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This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

This paper aims to analyze the impact of tax digitalization, focusing on artificial intelligence (AI), machine learning and blockchain technologies, on enhancing tax compliance behavior in various contexts. It seeks to understand how these emerging digital tools influence taxpayer behaviors and compliance levels and to assess their effectiveness in reducing tax evasion and avoidance practices.

Using a systematic review technique with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study evaluates 62 papers collected from the Scopus database. The papers were analyzed through textometry of titles, abstracts and keywords to identify prevailing trends and insights.

The review reveals that digitalization, particularly through AI and blockchain, significantly enhances tax compliance and operational efficiency. However, challenges persist, especially in emerging economies, regarding the adoption and integration of these technologies in tax systems. The findings indicate a global trend toward digital Tax Administration 3.0, emphasizing the importance of regulatory frameworks, capacity building and simplification for small and medium enterprises (SMEs).

The findings provide guidance for policymakers and tax administrations, underscoring the necessity of strategic planning, regulatory backing and global cooperation to effectively use digital technologies in tax compliance. Emphasizing the need for tailored support for SMEs, the study also calls for expanded research in less represented areas and specific sectors, such as SMEs and developing economies, to deepen global insights into digital tax compliance.

This study has attempted to fill the gap in the literature on the comprehensive impact of fiscal digitalization, particularly AI-based, on tax compliance across different global contexts, adding to the discourse on digital taxation.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries and provides insight into accessing the supply chain (SC) vulnerability in an uncertain environment.

This study involves measuring the resilience status of 37 organizations across 22 industries based on a subjective decision-making approach using fuzzy logic. Experts from industries rated the importance and level of implementation of 33 attributes of SCR, which are used to develop a fuzzy index of implementation that explains the resilience status of organizations.

A novel coexistent resilience index is computed based on mutualism to exhibit the proportion of contribution or learning of each attribute of an organization in an industry. The research will enhance the response plans and formation of strategic alliances for mutual coexistence by industry.

Evidence-based interpretations and suggestions are provided for each industry to enhance resilience through coexistence.

The work uniquely contributes to academic literature and SC strategy. The novel coexistent resilience index is computed based on mutualism, facilitating researchers to access SC resiliency.

Needs for information literacy, disparities in society, bridging digital divides, richness of information sources in electronic (e-)environments and the value of dictionaries have often been propagated. To improve information sources and information literacy training, information behaviour must be understood (i.e. all information activities). This paper conceptualises new opportunities for information sources (e.g. electronic dictionaries) to all society sectors, dictionary literacy and research lenses such as lexicography to supplement information literacy and behaviour research.

A scoping review of information literacy and behaviour, lexicography and dictionary literature grounds the conceptualisation of dictionary literacy, its alignment with information literacy, information activities and information behaviour and lexicography as additional research lens.

Research lenses must acknowledge dictionary use in e-environments, information activities and skills, meanings of information and dictionary literacy, the value of e-dictionaries, alignment with information behaviour research that guides the development of information sources and interdisciplinary research from, e.g. lexicography – thus contextualisation.

Research implications – information behaviour and information literacy research can be enriched by lexicography as research lens. Further conceptualisation could align information behaviour, information literacy and dictionary literacy.

Dictionary training, aligned with information literacy training, can be informed by this paper.

The value of dictionary literacy for all sectors of societies can be improved.

Large bodies of literature on information behaviour and lexicography individually do not cover combined insights from both.

Acute and chronic obstructive pulmonary disease (COPD) is a common and progressive lung disease that makes breathing difficult over time and can even lead to death. Despite this, there is no definitive treatment for it yet. This study aims to evaluate the studies on single and combined herbal interventions affecting COPD.

In this study, all articles published in English up to 2020 were extracted from the Web of Science (WoS) database and collected using Boolean tools based on keywords, titles and abstracts. Finally, the data required for bibliographic analysis, such as the author(s), publication year, academic journal, institution, country of origin, institution, financial institution and keywords were extracted from the database.

A total of 573 articles were analyzed. The number of papers in the lung disease field showed an upward trend from 1984 to 2021, and there was a surge in paper publications in 2013. China, Korea and Brazil published the highest number of studies on COPD, and Chinese medical universities published the most papers. Three journals that received the highest scores in this study were the Journal of Ethnopharmacology, International Immunopharmacology and Plos One. In the cloud map, expression, activation and expression were the most frequently researched subjects. In the plus and author keywords, acute lung injury was the most commonly used word. Inflammation, expression of various genes, nitric oxide-dependent pathways, NFkappa B, TNFalpha and lipopolysaccharide-dependent pathways were the mechanisms underlying COPD. Scientometric analysis of COPD provides a vision for future research and policymaking.

This study aimed to evaluate the studies on single and combined herbal interventions affecting COPD.

This study aims to analyze the tax compliance behavior of family firms by integrating social and psychological norms with legitimacy determinants, focusing specifically on the Moroccan context.

Employing a qualitative research design, the study conducted semi-structured interviews with 30 chief executive officers (CEOs) of Moroccan family firms. The data were analyzed using thematic analysis to unravel the interplay between individual beliefs and societal norms.

The findings reveal a complex interplay between the personal norms of CEOs and chief financial officers (CFOs) and wider societal and cultural expectations, significantly influencing tax compliance behavior. The study identifies the multifaceted nature of tax compliance, which is shaped by personal ethics, family values and the dominant societal tax culture.

The research is limited by its qualitative approach and focus on Moroccan family businesses, which may not be generalizable to other contexts. Future studies could use a quantitative approach and expand to other geographical settings for a more comprehensive understanding.

Insights from the study can assist policymakers and tax authorities in developing culturally sensitive tax compliance strategies that resonate with family business values.

The research underscores the importance of considering sociocultural dimensions in tax compliance, fostering a more cooperative relationship between family businesses and tax authorities.

The study contributes a novel perspective by synthesizing social, psychological and legitimacy factors in understanding tax compliance in the unique context of family businesses.

Effective translation has become essential for seamless cross-cultural communication in an era of global interconnectedness. Translation management systems (TMS) have redefined the translation landscape, revolutionizing project management and execution. This study examines the attitudes of translation agencies and professional translators towards integrating and utilizing TMS, with a specific focus on Saudi Arabia.

The study's design was based on a thorough mixed-methods strategy that purposefully combined quantitative and qualitative procedures to create an array of findings. Through a survey involving 35 participants (both project managers and professional translators) and a series of interviews, this research explores the adoption of TMS, perceived benefits, influencing factors and future considerations. This integrated approach sought to investigate the nuanced perceptions of Saudi translation companies and expert translators about TMS. By combining the strengths of quantitative data's broad scopes and qualitative insights' depth, this mixed-methods approach sought to overcome the limitations of each method, ultimately resulting in a holistic understanding of the multifaceted factors shaping attitudes within Saudi Arabia's unique translation landscape.

Based on questionnaires and interviews, the study shows that 80% of participants were familiar with TMS, and 57% had adopted it in their work. Benefits included enhanced project efficiency, collaboration and quality assurance. Factors influencing adoption encompassed cost, compatibility and resistance to change. The study further delved into participants' demographic profiles and years of experience, with a notable concentration in the 6–10 years range. TMS adoption was linked to improved translation processes, and participants expressed interest in AI integration and mobile compatibility. Deployment models favored cloud-based solutions, and compliance with industry standards was deemed vital. The findings underscore the evolving nature of TMS adoption in Saudi Arabia, with diverse attitudes shaped by cultural influences, technological compatibility and awareness.

This research provides a holistic and profound perspective on the integration of TMS, fostering a more comprehensive understanding of the opportunities, obstacles and potential pathways to success. As the translation landscape continues to evolve, the findings from this study will serve as a valuable compass guiding practitioners and researchers towards effectively harnessing the power of technology for enhanced translation outcomes.