Browse Items (16488 total)
Sort by:
-
Decentralized financial assets: An attraction beyond stock market investments
The Chapter revolves around the attraction that the blockchain-backed decentralized financial products have created in the investment market. These new-gen decentralized finances would be Defi, Metaverse coins, Stablecoins, Cryptocurrency, smart contracts, and privacy coins. The influence of social media and high internet penetration levels have made the retail and partial investors very well aware of many alternate financial investment products. Though there is an increase seen in the Stock Market investments as well a drastic increase in the alternate market is seen. In recent times major stock markets have created indexes separately. It is the icing on the cake with the highest attention of the globe. The regulations have put the Defi Assets under unpredictable volatility but the attraction towards cryptocurrency and other digital asset gain still exists. The study aims to identify the relationship between the increase in investment in the stock market and the alternative. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Cultivating wellness: Synergy of emerging technologies in spiritual and wellness tourism
This academic analysis explores the dynamic landscape of wellness tourism from 1998 to September 2021, emphasizing the surge in scholarly publications post-2010, primarily contributed by the United States. Key findings identify major authors, significant papers, and evolving trends while acknowledging research limitations and offering recommendations. Additionally, the 2022 study highlights 10 pivotal elements enriching holistic health resorts, coupled with strategic marketing and DMO influence. A comprehensive review utilizing the PRISMA methodology reveals two significant research clusters, focusing on tourist motivations and behavior, anticipating post-pandemic research and the need for diverse data sources. Wellness tourism embodies dynamism and diversity, driven by an ever-growing yearning for well-being, relaxation, and self-care. Travelers can choose from a broad spectrum of dimensions and destinations to personalize their wellness journeys according to their unique aspirations and goals, making it an exceptionally adaptable and customized form of travel. The industry's trajectory is set for continued expansion and transformation as more individuals acknowledge the paramount importance of nurturing their holistic well-being. However, a notable research gap is identified: the incorporation of cutting-edge technologies like wearable technology, virtual reality, and artificial intelligence in wellness tourism remains underexplored. This study addresses this void by investigating the utilization of technology to craft personalized and immersive wellness experiences, its impact on traveler preferences and behavior, and its role in destination management and marketing strategies. Recognizing technology's pivotal role in wellness tourism, this study seeks to bridge the gap between traditional wellness practices and contemporary technological advancements, fostering a holistic approach to well-being in the modern era. 2025 Anand Patil, M.S. Prathibha Raj, R. Gowri Shankar and R.B. Lakshmi. All rights reserved. -
Impact of ESG Factors on the Financial Performance of Corporates in India
This study investigates the connection between Indian companies' financial results and Environmental, Social, and Governance (ESG) performance, with a focus on socially and environmentally sensitive industries. There is little data on the financial effects of ESG integration in developing nations like India, even though it is becoming more and more crucial for long- term value development. Using a quantitative approach involving ESG ratings and financial indicators (Return on Equity [ROE], Return on Assets [ROA], and Return on Capital Employed [ROCE]) we analyze the performance of firms from 2021 to 2023 in five different sectors. Among the pillars, environmental issues had the most significant effect on material financial success, showing a positive but weak correlation with ESG scores. However, the study also investigates into sectoral distinctions, pointing to differential financial outcomes associated with ESG across industries. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Impact of green management on firm performance in the purview of AI and data analytics: A comprehensive review
Sustainability has evolved from being a goal to a fundamental practice in the business world. Organizational survival depends on its adaptability, and innovative practices are a dire necessity for businesses to stay afloat. From this perspective, data analytics and artificial intelligence highly influence business decisions. They are elemental, have a substantial impact, and hence need to be absorbed into each aspect of organizational management. Over the years, businesses embracing green management principles have witnessed a significant impact on overall organizational performance. This study aims to provide a broad-spectrum analysis of this discipline using bibliometric analysis conducted on publications in the Scopus database over the last three decades using VOSviewer software. Multiple aspects of the discipline are tested to provide comprehensive results. The subject, citations, contributions of authors, countries, and institutions, as well as the active sources of publication, are some of the disclosures in the current study. These aspects collectively reveal the positive relationship between the adaptation of green management and firm performance through the lens of data analytics and AI. It is necessary to understand the depth of previous and ongoing advancements to propose new postulates and ideologies, which is met in this chapter. 2026 Anand Patil, Swathi Shekar. All rights reserved. -
Utilizing Deep Learning Features to Categorize WBCs in Blood Smear Images
Automated categorization of white blood cells (WBCs) is essential not just to identify infections, autoimmune ailments, and blood-related disorders, but also in the pivotal decision-making process concerning patient treatment and the efficient management of diseases. In this paper, an advanced approach for WBC type classification using smear images is proposed. The VGG16 model is utilized to capture intricate features of the images, which are then provided to an XGBoost classifier. This integration enables precise classification into 5 distinct WBC types. Our model shows a significant accuracy score of 92.3%, demonstrating its capability in accurately identifying WBC types from smear images. Proposed technique provides a promising pathway for automating WBC classification, thereby enhancing efficiency in disease diagnosis and decision-making within clinical settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel Approach for Sensitive Crop Disease Prediction Based on Computer Vision Techniques
Agriculture is a vital sector that plays an essential role in ensuring global food security, supporting economic development, and promoting environmental sustainability. Sustainable agriculture is an essential approach that aims to address the diffculties posed by conventional farming practices and ensure the long-term viability of our food production systems. Worldwide, crop leaf diseases seriously threaten food security and agricultural production. Early and accurate detection of crop leaf diseases is essential for effective crop productivity management and food prevention. Computer vision approaches offer promising solutions for automating the identifcation and prediction of crop leaf diseases. Analyzing digital images of plant leaves enables the identifcation of disease characteristics, such as discoloration, lesions, and patterns, which are often imperceptible to the naked eye. Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNN), have been widely employed in this domain to learn from large datasets of annotated images and accurately classify leaf diseases. The process of crop leaf disease classifcation using computer vision involves several stages. Initially, highresolution images of plant leaves are acquired using cameras or mobile devices. Preprocessing techniques, including image enhancement and noise reduction, are applied to improve image quality. Subsequently, feature extraction approaches extract pertinent data from the images, including texture, shape, and color. Deep Learning (DL) models are then trained and fne-tuned using these extracted features. newlineAlthough computer vision techniques have shown effective results in the classifcation of plant diseases, however, several challenges remain. Tomatoes and Potatoes newlineare widely cultivated and consumed vegetables worldwide and are a primary economic newlinesource for many countries. These sensitive plants are prone to various diseases during newlinegrowth, leading to signifcant losses in productivity and fnancial impact on farmers. -
GraCoD: a disruptive graph-aware drift detection algorithm with a GCN-based time-varying module for concept drift detection in short text streams
Detection of concept drift in time-varying short text streams has numerous challenges since the data are volatile. According to research, 30% to 40% of the traditional drift detection methods are not able to detect change of the concept in the text stream and, therefore, produce high false positives and slow response time. To address the above issues, the proposed Graph based Concept Drift Detection (GraCoD) method suggests a novel concept drift detection (CoD) framework. GraCoD uses ConvBERT with Hopfield layers and temporal convolution to capture linguistic context and temporal dependencies. The model constructs a graph representation of text data using a text GCN with Time Varying Spatio Temporal-Graph Attention Module (TVST-GAT) and uses the Graph Aware Drift Detection Algorithm (GADD) to classify the change in the graph metrics such as node centrality and edge density. The approach is more helpful and effective than the traditional approaches of detecting the occurrence of drift. To react to the detected drifts proactively, Deep Reinforcement Learning (DRL) is merged with Deep Q-Learning to automatically adapt parameters and behaviors based on the outcomes of detected drifts. The severity and classification modules detect the severity and classify the detected drifts for further investigation. The proposed model demonstrates exceptional performance in CoD across five diverse datasets: Twitter datasets 1 and 2, Enron, News 20, and Amazon Reviews. It achieves high accuracy (98.7%-99.5%) and F1-scores (96%-98%), with low false positive (0.020.04) and false negative (0.010.03) rates. The model effectively identifies 2329 drifts, with drift indicators ranging from 81.3% to 86.6%, showcasing its robustness in handling dynamic data streams across various domains. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
GraphDrift-net: a dynamic graph-based framework for concept drift detection in short unstructured text streams
Detecting concept drift in text streams is challenging due to the rapid evolution of language, shifting user behavior, and temporal dependencies. Issues like data sparsity, high dimensionality, lack of labeled data, and multimodal drift further complicate real-time detection and adaptation. This paper proposes GraphDrift-net, a novel dynamic graph-based framework for detecting and adapting to concept drift in evolving text streams. The model comprise of the following components: evolving Time BERT (EvoTimeBERT), which captures temporal language evolution via historical token memory and multi-scale temporal convolutions, hierarchical temporal graph network with dynamic topics and adaptive memory (HTGN-DTAM), a heterogeneous graph neural network that dynamically constructs topic-aware graphs to track changing semantics and Chronograph Detection, a time-series-based drift detection method leveraging graph statistics such as node centrality and clustering coefficient changes. In addition, graph neural reinforcement learning framework (GNRL), a reinforcement learning-based adaptive learning module, enables model adaptability by word embedding update, memory decay rate tuning, and few-shot adaptation. Experimental evaluations over various real-world datasets, including Twitter-1, Twitter-2, Enron, and News20, demonstrate that GraphDrift-net outperforms other methods in accuracy, F1-score, and drift detection sensitivity. The model achieves accuracy as high as 99.7%, is able to identify more drift points, and is more stable with computational efficiency, making it extremely appropriate for real-time text stream applications. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Advanced Machine Learning Framework for Precision Rainfall Prediction for Jharkhand, India
Jharkhand, characterized by a substantial agricultural population predominantly reliant on rain-fed agriculture, faces significant challenges due to the erratic nature of precipitation. The study uses meteorological variables and historical rainfall data from the Jharkhand Space Agency Centre (JSAC) to predict rainfall with precision and resilience. Three supervised machine learning algorithms, Random Forest, K-Nearest Neighbour (KNN), and Ridge Regression, are employed to evaluate their performance across monsoon and non-monsoon periods. A novel algorithm is proposed for Jharkhand, offering better modularity and accuracy to predict the Rain Index. The results show the efficacy of these algorithms in capturing the temporal variability of rainfall in Jharkhand. The ensemble modeling model obtained an MSE score of 0.457, providing valuable insights into the viability and competence of machine learning algorithms for rainfall estimation. This research offers a valuable scope for policymakers, researchers, and stakeholders to formulate sustainable strategies to address climate variability and its impact on rain-fed agriculture. The study contributes significantly to meteorological research and offers valuable insights for policymakers, researchers, and stakeholders. 2025 IEEE. -
Enhancing Experiences: The Integration of AI in Augmented and Virtual Reality
This chapter examines the integration of Artificial Intelligence (AI) in Augmented Reality (AR) and Virtual Reality (VR), highlighting how AI-driven innovations enhance interactivity, personalization, and real-t ime adaptation in immersive experiences. Through technologies like computer vision, machine learning, and natural language processing, AI enables AR and VR applications to better understand, respond to, and anticipate user needs. Applications of AI-augmented AR and VR are explored across various sectors, including healthcare, education, and industry, where AI-driven systems offer personalized training, virtual assistance, and adaptive simulations. As Mixed Reality (MR) evolves, edge computing plays a key role in improving performance, minimizing latency, and enabling seamless real-time interactions. The chapter also addresses ethical and technical challenges, such as privacy, bias, and processing limitations, emphasizing AIs transformative role in the future of immersive technologies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Customer Segmentation and Future Purchase Prediction using RFM measures
Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories. 2022 IEEE. -
PA1 cells containing a truncated DNA polymerase ? protein are more sensitive to gamma radiation
Purpose: DNA polymerase ? (Pol?) acts in the base excision repair (BER) pathway. Mutations in DNA polymerase ? (Pol?) are associated with different cancers. A variant of Pol? with a 97 amino acid de-letion (Pol??), in heterozygous conditions with wild-type Pol?, was identified in sporadic ovarian tumor samples. This study aims to evaluate the gamma radiation sensitivity of Pol?? for possible target therapy in ovarian cancer treatment. Materials and Methods: Pol?? cDNA was cloned in a GFP vector and transfected in PA1 cells. Stable cells (PA1Pol??) were treated with60Co sourced gamma-ray (015 Gy) to investigate their radiation sensitivity. The affinity of Pol?? with DNA evaluated by DNA protein in silico docking experiments. Results: The result showed a statistically significant (p < 0.05) higher sensitivity towards radiation at different doses (015 Gy) and time-point (4872 hours) for PA1Pol?? cells in comparison with nor-mal PA1 cells. Ten Gy of gamma radiation was found to be the optimal dose. Significantly more PA-1Pol?? cells were killed at this dose than PA1 cells after 48 hours of treatment via an apoptotic pathway. The in silico docking experiments revealed that Pol?? has more substantial binding potential towards the dsDNA than wild-type Pol?, suggesting a possible failure of BER pathway that results in cell death. Conclusion: Our study showed that the PA1Pol?? cells were more susceptible than PA1 cells to gamma radiation. In the future, the potentiality of ionizing radiation to treat this type of cancer will be checked in animal models. 2022 The Korean Society for Radiation Oncology. -
Kibble-Zurek scaling and spatial statistics in quenched binary Bose superfluids
The emergence of order from an initially uncorrelated state across a phase transition is a central problem in quantum many-body physics, particularly in multicomponent systems where interactions between components lead to rich nonequilibrium dynamics. While defect formation is known to follow universal scaling laws, prior studies have focused mainly on defect density, leaving their spatial organization largely unexplored. Here we show that gradually tuning the chemical potential in a two-dimensional binary Bose gas drives condensation into either a miscible or immiscible phase. In the immiscible regime, domains form whose number, size, and boundary length obey Kibble-Zurek (KZ) scaling and evolve self-similarly. In the miscible regime, vortices emerge with KZ scaling. In both cases, the spatial distribution of vortices and domains is well described by a Poisson point process with KZ-determined density. These results reveal universal features of far-from-equilibrium dynamics and provide a framework to characterize stochastic geometry in multicomponent quantum systems. The Author(s) 2026. -
Moderating influence of critical psychological states on work engagement and personal outcomes in the telecom sector
Organizations want their employees to be engaged with their work, exhibiting proactive behavior, initiative, and responsibility for personal development. Existing literature has a dearth of studies that evaluate all the three key variables that lead to optimal employee performancecritical psychological states (CPSs), work engagement, and personal outcomes. The present study attempts to fill that gap by linking the variable CPSs (which measures experienced meaningfulness, responsibility, and knowledge of results) with the other two. The study surveyed 359 sales personnel in the Indian telecom industry and adopted standardized, valid, and reliable instruments to measure their work engagement, CPSs, and personal outcomes. Analysis was done using structural equation modeling (SEM). Findings indicated that CPSs significantly moderate the relationship between personal outcomes and work engagement. The Author(s) 2014. -
Development and Validation of Work Environment Services Scale (WESS)
Purpose: This study presents a nine-factor, 32-item measure of work environment scale in the service sector. A healthy work environment is one in which employees trust the people they work for, have pride in what they do, and enjoy working with the people (Levering and Moskowitz, 2004). Methodology: This instrument builds on the conceptual model espoused by Insel and Moos (1974), Gordon (1973), Fletcher and Nusbaum (2010), Amabile et al. (1996), and Spector (2003). The scale included items elicited through a literature review, the use of the Delphi technique with a panel of experts, and tested on 824 full-time employees from nine service sector industries and five major cities in India. Findings: The Work Environment Services Scale (WESS) is a reliable and valid scale useful for measuring the nine work environment factors in the Indian services organization, with its own norms and a detailed manual. Originality/Value: The prevailing scales for measuring work environment do not capture the influence of ethics, recreation facilities, and the impact of social giving on the work environment. Most scales were suitable for sectors in the Western context, and there were no Indian scales measuring service employees' perception of their work environment. 2021 Harold Andrew Patrick et al., published by Sciendo 2021. -
Managing workplace diversity: Issues and challenges
Diversity management is a process intended to create and maintain a positive work environment where the similarities and differences of individuals are valued. The literature on diversity management has mostly emphasized on organization culture; its impact on diversity openness; human resource management practices; institutional environments and organizational contexts to diversity-related pressures, expectations, requirements, and incentives; perceived practices and organizational outcomes related to managing employee diversity; and several other issues. The current study examines the potential barriers to workplace diversity and suggests strategies to enhance workplace diversity and inclusiveness. It is based on a survey of 300 IT employees. The study concludes that successfully managing diversity can lead to more committed, better satisfied, better performing employees and potentially better financial performance for an organization. The Author(s) 2012. -
Intention to Stay as a Moderator on Employee Job Satisfaction and Organizational Citizenship Behavior
International Journal of Management Studies, Statistics & Applied Economics, Vol-2 (2), pp. 65-74. ISSN-2250-0367 -
Commitment of Information Technology Employees in Relation to Perceived Organizational Justice
The IUP Journal of Organizational Behaviour Vol. XI, No. 3. pp 23-40, ISSN No. 0972-687X -
Socialization tactics and new entrants adjustments in the information technology context /
PES Business Review, Vol. 8, Issue 1, pp.19-28 ISSN No. 0973-919X -
Expression of dissatisfaction in relation to managerial leadership strategies and its impact in Iinformation technology organizations /
Skyline Business Journal, Vol.8, Issue 1, pp.29-35, ISSN: 1998-3425.