Search results

This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.

At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.

The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.

This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

There are many library automation packages available as open‐source software, comprising two modules: staff‐client module and online public access catalogue (OPAC). Although the OPAC of these library automation packages provides advanced features of searching and retrieval of bibliographic records, none of them facilitate full‐text searching. Most of the available open‐source digital library software facilitates indexing and searching of full‐text documents in different formats. This paper makes an effort to enable full‐text search features in the widely used open‐source library automation package Koha, by integrating it with two open‐source digital library software packages, Greenstone Digital Library Software (GSDL) and Fedora Generic Search Service (FGSS), independently.

The implementation is done by making use of the Search and Retrieval by URL (SRU) feature available in Koha, GSDL and FGSS. The full‐text documents are indexed both in Koha and GSDL and FGSS.

Full‐text searching capability in Koha is achieved by integrating either GSDL or FGSS into Koha and by passing an SRU request to GSDL or FGSS from Koha. The full‐text documents are indexed both in the library automation package (Koha) and digital library software (GSDL, FGSS)

This is the first implementation enabling the full‐text search feature in a library automation software by integrating it into digital library software.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

The purpose of this paper is to investigate the void coalescence and corrosion behaviour of titanium Grade 4 sheets during single point incremental forming (SPIF) process with various spindle rotational speeds. The development of corrosion pits in 3.5 (%) NaCl solution has also been studied during SPIF process.

In this current research work, the void coalescence analysis and corrosion behaviour of titanium Grade 4 specimens were studied. A potentio-dynamic polarization (PDP) study was conducted to investigate the corrosion behaviour of titanium Grade 4 processed samples with various spindle speeds in 3.5 (%) NaCl solution. The scanning electron microscope and transmission electron microscope analysis was carried out to study the fracture behaviour and corrosion morphology of processed samples.

The titanium Grade 4 sheets obtained better formability and corrosion resistance by increasing the CNC spindle rotational speeds. In fact that, the significant plastic deformation affects the corrosion rate with various spindle speeds were recorded.

The spindle rotational speeds and vertical step depths increases then the titanium Grade 4 sheets showed better formability, void coalescence and corrosion behaviour as the same is evidenced in forming limit diagram and PDP curves.

The purpose of this study is to determine the influence of technology visibility and subsequent perceptions of VFRs on consumers' intention to adopt VFRs in the online shopping context. A cross-cultural comparison was conducted to examine the different relationships among technology visibility, consumer perceptions and adoption intentions between the Chinese and Korean consumers.

Data were collected from 306 Chinese and 324 Korean consumers. The data were empirically analysed using structural equation modelling as well as multi-group comparisons.

Empirical results suggest significant influence of technology visibility on consumers' experiential and functional perceptions towards VFRs and accordingly on their adoption intention towards VFRs. Significant differences were also revealed between the Chinese and Korean consumers in their adoption behaviours towards VFRs.

The comparison was only conducted between the Chinese and Korean consumers. If two countries from two dramatically different cultures were compared, the results might be more significant.

An important implication is that enhancement of visibility is crucial for technology adoption considering its importance in shaping consumers' perceptions towards the technology.

The paper empirically tested the importance of technology visibility in consumers' new technology adoption in the VFR context from a cross-cultural perspective.

The purpose of this study is to investigate the use and usability of e‐books from the perspectives of users in an academic and research environment.

This study involved an e‐mail questionnaire to survey researchers in the academic and research environment of the Indian Institute of Science regarding their use of e‐books.

The responses indicated that the students tend to use this new technology more often than faculty members and staff. Those who did use e‐books mostly used reference and technical material. The highest response was from the Centre for Ecological Science, followed by the Supercomputer Education and Research Centre, and then the Department of Molecular Reproduction and Development and Genetics. The majority of the respondents have used computers for over five years for a variety of purposes including e‐mail communication, internet browsing and text processing as well as for other advanced uses such as numerical computing and DNA sequence analysis. However, the use of e‐books appears to be very low, indicating a requirement for creating awareness and user education about both software and hardware related to e‐books. Only 37 of the 104 respondents had used the free trial offer from Kluwer and Edutech eBooks during July 2004.

There has been no previous study reported which has investigated users' perspectives of e‐books in an academic and research environment in India using a questionnaire method.

Though electronic books (e‐books) are not new, they are slow in their uptake compared to other types of e‐publications such as journals, newspapers. The possible reasons for this could be because the technology for creating/accessing e‐books (both hardware and software) is not yet matured. However, the recent involvement of many commercial publishers and aggregators in publishing and marketing of e‐books has triggered their use. This trend suggests making an analytical comparative study of the e‐book access model. The main purpose of this study is to analyse and compare offline and online e‐book access models.

In this paper an attempt is made to analyse and compare three offline and three online e‐book access models by identifying various specific e‐book access model features. The access models are evaluated based on three‐point scale.

It is observed that among offline access models Microsoft Reader has most of the features well defined and among online access models, Ebrary has most of the features well defined.

Many publishers and aggregators have started producing and marketing e‐books using different types of access models. There are several access models available and each one has its own merits and demerits. However, there has been no study carried out in comparing and analyzing these models. Hence this study is useful for all the stakeholders of e‐book industry viz., creator (author), publisher, aggregator, librarians and users of e‐books.

This study investigated the potential of partially replacing cement with red mud (RM) in concrete and examined its effects on its mechanical properties and microstructure. This study aims to explore sustainable alternatives to traditional cement and evaluate the performance of concrete mixtures with varying percentages (%) of RM as cement replacement.

This research aims to comprehensively understand the impact of RM on concrete, aiming for both environmental sustainability and improved construction materials. Subsequently, concrete mixtures were prepared with varying RM contents, ranging from 0% to 21% in increments of 3%, replacing cement. The workability of these mixtures was evaluated using the Slump Cone Test, whereas their mechanical properties (compressive strength, flexural strength and split tensile strength) were assessed through standardized tests. The durability was further investigated via water absorption, acid attack, rapid chloride permeability tests, open porosity test and Sorptivity test. To gain deeper insights into the internal structure of concrete, microstructure analysis was conducted using X-ray diffraction and scanning electron microscopy. Finally, the results were analyzed and quantified.

The finding demonstrates that substituting 12% of cement with RM not only boosts the mechanical characteristics of concrete but also mitigates waste disposal. The microstructural analysis identified a denser cement matrix and improved bonding between the cement paste and the aggregates, suggesting potential improvements in strength and durability.

These results suggest that RM can be efficiently used to produce sustainable concrete with potential applications in construction projects with environmental considerations.

Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms.

The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020.

The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions.

The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers.

This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.

This study highlights the trends of qualitative intellectual capital disclosures and patent statistics in the Indian manufacturing context by considering the numerous patent applications, patent grants, forward citations and backward citations. Furthermore, the study investigates the relation among qualitative disclosures, patent statistics and firm performance.

All manufacturing companies of CNX 500 Index of National Stock Exchange of India Limited are considered. Based on data availability, 243 manufacturing firms spanning across seven major manufacturing sectors are included. Secondary data were obtained from the annual report of companies and patent databases from 2004 to 2005 to 2013–2014, generating a sample of 2,430 firm years. Content analysis and citation analysis are used for collecting the relevant data.

Overall, the study results indicated increasing trends for all types of intellectual capital disclosures. Similar trends are observed for patent applications and patent grants, indicating a surge in patenting activities across the manufacturing sector. However, increasing trends in patenting activities are not reflected for forward and backward citations. In addition, significant differences in means and trend coefficients for qualitative disclosures and patent statistics indicated industry specificity within the Indian manufacturing sector. Furthermore, industry specificity is observed when translating intellectual capital to firm performance. The measure of firm performance, that is, Tobin's Q, is having a significant positive association with qualitative disclosures and patent statistics.

As the study is based on secondary data, its accuracy is limited by the accuracy of the data sources such as the annual reports of companies and patent databases.

The study findings imply that policymakers should devise and execute sector-specific policy interventions. Moreover, managers and policymakers should emphasize the qualitative aspect of patenting activities.

The study is an original work that highlights the trends in qualitative disclosures in the Indian manufacturing context. The value relevance of intellectual capital and patent statistics has been established.