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The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss.

The proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 × 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process.

The results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity.

DRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process.

The IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis.

This proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture.

The performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.

This paper aims to focus on compliance of workplace sexual harassment-related provisions under Indian companies and securities law, based on an empirical analysis of companies’ sexual harassment-related disclosures contained within their directors’ annual reports (ARs). Specifically, sections devoted to sexual harassment-related disclosures, inbuilt within directors’ ARs for the financial year 2019–2020 for a selected sample of companies listed under the National Stock Exchange, have been analysed.

To examine the nature of companies’ disclosures to demonstrate their compliance with statutory requirements under the POSH law, aligned with the Companies (Accounts) Rules, 2014 and Securities and Exchange Board of India’s regulations, an empirical-based, descriptive content analysis of ARs of 200 listed companies were used.

This study primarily finds that the majority of companies from the sample have disclosed to have prepared a corporate-level policy, as required under the POSH law. As also required under the POSH law, companies, reportedly, have constituted an Internal Complaints Committee to adjudicate and dispose of incidents related to sexual misconduct reported at their workplaces. However, companies lack in disclosing qualitative information, with sufficient detail, on many important aspects related to prevention and resolution of reported cases of workplace sexual harassment.

This paper adds to the broader narrative of the lacunae within the disclosure and reporting requirements on enhancing the liabilities of the companies to prevent and address sexual harassment under India’s corporate and securities regulations.

This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.

To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.

This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.

This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.

According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.

Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).

Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.

Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.

To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

Internet of Things (IoT) has been widely adopted in the commercial sector. Although IoT enables traditional libraries to convert into digital ones, the implementation of the IoT in libraries is slow. The purpose of this paper is to report on the current state of research on applications of IoT in libraries, describe challenges that IoT applications face in libraries and discus directions of adopting IoT in libraries in the future.

To conduct this research, the literature of IoT and its application in libraries were reviewed by examining existing literature in Institute of Electrical and Electronics Engineers (IEEE) Xplore.

The literature review finds that radio-frequency identification has been adopted by digital libraries. The slow implementation of IoT is caused by security and privacy issues, lack of standards and the lack of financial, technological and organizational resources. This study provides a prospective for the application of IoT in libraries; the technologies of IoT have the potential in betterment of library services.

The limitation of this study is that only IEEE Xplore is included. Other database should be explored in future research.

The application of IoT in libraries is an emerging issue; a systematic and extensive review of recent research on applications of IoT in libraries is unavailable. This paper presents an overview of IoT in libraries, findings and potential research opportunities.

The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static.

The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review.

The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation.

The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

This study explores the countermeasures taken by retailers to mitigate the effects of COVID-19 on supply chain disruptions.

This research uses a multiple case study approach and undertakes 36 semi-structured interviews with senior management of the four largest retailers of the United Arab Emirates. The respondents were designated at different positions such as Vice President, Director and Project Manager.

Results reveal that retailers are employing six countermeasures to mitigate the effects of COVID-19 on supply chains. Particularly, retailers are securing required demand, preserving cash flows, redirecting inventory, adding capacity to their distribution centres, becoming more flexible with their direct or third-party logistics provider and finally widening delivery options for their suppliers to mitigate the impact of COVID-19.

This study has some limitations. First, the results of this study cannot be generalized to a broader population as it attempts to build an initial theory. Second, this study uses a cross-sectional approach to explore the countermeasures employed by retailing firms to mitigate the effects of COVID-19.

A notable weakness in a supply chain disruption literature is an unfulfilled need for research examining the strategies employed by retailers to respond to/address the challenges posed by COVID-19. Our study fills this gap.

This paper investigates the latest achievements of studies on industrial information integration engineering (IIIE).

This paper extends the research by Chen (2016) by reviewing studies from 2016 to 2019 in IEEE Xplore and Web of Science. Altogether, 970 papers related to IIIE are grouped into 27 research categories and reviewed.

The results obtained in this study indicate that the number of research studies on IIIE rose drastically in the past three years compared with the findings in Chen (2016). Particularly, energy, engineering, industrial control, information and communications technologies, instrumentation, manufacturing and transportation are the hot topics. This change proves that the Internet of things (IoT) and IIIE have integrated closely by providing more applications, such as industrial Internet of things (IIoT), cyber-physical system (CPS), smart grids and smart manufacturing. This change also proves the research direction of IIIE identified by Chen (2016).

The results present up-to-date development of IIIE and provide directions for future research on IIIE. The review identifies that energy, engineering, industrial control, information and communications technologies, instrumentation, manufacturing and transportation are the main fields that most of the reviewed papers focus on. Applications that integrate IoT and IIIE, including IIoT, CPS, smart grids and smart manufacturing, are attracting scholars' and practitioners' attention. Some new technologies, such as 5G and blockchain, have the potential to be integrated with IoT and IIIE.