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Human gait analysis is based on a significant part of the musculoskeletal, nervous and respiratory systems. Gait analysis is widely adopted to help patients increase community involvement and independent living.

This paper presents a system for the classification of abnormal human gaits using a Markerless 3D Motion Capture device. This study aims at examining and estimating the spatiotemporal and kinematic parameters obtained by 3D gait analysis in diverse groups of gait-impaired subjects and compares the parameters with that of healthy participants to interpret the gait patterns.

The classification is based on mathematical models that distinguish between normal and abnormal gait patterns depending on the deviations in the gait parameters. The difference between the gait measures of the control and each disease group was examined using 95% limits of agreement by the Bland and Altman method. The scatter plots demonstrated gait variability in Parkinsonian and ataxia gait and knee joint angle variation in hemiplegic gait when compared with those of healthy controls. To prove the validity of the Kinect camera, significant correlations were detected between Kinect- and inertial-based gait tests.

The various techniques used for gait assessments are often high in price and have existing limitations like the hindrance of components. The results suggest that the Kinect-based gait assessment techniques can be used as a low-cost, less-intrusive alternative to expensive infrastructure gait lab tests in the clinical environment.

Smart tourist destinations (STDs) make use of new technologies to facilitate and improve the experience of tourists. So why not use these technologies to efficiently manage the destination? The aim of this work is to define and implement a methodology that provides value to STDs by defining their most important characteristics to monitor and quantify them automatically in real time.

The authors developed a conceptual framework to the smart tourism approach presented in previous studies, the latest technologies and the application of the smart tourism system (STS). Based on the focus group method with stakeholders from the tourism industry of the Spanish tourist municipality of Puerto de la Cruz, they defined the main KPIs for a municipal STD. Likewise, the authors specified the necessary technologies to obtain, manage and represent the data, and the method for quantifying the quality of the STD by using the AHP method. Lastly, they implemented the framework for the aforementioned municipality.

The implementation in a real context of the STS proposed for Puerto de la Cruz demonstrates its validity and the possibility of adapting it to any other municipal destination. In addition, the authors corroborate how this STS improves on other versions.

This paper provides a theoretical methodology to improve STD management and implements it. Other studies have focused only on the theoretical aspect. Moreover, automated management tools are emerging for STDs, but they lack the quality provided by the scientific approach employed herein.

The purpose of this paper is to document similarities and differences between management practices of different types of organizations in India’s IT sector through an empirical survey. The authors expected these differences to be significant enough for us to be able to group a priori this set of companies meaningfully through cluster analysis on the basis of the similarity of their management practices alone.

Using a mixed-methods approach, 73 senior-level executives of companies working in India’s IT sector were approached with a pretested questionnaire to find out differences on eighteen management practices in the areas of operations management, monitoring management, targets management and talent management. The different types of organizations surveyed were small and amp; medium global multinationals, large global multinationals, small and medium Indian multinationals, large Indian multinationals and small and medium local Indian companies. The differences and similarities found through statistical testing were further validated a priori through cluster analysis and qualitative interviews with senior-level executives.

The management practices of multinationals in India are moving toward Western management practices, indicating that management practices converge as the organizations grow in size. Though the practices of large Indian multinationals were not significantly different from those of global multinationals, the surprising finding was that large Indian multinationals scored better than global multinationals on a few practices. The practices of small and medium Indian companies differed significantly from those of other types of organizations and hence they formed a cluster.

The finding that large Indian IT multinationals have an edge over global multinationals in certain people management practices is a confirmation of the role of human resource practices in their current success and their continuing competitive advantage.

This is perhaps the first study of its kind to document state of specific management practices across different types of organizations in India’s IT sector and then use measures on these practices to group a priori these organizations for validation.

This study explores the adoption of Industry 4.0 in developing countries' export industries, focusing on factors influencing this adoption, the moderating role of market pressure and prioritizing key factors for sustainable growth.

Based on the “TOE theory” this study has proposed a research framework to identify the factors influencing the adoption and sustainable implementation of Industry 4.0 in the export industry. This study has collected valid datasets from 387 export-oriented industries and applied SEM-ANN dual-stage hybrid model to capture linear and nonlinear interaction between variables.

Results revealed that Technical Capabilities, System Flexibility, Software Infrastructure, Human Resource Competency and Market pressure significantly influence the Adoption of Industry 4.0. Higher market pressure as a moderator also improves the Industry 4.0 adoption process. Results also pointed out that system flexibility is a gray area in Industry 4.0 adoption, which can be enhanced in the export industry to maintain a sustainable adoption and implementation of Industry 4.0.

Minute information is available on the factors influencing the adoption of Industry 4.0 in export-oriented industries. This study has empirically explored the role of influential factors in Industry 4.0 and ranked them based on their normalized importance.

This study aims to examine the relationship between pandemic sentiment (PS) and stock market returns in an emerging order-driven stock market like India.

This study uses nonlinear causality and wavelet coherence techniques to analyze the sentiment-returns nexus. The analysis is conducted on the full sample period from January to December 2020 and further extended to two subperiods from January to June and July to December to investigate whether the associations between sentiment and market returns persist even several months after the outbreak.

This study constructs two novel measures of PS: one using Google Search Volume Intensity and the other using Textual Analysis of newspaper headlines. The empirical findings suggest a high degree of interrelationship between PS and stock returns in all time-frequency domains across the full sample period. This interrelationship is found to be further heightened during the initial months of the crisis but reduces significantly during the later months. This could be because a considerable amount of uncertainty regarding the crisis is already accounted for and priced into the markets in the initial months.

The ongoing coronavirus pandemic has resulted in sharp volatility and frequent crashes in the global equity indices. This study is an endeavor to shed light on the ongoing debate on the COVID-19 pandemic, investors’ sentiment and stock market behavior.

The regulatory design of Indian stock market provides us with the opportunity to disaggregate initial returns into two categories, i.e. voluntary premarket underpricing and post market mispricing. This study explores the impact of investor attention on the disaggregated short-run returns and long-run performance of initial public offerings (IPOs).

The study employs regression techniques on the sample of IPOs listed from 2005 to 2019. It measures investor attention with the help of the Google Search Volume Index (GSVI) extracted from Google Trends. Along with GSVI, the subscription rate is used as a proxy to measure investor attention.

The empirical results suggest a positive and significant relationship between initial returns and investor attention, thus validating the attention theory for Indian IPOs. Furthermore, when the returns are analysed for a more extended period using buy-and-hold abnormal returns (BHARs), it was found that price reversal holds in the long run.

This study highlights the importance of information diffusion in the market. It emphasizes the behavioural tendency of the investors in the pre-market, which reduces the market efficiency. Hence, along with fundamentals, investor attention also plays an essential role in deciding the returns for an IPO.

According to the best of the authors’ knowledge, this is one of the first studies that has attempted to explore the influence of investor attention and its interplay with underpricing and long-run performance for IPOs of Indian markets.

Taking a career capital approach, this paper addresses the issue of “pipeline block” frequently experienced by women seeking career advancement. Focusing on the Arab Middle East (AME) region, the authors take a contextually relevant multi-level approach to examine these issues.

The study uses a qualitative, interview-based approach, drawing on data obtained from women leaders from the AME region. Drawing on Bourdieu's capital-field-habitus framework, we explore how women in the AME developed career capital in particular organisational fields.

The findings show the importance of human and social capital, as well as the influence of habitus for women's career advancement in specific fields. The study also highlights the unique contribution of cultural capital in helping women to navigate organisational fields where it is necessary to both challenge, and conform to, traditional norms.

Limitations of the study include assumptions of homogeneity across countries of the AME, whereas differences are known to exist. Future research should consider these contextual differences, and also include a study of women who were not successful in gaining career advancement.

The study’s multi-level approach highlights practical implications for women, organisations and society. For organisations, the authors propose some context-relevant coaching strategies that can help women to attain leadership positions.

The study’s multi-level approach highlights practical implications for women, organisations,and society. Focusing on organisations, the authors propose some context-relevant coaching strategies that can help women to attain advancement in their careers.

The study demonstrates originality in the findings by showing how women overcome the pipeline block in relation to their career advancement. The use of the Bourdieusian framework, an in-depth qualitative approach, and the AME context also add to the study's originality.

Sentiment analysis is the subfield of data mining, which is profusely used for studying the opinions of the users by analyzing their suggestions on the Web platform. It plays an important role in the daily decision-making process, and every decision has a great impact on daily life. Various techniques including machine learning algorithms have been proposed for sentiment analysis, but still, they are inefficient for extracting the sentiment features from the given text. Although the improvement in sentiment analysis approaches, there are several problems, which make the analysis inefficient and inaccurate. This paper aims to develop the sentiment analysis scheme on movie reviews by proposing a novel classifier.

For the analysis, the movie reviews are collected and subjected to pre-processing. From the pre-processed review, a total of nine sentiment related features are extracted and provided to the proposed exponential-salp swarm algorithm based actor-critic neural network (ESSA-ACNN) classifier for the sentiment classification. The ESSA algorithm is developed by integrating the exponentially weighted moving average (EWMA) and SSA for selecting the optimal weight of ACNN. Finally, the proposed classifier classifies the reviews into positive or negative class.

The performance of the ESSA-ACNN classifier is analyzed by considering the reviews present in the movie review database. From, the simulation results, it is evident that the proposed ESSA-ACNN classifier has improved performance than the existing works by having the performance of 0.7417, 0.8807 and 0.8119, for sensitivity, specificity and accuracy, respectively.

The proposed classifier can be applicable for real-world problems, such as business, political activities and so on.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.