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Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study aims to provide a holistic review of social media influencers (SMIs) research based on a unique approach of bibliometric analysis and content analysis between 2011 and 2020. The review examines the main influential aspects, themes and research streams to identify research directions for the future.

The sample selection and data collection were done from the Scopus database. The sample dataset was refined based on the inclusion and exclusion criteria to determine the final dataset of 183 articles. The dataset was exported in the BibTeX format and then imported into the BiblioShiny app for bibliometric analysis. The content analysis was done following the theory-context-methodology framework.

The several findings of this study include (1) Co-word analysis of most used keywords; (2) Longitudinal thematic evolution; (3) The focus of the research papers as per the theory-context-methodology review protocol are persuasion knowledge model, fashion and beauty industries, Instagram and content analysis, respectively; and (4) The network analysis of the research studies is known as the co-citation analysis and depicts the intellectual structure in the domain. This analysis resulted in four clusters of the research streams from the literature and two emergent themes (Chen et al., 2010)

In general, the previous reviews in the area are either domain, method or theory-based. Thus, this study aims to complement and extend the existing literature by presenting the overall picture of the SMI research with the help of a unique combined approach and further highlighting the trends and future research directions based on the findings of this study.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

This study aims to provide an excellent overview of current research trends in the construction sector in digital advancements. It provides a roadmap to policymakers for the effective utilisation of emergent digital technologies and a need for a managerial shift for its smooth adoption.

A total of 3,046 peer-reviewed journal review articles covering Internet of Things (IoT), blockchain, building information modelling (BIM) and digital technologies within the construction sector were reviewed using scientometric mapping and weighted mind-map analysis techniques.

Prominent research clusters identified were: practice-factor-strategy, system, sustainability, BIM and construction worker safety. Leading countries, authors, institutions and their collaborative networks were identified with the UK, the USA, China and Australia leading this field of research. A conceptual framework for an IoT-based concrete lifecycle quality control system is provided.

The study traces the origins of the initial application of Industry 4.0 concepts in the construction field and reviews available literature from 1983 to 2021. It raises awareness of the latest developments and potential landscape realignment of the construction industry through digital technologies conceptual framework for an IoT-based concrete lifecycle quality control system is provided.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states.

This study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts’ opinions to validate the authors’ inferences. For the secondary data, it relies on government data provided in websites.

Based on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency.

This paper’s analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery.

The contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.

In recent decades, a significant number of research contributions have been made to the intersection of digital technologies and the tourism industry. However, a thorough examination of digitalization and digital transformation in the tourism industry has not been given sufficient consideration. This study aims to provide a bibliometric review of digitalization and digital transformation research in the tourism industry and devise future research agendas to advance the research field.

This study uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol and a bibliometric analysis to examine the research progress and scientifically map the research domain of digitalization and digital transformation in the tourism industry from 2002 to 2023 using bibliographic data retrieved from the Scopus and Web of Science.

This study presents the trends in publications and citations within the digitalization and digital transformation research domain in tourism. The findings also provide insights into the four primary clusters of the research field: digital innovation, smart tourism ecosystem, eTourism and smart destination experience. To further augment the application of digital transformation, this study offers several recommendations for future research on digitalization and digital transformation of the tourism industry.

This study provides valuable implications to researchers, managers and policymakers seeking to understand the current state and future research directions in tourism’s digitalization and digital transformation research field.

This study advances the research field of digitalization and digital transformation in the tourism industry by thoroughly examining the primary research clusters in the research corpus of the past two decades. Furthermore, it guides future research, thereby setting the stage for further progress in this domain.

近几十年来, 数字技术与旅游业的交叉领域做出了大量研究贡献。 然而, 对旅游业数字化和数字化转型的深入审视尚未得到充分考虑。本研究旨在对旅游业数字化和数字化转型研究进行文献计量回顾, 并制定未来的研究议程以推进该研究领域的发展。

本研究利用系统文献综述的科学程序和原理（SPAR-4-SLR）协议和文献计量分析来检验研究进展并科学地绘制旅游业数字化和数字化转型的研究领域 使用从 Scopus 和 Web of Science (WOS) 检索到的书目数据从 2002 年到 2023 年。

本研究呈现了旅游业数字化和数字化转型研究领域出版物和引用的趋势。 研究结果还提供了对该研究领域的四个主要集群的见解：数字创新、智能旅游生态系统、电子旅游和智能目的地体验。 为了进一步增强数字化转型的应用, 本研究为旅游业数字化和数字化转型的未来研究提出了几点建议。

本研究通过深入研究过去二十年研究语料库中的主要研究集群, 推进了旅游业数字化和数字化转型的研究领域。 此外, 它指导了未来的研究, 从而为该领域的进一步进展奠定了基础。

本研究为寻求了解旅游业数字化和数字化转型研究领域现状和未来研究方向的研究人员、管理者和政策制定者提供了有价值的启示。

En las últimas décadas, se ha realizado un número significativo de contribuciones de investigación a la intersección de las tecnologías digitales y la industria del turismo. Sin embargo, no se ha prestado suficiente atención a un examen exhaustivo de la digitalización y la transformación digital en la industria del turismo. Este estudio tiene como objetivo proporcionar una revisión bibliométrica de la investigación sobre digitalización y transformación digital en la industria del turismo y diseñar futuras agendas de investigación para avanzar en el campo de la investigación.

Este estudio utiliza el protocolo de Procedimientos y fundamentos científicos para revisiones sistemáticas de la literatura (SPAR-4-SLR) y un análisis bibliométrico para examinar el progreso de la investigación y mapear científicamente el dominio de investigación de la digitalización y la transformación digital en la industria del turismo. de 2002 a 2023 utilizando datos bibliográficos recuperados de Scopus y Web of Science (WOS).

Este estudio presenta las tendencias en publicaciones y citas dentro del dominio de investigación sobre digitalización y transformación digital en turismo. Los hallazgos también brindan información sobre los cuatro grupos principales del campo de investigación: innovación digital, ecosistema de turismo inteligente, turismo electrónico y experiencia de destino inteligente. Para aumentar aún más la aplicación de la transformación digital, este estudio ofrece varias recomendaciones para futuras investigaciones sobre la digitalización y la transformación digital de la industria turística.

Este estudio avanza en el campo de investigación de la digitalización y la transformación digital en la industria del turismo al examinar en profundidad los principales grupos de investigación en el corpus de investigación de las últimas dos décadas. Además, orienta la investigación futura, sentando así las bases para mayores avances en este ámbito.

Este estudio proporciona implicaciones valiosas para los investigadores, administradores y formuladores de políticas que buscan comprender el estado actual y las direcciones futuras de la investigación en el campo de la digitalización y la transformación digital del turismo.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.