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A new Chatbot system is implemented to provide both voice-based and textual-based communication to address student queries without any delay. Initially, the input texts are gathered from the chat and then the gathered text is fed to pre-processing techniques like tokenization, stemming of words and removal of stop words. Then, the pre-processed data are given to the Natural Learning Process (NLP) for extracting the features, where the XLnet and Bidirectional Encoder Representations from Transformers (BERT) are utilized to extract the features. From these extracted features, the target-based fused feature pools are obtained. Then, the intent detection is carried out to extract the answers related to the user queries via Enhanced 1D-Convolutional Neural Networks with Long Short Term Memory (E1DCNN-LSTM) where the parameters are optimized using Position Averaging of Binary Emperor Penguin Optimizer with Colony Predation Algorithm (PA-BEPOCPA). Finally, the answers are extracted based on the intent of a particular student’s teaching materials like video, image or text. The implementation results are analyzed through different recently developed Chatbot detection models to validate the effectiveness of the newly developed model.

A smart model for the NLP is developed to help education-related institutions for an easy way of interaction between students and teachers with high prediction of accurate data for the given query. This research work aims to design a new educational Chatbot to assist the teaching-learning process with the NLP. The input data are gathered from the user through chats and given to the pre-processing stage, where tokenization, steaming of words and removal of stop words are used. The output data from the pre-processing stage is given to the feature extraction phase where XLnet and BERT are used. In this feature extraction, the optimal features are extracted using hybrid PA-BEPOCPA to maximize the correlation coefficient. The features from XLnet and features from BERT were given to target-based features fused pool to produce optimal features. Here, the best features are optimally selected using developed PA-BEPOCPA for maximizing the correlation among coefficients. The output of selected features is given to E1DCNN-LSTM for implementation of educational Chatbot with high accuracy and precision.

The investigation result shows that the implemented model achieves maximum accuracy of 57% more than Bidirectional long short-term memory (BiLSTM), 58% more than One Dimansional Convolutional Neural Network (1DCNN), 59% more than LSTM and 62% more than Ensemble for the given dataset.

The prediction accuracy was high in this proposed deep learning-based educational Chatbot system when compared with various baseline works.

This study aims to examine the impact of corporate governance (CG) on firm risk-taking in India.

The present study is based on a panel data set of 100 non-financial Indian firms randomly selected from the top 500 firms listed on the Bombay Stock Exchange. The study uses two market-based measures to capture firm risk-taking – total risk and idiosyncratic risk. Generalised method of moments model is applied to examine the relationship between CG and firm risk-taking. Additionally, the fixed-effects model is applied to check the robustness of the results.

The study reveals a significant negative impact of CG index, CEO duality, multiple directorships, promoter ownership and institutional ownership on firm risk-taking. Whereas board size, board independence, board gender diversity and the number of board meetings do not significantly impact firm risk-taking.

This study contributes to the existing literature by providing a comprehensive view of how various CG attributes shape firm risk-taking in India. It examines eight CG variables: board size, board independence, board gender diversity, CEO duality, multiple directorships, number of board meetings, promoter ownership and institutional ownership. Furthermore, the study incorporates idiosyncratic risk as an additional measure of firm risk-taking, largely overlooked in the Indian context. Moreover, to the best of the authors’ knowledge, this is the first study to examine the impact of CG index on firm risk-taking in India.

This paper offers a measure of sensitivity to change orders in the later stage of the construction phase to characterize the distribution of project activities around the time when the project is scheduled to finish.

This paper employs eigenvector centrality to identify the sensitivity of an activity to change orders based on the sensitivity of its neighboring activities. Next, a distance-based measure, called the radius of gyration, is adopted to describe the distribution of project activities around the time when the project is scheduled to finish. Finally, a sensitivity measure, which quantifies the dispersion of project activities from the project finish date, is developed.

Two real-life construction projects are used to measure the sensitivity to late changes. The results conform to the intuition of sensitivity to late changes and confirm that the negative effects of change orders are amplified when project activities are scheduled to start closer to the finish date.

This paper adds to the literature on change orders in construction projects by developing the first method for quantifying the sensitivity of projects to the issuance of late changes. The proposed method can provide valuable information to project owners and construction managers as they negotiate the pricing of change orders based on their time of occurrence.

The main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.

Based on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.

With zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.

As the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Formwork design and construction for reinforced concrete buildings take significant time, effort and money. Construction procedures are time-consuming for designers and costly for the contractor. Poor engineering decisions have led to several workplace accidents in the construction industry. This paper aims to present an integrated building information modeling – genetic algorithm (BIM-GA) model to automate formwork design, 3D visualization and optimization.

Data are precisely extracted from a 3D structural model and used to optimize formwork design based on available formwork components and prices. Optimization models are made using GA approach. A library of 3D formwork components was modeled and stored using Revit. The optimized design solution thereafter would be visualized automatically in Revit to readily acquire formwork quantities schedules and shop drawings.

A case study illustrating the proposed approach demonstrated that using BIM will reduce formwork design, quantification and drawing time by more than 50% of the traditional approach with safer design and accurate results due to process automation and optimize cost for the given data.

This research introduces an innovative integrated BIM and GA model for the optimization and automation of slab formwork design, which has significantly benefited the construction industry. The utilization of GA in the optimization process allows for the attainment of an optimal formwork design, ultimately leading to a reduction in construction cost and time.

A learning-focused culture promotes creativity, innovativeness and the acquisition of novel insights and competencies. The study aims to explore the relationship between human resource development (HRD) practice and employee competencies using organizational learning culture as a mediating variable.

Data were collected from 828 employees of 37 health care institutions comprising 24 (internationally-owned) and 13 (indigenously-owned). Construct reliability and validity was established through a confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling.

Data supported the hypothesized relationships. The results show that training and development and employee competencies were significantly related. Career development and employee competencies were significantly related. Organizational learning culture mediates the relationship between training and development and employee competencies. However, organizational learning culture did not mediate the relationship between career development and employee competencies.

The generalizability of the findings will be constrained due to the research’s health care focus and cross-sectional data.

The study’s findings will serve as valuable pointers to policy makers and stakeholders of health care institutions in developing system-level capacities that promote continuous learning and adaptive learning cultures to ensure sustainability and competitive advantage.

By evidencing empirically that organizational learning culture mediates the relationship between HRD practices and employee competencies the study extends the literature.

The study attempts to investigate the role of social media in spreading awareness regarding ayurvedic immunity boosters (AIB) and changes in diet. Further, the study examines the factors affecting the willingness to pay for ayurvedic immunity boosters (WPIB) during the pandemic and new normal situation with the moderating effect of the “fear of COVID-19 infection.”

The data were collected from millennials in two phases, i.e. the first phase (1 July–August 2021) with 300 respondents and a second phase with (June–August 2022) 257 respondents. An online questionnaire was shared with millennials using the snowball sampling technique. Descriptive statistics with SPSS and SmartPLS 4.0 software were applied to analyze the data.

The results found a variation in AIB content sharing on social media during 2021 and 2022. Results found that respondents reported significant changes in their lifestyle and diet, like consuming honey, khada, tulsi tea, etc. In 2021, health consciousness and trust significantly affected WPIB, whereas in 2022, only health consciousness was substantially affected. Fear of COVID-19 infection moderates the relationship between health consciousness, perceived fear and willingness to pay for ayurvedic products, whereas the effect on consumer preference and trust remains insignificant.

Results could help ayurvedic product manufacturing companies understand the consumers' mindset and the factors that stimulate consumers to buy these immunity boosters. Ayurvedic advertisers should design unambiguous messages that focus on health consciousness and have trustable components to encourage consumers to adopt a healthy lifestyle.

This is one of its kinds of studies that presents the contrasts of how the COVID-19 crisis has significantly changed individuals' dietary intake and affected lifestyle patterns.

This study aims to identify and rank the key success factors linked with digitalization in the Indian construction sector. Because the construction firms in India are in the early stages of implementing digitalization in their operations, it provides a framework from which they may focus on the effectiveness of digitalization.

This research study examines 12 success factors related to digitalization in construction, which are derived from various sectors. Furthermore, experts from the construction industry and academia have validated these factors with respect to the Indian construction sector. The multi-criteria decision-making techniques are further used to examine the interrelationship, ranking and weightage of digitalization success. Finally, the success factors are validated through a questionnaire-based empirical study followed by ranking using a t-test. The results from both approaches (company-specific and generalized) are compared and discussed.

This research identifies that selecting appropriate digital methods and techniques is a critical success factor as far as digitalization in the Indian construction scenario is concerned. Besides that, continuous monitoring and control in digital implementation significantly impact other factors.

While similar results are obtained from approaches adopted in the study, a few success factors appear to differ in terms of their ranking position. Further studies can explore the finer details that can explain the behavior pattern. This study can also be extended by assessing the structural relationship among the identified factors that can throw more light on the dynamics of the continuation of digitalization in construction which can further help in formulating policies or digitalization rollout.

The outcome of this study sheds light on construction business knowledge by stressing key success elements connected to digitalization in construction processes in the Indian construction sector. Moreover, this study shows that the success of digitalization in construction is similar to that of transformation in the information technology industry, where adopting suitable digital methods and techniques plays a vital role in the transformation process.

Despite the multiple benefits of construction digitalization, limited research focuses on digitalization success factors, making this study unique. Furthermore, this study demonstrates that integrating Fuzzy decision-making trial and evaluation laboratory and maximum mean de-entropy approaches may be used to successfully prioritize success factors in the nascent stage of construction digitalization.

The aim of this paper is to investigate the potential impact of Industry 4.0 (I4.0) digital technologies on promoting sustainability in small and medium-sized enterprises (SMEs) within developing economies such as Brazil. Additionally, we present a comprehensive framework that consolidates this correlation.

Qualitative research was conducted through semi-structured interviews with leaders of SMEs to identify the specific challenges in achieving sustainability. Additionally, interviews were conducted with technology provider firms to evaluate the existing solutions available to SMEs. The interview results were analyzed, and technological solutions were proposed through a focus group session involving four experts in I4.0. These proposed solutions were then compared with the offerings provided by the technology providers. Based on this, a second round of meetings was conducted to gather feedback from the SMEs.

The findings of this study confirm the feasibility of implementing I4.0 and sustainable practices in SMEs. However, it is crucial to tailor the technologies to the specific circumstances of SMEs. The study presents propositions on how specific applications of technology can address the economic, environmental and social demands of SMEs. Furthermore, a framework is proposed, emphasizing the integration of smart technologies as essential components across sustainability dimensions.

This study makes a significant contribution to the current body of literature as it pioneers the examination of the relationship between I4.0 technologies and sustainability, focusing specifically on SMEs in a developing country context.

El objetivo de este estudio es investigar el potential impacto de las tecnologías digitales de la Industria 4.0 en la promoción de la sostenibilidad en las pequeñas y medianas empresas (PYMES) en economías en desarrollo, como Brasil.

Realizamos una investigación cualitativa mediante entrevistas semiestructuradas a líderes de PYMES para identificar los desafíos que enfrentan en la búsqueda de la sostenibilidad. También llevamos a cabo entrevistas con empresas proveedoras de tecnología para evaluar las soluciones existentes. Los resultados de las entrevistas se analizaron y se propusieron soluciones tecnológicas a través de una sesión de grupo focal con cuatro expertos en la Industria 4.0. Estas soluciones se compararon con las ofertas proporcionadas por los proveedores de tecnología. Posteriormente, se realizaron una segunda reunión para recopilar comentarios de las PYMES.

Los hallazgos de este estudio confirman la viabilidad de implementar la Industria 4.0 y prácticas sostenibles en las PYMES. Sin embargo, es crucial adaptar las tecnologías a las circunstancias de las PYMES. Presentamos propuestas sobre cómo las aplicaciones de la tecnología pueden abordar las demandas económicas, ambientales y sociales de las PYMES. Además, proponemos un marco que destaca la integración de tecnologías como componentes esenciales de la sostenibilidad.

Este estudio es pionero en examinar la relación entre las tecnologías de la Industria 4.0 y la sostenibilidad, centrándose específicamente en las PYMES en un contexto de país en desarrollo.