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Behavioural Intention towards adoption of Robotic Accounting for a profitable leading digital transformation
Leading digital transformation accelerates impactful changes in business environments and work places and helps them thrive in this age dominated by physical, emotional, and financial disruptions. This is very much evident during the pandemic-induced current economic climate; the Robotic Process Automation (RPA) industry has been found to grow at an exponentially increasing rate throughout 2020, and based on the response towards it, it can be logically predicted that this trend will continue to be in vogue for several years into the future. The use of RPA technology enables auditing firms to not only automate business processes but also significantly improve the way the company currently completes tasks. In view of the above, the present study focuses on the nature of digital automation of business processes in auditing firms using RPA and its impact on revenue management and client engagement. The study proposes to make use of qualitative research methods and also aims to theorize the role of various antecedents that develop a strong intention among the auditing firms to adopt RPA for the purposes of accounting and auditing. 2022 IEEE. -
Envisaging an Intelligent Blockchain Network by Intelligence Sharing
Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. 2022 IEEE. -
Demography-Based Hybrid Recommender System for Movie Recommendations
Recommender systems have been explored with different research techniques including content-based filtering and collaborative filtering. The main issue is with the cold start problem of how recommendations have to be suggested to a new user in the platform. There is a need for a system which has the ability to recommend items similar to the users demographic category by considering the collaborative interactions of similar categories of users. The proposed hybrid model solves the cold start problem using collaborative, demography, and content-based approaches. The base algorithm for the hybrid model SVDpp produced a root mean squared error (RMSE) of 0.92 on the test data. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Performance Evaluation and Comparison of Various Personal Cloud Storage Services for Healthcare Images
In recent times, usage of personal cloud storage services for storing e-health records in on a rise. This is due to the constant accessibility, easy sharing, and safe storage of the data at a nominal cost. In this paper, we have analyzed the performance of four personal cloud storage services: Google Drive, Dropbox, Sync.com, and Icedrive using medical image data files of various sizes. The parameters checked were number of packets transmitted during file upload and duration of time to upload, download, and delete the files. The results show us a comparative analysis of the personal cloud storage services based on the parameters and also help us identify certain gaps for the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Approach for Credit Card Churn Prediction Using Gradient Descent
A very important asset for any company in the business sectors such as banking, marketing, etc. are its customers. For them to stay in the game, they have to satisfy their customers. Customer retention plays an important role in attracting and retaining the customers. Customer retention means to keep the customer satisfied so that they do not stop using their service/product in the domain of banking; the banks provide various kinds of services to the customers especially in the electronic banking sector. For this study, we have selected the service of credit card. For a bank to give a loan or amount on credit basis, the e-bank should make sure if its customers are eligible and can repay their money. The purpose of this project is to implement a neural network model to classify the churners and non-churners. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Moderation of Income and Age on Customer Purchase Intention of Green Cosmetics in Bangalore
Cracking the code of customer purchase behaviour is a challenge for market researchers as myriad factors interfere. Marketers are puzzled as competitors position a new product category in the market to create demand. Indian public perceived cosmetics composition as blend of healthy chemical extracts. Television commercials portrayed the presence of chemicals in cosmetics as a product performance booster. People attributed chemical presence to superior product performance. Saturated markets witnessed competitors aiming at increased sales with similar commercials. Under pressure to differentiate, the idea of organic cosmetics started. Companies invested heavily on product development, marketing and branding. Expected success was not achieved as buyers measured performance of cosmetics weighing the absence of chemicals. Scepticism on organic level of the products emerged as various brand commercials claimed their respective compositions a true organic product. Fewer studies explained purchase intention of green cosmetics without focus on health consciousness and consumer innovativeness. Product diffusions were strategized on the basis of consumer innovativeness. Health consciousness captured individuals weightage on health and well-being while purchasing a product. This paper explores relationship of health consciousness and consumer innovativeness with purchase intention development conducting exploratory factor analysis, regression analysis and interaction analysis on selected independent variables using dependent variables. The study found both consumer innovativeness and health consciousness leading to development of purchase intention of green cosmetics. Age and income moderated the relationship of consumer innovativeness and purchase intention. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Factors Affecting the Growing Economic Inequality: An Empirical Study with Reference to BRICS Countries
Economic inequality refers to the uneven distribution ofearningsand opportunity between various groups in society and is a point of major concern in almost all the nations in the world. This study aims to analyse the effect of various factors over the increasing inequality in BRICS nations. The study takes into consideration factors like trade openness, credit, net foreign assets and health and tries to assess their impact as a driving force behind the increasing inequality in these countries. The augmented DickeyFuller test for stationarity has been applied followed by multiple regression. To explore causality, Granger causality test is applied. All the models are tested for autocorrelation using the BreuschGodfrey Lagrange Multiplier test. Wald test is applied to examine the significance of independent variables. The study provides statistical evidence about the positive and negative effects of trade openness, healthcare finance, net foreign assets and healthcare expenditure on income inequality in BRICS nations. Findings may help to work intensively on the relevant causes of inequality for these five countries. This paper will add to the already present literature on inequality which is one of the important problems of the countries across the world. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review of Algorithms for Mental Stress Analysis Using EEG Signal
Mental stress is an enduring problem in human life. The level of stress increases exponentially with an increase in the complexity of work life. Hence, it is imperative to understand the causes of stress, a prerequisite of which is the ability to determine the level of stress. Electroencephalography (EEG) has been the most widely used signal for understanding stress levels. However, EEG signal is useful only when appropriate algorithms can be used to extract the properties relevant to stress analysis. This paper reviews algorithms for preprocessing, feature extraction and learning, and classification of EEG, and reports on their advantages and disadvantages for stress analysis. This review will help researchers to choose the most effective pipeline of algorithms for stress analysis using EEG signals. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification Framework for Fraud Detection Using Hidden Markov Model
Machine learning is described as a computer program that learns from experience E with regard to some task T and some performance measure P, if its performance on T improves with E as measured by P. Suppose we have a credit card fraud detection which watches which transactions we mark as fraud or not, and on the basis, it knows how to filter better fraudulent transactions then, E is watching your transactions is fraud or not, T is classifying your transactions as fraud or not, P is number of transactions correctly differentiated as spam or not spam. Machine learning has two types: supervised learning and unsupervised learning. Supervised learning is the type of machine learning where machine is provided with input mapped with its output, and these inputs and outputs are used to make a machine learn a particular function from the trained dataset. There are two branches of supervised learning, i.e., classification and regression. In unsupervised learning, we do not supervise model instead we allow machine to work on its own to discover information. Clustering is type of unsupervised learning. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Depth Comparison of Objects in 2D Images Using Mask RCNN
Getting distance of an object from a single 2D image has always been a task. Due to various reasons, it was difficult to compare from images whether an object is closer or farther from camera. In this paper, we propose an idea to compare multiple images taken from same focal length cameras and specifying the distance of an object in those images with respect to each other. Our dataset contains images of palm of hand with particular distance from camera, and the output difference can specify in which image the palm is closer to camera as compared to others and vice versa. For this model, we are using Mask RCNN to recognize the object; in our case, it has been trained to identify palm, and then giving the output of masked RCNN to a depth identifier model to specify the distance of the palm from the camera. Directly using depth identifier model cannot give correct output as distance of background from camera results in different value for distance of targeted object in different images. So, we will be using mask RCNN to specify which part of image depth model should find distance from the camera. In the final step, we take the output of the depth model and take the mean of the output generated by it and compare the means of various images to specify relative distance from each other. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Neural Network Based Customer Churn Prediction Algorithm for Telecom Sector
For telecommunication service providers, a key method for decreasing costs and making revenue is to focus on retaining existing subscribers rather than obtaining new customers. To support this strategy, it is significant to understand customer concerns as early as possible to avoid churn. When customers switch to another competitive service provider, it results in the instant loss of business. This work focuses on building a classification model for predicting customer churn. Four different deep learning models are designed by applying different activation functions on different layers for classifying the customers into two different categories. A comparison of the performance of the different models is done by using various performance measures such as accuracy, precision, recall, and area under the curve (AUC) to determine the best activation function for the model among tanh, ReLU, ELU, and SELU. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effect of Halloysite Nanotubes on Physico-Mechanical Properties of Silk/Basalt Fabric Reinforced Epoxy Composites
Natural fiber reinforced polymer composites have become more attractive due to their high specific strength, light weight and environmental concern. However, some limitations such as low modulus and poor moisture resistance were reported. This paper presents the role of halloysite nanotubes (HNTs) on physico-mechanical properties of bidirectional silk and basalt fiber reinforced epoxy (SF-BF/Ep) hybrid composites. Vacuum bagging and ultra-sonication method were used for the fabrication of hybrid composite slabs. The effect of HNT loadings (1.5, 3 and 4.5 wt. %) on physico-mechanical characteristics like density, hardness, flexural and impact properties of SF-BF/Ep composites were determined according to ASTM standards. Experimental results revealed that the incorporation of HNTs improves the mechanical properties. The impact strength of SF-BF/Ep is predominant at 3 wt. % HNT loading where the impact strength surges to 568.67 J/m, which may render HNT filled SF-BF/Ep desirable for various toughness-critical structural applications. The test results demonstrated that SF-BF/Ep-3HNT coded composites exhibited improved mechanical properties among the all composites. 2022 Trans Tech Publications Ltd, Switzerland. -
On Combinatorial Handoff Strategies for Spectrum Mobility in Ad Hoc Networks: A Comparative Review
Technological advancements have made communication on-the-go seamless. Spectrum mobility is a networking concept that involves access technologies that allow highly mobile nodes to communicate with each other. Ad-hoc networks are formed between mobile nodes where fixed infrastructure is not used. Due to the lack of such fixed access points for connectivity, the nodes involved make use of the best network available to transmit data. Due to heterogeneous networks involvement, the mobile nodes may face trouble finding the most optimal network for transmission. Existing technologies allow the nodes to select available networks, but the selection process is not optimized, leading to frequent switching. This leads to packet loss, low data rates, high delay, etc. Many researchers have proposed optimal strategies for performing handoff in wireless networks. This paper reviews combinatorial strategies that make use of multiple techniques to perform a handoff. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review
Ultrasonography is the most accepted and widely used imaging technique due to its non-invasive and radiation-free nature. The heterogeneous structure of kidney makes the disease detection a difficult task. Hence, more efficient models and methods are required to assist radiologists in making precise decisions. Since ultrasound imaging is considered to be the initial step in the diagnosis, more efficient processing techniques are needed in the interpretation of images. The presence of speckle noise is a challenge task in image processing. It diminishes the clarity of the images. In this article, an in-depth review has been performed on various machine learning and deep learning techniques, which are helping to improve the quality of images. The pre-processing, segmentation, feature extraction, and classification are described in detail using kidney cyst, stone, tumor, and normal kidney images. Deep learning techniques are enhancing the quality of the images with better accuracy. The remaining challenges and directions for future research are also explored. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Econometric Approach Towards Exploring the Impact of Workers Remittances on Inflation: Empirical Evidence from India
This paper attempts to study short and long run impact of increased workers remittances on general price level. It uses the real GDP growth, real effective exchange rate (REER), M3 (broad money), fiscal deficit to gauge the impact of foreign remittances on inflation. The study makes use of VAR/VECM framework to gauge the impact of workers remittances on inflation. Inflation is measured in terms of CPI and WPI, real income or GDP at constant prices is taken as a measure of GDP growth, REER is used for exchange rates and M3 is taken as a proxy for money supply. Monthly data of all these variables has been taken from Bloomberg and World Bank data base. The findings provide important insights into the nature of association between remittances and inflation suggesting causality between inflation, remittances, real GDP, real effective exchange rates and money supply due to increased workers remittances. The findings have policy implications for decisions to channelize workers remittances in a way to increase real GDP growth and money supply while at the same time not causing the general price levels to soar. The present study focuses on how increased (decreased) workers remittances is leading to increase (decrease) in general price levels in India. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Security Aspects for Mutation Testing in Mobile Applications
Due to the increase in the number of Android Platform Devices, there are more and more applications being developed across various domains. It is interesting to see the involvement of bugs/crashes even in the deployed applications even though it has been through various test phases. Unit tests are essential in a well-trusted testing environment; however, it does not guarantee that the range of test caries every component of the application. This writes up discusses the overview of mutation testing method concerning Android Applications. Even though mutation testing is found out to be very effective in other applications, it is not that easy to implement the same for an Android Developed Application because of additional resources it would hold. Further, various measures for mutation testing are discussed with types of mutant operators, tools etc. The current studies of mutation analysis mainly focus on testing all the functionalities irrespective of the resource usage. However, the target of the future mutation tests must be also to evaluate the efficiency of the applications under the same test cases. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Currency Exchange Rate Prediction Using Multi-layer Perceptron
Financial forecasting is an estimate of a future financial outcome and this outcome is related to some kind of value. We can measure this outcome for a company to predict its future stock or to detect the viability of a human for the sanction of a loan. In all these cases, we want to estimate the future outcome based on historical data. Various methods have been developed lately, to make time series predictions. In this work, we have used Multi-layer perceptron algorithm to predict the Currency Exchange rate between US dollar and EURO. The training network has been compiled using TensorFlow. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predictive Analysis of the Recovery Rate from Coronavirus (COVID-19)
Estimation of recovery rate of COVID-19 positive persons is significant to measure the severity of the disease for mankind. In this work, prediction of the recovery rate is estimated based on machine learning technology. Standard data set of Kaggle has been used for experimental purpose, and the data sets of COVID cases in Italy, China and India for these countries are considered. Based on that data set and the present scenario, the proposed technique predicts the recovery rate. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Survey on Domain-Specific Summarization Techniques
Automatic text summarization using different natural language processing techniques (NLP) has gained much momentum in recent years. Text summarization is an intensive process of extracting representative gist of the contents present in a document. Manual summarization of structured and unstructured text is a tedious task that involves immense human effort and time. There are quite a number of successful text summarization algorithms for generic documents. But when it comes specialized for a particular domain, the generic training of algorithms does not suffice the purpose. Hence, context-aware summarization of unstructured and structured text using various algorithms needs specific scoring techniques to supplement the base algorithms. This paper is an attempt to give an overview of methods and algorithms that are used for context-aware summarization of generic texts. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Novel Approach for Web Mining Taxonomy for High-Performance Computing
Web mining is a central part of data analysis. The fetching and discovering knowledge from the different web data in data mining mechanism is more important nowadays. Web usage mining customs data mining practice for the investigation of custom decoration from different data storages. In this article paper, introducing a new approach for web mining taxonomy for high-performance computing. The primary motivation of this research is on the data collection in different real-time web servers for implementation and analysis. This article is focussed the WebLog Expert lite 9.3 tools for our study. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.