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AI-Optimized Erection of Landslide-Resistant Retaining Structures Through Heterogeneous Composite Nanomaterials: A Computerized Algorithmic Breakthrough
The proposed work relates to the field of environmental protection and ensuring the environmental safety of urban development and the population from erosion and landslide phenomena. It can be used to create territorial plans for the development of recreational areas in areas subject to these natural and man-made impacts. The technical result of the proposed work is to ensure the reduction of natural and man-made impacts on urban and similar settlements through the use of new technological solutions for the creation of structures using heterogeneous composite nanomaterials. A technical result is achieved by equipping the territory with buildings and structures, creating a base and a soil-reinforced array, with the location of blocks in it, made as soil-filled shells, on the basis, a soil-filled shell-stay-base is mounted with a soil-filled shell base, a rigid frame is mounted and the front wall, which is fixed with the second attachment point, fix the soil-filled shell, its upper part is made waterproof and equipped with a water outlet through the second attachment point, to which the rigid frame and the front wall are fixed, they are fixed with the third fastening unit, to which a soil-filled shell-plate with a storm drain of one or more arm tapes with a drainage system filled with a sorbent and placed in a waterproof shell. The front wall is additionally covered with a polymer material with seeds. All structural elements are made of heterogeneous composite nanomaterials. As a polymeric material with seeds, the material PINEMA is used. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
IoT-Driven Dynamic Behavior Intervention Model for Sustainable Hygiene Practices: Insights from Household Water Consumption
IoT-enabled technologies have advanced so that smart sensor systems can observe and recognize human behavior in various contexts, including energy consumption and healthcare, with remarkable efficiency and effectiveness. One example is using the Internet of Things (IoT) technology to better comprehend human water consumption behavior and establish and maintain clean environments. Static models have typically been used to model the behavior intervention process throughout time. While these static approaches perform adequately when predicting general human behavior, they fall short when tracking and reacting to shifts in behavior in IoT settings. The authors of this study proposed a dynamic behavior intervention model to forecast the hygiene-related water-use habits of individual households. This model takes its cues from the structure equation model method and the notion of control engineering, which originated in the expanded theory of planned behavior (ETPB). The current ETPB dynamic behavior model with system parameter estimation using an artificial neural network (ANN)is assessed for its intervention trend using a residential water use case study. It has been shown that the ETPB dynamic model helps the process of intervening in peoples behavior. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Study on Revolution of Healthcare Industry with Transformational Artificial Intelligence Tool
The world is under the turbulence of the transformation from the age-old traditional healthcare systems to contemporary, patient centric and clinicians need based operations. The introduction of Artificial Intelligence in the healthcare industry though besets with a bunch of demerits deserves special mention with respect to its over brewing merits. In contrast with the global standard, India, a developing country has obvious financial and infrastructure specific bottlenecks in bringing the success of the mass implementation of Artificial intelligence in the healthcare operations. However, the genuine and continuous efforts are made in streamlining the critical and stereotyped operations for the benefits of the medical service seeker and also for the competitive survival of medical service provider. The present study focused on the historical development of Artificial Intelligence in healthcare, the reason of its gradual popularity, the application of the tool, some of the notable used cases where the Artificial Intelligence gained its momentum and a host of pros and cons in dealing with it. The study also featured the futuristic intensity of its application of Artificial Intelligence in the healthcare units in India to ease out the pain of availing the emergent medical services without the typical intervention of the medical experts and on the contrary also the administering the hassle-free diagnostic procedures with transparency and smart approach. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Deployable Solution for Real-Time Children Face Emotion Prediction System
Nowadays, many parents struggle to comprehend their children's emotions, which can hinder the creation of a nurturing environment. While numerous models focus on predicting adult emotions, there is a lack of standardised datasets for studying children's emotions. To address this gap, our work attempts to establish a comprehensive children's emotion dataset that can facilitate the study of emotions across various pose orientations. Furthermore, we propose an efficient and deployable system for real-time children's emotion prediction. An effective face detector with deep architecture is designed to handle all pose orientations from key image frames. Optimal features are then selected by re-ranking the features using a hybrid feature selection mechanism. The emotion category is declared by carefully analysing sequences of emotion identification from these features. This system holds promise for educational institutions and healthcare facilities, offering insights into children's behaviour through emotional analysis. Through experimental comparisons with three state-of-the-art emotion prediction models, we observed that our proposed system consistently outperforms existing models. Hence, we strongly recommend the adoption of our proposed system. With its achievement of state-of-the-art results in children's facial emotion recognition, it offers a practical solution for real-time deployment across diverse settings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research Perspectives on Load Balancing Strategies in Serverless Computing
Serverless computing, a groundbreaking trend in cloud computing, has transformed how applications are deployed and managed by abstracting the infrastructure layer. Serverless computing enables developers to concentrate exclusively on their code while cloud providers care for server provisioning, maintenance, and scaling. Services like AWS Lambda, Google Cloud Functions, and Azure Functions exemplify this model, offering s ubstantial advantages in terms of reduced operational complexity and cost. However, one persistent challenge in this domain is load balancing. Effective load balancing in serverless computing ensures efficient resource utilization, optimal performance, and cost-effectiveness. Unlike traditional load balancing, which typically relies on long-lived server instances, load balancing in serverless environments must accommodate the stateless and ephemeral nature of serverless functions. Traditional techniques are not directly applicable because serverless architectures functions that are instantiated on-demand in response to incoming requests. This paper surveys various strategies and approaches developed to address the unique load balancing challenges in serverless computing, providing a comprehensive overview of the current state of research and practice. The paper extends further research on serverless computing by analyzing the survey papers. The paper highly focuses the research areas in the field of edge computing, hybrid cloud models and distributed load balancing for the future usage. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Localizing and Classifying Kannada Texts Using a YOLO-Based Approach
Extracting handwritten characters from the scanned documents is a critical step due to the inherent complexities of various writing styles, inconsistent alignments, multi-touch scenarios, and overwriting characters. Expanding upon the real-time object detection capabilities of YOLOv8 (You Only Look Once), the current paper presents an experiment utilizing a dataset of 2000 handwritten images. This dataset combines the standard dataset (Chars74K) with the custom dataset featuring multi-touch handwritten text, encompassing both individual characters and character combinations that form words. The annotations were created using the Roboflow application and exported to a yaml (yet another markup language) file. The hybrid dataset was split into training, validation, and testing sets. The evaluation process yielded an accuracy of 96.8% at a threshold of 0.5 for recognizing and classifying the characters. The result suggests a positive correlation between training dataset size and model accuracy. Further, fine-tuning the hyperparameters could increase the accuracy upto 98.4%. Additional experiments were conducted to compare YOLOv8 and Detectron2 with Faster R-CNN. The results demonstrated that YOLOv8 offers substantially faster inference times, while Detectron2 with Faster R-CNN exhibited marginally higher accuracy in few classes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Unleashing the Potential: How the Internet of Things Catalyzes Progress Towards the Development Goals
The world stands at a critical juncture, grappling with intertwined challenges of environmental degradation, social inequity, and economic precarity. In response, an integrated framework has been established by the United Nations in terms of Sustainable Development Goals (SDGs), for achieving a more sustainable and equitable future by 2030. These 17 ambitious goals encompass a wide range of interconnected targets, from eradicating poverty and hunger to ensuring clean water and sanitation, fostering responsible consumption and production, and combating climate change. However, realizing this ambitious agenda necessitates a paradigm shift, one that leverages technological innovation to drive transformative change. This paper explores the burgeoning potential of the Internet of Things (IoT) as a powerful enabler in achieving the SDGs. The IoT, a webbing of interconnected physical devices embedded with sensors, software, and other technologies, offers unprecedented capabilities for data collection, analysis, and real-time communication. By harnessing this data-driven ecosystem, we can cultivate a more intelligent and sustainable world. The paper examines specific applications of IoT solutions across various SDG domains. In the realm of Clean Water and Sanitation (SDG 6), for instance, IoT-enabled smart meters can monitor water usage patterns, detect leaks promptly, and optimize water distribution networks, leading to significant water conservation efforts. Similarly, in Sustainable Cities and Communities (SDG 11), IoT-powered traffic management systems can optimize traffic flow, reduce congestion, and lower emissions, contributing to cleaner air and improved urban living. Furthermore, the paper explores the role of IoT in fostering Responsible Consumption and Production (SDG 12). By embedding sensors in products and packaging, manufacturers can gain valuable insights into resource utilization and product lifecycles. This later can then be used to optimize production processes, minimize waste, and extend product lifetimes, promoting a circular economy. In conclusion, the paper posits that the IoT, when harnessed strategically and ethically, has the potential to be a transformative force in achieving the SDGs. By fostering data-driven decision making, optimizing resource use, and promoting sustainable practices, IoT solutions can lay the foundation for a more sustainable and impartial future for all. The world faces a critical juncture, grappling with environmental degradation, social inequity, and economic precarity. The United Nations Sustainable Development Goals (SDGs) offer a blueprint for a more sustainable future. IoT technology, with its interconnected devices and real-time data capabilities, is a powerful enabler. In Clean Water and Sanitation (SDG 6), IoT-enabled smart meters equipped with water quality sensors and wireless communication protocols can monitor water usage patterns, detect leaks, and optimize distribution networks. In Sustainable Cities and Communities (SDG 11), IoT-powered traffic management systems utilizing advanced sensors and data analytics can optimize traffic flow, reduce congestion, and lower emissions. For Responsible Consumption and Production (SDG 12), IoT-enabled product tracking systems can monitor resource utilization and product lifecycles. This data can inform manufacturers on optimizing production processes, minimizing waste, and extending product lifetimes, promoting a circular economy. By harnessing IoT strategically and ethically, we can foster data-driven decision-making, optimize resource use, and promote sustainable practices, laying the foundation for a more equitable and sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Rectifying Whole Brain Segmentation Errors Using a Novel Under-Segmentation Correction Method
Pre-processing is a critical step in any data-driven study, particularly in the field of medical imaging, where it significantly enhances the reliability of disease and disorder diagnosis. In this context, medical image segmentation allows for more precise data analysis by isolating the regions of interest. Accurate segmentation of these regions can reveal influential variabilities in analysis, potentially leading to unique scientific findings. This article presents a novel under-segmentation error correction technique specifically designed for whole-brain segmentation. Additionally, it performs a set of pre-processing steps for the structural magnetic resonance imaging (sMRI) images, which are necessary to maintain the structural integrity and uniformity of MRI scans across different subjects. The proposed algorithm effectively eliminates under-segmentation errors, thereby improving the accuracy of whole-brain segmentation, particularly for structurally intact brain images. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Sustainable Financial Management (SFM) in E-Banking: Strategies and Challenges in Dubai
This paper explores the landscape of sustainable financial management (SFM) in e-banking within Dubai, focusing on the strategies banks implement to promote sustainability and the challenges they encounter. As e-banking continues to expand, sustainability has become crucial for banks aiming to minimise their environmental impact and enhance operational efficiency. The study examines various sustainable strategies, such as the adoption of energy-efficient technologies, the shift towards paperless transactions, and promoting digital customer engagement. Additionally, it considers the regulatory framework and policy support that facilitate sustainable practices in the banking sector, the challenges that banks face, including high implementation costs, technological hurdles, and issues related to customer adoption through case studies. The research illustrates the effectiveness of different approaches and solutions to these challenges. The findings underscore the significance of an integrated strategy aligning economic objectives with environmental sustainability, ultimately benefiting the banking industry and society over time. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An Improved Deep Learning Framework for Energy Management in Low-Energy Building Integrated Photovoltaics (LE-BIPV)
The possibility of incorporating photovoltaics (PV) as part of building design has opened a new approach to energy generation from sustainable resources. An effective method to facilitate the good operation of these systems would be efficient energy-level management. The existing Energy Management of LE-BIPV employs a conventional control strategy, which is inconvenient for operation and fails to properly deal with nonlinearity in the PV system. The proposed model aims to provide a new deep-learning framework for the energy management of LE-BIPV. The proposed neural network framework can learn the intricate relationships between PV generation and battery storage and enable accurate energy management predictions. This proposed deep learning framework can substantially upgrade the global energy control of building-integrated PV systems in low-energy buildings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Voice Assistants in Marketing: A Transformative Tool
The current world is moving in the digital platform. Transformation of technology is evident in the field of marketing, and customers are also very much interested in exploring the destructive technology in the field of marketing. Voice Assistants in Marketing are the recent tendency in the marketing field and assure an immersive experience for the customers. The study attempts to explore the role of voice assistants in marketing, its applications in various industries, the knowledge level of the respondents towards various technology interfaces in voice marketing, and the factors building the trust level of voice marketing techniques. It adopts the descriptive research design, and the required data for the study is collected using the structured questionnaire in the mail survey method from 210 respondents through purposive sampling. The data gathered was organized and analyzed using SPSS, and factor analysis was employed to reduce the dimensions of the variables. The results and the explanations are summarized at the end. Voice assistants in marketing are an emerging technique that is transforming the livelihood of the marketing phenomenon. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Empowering Tribal Communities: Unveiling the Role of Skill Proficiency, Demographics, and Economic Pathways in Wayanad
The complex relationship between skill proficiency, demographics, and economic empowerment among the tribal communities in Kerala's Wayanad district is examined in the study Empowering Tribal Communities, Unveiling the Role of Skill Proficiency, Demographics, and Economic Pathways in Wayanad. The study examines how gender, age, income, and education affect skill development and how that affects resilience and economic prospects using a quantitative research approach. Important insights are revealed by stratifying data from a representative sample of 200 tribal members based on important demographic characteristics. Chi-Square tests, regression models, and structural equation modelling (SEM) are examples of descriptive and inferential techniques that show how skill development promotes economic inclusion. The findings indicate that skill competency is strongly correlated with higher education levels (p?<?0.0001) and has a significant impact on monthly income levels (R2?=?0.263, p?<?0.05). Nevertheless, neither gender nor skill proficiency were shown to be significantly correlated with skill type or age group (p?>?0.05). The SEM framework's path analysis shows that skill development directly improves market connections (??=?0.55), digital literacy (??=?0.70), social inclusion (??=?0.50), and economic possibilities (??=?0.65), all of which contribute to economic empowerment (??=?0.40). However, these effects are sometimes hampered by obstacles like restricted access to resources. The results highlight the need for focused skill-development programs that tackle educational inequalities and infrastructure obstacles. Policymakers, educators, and community organisations looking to improve tribal communities through customised interventions that promote socioeconomic stability and resilience in the face of persistent difficulties can benefit from the practical insights this research offers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Key Motivational Factors Driving the Adoption of Buy Now, Pay Later (BNPL)
This research paper looks into the factors influencing motivation to use Buy Now, Pay Later (BNPL) facilities. Quantitative method was employed for the survey of 223 respondents through an on-line questionnaire to determine salient factors including easy access, low or zero interest rate support offers as well as marketing and promotional strategies impact financial performance; whereas peer pressure matures a relationship between ease full comfort acceptance directly proportional with technology savvy. Top BNPL predictors in analysis: Access/convenience Low or zero interest rates Tech-savvy Peer effect and schemes to promote discount do influence but are less significant. This study offers pragmatic insights for BNPL providers, suggesting the necessity of offering a simple entry channel and low interest rates but targeting tech consumers by using social approaches or promotion tactically. These results contribute to a refined conception about consumer behavior in the BNPL market and indicate future areas of research that could be relevant for any type of consumer finance products. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An Exploratory Analysis of Neuromarketing Techniques and Their Impact on Consumer Purchase Decisions
Neuromarketing has been playing a signification role in consumers purchase decisions. It has played a significant role in the area of business, especially in the area of marketing. Today every medium and large-scale company is using neuromarketing to promote their product and to influence the purchase decision of the consumer. The study aims to assess the impact of neuromarketing on customers and its influence on the purchase decision of the consumer. The study has employed various statistical tools and analyses to conduct the study. The study highlights the importance of using neuromarketing in business and marketing activities, by ensuring transparency to build the trust of the consumer. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Virtual Influencers (VIs): A Bibliometric Analysis and Future Agenda
This study aims to comprehensively understand qualitative and quantitative information about the emerging trends in VIs. It examines 106 articles published in Scopus-indexed journals between 2020 and 2024. The analysis was done with the help of Biblioshiny, an R-developed online application from the Bibliometrix package, and VOSviewer software for analytical and visualization purposes. This study was conducted using the SPAR-4-SLR protocol. The findings showed that recent years have been more productive, and many authors have demonstrated their interest in studying the VIs. Recent trends are social media, virtual reality, marketing, social networking, etc. The study employs a systematic review and bibliometric analysis to extract valuable insights from the extensive body of literature. These insights suggested several areas for future research, providing a roadmap for future researchers to proceed with their research in this area. The comprehensive scientific cartography of the area has yet to be presented; therefore, this study aims to synthesize the current knowledge frameworks within the field and determine the dominant research patterns in the specific area of investigation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing
Question paper generation is a crucial task in education, where the objective is to design an assessment that effectively evaluates students knowledge and understanding of various subjects. Traditional methods of question paper generation can be exceedingly difficult, time-consuming, and inappropriate and may not be fully optimized. They ensure a comprehensive assessment of students knowledge. The system also offers the flexibility to customize question papers based on specific preferences and requirements. This research introduces a comparative approach to question paper generation using Latent Semantic Analysis (LSA), Word Embedding, and Sequence-to-Sequence (Seq2Seq) models, leveraging the power of Artificial Intelligence (AI) and Natural Language Processing (NLP). This model compares their Semantic Representation Quality, Context Understanding, and Computational Complexity. Comparing these techniques shows that LSA offers simplicity but may lack precision, while word embedding balances complexity and semantic understanding. Seq2Seq models, despite their complexity, provide contextually rich mappings with the highest degree of fine-tuning potential. This comparative analysis underscores the importance of understanding the nuances and trade-offs of each approach, enabling educators to make informed decisions in adopting these technologies to enhance educational practices and student learning experiences. A few modules are included in this system, including the admin module, add user, subject selection, question entry, question management, paper management and difficulty level. By capitalizing on the capabilities of LSA, Seq2Seq models, and word embedding, educators can revolutionize the process of question paper generation, ultimately leading to more effective and impactful student learning outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Machine Learning Based Depression Prediction Using Gradient Boosting Algorithm
Depression is one of the major diseases, more than one million people are facing this issue. To achieve the best results possible, it is essential to monitor and intervene when needed regularly. While there are many ways to observe the mental well-being of an individual in a workplace environment, AI has the potential to enhance the accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing an ML system for distinguishing symptoms of depression among individuals in the workplace. The dataset comprises detailed information on the signs and symptoms of depression among individuals, it particularly focuses on the observed negative consequences at the workplace, physical health issues and their negative consequences, treatment. In this experimental process two main machine learning algorithms were used, the Random Forest and Gradient Boosting algorithm. Both the algorithms have an overall accuracy of 82%, but based on maximization of the overall performance, the Gradient Boosting model is slightly better than the Random Forest. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct depression analysis class systems. This study highlights the power of machine learning that could revolutionize depression care by identifying mental health problems early. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
AI-Powered Disaster Management System Using Satellite Imagery: A Survey
Disaster management is all about time; timely response and an accurate assessment are the basis on which disaster damage may be limited and lives saved. Traditional methods of disaster response rely on human analysis and manual interpretation of satellite images, which are slow and prone to human error. Here, AI can prove to be a technology capable of using ML and DL algorithms to analyze vast quantities of satellite imagery in real time. AI-based systems can work to detect areas affected, assess the severity of the damage, and predict the evolution of disasters for better response and resource allocation. The paper presents recent developments in AI-based disaster management with the assistance of satellite imagery, sketching out major challenges and future research directions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Hybrid Machine Learning Approach for Gait Type Classification Using Pose-Based Feature Extraction
Gait analysis is essential for the diagnosis of neuromuscular and musculoskeletal disorders. Traditional methods are vulnerable and lead to inconsistency as they rely on subjective assessments. An angle-based approach which uses advanced machine learning techniques have been used address this. Extracted joint angle measurements have been extracted from the video data using computer vision methods. The characteristics used in this research were used to train a hybrid model of a Random Forest classifier and a Fuzzy C-Means clustering algorithm. Random Forest model was used as it is stable and capable of dealing with intricate nonlinear relationships and Fuzzy C-Means was used as it can manage ambiguity in the data as well as overlapping class distributions. The results showed that the Random Forest classifier has a classification accuracy of 94.62%, which is better than the other models in distinguishing between normal and abnormal gait patterns. Fuzzy C-Means also shows high accuracy is capable of clustering various forms of gait and extracting detailed features in gait dynamics. Results suggest that integrating joint angle analysis with machine learning methods provides a credible tool for gait analysis, which can aid clinicians in the early detection and treatment of gait related disorders. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Smart Finances: A Web-Based System for Personalized Financial Management
Smart Finances is a dynamic and user-friendly web application designed to assist users in managing and understanding their personal finances with ease. Built using HTML, CSS, JavaScript, and PHP with MySQL for backend functionality and data persistence, the platform offers a suite of tools, including an interest calculator, investment calculator, budget manager, and interactive spending analysis. The intuitive interface enhances usability and promotes financial literacy, especially among students and individuals new to personal finance. The application supports both guest and registered user modes, offering tiered access to features based on authentication. Smart Finances aims to make money management more approachable and insightful through accessible design and practical functionality. Initial user testing with 20 participants indicated a 30% improvement in budgeting accuracy and a high satisfaction rate (88%) with the usability of the interface. These results validate the systems impact on financial literacy and user engagement. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.