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Transforming Teletherapy: Using Transfer Learning and NLP for Improved Mental Health Care
The increasing reliance on tele-therapy for mental health support highlights the need for advanced methodologies to improve diagnostic precision and patient outcomes. This study explores the transformative potential of transfer learning in natural language processing (NLP) to enhance the detection of mental health conditions during tele-therapy sessions. Leveraging a dataset sourced from mental health-related subreddits, which includes conversations mapped to five target categories (Stress, Depression, Bipolar Disorder, Personality Disorder, and Anxiety), we fine-tuned a pre-trained BERT model for multi-class classification. Our study's results highlight significant performance enhancements achieved through the implementation of transformer-based models. The proposed framework achieved an accuracy of 83%, with macro average precision, recall, and F1-score values of 0.84, 0.83, and 0.83, respectively. Class-specific analysis further underscores the model's robustness, with precision ranging from 0.75 to 0.92 and recall values exceeding 0.80 for most categories. These outcomes significantly outperform traditional machine learning models such as Random Forest (accuracy: 72.65%) and Support Vector Machines (accuracy: 69.71%), demonstrating the superior capacity of BERT to capture complex linguistic patterns and semantic nuances in patient interactions. This research underscores the transformative role of transfer learning in NLP applications for tele-therapy, offering a scalable and precise solution for mental health assessment and paving the way for personalized, AI-driven interventions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
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. -
Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models
Adversarial attacks are a serious threat to machine learning models, both for conventional architectures, like neural networks, and for more sophisticated frameworks, like Vision Transformers (ViTs). Although a lot of work has been done to defend state-of-the-art deep learning models against attacks like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Gaussian noise perturbations, classical machine learning models like logistic regression, support vector machines (SVMs), and decision trees are relatively less explored despite their extensive use in situations where low computational complexity and high interpretability are needed. This work presents a rigorous evaluation of the adversarial vulnerability of binary and other classical models on the MNIST dataset and explores the effectiveness of various defense mechanisms, including adversarial training, input pre-processing (Gaussian smoothing), and defensive distillation. Experiments demonstrate that adversarial training is the most effective defense that improves model robustness with classification accuracies of up to 96% in all attack scenarios. In contrast, defensive distillation and input preprocessing make modest gains, with accuracy levels ranging from 61 to 81% based on the nature of the attack. Through adversarial threat analysis of typical machine learning models, this work points out their inherent susceptibility to adversarial perturbations and introduces robust defense techniques. These results identify the necessity for robust security and reaffirm the practical viability of typical models in the scenario of resource-constrained environments, contributing towards a more complete picture of adversarial defenses for the entire spectrum of machine learning architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Examining the Relationship Between Acceptance of Technology Integration and Dimensions of TPACK Among Higher Secondary School Teachers in Kerala
The study uses the Unified Theory of Adoption and Use of Technology (UTAUT) to investigate how higher secondary school teachers in Kerala, India, connect to Technological Pedagogical Content Knowledge (TPACK) and technology adoption. Using 496 teachers from diverse backgrounds, the study revealed some important positive correlations between TPACK characteristics and UTAUT results. Strong technologically minded teachers are interested in incorporating technology into the classroom. Crucially for the quality of education, PK, PCK, and CK have a modest influence on technology acceptance. The study emphasises the need for specific professional development and supportive policies to fit Kerala's unique educational scene. With consequences for the whole educational scene, TPACK can encourage improved technology acceptance, particularly in sectors connected to technology. Future research should look at long-term changes in technology use, geographical comparisons, and how new technologies impact teaching approaches. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Evaluating the Effectiveness of GraphSAGE with Reinforcement Learning in Suicide Risk Prediction
Suicide is considered to be a major mental health issue that has affected most individuals worldwide. According to World Health Organization, it shows the rise of suicidal rates among students has increased drastically. This vulnerability shows the rising need to encounter this issue with immediate effect. Therefore, proper detection methods have to be incorporated so that we can reduce the number of suicidal rates. Many computational models were implemented to address this issue. This study was conducted to compare various algorithms such as traditional machine learning models random forest and also various deep learning models like GraphSAGE, Graph Convolutional Network, Convolutional Neural Network, and Convolutional Neural Network with Long Short Term Memory with the proposed GraphSAGE Reinforcement Learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Consumer Trust and Online Security in the UK Banking Sector
The expanded use of online banking has generated major worries about consumer trust and the security of their financial information. As online banking becomes more prevalent in the United Kingdom it becomes essential to ensure consumers trust the security measures of these platforms. This research examined what elements create trust and security among consumers in the UK online banking industry. The study examined critical components including perceived usefulness, navigation simplicity and security effectiveness to understand their impact on consumer trust. The study proves significant because it fills research gaps while delivering valuable insights that help financial institutions improve consumer trust through enhanced user experience and security measures. The research objectives were met through quantitative methods which involved gathering data using an online survey from 55 participants from 1st October to 30th October 2024. The survey targeted multiple aspects such as ease of navigation through the platform, the perceived usefulness of the system, user concerns about information security, and trust levels in online banking security measures. Through descriptive statistics, correlation analysis and linear regression techniques researchers analysed data to uncover how different factors affect consumer trust in online banking. The study utilized the Technology Acceptance Model (TAM) and Protection Motivation Theory (PMT) as guiding theoretical frameworks for analysis. Three main factors including ease of navigation, the perceived usefulness of the system, and security information concerns emerged as significant drivers of consumer trust in online banking security. Although customers trust existing security protocols they require ongoing improvements and clear communication to sustain their confidence levels. Banks can use these findings to improve online platform trust by enhancing security measures and user experience. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
The Mediating Role of Attitude Towards Behaviour Between Brand Image and Purchase Intention: Evidence from Indias Housing Market
This study investigates the association between Brand Image, Attitude Toward Behaviour, and Purchase Intention within the Indian real estate sector. The research employs the Theory of Planned Behaviour (TPB) to examine the impact of brand image on purchase intention, with attitude toward behaviour serving as a mediating role. Data were gathered from 422 respondents. The collected data is inspected using Structural Equation Modeling (SEM hereafter) utilising JMP Software. The findings demonstrate that brand image has a substantial effect on purchase intention, both directly as well as indirectly, via attitude towards behaviour. The results emphasize the need to establish a robust brand image to cultivate favourable consumer perceptions and influence purchasing decisions, providing essential insights for real estate developers in a competitive landscape. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Export Rhythms in Indian Agriculture: Trend and Seasonal Decomposition of Indian Cereal Products Exports
This study investigates the long-term trends and seasonal dynamics of Indias cereal exports specifically Basmati rice, non-Basmati rice, other cereals, and wheat using trend modelling and decomposition techniques. Drawing on monthly export data from April 2006 to November 2024 (with wheat data beginning in 2013), linear, log-linear, and quadratic trend models were estimated alongside additive, multiplicative, and STL (Seasonal-Trend decomposition using Loess) seasonal models. Results indicate strong linear and exponential growth in Basmati and non-Basmati rice exports, wheat exports exhibited no statistically significant trend and displayed high volatility. Durbin-Watson statistics revealed serial autocorrelation in most models, highlighting the importance of incorporating seasonality and external shocks in trend analysis. Additive decomposition reveals pronounced seasonal effects in Basmati rice exports, STL analysis confirms these patterns. Wheat shows moderate seasonal strength, while non-Basmati rice and other cereals exhibit mild seasonality. These findings underscore the necessity of commodity-specific export strategies aligned with harvest cycles and global demand windows. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Unlocking Access: Technology as a Tool for Transgender Economic Justice
The Transgender community across the world face discrimination from all walks of life. The transgender, even if educated and skilled, faces multiple hurdles during the employment process and within the workplace once employed. The challenges have even increased with COVID-19 and mass layoffs affecting employees across the globe. Transgender people have to be empowered to face the challenges of employment processes and workplace challenges as and when they arise. With the advancement of digital technologies, the Fourth Industrial Revolution can be leveraged for training, empowerment, awareness, network building, grievance redressal and sustainability for the transgender community. Digital technologies leverage the community to collaborate and be vocal about their needs and rights at a global level. Digital technology also helps the transgender community reach the proper forum to implement the required program for the benefit of the community. The decentralized and anonymous system for recording data and reporting incidents will also be a helpful, transparent process without being subject to unnecessary scrutiny. The technology-enabled system and practices will also help the companies and institutions to ascertain appropriate and efficient methods for creating gender gender-neutral environment devoid of any discrimination. Recently, there was a significant rise in concerns about employment practices for the LGBTQ community around the globe during the pandemic, such as lockdown protocols affecting employment conditions, vaccine access, support from employers, severance pay, mental health, etc. The International ecosystem has seen minimal regulatory discussion, and individual countries and companies are now implementing schemes to address the issue faced by transgender individuals in the employment and work ecosystem through the infusion of technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Technology, Informality, and the Future of Work: Enhancing Socio-Economic Status of Domestic Workers in Karnataka
This study examines the impact of technology-based skill development on the socio-economic status of domestic workers in Karnataka, India. Using structural equation modelling with data from 433 female domestic workers across five districts, the research investigates how digital skill interventions influence employability, job security, and access to government welfare schemes. The findings reveal that technology-based skill development significantly enhances socio-economic outcomes through improved employability (? = 0.53, p < 0.001), with job security serving as a crucial mediating factor. However, government welfare schemes showed limited effectiveness in mediating the relationship between skill development and socio-economic advancement. The study highlights that 78% of respondents work part-time, with 88% receiving cash payments, indicating persistent informality. While digital skill programs create pathways for economic mobility, their success depends on facilitating stable employment rather than mere knowledge transfer. The research underscores the need for better integration between skill development initiatives and social protection systems to maximize benefits for marginalized domestic workers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Unpacking Insurance: The Challenges and Innovations in Tying and Bundling Practices in Insurance
The insurers market share mainly depends on the channels of distribution they have adopted to make the business cost-effective. IRDAIs working paper supported the idea that the lengthier the distribution channel, the more conflict of interest there was, and it had a mediating effect. There is a chance of misrepresenting policy information, which is an outcome of that effect. In India, as per IRDAI, the incurred insurance claim ratio, the ratio of claims settlement against the total premiums received in a year, varies yearly. Even though the ratio indicates the financial performance of each sector, it can also be a benchmark for explaining the scope of the product utilization by the insured. During 202223, the ratio ranges between 72 to 86% for the general insurance companies. It also differs for the Fire, Health, motors, and marine sectors. The highest ratio is for the health sector, around 86%. At the same time, the ratio for the domestic travel insurance business is around 19%. Travel insurance is an example of bundling an insurance policy with another product. However, there is no specific information about the scope of bundling of insurance policies and the claims arising out of it. To combat these challenges, innovative solutions must be explored. One such approach is the introduction of modular insurance plans, allowing consumers to customize their coverage based on specific needs without being forced into unnecessary add-ons. AI-driven policy comparison tools can further empower consumers by offering real-time insights into the value of bundled versus standalone options. Regulatory mandates for opt-out provisions in bundled offerings can ensure greater autonomy, allowing policyholders to reject unwanted components. Additionally, the implementation of blockchain-based smart contracts can bring transparency by clearly defining coverage terms and pricing, reducing hidden costs. By adopting these solutions, the insurance sector can enhance consumer trust, encourage informed decision-making, and create a more competitive marketplace, ultimately leading to fairer and more accessible insurance options for all. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Family Functioning and Differentiation in Indian Homeschooling Families: A Systems Perspective on Stress and Coping
Family system and functioning play an integral part in individuals emotional development and channel the various coping mechanisms adopted by individuals to deal with multiple situations in life. The study explored family functioning, family differentiation, family stress, and family coping strategies among homeschooling families. A sample of 115 homeschooling families was selected by snowball sampling from India. A quantitative approach using correlation and regression analysis was used for the study. There has been a surge in families opting for home-based education post-pandemic. There is a need for an in-depth exploration of how these families function, develop differentiation, experience stress, and cope in a collectivistic culture like India. The results indicate that balanced cohesion in families predicts better differentiation in the motherchild subsystem, while family satisfaction predicts better differentiation in the husband and wife subsystem. The research findings can form a basis for developing family therapy specific to homeschooling and enhance the knowledge and understanding regarding the benefits and costs of homeschooling. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Sacred Roots: Rethinking Urban Landscapes via Ethnobotanical Narratives
This chapter discusses the synthesis of interdisciplinary research on the integration of sacred ethnobotanical knowledge with artificial intelligence (AI) into the present-day urban planning. This chapter draws upon a wide range of literature in the field of ethnobotany, ecotheology, urban ecology and digital innovation to explore how relationships between religious worldviews (including TEK), AI and green infrastructure can be used toward the enhancement of sustainable development of the urban. Review of current academic discourse on sacred plant landscape is emphasized above all, also examining the academic discourse on the nature of faith based ecological ethics and AI assisted urban greening strategies. I begin by reviewing ethnographic approaches and field-based studies that discuss the cultural and spiritual significance of sacred plants in Hindu, Islamic and Christian traditions, then examine service and trust as both a source and outcome for social infrastructure. It is critically analyzed how theological frameworks are ecologically applicable on the plural urban context. The review of AI integrated urban gardening initiatives provides a glimpse of how sensor data, machine learning models as well as mobile platforms are used to monitor plant health and plant biodiversity and how these can also be problematic on ethical front, justice, appropriation of knowledge and autonomy of community. The case studies from projects in Tokyo, Singapore, Ethiopia and Barcelona are placed within a global context and globally applied with a thematic synthesis in order to explore how, in practice, the coalescence of sacred ecological values and technological interventions occurs. The chapter discusses challenges of implementing policy, of cultural commodification, and of current interfaith collaboration models. The end of the review discusses the best practices and policy recommendations that can assist cities to join spiritual stewardship with digital ecological management to coalesce inclusive, biodiverse, and culturally grounded urban ecosystems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Topophilia in Transition: KeralasSacred Landscapes and Smart CityDevelopment
The rise of smart cities in India presents a complex phenomenon and a significant challenge for the preservation of sacred landscapes. To address thisthe chapterproposes Smart Theology as a conceptual framework that integratesecological ethics and cultural resilience with digital innovation. This approach is examined through fieldwork in Keralas kavussacred sites reflecting Indigenous ecological knowledge and ritual practicefunded by the Centre for Cultural Resources and Training (CCRT), Government of India. Digital interventions includingGIS, Virtual Reality, and community-driven tools serve as a vital bridge mediating the tension between cultural legacy and urban innovation. By investigating these applications the study demonstrates how technology facilitate inclusive preservation while maintaining the spiritual essence of sacred landscapes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Water Sustainability and Smart Monitoring: IOT and AI for Water Use
This chapter discusses the contributions of Artificial Intelligence (AI) and the Internet of Things (IoT) to sustainable water management. It considers how traditional water management, including reservoirs, qanats, and aqueducts, has provided important lessons in climate adaptation and sustainability, while contemporary technologies effectively address challenges of scarcity, leakage, and inefficient management. AI-supported predictive analytics, IoT-enabled smart sensors, together with precision irrigation, can support leak detection and water demand forecasting, detecting soil moisture through sensors, and water quality monitoring. The chapter also touches on trends such as blockchain capabilities, circular water systems, and the use of citizen science. It offers new opportunities to innovate, focus on global partnerships, and small-scale capabilities, to help combine ancient wisdom with innovation to promote equitable, efficient, and durable approaches to water sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Study on the Impact of Brick and Mortar Stores and e-Commerce on Impulsive Buying Behaviour of Consumers
In the changing business landscape in retail and e-commerce, impulsive buying behaviour has played a significant role in the field of marketing and consumer psychology. The study aims to compare the impulsive buying behaviour in brick-and-mortar stores and e-commerce platforms. To execute the research, the primary data was collected through a structured questionnairethe data from collected from 300 respondents who were living in urban Bengaluru. The findings revealed that the physical stores stimulate impulsive buying through the sensory cues, through various internal promotional activities, and also through product gratification. The e-commerce also triggers impulsive buying with the help of recommendations, which are driven by algorithms, with limited-period offers, lucrative deals, discounts, and bundle offers. On the other hand, the demographic variables also have an impact on impulsive buying behavior. The research highlights the ever-evolving nature of consumer behaviour in the digital era and provides quality information that would be used as tactics to trigger impulsive buying. The research paper concludes with practical inputs for companies and marketing agencies. The research can be further extended to various other Omni-channel. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Sway of Social Media Financial Content on Financial Literacy of Youngsters in a Metropolitan City
On social media, the financial material has steadily been shaping the financial actions and knowledge levels of youngsters globally. This research was intended to study the influence of social media financial content on the financial literacy of youngsters in Bengaluru, a metropolitan city in India. The study looked into the patterns of social media usage for financial information acquisition, the types of financial content consumed, and the perceived impact on financial decision-making processes and overall financial literacy levels. The results showed that while gender and marital status significantly influence the type of financial content consumed, factors such as age, educational background, occupation, and income level did not exhibit significant variations in content preferences. The study highlighted that the diversity of social media platforms and the nature of content available on these platforms play pivotal roles in shaping the financial literacy levels of youngsters. The conclusion reflects that there is a clear relationship between social media engagement and financial information acquisition. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Coping with Burnout Across Cultures
The well-being of employees is impacted by numerous factors within their work realm. These factors consist of internal elements, such as the work environment, relationships with coworkers, and satisfaction with their jobs, as well as external factors like job security, working conditions, pay, and growth opportunities. Unfortunately, the COVID-19 pandemic has introduced significant changes that have greatly disrupted the factors that were crucial for employees to maintain a healthy and productive career. These changes include the global economic downturn, shifts in workplace culture, and a decline in worklife balance, all contributing to increased job insecurity among employees. The weight of unemployment and job insecurity often materialises as burnout and enduring fatigue among employees, consequently lessening their peak efficiency level. However, while exploring coping techniques for burnout, cultural practices are persistently overlooked. However, each culture possesses distinctive norms that shape an individuals way of handling workplace stress and pressure. This paper will predominantly look at secondary data published in online databases to explore previously existing literature on differences in culture while coping with burnout. Through the literature review, the authors compare the coping mechanisms employees adhere to between individualistic cultures and collectivist cultures. The paper highlights employees routines at a broader level, emphasising the need for organisations to be aware of diverse coping styles, especially on sites that act as a melting pot of cultures. It aims to promote safer work environments by articulating the differences in coping mechanisms of employees in different cultures. The paper explores sustainable practices for employees and workers to enhance their job satisfaction and well-being. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-Driven Real-Time Decision Making at the Edge: Overcoming Latency, Bandwidth, and Scalability Challenges for Smarter Data-Intensive Applications in Healthcare, Manufacturing, and Smart Cities
Aim and Purpose: This chapter explores the crucial role of artificial intelligence (AI) in enabling real-time decision-making at the edge, particularly within data-intensive applications. It aims to identify and address fundamental challengessuch as latency, limited bandwidth, and scalabilitythat frequently hinder the efficient deployment of AI models near data sources. The objective is to propose a coherent and implementable framework to mitigate these obstacles, thereby facilitating the development of intelligent, responsive systems. The chapter emphasizes the transformative potential of edge AI across three key sectors: healthcare, manufacturing, and smart cities, illustrating how localized intelligence can enhance performance, efficiency, and autonomy in time-sensitive environments. Methodology: We adopt a comprehensive methodological approach that includes studying optimization techniques such as model compression, quantization, and distributed inference. Special attention is given to federated learning, which supports collaborative training without the need to transfer raw dataenhancing both privacy and scalability. The examination of edge-optimized hardware accelerators (e.g., NPUs, FPGAs) and streamlined software frameworks will highlight their role in overcoming processing bottlenecks and ensuring low-latency performance. Limitations: Despite the promise of edge AI, challenges persist. These include limited processing and energy resources, security vulnerabilities, and device heterogeneity. Managing updates and maintaining consistency across distributed systems complicate widespread implementation further. Applications and Novelty: This chapters novelty lies in its integrated focus on practical, real-world applications of edge AI in healthcare, manufacturing, and smart cities. By presenting targeted solutions to known constraints, it contributes a practical, implementation-ready perspective to the growing body of edge AI research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-Based Security and Privacy Solutions for Edge Computing Using Federated Learning
Edge computing, which reduces latency and bandwidth usage by performing computations on data closer to where they are generated, is generating considerable interest due to the rapid growth of the Internet of Things (IoT) and real-time applications. However, this new architecture brings more security issues, such as cyber-attacks, unauthorized access, and data leakage. This architectural change improves performance, but it also increases the risk for data leakage, unauthorized access and cyberattacks. These distributed/architecturally decentralized scenarios are not something traditional cloud security models are suitable for. Federated learning (FL), a privacy-preserving setting for distributed learning, comes as a new solution which enables edge devices to collaboratively update their models without disclosing locally learned models. When integrated with AI, FL offers intelligent, flexible, and privacy-preserving security to edge ecosystems. This chapter investigates the interplay between edge computing, FL, and AI, providing a detailed analysis of possible future developments, risk mitigation strategies, and existing threats. This chapter studies the pioneering role of AI-supported federated systems in defending the future generation of edge networks through recent research and applied studies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.