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This study aims to present a novel system for detection and prevention of black hole and wormhole attacks in wireless sensor network (WSN) based on deep learning model. Here, different phases are included such as assigning the nodes, data collection, detecting black hole and wormhole attacks and preventing black hole and wormhole attacks by optimal path communication. Initially, a set of nodes is assumed for carrying out the communication in WSN. Further, the black hole attacks are detected by the Bait process, and wormhole attacks are detected by the round trip time (RTT) validation process. The data collection procedure is done with the Bait and RTT validation process with attribute information. The gathered data attributes are given for the training in which long short-term memory (LSTM) is used that includes the attack details. This is used for attack detection process. Once they are detected, those attacks are removed from the network using the optimal path selection process. Here, the optimal shortest path is determined by the improvement in the whale optimization algorithm (WOA) that is called as fitness rate-based whale optimization algorithm (FR-WOA). This shortest path communication is carried out based on the multi-objective function using energy, distance, delay and packet delivery ratio as constraints.

This paper implements a detection and prevention of attacks model based on FR-WOA algorithm for the prevention of attacks in the WSNs. With this, this paper aims to accomplish the desired optimization of multi-objective functions.

From the analysis, it is found that the accuracy of the optimized LSTM is better than conventional LSTM. The energy consumption of the proposed FR-WOA with 35 nodes is 7.14% superior to WOA and FireFly, 5.7% superior to grey wolf optimization and 10.3% superior to particle swarm optimization.

This paper develops the FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN. To the best of the authors’ knowledge, this is the first work that uses FR-WOA with optimized LSTM detecting and preventing black hole and wormhole attacks from WSN.

This paper aims to propose that the literature on customer engagement has emphasized the benefits of customer engagement to the firm and, to a large extent, ignored the customers’ perspective. By drawing upon co-creation and other literature, this paper attempts to alleviate this gap by proposing a strategic framework that aligns both the customer and firm perspectives in successfully creating engagement that generates value for both the customer and the bottom line.

A strategic framework is proposed that includes the necessary firm resources, data, process, timeline and goals for engagement, and captures customers’ motives, situational factors and preferred engagement styles.

The authors argue that sustainability of data-driven customer engagement requires a dynamic and iterative value generation process involving customers recognizing the value of engagement behaviours and firm’s ability to capture and passing value back to customers.

This paper proposes a dynamic strategic value-creation framework that comprehensively captures both the customer and firm perspectives to data-driven customer engagement.

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.

This study aims to introduce a novel rank aggregation algorithm that leverages graph theory and deep-learning to improve the accuracy and relevance of aggregated rankings in metasearch scenarios, particularly when faced with inconsistent and low-quality rank lists. By strategically selecting a subset of base rankers, the algorithm enhances the quality of the aggregated ranking while using only a subset of base rankers.

The proposed algorithm leverages a graph-based model to represent the interrelationships between base rankers. By applying Spectral clustering, the algorithm identifies a subset of top-performing base rankers based on their retrieval effectiveness. These selected rankers are then integrated into a sequential deep-learning model to estimate relevance labels for query-document pairs.

Empirical evaluation on the MQ2007-agg and MQ2008-agg data sets demonstrates the substantial performance gains achieved by the proposed algorithm compared to baseline methods, with an average improvement of 8.7% in MAP and 11.9% in NDCG@1. The algorithm’s effectiveness can be attributed to its ability to effectively integrate diverse perspectives from base rankers and capture complex relationships within the data.

This research presents a novel approach to rank aggregation that integrates graph theory and deep-learning. The author proposes a graph-based model to select the most effective subset for metasearch applications by constructing a similarity graph of base rankers. This innovative method addresses the challenges posed by inconsistent and low-quality rank lists, offering a unique solution to the problem.

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.

The purpose of this paper is to define big data and draw its deep understanding. Moreover, trends of big data research in the field of library and information management are explored. With the purpose to explore the research trends, papers indexed in Thomson Reuters’ ISI Web of Knowledge were analyzed.

It is a literature-based and scientometric paper. A formal definition is constructed through a review of literature. Moreover, scientometric analysis of papers indexed in Thomson Reuters’ ISI Web of Knowledge has been done to explore the research trends associated with big data in the field of library and information science, using Vosviewer software.

The findings of the study indicate the reshaped definition of big data. The findings further indicate major research trends associated with big data. The analysis indicated “Risk”, “Industry”, “Market”, “Creditworthiness” and “Big Data Analytics”, the most repeated research trends associated with big data.

The paper sums up the learnings required to be a successful data-literate manager. First, the study defines big data. Second, the study describes current research trends associated with big data. Third, on the basis of the explored trends, data managers and library and information management professionals are guided about the learnings they require to be a successful data manager. Where thousands of data-literate managers are predicted to require in future, the present study is a guide to trends associated with big data.

It is a first study of its type which provides a reshaped definition of big data. It portrays its landscape and associated research trends in the field of information and library management (ILM).

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

This review aims to provide synoptic documentation on acclaimed anecdotal plant-based remedies used by Latin America and the Caribbean (LAC) communities to manage COVID-19. The theoretical approaches that form the basis for using the anecdotally claimed phytotherapies were reviewed against current scientific evidence.

In this paper plant-based remedies for managing COVID-19 were searched on social and print media to identify testimonies of people from different communities in LAC countries. Information was extracted, evaluated and reviewed against current scientific evidence based on a literature search from databases such as Journal Storage (JSTOR), Excerpta Medica Database (EMBASE), SpringerLink, Scopus, ScienceDirect, PubMed, Google Scholar and Medline to explore the scientific basis for anecdotal claims.

A total of 23 medicinal plants belonging to 15 families were identified as phytotherapies used in managing COVID-19 in LAC communities.

The plant-based remedies contained valuable phytochemicals scientifically reported for their anti-inflammatory, antiviral, antioxidant and anticancer effects. Anecdotal information helps researchers investigate disease patterns, management and new drug discoveries. The identified acclaimed plant-based remedies are potential candidates for pharmacological evaluations for possible drug discovery for future pandemics.

The purpose of this paper is to optimize production of cellulase enzyme from agricultural waste by using Aspergillus fumigatus JCF. The study also aims at the production of bioethanol using cellulase and yeast.

Cellulase production was carried out using modified Mandel’s medium. The optimization of the cellulase production was carried out using Plackett-Burman and Response surface methodology. Bioethanol production was carried out using simultaneous saccharification and fermentation.

Maximum cellulase production at optimized conditions was found to be 2.08 IU/ml. Cellulase was used for the saccharification of three different feed stocks, i.e. sugar cane leaves, corn cob and water hyacinth. Highest amount of reducing sugar was released was 29.1 gm/l from sugarcane leaves. Sugarcane leaves produced maximum bioethanol concentration of 9.43 g/l out of the three substrates studied for bioethanol production.

The present study reveals that by using the agricultural wastes, cellulase production can be economically increased thereby bioethanol production.

The purpose of this study is to determine the level of awareness among library and information science (LIS) professionals regarding the perceived utility of big data (BD) and data analytics (DA) in academic libraries, as well as their influence on the provision of data services (DSs).

A cross-sectional survey was carried out to collect the data for this study. The population of this study comprised LIS professionals working in public sector university libraries. A four-factor measurement model estimating the influence of BD and DA on the provision of DSs in academic libraries was tested using the structural equation modelling.

The findings revealed that awareness (AW) (β = 0.141, CR = 2.534, p = 0.011) demonstrated a significant positive influence on the provision of DSs. The perceived utility of BD (β = 0.058, CR = 0.582, p = 0.561), and perceived utility of DA (β = 0.141, CR = 2.534, p = 0.905) exhibits a positive but statistically non-significant impact on the provision of DSs (β = 0.010, CR = 0.120, p = 0.905). The goodness of fit indices suggest a favourable fit for the model, as evidenced by the following values: χ² = 1.400, DF = 164; p = 0.001; IFI = 0.954; TLI = 0.946; CFI = 0.953; GFI = 0.906; and RMSEA = 0.043.

A new perspective on the use of BD and DA in academic libraries is presented in this study. It presents a four-factor measurement model on the influence of BD and DA on the provision of DSs in university libraries.