Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like – SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market

Computer-aided automated detection of kidney disease using supervised learning technique

In this paper, we propose an efficient home-based system for monitoring chronic kidney disease (CKD). As non-invasive disease identification approaches are gaining popularity nowadays, the proposed system is designed to detect kidney disease from saliva samples. Salivary diagnosis has advanced its popularity over the last few years due to the non-invasive sample collection technique. The use of salivary components to monitor and detect kidney disease is investigated through an experimental investigation. We measured the amount of urea in the saliva sample to detect CKD. Further, this article explains the use of predictive analysis using machine learning techniques and data analytics in remote healthcare management. The proposed health monitoring system classified the samples with an accuracy of 97.1%. With internet facilities available everywhere, this methodology can offer better healthcare services, with real-time decision support in remote monitoring platform

A Scoping Review of Classification of Concrete Cracks using Deep Convolution Learning Approach

An important factor that causes defect in the concrete structure is the systematic damage and it is very difficult to detect the cracks by visual examination. Digital image processing has proven to be one of the best substitutes for the monitoring of the cracks. A traditional filter based on image processing algorithm is a classical approach for monitoring the cracks. Thereafter, the deep learning-based methods have been implemented to detect and classify the cracks on the concrete images and have shown significant results. The convolution neural network-based models have fairly observed and graded the cracks giving better performance in terms of accuracy, precision and recall. After the bibliometric review of the existing literature, comparison of the performance of different models and existing methods can be observed

Bibliometric Survey on Reconfigurable Antenna for MIMO systems

The aim of this study is to analyse the work done on the frequency and polarization reconfigurability of antenna for various design types using the bibliometric study methods. Different articles on reconfigurable antennas for MIMO systems were retrieved using SCOPUS which is one of the most popular databases. The research articles published between 2004 to 2020 were considered and Scopus Analyser was used to fetch the analysis results namely document by source, author, subject, year and country. In the currently available literature, a lot of survey articles on the reconfigurable antennas and MIMO systems is available but there is no bibliometric analysis conducted till date. Hence, this in this article, the bibliometric study with an emphasis on the reconfigurable antennas and their applications in the MIMO systems is undertaken. The aim of this article is to explore the present research conducted referring to the articles published each year, keywords used over time, topmost keywords used each year, journals publishing most papers over time and each year as well. The data that is articulated will support the basic understanding of the topic and emphasize the fact that there is an enormous opportunity for the research clusters to explore the field of reconfigurable antennas

Bibliometric Survey on Multipurpose Face Recognition System using Deep Learning

Face recognition is a new concept coming up lately and flourishing in the field of network access and multimedia information systems. Since humans are at the focus of attention in variety of applications containing videos, the concept of face recognition is rising in popularity. Several areas involving network security, retrieval and indexing of the content, and compression of videos gain a lot of benefit from the face recognition systems and related technology. Controlling the access of the networks with the help of facial recognition not only makes it difficult for the hackers to steal the information but also makes the system more fool proof, user friendly and manageable. Out of all the available tools for processing the biometric information, the face recognition system is the most popular one and used worldwide due to its ease of use and adaptability along with a wider range of working. The overall system may consist of hardware and software modules where in the detection of the facial features can be done using the available hardware and deep learning algorithms can be used to process the retrieved information. To this end, a system can be built considering face detection and face recognition as two major parts. This article shows the systematic bibliometric survey of the existing literature for the face recognition system using deep learning techniques. The survey is undertaken using the Scopus database for data analysis and several other tools like Gephi, science scape and minivan for visualisation of the fetched data. In this article, the information drawn from the Scopus database is articulated with respect to the vital aspects of bibliometric analysis such as documents fetched by affiliation, country or territory, funding sponsor, source, subject area, type and year. The information is then related to each other with the help of network diagrams for coappearance of information like - authors and source titles, authors and keywords, authors linked by co-publication etc. This survey reinforces the point that there are ample opportunities for the researchers to work in the field of face recognition system especially using deep learning techniques

An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module

Circularly Polarised Reconfigurable Antenna in 5G Application:A Bibliometric Study using Scopus Database

The field of wireless technology has come a long way from connecting humans to humans, human to machines and now machines to machines. The boom in the wireless communication and increased number of systems used in latest wireless and radar applications creates the need for reconfigurable antennas. This paper presents an analysis of a circularly polarized reconfigurable antenna for 5G applications. The activation mechanisms, design and ways to optimize the operation of reconfigurable antennas are discussed. With the world moving towards 5G, which expects its reach in remote areas as well, the circular polarization patch antennas are well suited for such purpose, and they can work efficiently in densely populated areas as well. The importance of reconfigurable antennas in a world which awaits the transformation of technology with the coming of 5G is discussed briefly. This review digs deeper into the factors which can optimize the performance of a reconfigurable antenna and the reasons for which the circular polarization is widely sought after. Reconfigurable circularly polarized antennas are used in wireless and satellite communication systems and finds its application in various areas. We have used numerous research papers for our literature survey which were published between 2002 and 2021 in this feild. The bibliometric survey done in this literature review were mainly based on the Scopus database and tools such as VOSviewer, Graph Receipe and ScienceScape

A Brief Bibliometric Survey on Microstrip Antennas for Machine-to-Machine (M2M) Communication in Smart Cities

Stupendous progress in heterogeneous communication technologies has allowed smart city gadgets to communicate with one another. However, these communication technologies are not able to offer the connectivity that is needed in smart cities because of the coexistence of hundreds and thousands of devices, which leads to various problems like, high energy consumption, interoperability support among the heterogeneous wireless networks, interference management, scalable wireless solutions, and mobility management. Machine-to-Machine (M2M) communication is one of the key enablers for advanced applications and services. The aim of this bibliometric review is to understand the extent of the existing literature for the area of M2M communications in smart cities using Microstrip antennas. This bibliometric analysis is majorly based on the Scopus database and tools such as VOSviewer and ScienceScape. The research articles published between the years 2013 to 2021 were considered. We observed from this bibliometric analysis that the major publications found are from conference papers, articles and conference reviews by Indian publications followed by Irish, Japanese and Chinese publications. The majority of the contribution is by the subject areas of Engineering, Computer Science, Physics and Astronomy, Material Science and Mathematics

Bibliometric Study on Analysing Impact of newly launched products over existing ones through AI

Different analysis models like Conditional Mean Analysis, Trend Analysis, Correlation Analysis helps us to analyse the delicate equilibrium between businesses that gets impacted when a new product is launched in a cluster. This paper shows a statistical report of research done on the businesses in a cluster based on ongoing trends and current customer needs . There is surplus data present on various platforms related to every product following the ongoing trends in the form of customer reviews.The research mainly speculates mainly how the businesses get impacted with change in consumer needs, wants and demands. With the help of datasets that are available from online sources incorporating various machine learning techniques which would help us analyze the correlation of two businesses and by checking on various algorithms for analyzing the results obtained regarding the study made covering various aspects of businesses. On top of that, the precision largely depends on the evaluating parameters that are taken into consideration along with finding helpful patterns in those evaluating parameters to characterise the main problem. In this report, to perform bibliometric analysis Scopus Database is employed. This bibliometric analysis considers essential keywords, datasets, and significance of the selected research papers. Moreover it offers details regarding types, sources of publications, yearly publication trends, affiliations and so on from Scopus. Furthermore, it captures details concerning co-appearing keywords, authors, titles of sources through networked diagrams. From this research paper it is perceived that there is a lot of research for the considered research area. This kind of research will also be helpful for speculating how the new businesses impact the awareness of the customers on the existing ones