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Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

The purpose of this paper is to explore the trends of changing temperature of Lahore in Pakistan due to invigorating urbanization process since 1950‐2007.

This research is designed by using the numerical time series data of mean minimum temperature (MMiT), mean maximum temperature (MMxT) and mean annual temperature (MAT). The growth in urban population, area and transportation are also evaluated by using the available data. Linear regression method is applied to investigate the results of change in temperature. Three different approaches to examine the MAT are testified; first as an entire period (1950‐2007), and then by dividing the entire period into two equal phases as Phase I (1950‐1974) and Phase II (1975‐2207). MMiT and MMxT are analysed for the entire period without making any division.

The results of the paper are significantly indicating an increase in MAT and MMiT which have risen up to 0.89 and 2.51°C, respectively, while MMxT remained resolute throughout the study period. Change in MMiT is observed regular and brisk than other parameters of temperature. Increase in temperature in Phase I is observed only 0.062°C and in Phase II it is observed 0.94°C.

This research can be further worked out by using different meteorological models to study the effects of urbanization on lower surface atmosphere and urban heat island effects in Lahore.

By taking into consideration these results, the town planners and government can make different strategies to mitigate the urban effects on rising temperature in Pakistan.

African countries are endowed with both human and natural resources. These resources constitute integral components for any economic development due to the long-lasting relationship with all sectors in an economy, yet there is an obvious disagreement between growing economy and employment generation in Africa. Though there has been a growing pattern of economic size, particularly the gross domestic product (GDP) among African countries, most of these economies are low in human development. The disagreement between economic growth and employment generation in Africa despite abundant natural resources located on the continent calls for public discourse among scholars. Therefore, the purpose of the study is to examine the peculiar drivers of unemployment intensity in a region characterized by endowed resources.

The paper adopts two approaches; the authors employed the pooled mean group (PMG) estimator and utilised stochastic frontier analysis (SFA) to generate a government efficiency index between the period 1991 and 2017 among sub-Saharan Africa (SSA) countries.

The empirical results through the single output-multiple inputs framework indicate that on average, there is a low level of government efficiency towards increasing the objective of human development in Africa. However, in the long run, natural resource endowment has a positive and significant relationship with employment generation for SSA. Hence, the study established that a low level of government efficiency has a long-lasting effect on low human development experienced in Africa.

The need to improve the level of government efficiency towards economic development by making both human and physical capital more effective will spur the exploration of natural resources.

The paper provides an empirical study of the effectiveness and efficiency of government through PMG and SFA in establishing the relationship between government approaches and employment level in selected SSA countries.

The purpose of this paper is to examine to what extent Finnish university students endorse entrepreneurial intent and the ways in which they position themselves in relation to entrepreneurship according to their self-perceived abilities or “ability self”.

The study was conducted by means of an e-survey, and the participants comprised the sample of students (n =1,819) from two Finnish universities, representing diverse fields of study.

It was found that a great majority of the students showed a relatively low intent to become an entrepreneur. The perception of abilities, such as innovativeness and ambitiousness-competitiveness, was positively related with entrepreneurial intent, whereas the perception of academic abilities and “conventional” employee skills indicated inverse associations.

The findings suggest that in terms of self-perceived abilities, entrepreneurship in an academic context is perceived as a rather restricted category to which only a few specific individuals have access. Accordingly, there is a certain tension between the tenets of entrepreneurship and corresponding abilities, and the ethos of universities and related high-valued abilities such as theoreticality and criticality.

Although employability and entrepreneur intent have been widely studied, little is known about students’ identification with entrepreneurship according to their ability perceptions. The present study contributes to the existing body of knowledge on university students’ “internal employability” that involves students’ self-assurance and views of work-related relevance with regard to supposed abilities.

The purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.

In this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.

Analysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.

The paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

In cities with high density, heat is often trapped between buildings which increases the frequency and intensity of heat events. Researchers have focused on developing strategies to mitigate the negative impacts of heat in cities. Adopting green infrastructure and cooling pavements are some of the many ways to promote thermal comfort against heat. The purpose of this study is to improve microclimate conditions and thermal comfort levels in high-density living conditions in Seoul, South Korea.

This study compares six design alternatives of an apartment complex with different paving and planting systems. It also examines the thermal outcome of the alternatives under normal and extreme heat conditions to suggest strategies to secure acceptable thermal comfort levels for the inhabitants. Each alternative is analyzed using ENVI-met, a software program that simulates microclimate conditions and thermal comfort features based on relationships among buildings, vegetation and pavements.

The results indicate that grass paving was more effective than stone paving in lowering air temperature and improving thermal comfort at the near-surface level. Coniferous trees were found to be more effective than broadleaf trees in reducing temperature. Thermal comfort levels were most improved when coniferous trees were planted in paired settings.

Landscape elements show promise for the improvement of thermal conditions because it is much easier to redesign landscape elements, such as paving or planting, than to change fixed urban elements like buildings and roads. The results identified the potential of landscape design for improving microclimate and thermal comfort in urban residential complexes.

The results contribute to the literature by examining the effect of tree species and layout on thermal comfort levels, which has been rarely investigated in previous studies.

The purpose of this study is to examine the impact of organizational cynicism (OCyn) on organizational citizenship behavior (OCB) among bank employees with a focus on the role of work alienation (WA) as a potential mediator.

Using standardized questionnaire, data were collected from 381 employees working in the banking sector of Punjab, a northern state of India. Statistical techniques such as hierarchical multiple regression and confirmatory factor analysis along with PROCESS macro were used for data analysis.

Results reveal that OCyn has a significant negative effect on OCB. Further, WA is found to be significantly partially mediating the relationship between the aforementioned constructs.

This study pertains to a single sector i.e., banking sector restricting the generalizability to other industrial and vocational settings. Further, it may be difficult to draw any causal inferences as the research design adopted for this study is cross-sectional in nature.

In order to promote OCBs among bank employees, the formation of negative workplace attitudes such as OCyn and WA needs to be regulated. This can be achieved through improving communications network, encouraging participative decision-making activities, conducting psychological counseling and stress management training sessions.

This study is one of the scarce empirical research works that have substantiated the direct impact along with the indirect impact of OCyn (through work alienation) on OCB among bank employees.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.

Recognising the significance of international trade in economic growth, this research explores the drivers of exports in South Asian Association for Regional Cooperation countries from 2008 to 2021. The study employs the export demand model and the augmented exports supply model and utilises pooled time-series data. This study questions whether export supply decisions are based on traditional trade model factors, emerging trading realities or macroeconomic variables. The model based on fixed effects evaluates the connection between exports and their possible drivers. Traditional export supply models suggest determinants like production capacity, variable cost and relative pricing influencing South Asian export supply performance substantially. Changes in trade, for example, have a substantial impact on export supply, demonstrating that the trade liberalisation procedure promotes growth in exports, compression in imports and technological advancement. The worsening state of the energy industry and growing levels of corruption have proved to be significant deterrents to export supply decisions. The results verify foreign direct investment's positive and medium influence on the expansion of exports. Other variables, however, such as GDP and its growth, Official Development Assistance (ODA), development expenditure, indirect taxation, labour supply and the exchange rate of currencies, have a positive impact on the flow of exports. Furthermore, the data corroborate the notion that increased savings have a significant beneficial influence on the flow of exports. The study proposes that concerned governments examine their export policies and adopt new policies adapted in accordance with changing circumstances with the goal of increasing and enhancing the performance of exports.