Browse Items (11810 total)
Sort by:
-
Moderation effect of flexibility in projects on senior management commitment in achieving success in financial services IT projects
Senior management commitment and flexibility improve project responsiveness to volatile and high-impact scenarios, especially in large projects and programs. The aim of this study is to determine how project flexibility interacts with and affects the relationship between senior management commitment and success in IT projects. A cross-sectional survey of 166 managers was used to derive empirical data from the financial services industry and used to test the conceptual framework based on recent project management literature. Ordinal regression analysis demonstrated a significant relationship between senior management commitment and success in projects which is influenced by significantly positive moderations established through flexibility in projects. The study findings can assist project managers and senior leaders to accomplish their short-term and long-term project goals and achieve success in projects by reducing the chances of failures. This paper adds value to existing research in the context of IT projects and the role of project flexibility on their performance. Copyright 2023 Inderscience Enterprises Ltd. -
Impact on cardioprotective effect of Psidium guajava leaves extract in streptozotocin-induced Wistar mice with molecular in silico analysis
Cardiovascular disease (CVD) and its complications have been regarded as the leading cause of morbidity and mortality. The drugs available in the market are effective to treat CVD, but with many adverse reactions. Nowadays, herbal products are the attention of researchers because of their less adverse effects. In this study, the cardioprotective effects of ethanolic leaves extract of Psidium guajava Linn. (Guava) (P. guajava) were evaluated in streptozotocin (STZ)-treated animal models. Mice acquired for the study were divided into five groups, each consisting of six mice. The toxin-induced mice were treated with the ethanolic leaves extract of P. guajava (300 mg/ kg body weight [b.w.]). The results were compared to the standard drug (glibenclamide)-treated mice (3 mg/kg b.w.). The following parameters were considered for further investigations: creatine kinase-muscle brain (CK-MB), creatine kinase (CK), troponin, lysosomal, and mitochondrial enzymes. Then the docking study was accomplished. The levels of cardiac marker enzymes and lysosomal enzymes increased significantly in the toxin-induced mice, while the level of mitochondrial enzyme decreased significantly. During treatment with the ethanolic leaves extract of P. guajava, the levels of all parameters were notably reversed to normal range (P < 0.05). Further, in docking analysis, the interaction of compounds, such as alpha-terpineol, cyclopentanecarboxamide, guaiol (a sesquiterpenoid alcohol), 1H-cyclopropanaphthalene, tetracyclotridecan-9-ol, dormin/abscisic acid, and epiglobulol, with the respective protein molecules, evidenced the cardioprotective effect of P. guajava leaves. Hence, it was concluded that the ethanolic leaves extract of P. guajava leaves have a cardioprotective effect. 2023 Codon Publications. -
Student engagement and learning during COVID-19: an empirical analysis
COVID-19 pandemic brought along with it a widespread disruption of education system around the world. Schools, colleges and universities were shut all over the world. In order to maintain the continuity of education, educators and students alike adopted the online mode of teaching and learning. While mainstream education was mostly face-to-face; a sudden shift to the online mode of teaching and learning required teachers and students to get acquainted with the platform and tools. This study attempts to test a model to understand the impact of online education on students engagement levels in the context of higher education and the COVID-19 pandemic. Results indicate that access to digital resources and teacher effectiveness has positive impact on engagement and student engagement in turn has positive impact on learning outcomes. Stress has negative impact on student learning. The paper also discusses implications of the study and future direction for research. Copyright 2022 Inderscience Enterprises Ltd. -
Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm
Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset's attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. 2023 S. Krishnan et al. -
Impact of Mahatma Gandhi National Rural Employment Guarantee Act on Rural Credit System in India: A Standard Logit Difference in Difference Approach
The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) of India is one of the most extensive social safety nets programs in the developing world. The initiative attempts to enhance rural livelihoods in India by lowering rural poor vulnerability and misery. The programs nature and extent of execution vary from state to state. Using panel data sets from the Indian Human Development Survey (IHDS), which covering India for two waves, 200405 and 201112. We used a quasi-experimental approach, such as the difference-in-difference technique of effect evaluation, to quantify the programs influence on rural families credit and debt structures. The empirical analysis shows evidence of changing the behavior of taking loans from formal sources among non-poor households actively participating in the MGNREGA program. But the difference-in-difference results shows that among poor households participating in the MGNREGA scheme, the tendency to depend on formal sources to take loans is still insignificant. That means informal lending sources are still more prevalent among poor people. This tendency has not changed even after the initiation of this program. The article finishes with policy recommendations for successfully targeting the program, notably the social safety net benefits to disadvantaged households in India. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Quantitative assessment of blockchain applications for Industry 4.0 in manufacturing sector
Blockchain is one of the emerging digital technologies that will play a role in the breakthroughs of the fourth industrial revolution. The use of blockchain technology has the potential to greatly benefit businesses of all sizes by increasing their data's integrity, privacy, and openness. The term Industry 4.0? refers to the amalgamation of recent advances in manufacturing technology that have helped businesses cut production times significantly. The industrial and supply chain industries can benefit from these technological advancements in a number of ways. Increased efficiency in production and a more stable supply chain are just two of the many benefits that blockchain promises to bring to the manufacturing industry. The study focuses on Blockchain's huge potential in the context of Industry 4.0. Understanding the role of Blockchain technology in Industry 4.0 is examined, along with its various drivers, enablers, and associated capabilities. The several sub-domains of Industry 4.0 that can benefit from the implementation of Blockchain technology are also covered. The present research is primary and exploratory in nature. The sample size of the study is 256. The responses obtained from workers working in manufacturing sector in Delhi/NCR. The responses from workers obtained through structured questionnaire. The several sub-domains of Industry 4.0 found that can benefit from the implementation of Blockchain technology. At last, the existing study found the most important uses of Blockchain technology in the fourth industrial revolution. 2023 -
A STRUCTURAL EQUATION MODELLING APPROACH TOWARDS TAXPAYERS PERCEPTIONS ON GOODS AND SERVICES TAX IN INDIA; [UMA ABORDAGEM DE MODELAGEM DE EQUAES ESTRUTURAIS PARA AS PERCEPES DOS CONTRIBUINTES SOBRE O IMPOSTO SOBRE BENS E SERVIS NA DIA]; [UN ENFOQUE DE MODELADO DE ECUACIONES ESTRUCTURALES HACIA LAS PERCEPCIONES DE LOS CONTRIBUYENTES SOBRE EL IMPUESTO SOBRE BIENES Y SERVICIOS EN LA INDIA]
Purpose: The Purpose of this article is to comprehend how Indian taxpayers perceive the goods and services tax. Theoretical Framework: India has completed five years after the successful implementation of Goods and Services Tax (GST). Many economic benefits were promised at the time of implementation of this tax regime. Thus, it becomes essential to understand tax payers perceptions by developing a strong framework that influences their perceptions. Design/Methodology/Approach: A descriptive study approach was adopted for this objective. 200 replies were obtained in total. Using SPSS Amos, structural equation modelling was utilised to assess the assumptions produced. Attitude, knowledge, Equity, and fairness of taxpayers served as exogenous factors, while taxpayer impression served as the dependent variable. The real-world implication is used as a mediating variable in order to examine the impacts. Findings: The findings of the research indicate that tax knowledge, Equity, and fairness impact tax attitudes. This study provides some useful recommendations for further research in this sector. Research Implications: This study considers tax knowledge, tax equity and fairness and tax attitudes to measure tax payers perception. However, tax rates, regular amendments, circulars, technology and other variables could also be considered by future researchers on this study. Originality/Value: Using a Structural Equation Modelling in understanding Tax Payers Perceptions was hardly adopted in these types of studies. Variables considered for this study were also unique. 2023 AOS-Estratagia and Inovacao. All rights reserved. -
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the arterys stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Improving Organizational Sustainable Performance of Organizations Through Green Training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour, in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance, and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023 IGI Global. All rights reserved. -
A novel map matching algorithm for real-time location using low frequency floating trajectory data
The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright 2023 Inderscience Enterprises Ltd. -
Fault analysis in the 5-level multilevel NCA DCAC converter
The existing neutral clamped active inverter has common mode voltage with the high frequency which can reduce the severity with less voltage gain. The traditional active neutral point clamped (APC) DCAC converter maintains great common mode voltage with high-frequency (CMV-HF) reduction capability so, it has limited voltage gain. The paper presents a new 5-level active neutral point clamped DCAC converter that can change voltage step-up in a single-stage inversion. In the suggested design, a common ground not only reduces the CMV-HF but also improves DC link voltage use. Compared with the traditional two-stage 5-level APC DCAC converter, the proposed design has lower voltage stresses and greater uniformity. While improving the overall efficiency, the suggested clamped DCAC converter saves three power switches and a capacitor. Modelling and actual tests have proven the suggested active neutral point clamped inverters overall operation, efficacy and achievability. The proposed circuit is finally tested with fault clearance capability. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
FUZZY SEMI-ESSENTIAL SUBMODULES AND FUZZY SEMI-CLOSED SUBMODULES
In this paper, we prove some properties of fuzzy semi-essential submodules and fuzzy semi-closed submodules I??k University, Department of Mathematics, 2023; all rights reserved -
Student engagement in online learning during COVID-19
Online teaching and learning have become the novel norm amidst COVID-19 pandemic crisis across the world. The educational institutions across the world have switched to online mode of instruction to continue to provide education. Thus, research on effectiveness of online teaching and factors affecting the students engagement in a virtual classroom has gained importance. Students during pandemic are learning at home and lack motivation and confidence in their academic life. The present study aimed to analyze the student engagement and the factors that affect the student engagement in online learning environment. The study employed a quantitative research design to collect data from 600 students attending online classes in schools and colleges of Bangalore, India. The study found that there is a positive correlation between students intrinsic motivation and student engagement. Student engagement increases as the academic pressure or tension decreases. The core findings of the study showed that interest towards learning, perceived competence, and perceived choice of students determines student engagement in online classroom. Almost 33.7 % variance in student engagement is because of students intrinsic motivation. Future researchers may explore external factors affecting student engagement. Student engagement is significant for meaningful learning in online learning environment. Italian e-Learning Association. -
A Comparative Study of Pollution Levels in Major Cities of India During Covid-19 in India
This paper aims to study the major pollutants of the four metro cities of India before and after covid 19 first wave. The cities considered for the study are Bangalore, Delhi, Mumbai, and Kolkata. The major pollutants considered for the study are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The basic aim of the study is to find the effect of lockdown and covid restrictions on the level of pollutants across the four major cities of India. We used both parametric and non-parametric tests for the analysis using SPSS. From the study, it is clear that there is a significant decrease in all the major pollutants across India's major cities.6. 2023, University of Wollongong. All rights reserved. -
Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved. -
Comparative Performance of LSTM and ARIMA for the Short-Term Prediction of Bitcoin Prices
This research assesses the prediction of Bitcoin prices using the autoregressive integrated moving average (ARIMA) and long-short-term memory (LSTM) models. We forecast the price of Bitcoin for the following day using the static forecast method, with and without re-estimating the forecast model at each step. We take two different training and test samples into consideration for the cross-validation of forecast findings. In the first training sample, ARIMA outperforms LSTM, but in the second training sample, LSTM exceeds ARIMA. Additionally, in the two test-sample forecast periods, LSTM with model re-estimation at each step surpasses ARIMA. Comparing LSTM to ARIMA, the forecasts were much closer to the actual historical prices. As opposed to ARIMA, which could only track the trend of Bitcoin prices, the LSTM model was able to predict both the direction and the value during the specified time period. This research exhibits LSTM's persistent capacity for fluctuating Bitcoin price prediction despite the sophistication of ARIMA. 2023, University of Wollongong. All rights reserved. -
Using machine learning architecture to optimize and model the treatment process for saline water level analysis
Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved. -
A Novel Fuzzy-Based Thresholding Approach for Blood Vessel Segmentation from Fundus Image
Retinal vessel segmentation is a vital part of pathological analysis in Fundus imaging. The automatic detection of blood vessels resolves several issues in the manual segmentation process. Most unsupervised segmentation methods depend on conventional thresholding techniques for final vessel extraction. It may lead to the loss of some vessel pixels, leading to inaccurate analysis of retinal diseases. In this work, we incorporate fuzzy concepts into two threshold-based vessel detection methods, namely mean-c thresholding and Iso-Data thresholding, which results in a mask consisting of membership values rather than binary values. The two fuzzy-based thresholding algorithms are applied independently on each image, and the resultant membership image (mask) is fused to get a single membership mask. The fusion is performed using fuzzy union operation. Experiments are carried out with Fundus images from DRIVE, STARE and CHASE_DB1 databases.ses. The proposed fusion framework gives a 3%, 6%, and 5% increase in sensitivity compared to traditional thresholding methods when applied to the DRIVE, STARE, and CHASE_DB1 databases, respectively. The accuracy obtained for the datasets is 96.02%, 94.57%, and 94.34%, respectively. 2023 by the authors. -
A pharmacognostic approach, including phytochemical and GC-MS analysis, targeted towards the authentication of Strobilanthes jomyi P. Biju, Josekutty, Rekha & J.R.I.Wood
The genera Strobilanthes Blume have a rich history in therapeutic culture all over the world. Asian countries like India, China, Myanmar and Thailand still use Strobilanthes genus-based medicinal preparations for various diseases. Strobilanthes jomyi is a newly discovered species from Kerala, India. Some tribal communities of Kasaragod district still use S. jomyi leaf extract as a wound healing medication. The current study aims to investigate the pharmacognostic, phytochemical and GC-MS analysis of the leaves, stems and roots of S. jomyi. The microscopic, macroscopic, organoleptic, fluorescent, phytochemicals and GC-MS analysis of the leaves, stem, and root of S. jomyi were estimated using various standard protocols. The macroscopic and microscopic characters of leaves revealed the presence of non-glandular trichomes with paracytic stomata in the leaves. The transverse section of the stem and petiole showed the presence of raphides and the root showed the presence of tannin cells. Cystoliths were observed only in the petiole. Powder morphology of leaves, stems and roots revealed the presence of fibers, trichomes, palisade cells, spiral xylem vessels, bordered pit vessels and raphides. The vegetative part of S. jomyi powder exhibited various fluorescent coloration based on numerous chemical treatments along with different tastes, smells, colors and textures by organoleptic assays. Qualitative phytochemical analysis of different vegetative parts revealed the presence of flavonoids and other phytochemicals. GC-MS study revealed that lupeol a significant bioactive compound was present in all the vegetative parts of S. jomyi. The results acquired from this study can be used for the standardization, identification, quality and purity check of plant samples. The Author(s). -
Green Bonds: A Propitious Financial Instrument of Climate Finance
Green bonds are a comparatively recent investment mechanism for green initiatives and are perceived as the first line of climate change protection. The aim of the article is to decide if the issuing of a green bond is perceived to be good news for market players, and also to ascertain whether developing markets, relative to established markets, are more inclined towards green bonds to tackle climate change. The study used an international sample of recent green bond issues and illustrated the possible effects of the issuing of a green bond for the issuer. A sample of 392 green bonds released from 2017 to 2020 is included. Event study methodology is used to analyse investor response to green bond issuance. Over the years, emerging markets have been found to be keen on greening projects by green bonds, much in line with established markets. The findings suggest that on the day of issuance of the green bonds the stock market responds adversely and reacts positively after the day. Hence statistical technique is applied on different event windows to obtain the cumulative abnormal returns (CAR). Statistical analysis concludes that the market responds adversely to the issuing of a green bond. This influence is particularly evident in the first issuing of green bonds and in developing markets. This research shows that proposals of green debt transmit unfavourable knowledge about the issuing companies. These results are relevant only in the case of green bonds issued by listed firms. 2023 MDI.