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An Empirical study on price pressure and liquidity effect of stock split announcement- Evidence from Indian Market
International Journal of Marketing and Technology Vol.3,No.1,pp.138-156 ISSN No. 2249-1058 -
An empirical study on impact of changes in macro economic variables on bond yield curve - Evidence from Indian corporate bond market /
Research Journal Of Finance And Accounting, Vol.6, Issue 11, pp.396-403, ISSN No: 2222-2847 (Online) 2222-1697 (Print) -
An Empirical Study on Gold ETF Volatility - Evidence From Indian Market
Zenith International Journal of Business Economics & Management Research, Vol-3 (7), pp. 223-232. ISSN-2249-8826 -
An Empirical Study on announcement effects of right issues on share price volatility and liquidity and its impact on market wealth creation of informed investors in Bangalore with special reference to CNX Nifty Stocks of NSE
Excel International Journal of Multidisciplinary Management Studies Vol. 2, No. 7, pp 41-58, ISSN No. 0972-687X -
An Empirical Study ofSignal Transformation Techniques onEpileptic Seizures Using EEG Data
Signal processing may be a mathematical approach to manipulate the signals for varied applications. A mathematical relation that changes the signal from one kind to a different is named a transformation technique in the signal process. Digital processing of electroencephalography (EEG) signals plays a significant role in a highly multiple application, e.g., seizure detection, prediction, and classification. In these applications, the transformation techniques play an essential role. Signal transformation techniques are acquainted with improved transmission, storage potency, and subjective quality and collectively emphasize or discover components of interest in an extremely measured EEG signal.The transformed signals result in better classification. This article provides a study on some of the important techniques used for transformation of EEG data. During this work, we have studied six signal transformation techniques like linear regression, logistic regression, discrete wavelet transform, wavelet transform, fast Fourier transform, and principal component analysis with Eigen vector to envision their impact on the classification of epileptic seizures. Linear regression, logistic regression, and discrete wavelet transform provides high accuracy of 100%, and wavelet transform produced an accuracy of 96.35%. The proposed work is an empirical study whose main aim is to discuss some typical EEG signal transformation methods, examine their performances for epileptic seizure prediction, and eventually recommend the foremost acceptable technique for signal transformation supported by the performance. This work also highlights the advantages and disadvantages of all seven transformation techniques providing a precise comparative analysis in conjunction with the accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Empirical Study of Blockchain Technology, Innovation, Service Quality and Firm Performance in the Banking Industry
Despite the potential promises that blockchain technology (BT) offers to the financial services sector, its large-scale implementations are still in a nascent stage. There is no consensus on what benefits BT may bring, and there is always a possibility of difference between expected benefits and experienced real-world impact. Since the actual impact can be assessed only after large-scale implementations by financial institutions, there is little empirical evidence available in the literature. In this context, this research seeks to explore the potential impact of BT by developing and empirically testing a model. For this purpose, we have identified four dimensions of BT, namely, Decentralization, Transparency, Trustlessness, and Security. The impact of BT on innovation, service quality, and firm performance is assessed based on the extent to which these dimensions are present in the organization. The linkages of the latent constructs are estimated by analyzing the primary data collected from senior managers of various banks in India. The findings of this study provide several important considerations regarding the implementation of BT. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
An Empirical Model for Geopolymer Reactions Involving Fly Ash and GGBS
Numerous works are reported in literature regarding the enhancement of compressive strength of fly ash-GGBS geopolymer combinations with addition of alkali activators of varying concentrations. However, a limited study has been chronicled, revealing the specific role of alkali or alkaline earth contributed by the fly ash-GGBS combinations on the compressive strength development. It is well known that the strength of a geopolymer is dependent on gel formation from Al/Si ratio, Ca/Si ratio, and Ca/(Si + Al) ratio but their exact role when cured for various extended periods is unknown as yet. In the present study, alkali concentration in a fly ash-GGBS geopolymer combination was varied from 6 M to 12 M with increments of two mol in six different fly ash-GGBS combinations with a minimum of 20 percent and a maximum of 70 percent GGBS. The correlation coefficients between compressive strength and Al/Si, Ca/Si, and Ca/(Si + Al) ratios exhibited values higher than 0.95 taken individually. Multiple linear regression analysis with compressive strength (as dependent parameter) and individual values of Al/Si, Ca/Si, and Ca/(Si + Al) ratios (as independent parameters) was effectuated. It was observed that, depending on the composition, the compressive strength circumstantiated a changeover from Ca/Si to Ca/(Si + Al) ratio in the intermediate composition range. Such a detailed analysis is considered supportive of developing a suitable composition which will provide the optimum compressive strength of the combination. 2022 Beulah M et al. -
An empirical investigation of foreign direct investment and economic growth in SAARC nations
Purpose This paper aims to investigate the causal nexus between foreign direct investment (FDI) and economic growth in SAARC countries. Design/methodology/approach Johansen's cointegration test was employed to examine the long?run relationship between foreign direct investment and economic growth in SAARC countries. Besides, the vector error correction model (VECM) was employed to examine the causal nexus between foreign direct investment and economic growth in SAARC countries for the years 1970?2007. Finally, the impulse response function (IRF) has been employed to investigate the time paths of log of foreign direct investment (LFDI) in response to one?unit shock to the log of gross domestic product (LGDP) and vice versa. Findings The Johansen cointegration result establishes a long?run relationship between foreign direct investment and gross domestic product (GDP) for the sample of SAARC nations, namely, Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka. The empirical results of the vector error correction model exhibit a long?run bidirectional causal link between GDP and FDI for the selected SAARC nations except India. The test results show that there is a one?way long?run causal link from GDP to FDI for India. Research limitations/implications This paper employed annual data to examine the causal nexus between FDI and economic growth. Therefore, researchers are encouraged to test the FDI?growth relationship further by using quarterly data. Practical implications The SAARC nations should adopt effective policy measures that would substantially enlarge and diversify their economic base, improve local skills and build up a stock of human capital recourses capabilities, enhance economic stability and liberalise their market in order to attract as well as benefit from long?term FDI inflows. Originality/value This paper would be immensely helpful to the policy makers of SAARC countries to plan their FDI policies in a way that would enhance growth and development of their respective economies. 2011, Emerald Group Publishing Limited -
An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection
Artificial intelligence (AI)-based systems are normally data driven applications, where the model is trained to think on its own based on the external circumstances. The power of AI has reached every facet of business and common life and is even being largely explored to be adopted in life sciences and medical domains. It supports the human in decision-making through the cognitive utilities which arises out of self-learning capabilities of a model. With the exponential growth of data, supply chain management and analytics have attracted a large community of researchers to build intelligent systems which can lead to re-invention of data-driven decision systems powered by AI. Systems and literature of the past shows that AI-based technologies are promising in intelligent supply chain management (SCM) and building resilient SCMs. There is a gap in literature which addresses on the framework for decision support systems in SCM and application of AI methods for building a robust supply chain resilience (SCR) leading to more exploration on the topic. In this paper, a decision framework is proposed by incorporating fuzzy logic and recurrent neural networks (RNN) for disclosing the patterns of various AI-enabled techniques for SCRs. The proposed analysis involved data from leading literatures to determine the most adoptable and significant applications of AI in SCRs. The analysis shows that techniques such as fuzzy programing, network based algorithms, and genetic algorithms have large impact on building SCRs. The results help in decision-making by exhibiting an integrated framework which can help the AI practitioners for developing SCRs. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
An Empirical Examination of the Factors of Big Data Analytics Implementation in Supply Chain Management and Logistics
Numerous companies have effectively exploited Big Data Analytics (BDA) potential to enhance their effectiveness in the Big Data period. Given that big data application in logistics and supply chain management (SCM) is nevertheless in its early stages, assessments of BDA could differ from various viewpoints, producing certain difficulties in comprehending the significance and potential of big data. Based on past research on BDA and SCM, this work examines the factors that influence organizations' willingness to implement BDA in their everyday activities. This research divides potential elements into 4 groups: technical, firm, ecological, and supply chain issues. A framework consisting of direct factors like technical, firm, and mediators was presented based on the technology diffusion hypothesis. The experimental findings demonstrated that anticipated advantages and high-level management assistance might have a considerable impact on intended adoption. Furthermore, ecological variables like competitive adoption, administration legislation, and supply chain connection can greatly alter the direct connections between influencing causes and intended adoption. 2023 IEEE. -
An Empirical Evaluation of the Relationship Between Economic Growth, Population and Solid Waste Generation in India
Municipal solid waste (MSW) poses a hazard to the environment, human health and well-being and economic growth, if not managed correctly.It is essential to study the determinants of municipal solid waste generation for efficient waste management planning and achieving sustainable growth.The main objective of this paper is to establish a relationship between economic growth, population and MSW generation.Secondly, it aims to verify whether the environmental Kuznets curve (EKC) hypothesis is valid in the Indian context with MSW generation as the proxy variable for environmental degradation.Panel regression has been run using statewise data of MSW generation, state net domestic product (SNDP) per capita and population for the years 2000-2019.The results show a significant positive relationship between the selected variables.Least square regression was applied to verify the validity of the environmental Kuznets curve (EKC) hypothesis in India using nationwide data for MSW generation and GDP per capita for the years mentioned above.The results depicted inverted U-shaped curve with MSW as the dependent variable and GDP per capita as the independent variable and confirmed the validity of the EKC hypothesis. 2022 Scientific Publishers. All rights reserved. -
An empirical evaluation of stress and its impact on the engineering colleges faculty members in Tamil Nadu
The present study examined the specific causes, levels and effects of stress experienced by faculty members who worked in unaided private engineering and technology (UPET) colleges in the state of Tamil Nadu, India. Factor analysis and structural model were used to evaluate the relationship between the causes, subsequent effects at different level of stress and the appropriate personal and organisational stress coping strategies. Primary data were collected from 560 faculty members by employing convenience sampling method during the academic year 20172018. The study revealed that causes of stress influencing considerably the level of stress and the level of stress explained the high influence on the effect of stress variables. Both effects of stress and causes of stress were negatively influencing the stress coping strategies of personal as well as organisational level strategies. Copyright 2020 Inderscience Enterprises Ltd. -
An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction
In today's focus on mental well-being, technology's capability to predict and comprehend mental fitness holds substantial significance. This study delves into the relationship between mental health indicators and mental fitness levels through diverse machine learning algorithms. Drawing from a vast dataset spanning countries and years, the research unveils concealed patterns shaping mental well-being. Precise analysis of key mental health conditions reveals their prevalence and interactions across demographics. Enriched by insights into Disability-Adjusted Life Years (DALYs), the dataset offers a comprehensive view of mental health's broader impact. Through rigorous comparative analysis, algorithms like Linear Regression, Random Forest, Support Vector Regression, Gradient Boosting, K-nearest neighbors and Theil Sen Regression are assessed for predictive accuracy. Mean squared error (MSE), root mean squared error (RMSE), and Rsquared (R2) scores are used to assess the predictive accuracy of each algorithm. Results show that Mean Squared Error (MSE) ranged from 0.030 to 1.277, Root Mean Squared Error (RMSE) from 0.236 to 1.130, and R-squared (R2) scores ranged between 0.734 and 0.993, with Random Forest Regressor achieving the highest accuracy. This study offers precise prognostications regarding mental fitness and establishes the underpinnings for the creation of effective tracking tools. Amidst society's endeavor to tackle intricate issues surrounding mental health, our research facilitates well-informed interventions and individualized strategies. This underscores the noteworthy contribution of technology in shaping a more Invigorating trajectory for the future. 2023 IEEE. -
An Empirical and Statistical Analysis of Fetal Health Classification Using Different Machine Learning Algorithm
The health of both the mother and the baby is affected by how well the fetus is doing during pregnancy, making it a matter of utmost importance. To achieve the best results possible, it is essential to regularly monitor and intervene when needed. While there are many ways to observe the wellbeing of the fetus in the mother's womb, using artificial intelligence (AI) has the potential to enhance accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing a machine learning-driven system for accurate fetal health classification. The dataset comprises detailed information on the signs and symptoms of pregnant individuals, particularly those at risk or with emerging fetal health issues. Employing a set of ten machine learning models namely Nae Bayes, Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Linear Discriminant Analysis, Quadratic Discriminant Analysis Light Gradient Boosting Machine (LGBM) along with ensemble-based processes, the Light Gradient Boosting Machine (LGBM) has been identified as a standout performer, accomplishing an accuracy of 96.9%. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct fetal fitness class systems. This study highlights the power of machine learning that could revolutionize prenatal care by identifying fetal health problems early. 2024 IEEE. -
An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
The risk of dying from a heart attack is high everywhere in the world. This is based on the fact that every forty seconds, someone dies from a myocardial infarction. In this paper, heart attack is predicted with the help of dataset sourced from UCI Machine Learning Repository. The dataset analyses 13 attributes of 303 patients. The categorization method of Data Mining helps predict if a person will have a heart attack based on how they live their lives. An empirical and statistical analysis of different classification methods like the Support Vector Machine (SVM) Algorithm, Random Forest (RF) Algorithm, K-Nearest Neighbour (KNN) Algorithm, Logistic Regression (LR) Algorithm, and Decision Tree (DT) Algorithm is used as classifiers for effective prediction of the disease. The research study showed classification accuracy of 90% using KNN Algorithm. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
An Empirical Analysis of Turnaround and its Benefits to Stakeholders
International Journal of Applied Management Research, Vol-6 (1(3), pp. 470-473. ISSN-0974-8709 -
An empirical analysis of the antecedents and barriers to adopting robo-advisors for investment management among Indian investors
This study aims to provide a research framework to understand the antecedents and barriers to adopting Robo-advisors for investment decision-making in India. The study employed a research model based on the extended UTAUT 2, along with three additional constructs, i.e. personal innovativeness (PI), perceived risk (PR), and technological anxiety (TA). Data collected were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with the help of SmartPLS 4.0 software. This research will help banks, wealth management service providers, FinTech companies, and Robo-advisor developers improve their platforms, offers, products, and marketing tactics for these automated advisory services. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
An empirical analysis of sustainability of public debt among BRICS nations
The main objective of this paper is to verify the sustainability of public debt among Brazil, Russia, India, China and South Africa (BRICS) in a political economy framework. Annual panel data have been used for BRICS countries from World Development Indicators of World Bank for the period 19802017 for the analysis. Bohn's sustainability framework is used to examine the sustainability of public debt in BRICS nations and verify the influence of political economic variables such as election year, coalition dummy, ideology of the government and unemployment on public debt sustainability. The results suggest that public debt sustainability is weak for BRICS as a whole. China and India have a better public debt sustainability coefficients compared to the same for Brazil, Russia and South Africa. Structural change dummy included in the model suggests that debt sustainability is severely affected after the 2008 crisis period. Political factors have influence on debt sustainability in BRICS. Electoral cycle year and coalition dummy variables adversely affect public debt sustainability in BRICS. While centrist political ideology is found to be significant and negative, left and right ideologies are not significant for debt sustainability. Since debt sustainability is found to be weak in BRICS, countries in the region need to adopt necessary measures to improve their primary balance through appropriate fiscal and debt management. Besides, it is important for the governments to prioritize fiscal prudence irrespective of their ideologies and political compulsions. 2020 John Wiley & Sons, Ltd -
An empirical analysis of similarity measures for unstructured data /
An International Journal of Advanced Computer Technology, Vol.8, Issue 8, pp.3302-3306, ISSN No: 2320-0790. -
An empirical analysis of similarity measures for unstructured data
With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role. The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document organization, plagiarism detection, query matching etc. This task is one of the most fundamental task in the area of information retrieval, information extraction, document organization, plagiarism detection and text mining problems. But effectiveness of document clustering is highly dependent on this task. In this paper four similarity measures are implemented and their descriptive statistics is compared. The results are found to be satisfactory. Graphs are drawn for visualization of results. 2019 COMPUSOFT, An international journal of advanced computer technology.