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Natural convection of water-copper nanoliquids confined in low-porosity cylindrical annuli
Natural convection in cylindrical porous annuli saturated by a nanoliquid whose inner and outer vertical radial walls are respectively subjected to uniform heat and mass influxes and out fluxes is studied analytically using the modified Buongiorno-Darcy model (MBDM) and the Oseen-linearization technique. Nanoliquid-saturated porous medium made up of water as base liquid, copper nanoparticles of five different shapes, viz., spheres, bricks, cylinders, platelets and blades, and glass balls porous material is considered as working medium for investigation. The thermophysical properties of nanoliquid -saturated porous medium is modeled using phenomenological laws and mixture theory. The effect of various parameters and individual effects of five different shapes of copper nanoparticles on velocity, temperature and heat transport are found. From the study, it is clear that the addition of a dilute concentration of nanoparticles increases the effective thermal conductivity of the system and thereby increases the velocity and the heat transport, and decreases the temperature. In other words, the heat transport is more in the case of heat and mass driven convection compared to purely heat-driven convection. Among the five different shapes of nanoparticles, blade-shaped nanoparticles facilitate the transport of maximum temperature compared to all other shapes. Maximum heat transport is achieved in a shallow cylindrical annulus compared to square and tall circular annuli. The increase of the inner solid cylinder's radius is to decrease heat transport. The results of the KVL single-phase model are obtained from the present study by setting to zero the value of the nanoparticles concentration Rayleigh number. Also, neglecting the curvature effect in the present problem, we obtain the results of the rectangular enclosure problem. 2020 The Physical Society of the Republic of China (Taiwan) -
Study of rotating Bard-Brinkman convection of Newtonian liquids and nanoliquids in enclosures
Taylor-Bard convection of water and water-based nanoliquids confined in three different types of high porosity rectangular enclosures, viz., shallow, square and tall, is studied analytically using both infinitesimal and finite amplitude stability analyses. We make use of the modified-Buongiorno-Brinkman model(MBBM) for the governing equations concerning nanoliquid-saturated porous enclosures bounded by rigid-rigid boundaries and obtain analytical results. Among three types of enclosures, maximum and minimum heat transfers are observed in tall and shallow enclosures respectively. Water well dispersed with a dilute concentration of single-walled carbon nanotubes(SWCNTs) is considered as a working medium. The water-SWCNTs is able to flow in the porous medium because the medium is loosely-packed with porosity in the range 0.5 ? ? ? 1. In addition to this, the maximum volume fraction of nanoparticles considered in the system is 6% and thus this does not alter the fluidity of the system. We found from the study that the presence of low concentration(volume fraction-0.06) of SWCNTs in a water-saturated porous medium effectively improves the heat transport of the system due to its high thermal conductivity and large surface area. Due to the presence of a porous medium, however, the onset of convection gets delayed and heat transport in nanoliquids gets substantially reduced in a Bard-Brinkman configuration resulting from the weak thermal conductivity of the porous medium. Thus the porous medium acts as the heat storage system. Also, in a rotating frame of reference the heat transport gets reduced and rotation serves as an external mechanism of regulating heat transport in the system. The nonlinear dynamics of the system is studied using the 6-mode Lorenz model. Chaotic motion in the system is studied using the maximum Lyapunov exponent(MLE). The Hofp-bifurcation point of the system along with the MLE is used to investigate periodic, nearly periodic and mildly chaotic behaviors of the system. 2020 -
Achievenment motivation and self esteem among handicapped children
How the children with handicap perceive themselves and their self esteem levels are important yet not much focussed aspect in disability research. If we have a correct evaluation of their motivational level and self esteem it may help us to modify their training interventions and also would make them feel more satisfied and confident. So we planned to study achievement motivation and self esteem levels of handicapped children. The Objective of the study is that to to compare achievement motivation of physically handicapped to that of non-handicapped school children, and to compare self esteem of physically handicapped to that of non-handicapped school children. Methodology 40 physically handicapped school students and 40 age, gender and education matched non handicapped students were included in the study. Handicapped children of other categories like sensory disability, visual impairment, hearing impairment and speech impairment were excluded. Achievement motivation questionnaire was used to measure the motivational behaviour and Rosenberg self-esteem scale was applied by asking the respondents to reflect on their current feelings. Results and Conclusions Achievement motivation and self esteem were observed to be significantly lower in physically handicapped students compared to healthy controls. Significant gender difference in favour of females was observed i.e., self esteem and achievement motivation was significantly higher in females of both the groups compared to males. The study emphasizes need for interventions to improve self esteem and motivation levels of handicapped children. -
A Hybrid Approach Against Black Hole Attackers Using Dynamic Threshold Value and Node Credibility
Detecting black hole attackers is tedious in Vehicular Ad Hoc Networks due to vehicles' high mobility. The main consequence faced because of these attackers is an increase in the number of dropped packets which converts secure and fastest paths to compromised ones. Since these attackers can act individually and collaboratively as a group, early detection of these attackers must be feasible to preserve the network's performance. The majority of current methods rely on predetermined threshold and trust score values, which are ineffective in accurately identifying black hole attackers. Hence, this paper proposes a hybrid approach using dynamic threshold value and node credibility for early detection of black hole attackers. RSUs periodically compute the dynamic threshold value and categorize the vehicles into categories 1, 2, and 3. Vehicles classified as Category 1 are legitimate, whereas Category 3 vehicles are attackers. Vehicles in Category 2 are suspicious, requiring further analysis using node credibility values to identify attackers. It is protected against single, multiple, and collaborative black hole attackers. The NS2 simulation results demonstrate that the suggested method is optimal concerning PDR, Throughput, Delay, and Packet Loss Ratio compared to recent techniques. Since the proposed scheme efficiently identifies the attackers, it has 89.67% PDR, which is higher when compared to other schemes. 2013 IEEE. -
A Cognitive Workload-Aware Machine Learning Model for Performance Enhancement in Cyber-Physical Systems
Cyber-Physical Systems increasingly demand seamless coordination between human operators and autonomous processes, which increases the complexity. High cognitive workload in those environments amounts to a degradation of performance, decision fatigue, and increased susceptibility to system failure and cyber threats. To address these challenges, we propose a Neuro-inspired Cognitive Workload Optimizer (NCO), a novel machine-learning-based model for the monitoring, prediction, and optimization of cognitive workload for CPS performance improvement. The NCO framework employs neuro-inspired deep learning techniques, with LSTM networks coupled with an attention mechanism for assessing workload patterns dynamically in time. The adaptive operation of the system depends on executing a contextual analysis of system data and operator interaction metrics, whereby NCO recognizes fluctuations in workload and adjusts the operations of the system in real-time to maintain an optimal state for cognitive functioning. Thus, the model implements an adaptive feedback loop that prioritizes task distribution, resource allocation, and security management based on cognitive load estimations. In this way, CPS environments are hereby enabled to proactively mitigate operator overloads, minimize latencies, and enhance accuracy in decision-making, all while ensuring this is happening under dynamic conditions ensuring robust system performance. Experimental results on simulated CPS datasets indicate that NCO can reduce workloads peaks by 35%, improve system throughput by 28%, and provide better anomaly detection performance in conditions of high stress. The NeuroCPS-Optimizer thus opens up a new paradigm for cognitive-aware CPS management, ensuring that human and machine components are kept within safe and efficient bounds. This research thereby advances the creation of resilient and intelligent CPS that can self-adjust and sustain performance levels in complex and demanding environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption
The study presents a machine learning approach to predict carbon dioxide (CO2) emissions by analysing key factors such as energy consumption, renewable energy adoption, and economic growth (GDP). Traditional forecasting methods struggle to capture the complex and nonlinear patterns of emissions. To overcome the limitations and improve the accuracy, research combines classical statistical models like ARIMA and VAR with advance techniques, including deep learning (LSTM) and ensemble methods (XGBoost, stacking). The models are trained on a global dataset of energy and economic records. The results shows that the hybrid models, particularly the LSTM + XGBoost and stacked approaches, have outperformed the traditional methods by obtaining a lower Root Mean Square Error (RMSE) and a higher coefficient of determination (R2). Apart from advancing environmental data science, the research offers a solid predictive framework to support policy initiatives related to the Sustainable Development Goals, specifically SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). 2025 IEEE. -
Gestational diabetes prediction using hybrid probabilistic machine learning models
[No abstract available] -
Nonlinear steady Darcy-Bard convection problem: Revisit using the heatlines approach
The classical problem of Darcy-Bard convection(DBC) in enclosures is revisited using the method of heatlines to have a better perspective of the problem. General aspect ratio is chosen in the analysis which helps in obtaining the results of four different types of enclosures, viz., tall, square, shallow and very shallow. Three different water saturated porous media(WSPM) and their actual thermophysical properties are used in the computation of the results. The method of heatlines facilitates the observation of fluid and heat flow lines in order to have a good understanding of the dynamics. The neo-classical approach not only accurately predicts the critical Darcy-Rayleigh and wave numbers but also picturizes the heat flow of the problem in the most natural way. The Galerkin method is used in the paper for the normal and convective modes of convection yields accurate analytical results in the heatlines formulation. Theoretical expression to calculate the number of Bard cells that form in the system at onset is obtained by linear theory, and ranges of aspect ratio at which unicellular, two-cellular and multicellular convection are possible are determined and documented. The weakly non-linear stability analysis is performed to determine the heat transport. Among four considered enclosures, maximum heat transport is achieved in the case of a square enclosure. Out of three chosen WSPM, the water-saturated glass balls porous medium and the water-saturated aluminium-foam porous medium show most stable and least stable behaviours. Results obtained from the heatlines approach are validated by comparing with the results of the classical DBC problem in the case of a very shallow enclosure. From the study, we conclude that the square enclosure with water-saturated aluminium-foam porous medium has possible application in heat removal systems. 2025 The Physical Society of the Republic of China (Taiwan) -
The Role of Social Media in Shaping Gen Zs Awareness in Understanding Social Issues
Many social movements (such as #Black Lives Matter Movement, #MeToo Movement and #EndAcidSale Movement) have gained momentum through social media, but there is a research gap in understanding Generation Zs individuals participation and engagement in these social movements and also the awareness they have regarding social issues through social media. This study aims to understand how social media engagement affects Gen Zs understanding of social issues and how the perceptions of Gen Z regarding social issues is influenced by it. It is crucial to look into Gen Zs engagement in these social movements and also the awareness they get regarding social issues through social media. Algorithms tend to dictate the content that reaches the users; therefore, we want to analyse whether social media algorithms is effective in ensuring Gen Z social media users get an understanding of different viewpoints regarding the social issue. Understanding Gen Zs engagement in social activism through social media requires an in depth analysis on the factors that motivate them to do so. These factors can include their belief that it would lead to a systematic change, a sense of community they get when they engage with others on social media about social issues, pressure from their social circle to engage with social media content related to social issues and the desire to be seen as socially conscious by their peers. This study also aims to find out whether this has an effect on their mental health. The findings of the study show that social media content engagement moderates the relationship between social issue awareness and both real-world activism and mental health among Gen Z. Increased social issue awareness and social media content engagement lead to higher activism, but excessive engagement is found to negatively impact mental health among Gen Z. Results from the study indicates that respondents believe in the effectiveness of social media activism but also report feeling stressed, anxious, and overwhelmed by social issue-related content. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Role of digital marketing tactics in enhancing financial performance in e-commerce enterprises
In recent years, the exponential growth of e-commerce has revolutionized how businesses operate and interact with consumers. This surge in online shopping has been accompanied by a parallel rise in digital marketing strategies employed by e-commerce companies to attract, engage, and retain customers. Digital marketing encompasses various techniques, including search engine optimization (SEO), social media marketing, content marketing, email marketing, and pay-per-click advertising. The effectiveness of these strategies in enhancing a company's financial performance has become a subject of significant interest and scrutiny. Companies can boost their bottom line and achieve greater profitability by reducing customer acquisition costs, increasing customer lifetime value, and improving overall marketing efficiency. Ecommerce businesses could better reach their target audience on these channels by increasing their brand visibility and reach and driving traffic to their websites. 2025, IGI Global Scientific Publishing. All rights reserved. -
The Role of Al in Customer Relationship Management for Tailored Financial Services
Much as the financial services industry is characterised by dynamism, Artificial Intelligence (Al) is gradually transforming the Customer Relationship Management (CRM). Among the AI-related advances is the advancement of bespoke financial services that increases involvement and experience of the customers. This chapter seeks to address the following research question: how has the incorporation of Al tools influenced the CRM strategies adopted by the industry? Newer, implementing Al in CRM systems, banks can go through terabytes of customers' information looking for more about each other's preferences, tendencies and needs. This information may therefore allow for financial services, proactive customer care and consumption point marketing based on segments of the population. Information on financial organisations' successful implementation of Al-based CRM systems is provided in the chapter; in addition, challenges and ethical issues regarding the use of these technologies are also described. The findings show how the use of Al can enhance CRM in the financial services sector resulting in satisfied and loyal customers alongside more organisation productivity. As for the maximisation of CRM results, the report also suggests that financial institutions must use Al appropriately while addressing the problems associated with the latter. 2025 Lakshmi S.R., Rajimol K.P., Y.K. Sunitha, Ajatashatru Samal, Priya R.P. and Srija H.R. All rights reserved. -
Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review
Cancer is one of the most fatal diseases in the world and the leading cause for most deaths worldwide. Diagnosing cancer early has become the need of the day for doctors and researchers as it allows them to categorize patients as high-risk and low-risk categories which will eventually help them in correct diagnosis and treatment. Machine learning is a subset of artificial intelligence that makes use of raw data to make predictions and insights. Using machine learning for cancer prognosis has been under study for a long time and several papers have been published regarding the same. Even though many papers have been published on the usage of statistical methods for cancer prognosis, it has been proved that machine learning models provide more accuracy when compared to conventional statistical methods of detection. These machines can be trained to detect abnormalities such as a tumour by looking at real-world examples. Models such as artificial neural networks, decision trees, clustering techniques, and K-Nearest-Neighbours (KNNs) are being used for cancer prediction, prognosis and also research purposes. The key aim of this article is to go through the popular key trends in using machine learning algorithms for cancer prognosis, types of input datasets to be fed, different types of cancers that can be studied and also the performance of these models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Building Robust FinTech Applications and Reducing Strain on Strategic Data Centers using the LoTus Model
Agile is a well-known project management approach that has been used for many years. It places a strong emphasis on client satisfaction, adaptability, and teamwork. Agile was first developed as a software development approach, but it has now been modified for application in other sectors including marketing and finance. The Agile Manifesto, which was released in 2001 and explains the principles and ideals of Agile development, is the foundation of the Agile ideology. One or more of the guiding principles is to adapt to change instead of following a plan, prioritize functional software over thorough documentation, and collaborate with customers over negotiating contracts. Agile has gained popularity over time as businesses try to be adaptable and responsive to their customers' constantly changing business demands. The lack of predictability in Agile is one of its key drawbacks. Agile stresses client cooperation and adaptation, therefore the finished product could differ somewhat from what was originally planned. For businesses that depend on meticulous planning and a rigid schedule, this lack of predictability can be problematic. It faced a serious problem during the process of building a finance application called JazzFinance. This has led to build another robust and systematic software development method called as LoTus model. The proposed LoTus is an acronym for two abbreviations. Those are lean optimization TypeFace for Unified Systems (LoTus) and Locate dependencies, optimize for reusability, Test-Driven environment, Unify Design and Scalability. This article goes through the development of LoTus and how it has helped us build a stable finance application within a small amount of stipulated time. 2023 IEEE. -
Strengthening of brick masonry using biaxial polypropylene geogrid as confinement reinforcement
Recent and past earthquakes have once again reiterated the requirement of strengthening the masonry structures to withstand both in-plane and out-of-plane loads. In this experimental investigation, biaxial polypropylene geogrid was used as a confinement reinforcement on the surfaces to strengthen masonry specimens. The masonry specimens without and with geogrid have been subjected to a compression test, flexural bond strength test and diagonal tension (shear) test as per IS 1905, ASTM E518 and ASTM E519, respectively. From the results, it has been found that biaxial polypropylene geogrid significantly enhances the strength in masonry specimens with geogrid and also reduces crack propagation in all three tests. The relationship between compressive strength and flexural bond strength, compressive strength and shear strength of masonry specimens with geogrid has been established. Furthermore, based on the cost analysis of various strengthening techniques, it was concluded that the use of biaxial polypropylene geogrid is an economically feasible alternative to other reinforcing materials, such as stainless-steel wire mesh and polyester geogrid. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Digital Transformation in Higher Education: Impact of Instructor Training on Class Effectiveness During COVID-19
Digital technology is transforming society and business like never before. Digital technology has made inroads into all sections of society, especially with the pandemic restricting interaction and movement in the physical space. Education systems and institutions have witnessed a drastic change in their pedagogy. Education institutions adopting digital technologies can become drivers of growth and development for their ecosystems bringing significant changes in education, engagement, and management of class activities of educational institutions. The education system will have to adapt and evolve to take advantage of the new technologies and tools and develop strategies to play an active role in the digital transformation process. In the wake of the COVID-19 situation, higher education institutions have adopted digital platforms for teaching and learning. The study attempts to understand the instructors/academician/teachers training process adopted by selected higher education institutions in India to facilitate migration to digital platforms. Further, the study analyses the challenges faced in the new normal of education and the levels of training process initiated by institutions for teaching faculty. The authors have tried to analyse how this has enabled instructors to meet the challenges of conducting online classes and increase class effectiveness. The study unfolded the impact of high-level institutional training on class effectiveness and how individual digital preparedness is essential in engaging virtual classrooms. Further, the positive impact of training in reducing anxiety in engaging online sessions and the extent of motivation to continue online teaching as it has become inevitable with the second wave of the pandemic were examined across age and gender. An attempt is made to suggest few strategies for continued effective online class engagement as India battles through the second wave of the pandemic. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
FOXS HEAD OR LIONS TAIL? WORK LIFE BALANCE OF WOMEN ENTREPRENEURS IN AGRICULTURE AND FARM VENTURES AND ITS ANTECEDENT EFFECT ON QUALITY OF LIFE; [CABE DE RAPOSA OU RABO DE LE? EQUILRIO DA VIDA PROFISSIONAL DAS MULHERES EMPREENDEDORAS NA AGRICULTURA E EMPREENDIMENTOS AGROLAS E SEU EFEITO ANTECEDENTE NA QUALIDADE DE VIDA]; [CABEZA DE ZORRO O COLA DE LE? LA CONCILIACI DE LA VIDA LABORAL Y FAMILIAR DE LAS MUJERES EMPRESARIAS EN LA AGRICULTURA Y LAS EXPLOTACIONES AGROLAS Y SU EFECTO ANTECEDENTE EN LA CALIDAD DE VIDA]
Purpose: The objective of this study was to identify the factors that influence work life balance of women entrepreneurs in the field of agriculture and allied products and how the family demands affect their work-life balance. Further, the paper explores the conflict between parental demand and running a business. Theoretical framework: Literature review points out that despite, an increase in the number of women entrepreneurs over the years, according to the (Global entrepreneurship monitor report, 2020), fewer women pursue entrepreneurship due to various challenges of managing personal and business responsibilities and striking the right balance. Work-life balance is frequently examined in the context of human resource management (Etienne St-Jean and Duhamel M.,2020)but not much has been explored in an entreprenurial context.Hence this study is to investigate and understand the influence of various factors affecting work life balance from an entrepreneurial standpoint. Design/methodology/approach: Triangulation method was used for the study by utilizing both quantitative and qualitative data. The researchers developed a questionnaire to measure work-life balance experienced by women entrepreneurs with 12 independent variables to measure the dependent variable work-life balance.The sample consisted of 450 women agripreneurs Findings: The findings reveal that the age of the children is a major determinant of the extent of parental demand a woman goes through in her life and family support systems are critical in reducing overlap and conflict between the life domains. A positive spillover between the domains significantly enhances quality of life of women entrepreneurs. Research, Practical & Social implications: We suggest a future research into other Personality traits and macro environmental factors which can have a bearing on work life balance of women entrepreneurs which would enable an inclusive entrepreneurial ecosystem. Originality/value: The researchers have concluded that positive spillover between the domains significantly enhances quality of life of women entrepreneurs. 2022 The authors. -
Indigenous tribes and inclusive engagement: An integrated approach for sustainable livelihood into the future
Tourism acts as a stimulant in rural poverty reduction and inclusive socioeco-nomic development. Sustainable tourism can significantly contribute to the economic diversification and local economic development of rural areas with its ability to create jobs and encourage infrastructural development focusing on preserving the environment, culture and indigenous groups. The detrimental effects of tourism on the economy, society and culture have shifted attention to sustainable travel. As a result, terms like 'tribal tourism', 'ecotourism' and 'sustainable tourism' have become popular. Inclusive engagement is a crucial agenda item in future tourism development and a major concern of many international organisations, including the United Nations. This chapter focuses on exploring the tribal communities and their involvement in sustainable tourism initiatives with an overarching focus on the role of the indigenous community and their skill sets in creating sustainable livelihoods through tribal tourism. Apart from creating direct and indirect employment opportunities, 2024 Kottamkunnath Lakshmypriya and Bindi Varghese. All rights reserved. -
IOT-BASED cyber security identification model through machine learning technique
Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources. 2023 The Authors -
Anti-caste Movement and Rise of Dalit Womens Voices from South Asia
There have been cohesive attempts at forging alliance through the sustained efforts of emergent Dalit Civil society network, Dalit academicians and the renaissance of Ambedkarite thought among the Dalit youth around the question of political representation and social justice. This has led to a renewed and greater visibility of caste-based social relations and interactions in the present millennium, which was otherwise, treated as a long-forgotten age-old tradition. The lived experiences of exclusion and atrocities faced by members of the Dalit community especially the violence against women and girls reflect the grim reality of the prevalent casteist and patriarchal society. In this background, the emergence of Dalit Womens collectives raising their voices not just on caste but also on the intersectionality of gender provides a new dimension of analysis based on the critical race theory. Thereby, the attempt has been on forging an alliance and building collective voices. The chapter seeks to highlight the numerous struggles and triumphs along the way made by Dalit Women (also with building alliances with Black Womanists and Feminists Movement) in challenging the way in which feminists discourses have been held leading to rethinking and reimagining womens collectives by way of building solidarities, recognizing the difference of experience and positioning in caste and gender ladder that have influenced access to resources, rights, political representation and decision-making power from the local governance to national level. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Artificial intelligence in higher education: the challenges, opportunities and the road ahead
This paper investigates to deliver an overview of literature from 2012 to 2023 on the phenomena of implementing artificial intelligence in education (AIEd). With the help of the Scopus indexing database, data from 441 articles were extracted, analysed based on the keywords and preliminary reading and synthesised according to explicit inclusion and exclusion criteria and article compilation was on the parameters of scientific procedures and rationales for systematic literature review protocol (SPAR4SLR). Drawing on the recent literature depicts that the inception of artificial intelligence in education is still in its initial stage and much research is required. This article implies that although there are benefits and challenges talked about in the article delving into the application of AIEd in higher educations system of teaching and learning that shall lead the education system to newfound intelligence and automation, however, things are at the very initial stage and filled with conjectures. The findings demonstrate that the artificial intelligence-based teaching and learning phenomenon has a bright future as educational institutes understand its upcoming impact. The greatest challenge for educational institutes now is to start planning, designing, developing and implementing artificial intelligence-based courses for multidisciplinary and holistic training for future employees. Copyright 2025 Inderscience Enterprises Ltd.