. Precision largely depends on usefulness of features that are taken into consideration along with finding useful patterns in those features to characterize the main problem. The Performance of the optimized algorithm finds the overall optimum with less function evaluation. The principle target of this examination is to optimize feature selection technique to bring an optimized and efficient model to cater to various health issues. In this research paper, to do bibliometric analysis Scopus and Web of Science databases are used. This bibliometric analysis considers important keywords, datasets, significance of the considered research papers. It also gives details about types, sources of publications, yearly publication trends, significant countries from Scopus and Web of Science. Also, it captures details about co-appearing keywords, authors, source titles through networked diagrams. In a way, this research paper can be useful to researchers who want to contribute in the area of feature selection and optimization in healthcare. From this research paper it is observed that there is a lot scope for research for the considered research area. This kind of research will also be helpful for analyzing pandemic scenarios like COVID-19

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Improving K-means clustering with enhanced Firefly Algorithms

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for undertaking the obstinate problems of initialization sensitivity and local optima traps of the K-means clustering model. To enhance the capability of both exploitation and exploration, matrix-based search parameters and dispersing mechanisms are incorporated into the two proposed FA models. We first replace the attractiveness coefficient with a randomized control matrix in the IIEFA model to release the FA from the constraints of biological law, as the exploitation capability in the neighbourhood is elevated from a one-dimensional to multi-dimensional search mechanism with enhanced diversity in search scopes, scales, and directions. Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. This dispersing mechanism ensures sufficient variance between fireflies in comparison to increase search efficiency. The ALL-IDB2 database, a skin lesion data set, and a total of 15 UCI data sets are employed to evaluate efficiency of the proposed FA models on clustering tasks. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality. The empirical results indicate that the proposed FA models demonstrate statistically significant superiority in both distance and performance measures for clustering tasks in comparison with conventional K-means clustering, five classical search methods, and five advanced FA variants

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Cancer prediction using graph-based gene selection and explainable classifier

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy

Automobile Insurance Fraud Detection Using Data Mining: A Systematic Literature Review

Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process

IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective

With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges, specifically, effective model selection, design/tuning, and updating, which have brought massive demand for experienced data scientists. Additionally, the dynamic nature of IoT data may introduce concept drift issues, causing model performance degradation. To reduce human efforts, Automated Machine Learning (AutoML) has become a popular field that aims to automatically select, construct, tune, and update machine learning models to achieve the best performance on specified tasks. In this paper, we conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML in order to identify and summarize the optimal solutions for every step of applying ML algorithms to IoT data analytics. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted in this work. Lastly, we discuss and classify the challenges and research directions for this domain.Comment: Published in Engineering Applications of Artificial Intelligence (Elsevier, IF:7.8); Code/An AutoML tutorial is available at Github link: https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytic