Browse Items (16481 total)
Sort by:
-
AI-driven service marketing in accessible tourism: Digitizing hampi for all
This book chapter, which focuses on the historic site of Hampi, India, investigates how artificial intelligence (AI) might be included into service marketing to improve accessible tourism. Hampi presents special accessibility issues because it is a UNESCO World Heritage Site, especially for those with impairments. In addition to introducing AI-driven technologies like machine learning, natural language processing, and augmented reality and discussing how they might be used to enhance tourist experiences at Hampi, the chapter also examines current trends and difficulties in accessible tourism. A focus on personalized service delivery, increased tourist engagement, and inclusivity, case studies and examples from international practices demonstrate effective AI implementations in accessible tourism. In the end, this chapter offers perspectives and suggestions for utilizing AI-driven service marketing to promote universal accessibility and digitize Hampi, improving visitors' encounters with the local cultural heritage. 2025, IGI Global Scientific Publishing. -
Price Minds: AI-Driven Insights, Recommendations and Dynamic Pricing
This research aims to enhance e-commerce systems by leveraging customer behavior analysis, dynamic pricing, and personalized recommendations. With the increasing demand for tailored shopping experiences and competitive pricing, businesses require adaptive solutions. The study integrates synthetic and real-time customer data to identify purchasing patterns and segment customers effectively. Dynamic pricing strategies are applied to optimize revenue while maintaining customer satisfaction. A unified framework combines clustering techniques, real-time data streams, and decision-making models to deliver actionable insights for business operations. The proposed system dynamically adjusts pricing and recommends products based on individual customer preferences and behavior. The approach addresses the growing need for intelligent systems that adapt to market trends and consumer demands. Results demonstrate improved operational efficiency, better customer engagement, and enhanced profitability. This work highlights the importance of real-time analytics and intelligent pricing mechanisms in advancing e-commerce and creating competitive advantages in rapidly evolving markets. 2025 IEEE. -
Structural investigation of higher order members of bismuth system superconductors
Structural formation of higher order Bismuth superconducting compounds Bi1.65Pb0.35Sr2Ca4Cu5Oy (2245) phase and Bi1.65Pb0.35Sr2Ca8Cu9Oy(2289) phase were investigated. The samples were synthesized by solid state reaction technique. Morphological and micro-structural features of the synthesized samples were analysed by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDAX). The XRD of both the samples revealed the presence of Bismuth - (2212)& (2223) phases of which (2223) phase was found to be the predominant. The average grain size was found to be around 65 nm. Traces of Ca2PbO4 was also noticed when the samples were synthesised at 835 C. Superconducting transition temperature (TC) of the samples measured by self-inductance method, showed a two-step transition, one around 110 K and the other around 90 Kindicating the presence of (2223)&(2212) phases respectively. But the TC value observed for (2212) phase is about 10 K more than the expected value of 80 K. There was no signature of the formation of (2245) or (2289) phases in this synthesis. -
Investigation of structural formation of starting composition 2245 in the Bi-Pb-Sr-Ca-Cu-O system superconductors /
Journal of Solid State Physics, Vol.2014, pp.127-133, ISSN No: 2356-7643 (Print), 2314-6842 (Online). -
Harary Spectra and Energy of Certain Classes of Graphs
Aims: To investigate the H-eigenvalues and H-energy of various types of graphs, including k-fold graphs, strong k-fold graphs, and extended bipartite double graphs and establish relationships between the H-energy of k-fold and strong k-fold graphs and the H-energy of the original graph G, we explore the connection between the H-energy of extended bipartite double graphs and their ordinary energy and find the graphs that share equienergetic properties with respect to both the ordinary and Harary matrices. Background: The H-eigenvalues of a graph G are the eigenvalues of its Harary matrix H(G). The H-energy {Formula Presented} of a graph, G is the sum of the absolute values of its H-eigenvalues. Two connected graphs are said to be H-equienergetic if they have equal H-energies. They are said to A-equienergetic if they have equal A-energies. Adjacency and Harary matrices have applications in chemistry, such as finding total electron energy, quantitative structure-property relationship (QSPR), etc. Objectives: We determined the H-spectra of k-fold graphs, strong k-fold graphs and extended bipartite double graphs and established connections between the H-energy of different types of graphs and their original graph G for investigating the relationship between the H-energy of extended bipartite double graphs and their ordinary energy and the graphs that share equienergetic properties with respect to both the adjacency and Harary matrices. Methods: Spectral algebraic techniques are used to calculate the H-eigenvalues and H-energy for each type of graph and compare the H-energies of different graphs to identify the equienergetic properties and derive relationships between the H-energy of extended double cover graphs and their ordinary energy. Results: We determined the H-spectra of k-fold graphs, strong k-fold graphs and extended bipartite double graphs and established relationships between the H-energy of k-fold and strong k-fold graphs and the H-energy of the original graph G. Then, we explored the connection between the H-energy of extended bipartite double graphs and their ordinary energy and presented graphs demonstrating equienergetic properties concerning both adjacency and Harary matrices. Conclusion: The study provides insights into the H-eigenvalues, H-energy and equienergetic properties of various types of graphs. The established relationships and connections contribute to a deeper understanding of graph spectra and energy properties and the findings enhance the theoretical framework for analyzing equienergetic graphs and their spectral properties. Scope: Possible extensions of this research could include investigating additional types of graphs and exploring further explicit connections between different graph energies and spectral properties. Harary matrices are distance-based matrices, which can model distances between atoms in molecular structures and could be useful in organic synthesis to predict how molecular structures behave. 2025, Bentham Science Publishers -
Study of substituion effectson structure and properties of high temperature superconductors and isostructure compounds
The thesis mainly describes the investigation of the structural formation of higher order members of bismuth system of superconductors Bi1.6sPb0.35Sr2CazCu4Oy (n = 4, 2234 phase), Bi1.6sPb0.35Sr2Ca4CusOy (n = 5, 2245 phase) and Bi1.65Pb0.35Sr2CasCu,Oy (n = 9, 2289 phase). The samples were synthesized by solid state reaction technique. Micro-structural and morphological features of the synthesized samples were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDS). The XRD pattern of all the samples revealed the presence of Bismuth 2212 and 2223 phases of which 2223 phase was found to be the predominant. Superconducting transition temperature (Tc) of the samples measured by self-inductance method and dc four probe method showed Tc value around 110 K. There was no signature of the formation of 2234, 2245 or 2289 phases in this investigation. newlineFormation of Bi-2245 compound was further investigated by preparing the sample in a new matrix route. The Tc on set of this sample was found to be 127 K which was the highest reported ever in bismuth system superconductors. The complete replacement of copper by nickel in bismuth system superconductor Bi2SraCu06 (2201) was ttempted by preparing the sample in air by solid state reaction method under open and closed environment. Morphological and microstructural features of the synthesized sample Bi2Sr2Ni0g was investigated by X-ray diffraction, SEM and EDAX. The analysis of X-ray diffraction pattern revealed that nickel can replace copper completely and form a single phase Bi2Sr2Ni06 only when prepared in a closed environment in air. This phase formation of BizSr2NiOo was reported first time. -
-
The asymmetric relationship between foreign direct investment, oil prices and carbon emissions: evidence from Gulf Cooperative Council economies
We investigate the asymmetric nonlinear link between foreign direct investment, oil prices, and CO2 emissions for the Gulf Cooperation Council nations, using foreign direct investment and oil price data. As foreign direct investment is positively associated with carbon emissions in the long run and oil prices have positive, significant effects on CO2 emissions, our findings support the pollution-haven hypothesis. Furthermore, these variables have an asymmetric nonlinear relationship, which corresponds to the theoretical expectations of the pollution-haven hypothesis. We also find that negative changes in foreign direct investment have positive, significant impacts on carbon emissions in the short run, implying that foreign enterprises utilize green technologies in their manufacturing processes in the short run. In the long run, however, negative changes in oil prices are positively associated with carbon emissions. These findings should help Gulf Cooperation Council economies focus on policies that encourage foreign direct investment in green rather than dirty industries in order to ensure environmental sustainability. 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence
The emergence of Web 3.0 has left very few tag recommendation structures compliant with its complex structure. There is a critical need for newer novel methods with improved accuracy and reduced complexity for tag recommendation, which complies with the Web 3.0 standard. In this paper, we propose KMetaTagger, a knowledge-centric metadata-driven hybrid tag recommendation framework. We consider the CISI dataset as the input, from which we identify the most informative terms by applying the Term Frequency - Inverse Document Frequency (TF-IDF) model. Topic modeling is done by Latent Semantic Indexing (LSI). A heterogeneous information network is formalized. Apart from this, the Metadata generation quantifies the exponential aggregation of real-world knowledge and is classified using Gated recurrent units(GRU). The Color Harmony algorithm filters out the initial feasible solutions into optimal solutions. This advanced solution set is finalized into the tag space. These tags are recommended along with the document keywords. When the suggested KMetaTagger's performance is compared to that of baseline techniques and models, it is found to be far superior. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Gender diversity and organizational performance: A study of IT industries in Bangalore
Humans are considered to be different from each other with their acumen and intelligence. The working condition in IT sector has changed over the past few decades. There has been a drastic increase in the number of female employees towards the development of IT sector over the past few years. Gender diversity is creating a wide range of awareness and helps understand the importance of gender identity. IT industry which is a dominant industry in India has reckoned gender diversity as a major tool to ensure it stands on criteria of being competent and innovative in the ever changing dynamic business environment. The main objective of this paper is to analyze the relationship between acceptance of gender diversity among the employees, diversity practices and programs adopted by the IT industries and barriers to the same. Limited Liability Company Consulting Publishing Company Business Perspectives, 2017. -
A comprehensive record of fishes and crustaceans in a poorly known tropical estuary-Kavvayi from the west coast of India
A comprehensive study conducted from 2019 to 2021 in the Kavvayi estuarine wetland along Indias southwest coast documented its fish and crustacean diversity, providing valuable insights for conservation. Monthly surveys across 19 sampling stations recorded 151 species, including 79 demersal fish, 55 pelagic fish, and 17 crustaceans from 63 families. According to IUCN criteria, 98 species are classified as Least concern, 32 as Not evaluated, 14 as 'Data Deficient',four as Vulnerable, two as Near threatened, and one as Critically endangered. Marine migrant species dominate the estuary, while freshwater species are rare. The Eupercaria order contributes significantly to finfish diversity, representing 12.58%. Families such as Carangidae (14 species), Portunidae (8), and Clupeidae (7) exhibit notable species richness. Prominent species like Ambassis gymnocephalus, Mugil cephalus, Planiliza macrolepisStolephorus indicus, Etroplus suratensis, Pseudetroplus maculatus, Sillago sihama, Caranx ignobilis, and Gerres filamentosus are consistently present throughout the year, highlighting the estuarys reliability as a habitat. This dataset not only offers a crucial inventory of Kavvayis biodiversity but also emphasizes its conservation potential. The scarcity of information on the fish and crustacean diversity underscores the importance of the dataset provided in this paper, as it will significantly contribute to the assessment for designating Kavvayi estuary as a wetland of international importance. This dataset enhances local, regional, and global fish community data for estuarine fisheries. It also addresses the challenges faced by the fishing community, emphasizing the need for conservation strategies to ensure the long-term health of the estuarine ecosystem. The Author(s), under exclusive licence to Senckenberg Gesellschaft f Naturforschung 2025. -
Biosynthesized carbon quantum dots/g-C3N4/Co3O4 composites for effective methylene blue dye degradation and DFT study
In this study, we aimed to develop a new, efficient photocatalyst, graphitic carbon nitride/carbon quantum dots/cobalt oxide (g-C3N4/CQDs/Co3O4 (CCC)), via a hydrothermal route. The composite was synthesized through a simple hydrothermal method, with the Co3O4 nanoparticles (NPs) systematically varied to 3, 5, and 10 %. The resulting samples are comprehensively characterized using various techniques, including X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), BrunauerEmmettTeller (BET) surface area analysis, vibrating sample magnetometry (VSM), thermogravimetric analysis (TGA), and ultraviolet-visible (UVVis) spectroscopy. Photocatalytic activity was evaluated using methylene blue (MB) dye under UV light. Among the prepared samples, the 3 % Co3O4 NPs loaded CCC catalyst has shown superior photocatalytic efficiency of 94.5 % within 120 min, which is higher than that of the 5 and 10 % Co3O4 NPs loaded CCC composite and better than that of the pristine materials. The results are obtained for optimized conditions at a concentration of 5 ppm, 0.05 g and pH 10. The 3 % CCC composite has exhibited excellent reusability and stability upto five cycles. Furthermore, Density Functional Theory (DFT) was used to understand the crystal structure and electronic properties of the prepared composite. The results have demonstrate that the novel CCC composite is a promising catalyst for the degradation of MB dye in aqueous solutions and environmental remediation. 2025 Elsevier B.V. -
Facile synthesis of novel ternary g-C?N?/MnO?/CQDs nanocomposite for efficient photocatalytic degradation of methylene blue and DFT study
A novel type II staggered heterojunction photocatalyst, g-C?N?/MnO?/CQDs (CCM nanocomposite), has prepared through a facile hydrothermal route using MnO? nanoparticles (NPs) varying between 3 % and 5 %. The 5 % CCM nanocomposite has exhibited superior photocatalytic performance, achieving a maximum degradation efficiency of 98.38 % for methylene blue (MB) under UV light irradiation within 120 min. This performance significantly surpassed those of pristine g-C?N? and MnO?. Kinetic analysis has demonstrated a rate constant of 2.96 10? min? and a half-life of 23.42 min under optimal conditions. The degradation efficiency gets increased from 93.15 % to 98.38 % with the increase in pH from 3 to 7, whereas higher dye concentrations (10 and 20 ppm) has resulted with the decreasing efficiencies of 87.1 % and 73.9 %, respectively. BrunauerEmmettTeller (BET) analysis has confirmed the mesoporous nature of the 5 % CCM nanocomposite, with a specific surface area of 5.27 m g?, an average pore size of 11.52 nm, and a pore volume of 0.03 cm g?. The bandgap energies are determined to be notably reduced to 2.7 eV for 5 % CCM nanocomposite. Thermogravimetric analysis (TGA) has shown the excellent thermal stability of the 5 % CCM nanocomposite up to 750 C. Vibration Sample Magnometer (VSM) analysis has specified the weak ferromagnetic behaviour. Density functional theory (DFT) calculations were performed to evaluate the electronic structure and charge-transfer mechanism underlying the improved performance. Importantly, reusability tests over five consecutive cycles showed that the 5 % CCM nanocomposite has retained 80 % of its initial photocatalytic activity, demonstrating excellent catalyst stability and potential for practical applications. 2025 Elsevier B.V. -
Trajectories forSpace Missions: Bridging Tradition andInnovation
Spacecraft trajectory optimization has always been a determining factor in successful space missions as it should be precise and efficient in automatically exploiting new opportunities present in the complex and dynamic environment. Traditional optimization algorithms cannot meet the increasing demand for fast computation, adaptation ability, or overcoming real-time constraints. A recently developed technique called reinforcement learning is quite promising in dealing with such issues by proposing innovative solutions for trajectory optimization. This paper surveys cutting-edge reinforcement learning solutions for optimizing spacecraft trajectory problems. Comprehensive and pragmatic analysis based on different aspects of currently available solutions, and concise reports are generated to get the latest update on this field, as well as provide reference on designing future-related solutions. The survey suggests that more efforts from the research field should be spent on reinforcement learning solutions especially when applied in the real mission scenario because there are still many challenges unattended by the community that were pointed out before being delivered at the end-user level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management
Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. The annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately. 2023 IEEE. -
Classification of Disaster Tweets using Machine Learning and Deep Learning Techniques
Social networks provide a plethora of information for gathering extra data on people's behavior, trends, opinions, and feelings during human-affecting occurrences, such as natural catastrophes. Twitter is an inevitable communication medium during calamities. People mainly depend on Twitter to announce real-time emergencies. However, it is rarely straightforward if someone is declaring a tragedy. Sentiment analysis of disaster tweets aid in situational awareness and realizing the disaster dynamics. In our paper, we perform a sentimental analysis of disaster tweets using techniques based on machine learning and deep learning. The tweets are pre-processed before being converted into a structured form using Natural Language Processing (NLP) methods. Supervised learning techniques such as the Support Vector Machine and the Naive Bayes Classifier algorithm are used to develop the Classifier, which categorizes tweets into distinct catastrophes and selects the most appropriate algorithm. The chosen algorithm is further enriched with an emoticon detection algorithm for explicit elucidation. Our research would help disaster relief organizations and news agencies to conclude about the state of affairs and do the needful. 2022 IEEE. -
Characterization of the Forgotten Topological Index and the HyperZagreb Index for the Unicyclic Graphs
Let G be a molecular graph with V (G) and E(G) be the vertex set and edge set, respectively. Various investigations show that many degree and distance based topological indices are used to exhibit strong intrinsic connection between the molec- ular structures and the physico-chemical properties of the chemical compounds. In this paper, we focus on two degree-based topological indices, namely, the forgotten topological index and the hyper-Zagreb Index expressed as F(G) = P u2V (G) d(u)3 and HM(G) = P uv2E(G)(d(u) + d(v))2, respectively, where d(u) and d(v) are the degrees of the vertices u and v, respectively, in the graph G. We show that the unicyclic graphs can take any even positive integer except 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 26, 28, 30, 34, 36, 38, 42, 46, 50, 50, 54, 58, 62, 66, 70, 74, 78, 86 and 94 for the forgotten index. A comparable result for the hyper-Zagreb index is also presented. 2020 University of Kragujevac, Faculty of Science. All rights reserved. -
QSPR analysis of certain degree and eccentricity based topological indices and butane derivatives
Butane derivatives are chemical compounds formally derived from Butane C4H10 by replacement of one or more hydrogen atoms with other atoms or functional groups. In this paper, we do the QSPR analysis of few Butane derivatives with respect to some selected degree based topological indices and one eccentricity based topological index. In QSPR studies, topological indices are extensively used in determining specific bioactivity of chemical compounds. Our study showcases some important results on the correlation between Heavy atomic count, Complexity, Density, Surface tension and Index of refraction of Butane derivatives with the selected topological indices which further helps in characterizing the predicting power of these topological indices. RAS?YAN. All rights reserved. -
The QSPR study of butane derivatives: (A mathematical approach)
The QSPR analysis provides a significant structural insight into the physiochemical properties of butane derivatives. We study some physiochemical properties of fourteen butane derivatives and develop a QSPR model using four topological indices and butane derivatives. Here we analyze how closely the topological indices are related to the physiochemical properties of butane derivatives. For this we compute analytically the topological indices of butane derivatives and plot the graphs between each of these topological indices to the properties of butane derivatives using Origin. This QSPR model exhibits a close correlation between Heavy atomic count, Complexity, Hydrogen bond acceptor count, and Surface tension of butane derivatives with the Redefined first Zagreb index, the Redefined third Zagreb index, the Sum connectivity index and the Reformulated first Zagreb index, respectively. 2018 Oriental Scientific Publishing Company. All Rights Reserved.