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Transforming Finance into an AI-Driven, Data-Centric, and Analytics-Focused Function: Implications for Organizational Impact
The financial function within organizations is evolving from a traditional cost-center role to a strategic partner powered by artificial intelligence (AI), data analytics, and advanced technologies. This transformation is reshaping how financial professionals analyze data, predict trends, and support decision-making. By leveraging AI, finance teams can automate repetitive tasks, enhance data accuracy, and deliver actionable insights in real-time. Integrating predictive and prescriptive analytics further empowers organizations to forecast financial outcomes, optimize resource allocation, and mitigate risks with precision. This shift to an AI-driven, data-centric finance model fosters agility, innovation, and enhanced collaboration across departments. It aligns financial strategies with organizational goals, improving operational efficiency and driving sustainable growth. However, the transition demands investments in digital infrastructure, a redefinition of roles, and an upskilling of personnel to handle complex analytics tools. The implications for organizational impact include enhanced competitiveness, informed decision-making, and a stronger ability to navigate an increasingly volatile economic landscape. This chapter explores these transformations, their challenges, and the opportunities they unlock for organizations aiming to thrive in a data-dominated era. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Bridging Signs to Sentences: Enhancing Sign Language Interpretation
This paper mainly focuses on bridging the gap between sign language recognition (SLR) and sentence formation by integrating the recognition of different signs or machine learning models with large language models (LLMs), which results in contributing and enhancing the communication of the deaf community. There are around 466million deaf individuals worldwide, where they primarily rely on Sign language for communication. Current SLR technologies have certain limitations that deal with difficulty in sentence formation and high processing requirements. This papers dataset consists of 36 classes, where 26 of them are alphabets, and the rest 9 are numbers, each consisting of 500 images. Therefore, a total of 18,000 images are present in the dataset for accurate prediction of sign languages. MediaPipe, developed by Google, is used as a tool for feature extraction by identifying 21 hand landmarks. The features extracted are then passed onto an ML model (like a Random Forest), and then the result of this model is passed on to an LLM (here, Groqs Gemma-7b-it) that forms a sentence based on the predictions. The ML model achieved a high accuracy of 99.54% and the LLMs achieved an accuracy of 93.75%. With the combination of sign language recognition and advanced modeling, this work helps to bridge signs to sentences, providing to the deaf community. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
New Empirical Equation for Fundamental Time Period of RC Moment-Resisting Frame Buildings Using Machine Learning Algorithms
The fundamental time period of reinforced concrete (RC) buildings is a critical parameter in structural engineering, influencing their dynamic behavior and response to seismic and wind loads. This study aims to propose a new empirical formula for estimating the fundamental time period of RC buildings through regression analysis. Leveraging the SAP2000 API with VBA code, a dataset comprising 200 two-dimensional RC building models was rapidly generated, allowing for efficient exploration of various building configurations. Modal analysis was conducted for each model to determine the fundamental time period, and regression analysis was performed using both multiple linear regression and curve estimation regression techniques. The input parameters included total building height and base dimensions, while the output variable was the fundamental time period obtained from SAP2000 results. Multiple linear regression yielded two best-fit models, while curve estimation regression produced logarithmic and exponential models. The proposed models were compared with the fundamental time period values obtained from SAP2000 results and those calculated using the formula specified in the Indian Standards (IS) code. Further the results obtained are used to develop a machine learning model that can be used to estimate the time period of RC structures for a given height. The model is chosen after estimating the coefficient of regression for various individual machine learning algorithms and ensemble algorithms. This research contributes to the advancement of structural engineering by providing a systematic approach to developing empirical formulas tailored to RC buildings. The proposed formula, enabled by the automation capabilities of the SAP2000 API, offers a more accurate and reliable method for estimating the fundamental time period, facilitating improved seismic design and analysis practices. Further validation and verification of the formulas performance using additional datasets and real-world case studies are recommended to enhance its applicability and robustness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Disease Identification from Illegible Medical Prescriptions Using OCR and NLP Techniques
Medical prescriptions that are challenging to interpret present significant issues for the healthcare industry because they increase the possibility of errors in patient care and medication administration. This study presents an efficient workflow that uses Optical Character Recognition (OCR) technology, specifically, Tesseract OCR, along with a preprocessing step to extract text from handwritten prescriptions. The preprocessing stage uses grayscale conversion, noise reduction, and contrast enhancement to increase the accuracy of OCR. Significant results from experiments on a publicly accessible dataset show that preprocessing greatly improves performance, lowering the error rate from 34.7 to 18.3% and raising average accuracy from 65.3 to 81.7%. The enhanced accuracy outweighs the modest increase in processing time (from 0.8 to 1.2s), emphasizing the potential of using these techniques in practical healthcare applications. The studys findings also demonstrated the successful analysis of the text using Natural Language Processing (NLP) and Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) techniques by identifying four distinct diseases, Common Cold, Diabetes Mellitus, Bronchitis, and disease caused by Anemia, as validated by a medical professional. This demonstrates the systems potential to improve health care processes by automatically digitizing handwritten prescriptions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
PM2.5 Prediction Models: A Systematic and Comparative Review
Airborne particulate matter (PM) is an amalgam of liquid droplets found in air and microscopic solid particles. The particles differ in size, shape, and chemical composition. PM has a significant impact on climate and precipitation and adversely affects human health as it can infiltrate the lungs and enter the cardiovascular system. This article explores the various PM2.5 prediction models proposed to date to predict a region's particulate matter (PM2.5) concentration. As prediction techniques evolve rapidly, this study aims to assess the various methodologies proposed for predicting PM2.5 concentration in different regions according to the factors that influence it. Various machine learning, deep learning, and statistical models have been proposed to predict hourly or daily PM2.5 concentrations in the air. The previously proposed models were compared using the RMSE, MAE, and R2 scores as the evaluation metrics. Since most of these models were region-specific and mostly used different parameters for the prediction, the comparison highlighted the need for a generalized model that could be fine-tuned based on the parameters of a particular region. Thus, this review points to the need for a high-accuracy generalized prediction model for PM2.5 that adapts to the diverse parameters region-wise. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Maximizing Efficiency: Unveiling thePotential ofKubernetes Metrics
In the realm of Kubernetes cluster management, the importance of metrics cannot be overstated. Metrics serve as a powerful lens, providing a quantitative perspective into a clusters performance, behavior, and resource utilization. In the ever-evolving landscape of cloud-native computing, metrics are the key to informed decision-making. They empower administrators to navigate scaling, resource allocation, and the holistic optimization of Kubernetes clusters with a data-driven confidence. This paper stands as a vital contribution, placing metrics at the forefront of the discussion. It underscores their transformative potential by shedding light on how they drive administrators decisions, enable the identification of performance bottlenecks, and enhance application responsiveness. Moreover, metrics play a pivotal role in proactive capacity planning, ensuring resources are allocated with precision to meet both current and future workload demands. In essence, this papers core contribution lies in providing a comprehensive overview of Kubernetes metrics and highlighting their profound impact on Autoscaling strategies. By revealing the constraints that metrics may impose on the efficient scaling of application resources, it equips administrators with a navigational tool for building dynamic and resilient computing environments within Kubernetes clusters. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation
Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Machine Learning-Based Cross-Layer DoS Attack Detection Technique for IoT
The most potent and common attacks on Internet of Things (IoT) are Denial of Service (DoS) attacks. Unfortunately, because the attack occurs on numerous layers, a single layer detection method is insufficient and ineffective to counteract these attacks. The current work focuses on the detection of cross-layer DoS assaults using Machine Learning-based multiclass classifiers. Three attacks against Routing Protocol (RPL) and Transmission Control Protocol (TCP) are detected using three ML Classifiers (KNN, Gradient Boosting, Random Forest). The novelty of the study is the design of cross-layer attack datasets using feature engineering technique. The performances of the classifiers are analyzed in presence of both balanced and imbalanced datasets. The results show that Gradient Boosting classifier has highest accuracy of up to 98% with deviation of up to 97%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Impact of Homophily on Patient Empowerment: A Study of Online Patient Support Groups
Internet facility has led to emergence of patient support groups. These have gained prominence as these fulfils important benefits to patients. One such benefit is patient empowerment. These online groups provide opportunity to patients to interact with similar ailments and predicaments and who can understand the pain and discomfort felt by the patient. This provides validation for the patient and patients experiences. How does this homophily impacts patient empowerment? This question has been explored in this study. The methodology is based on an online survey of patients visiting such online platforms. In all 701 patients provided the data. Independent variable (homophily) and dependent variable (patient empowerment) have been measured using a 7-point Likert scale. Findings provide that both are weakly correlated, but this correlation is significant. Regression analysis led to a regression model that is fit statistically. This provides basis to encourage patients to visit online support groups. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Bibliometric Analysis of Industry 4.0 and Health-Care Services
A key moment in health care is marked by the Fourth Industrial Revolution, commonly referred to as Industry 4.0. This transformation, driven by the convergence of digital technologies with automation and data driving processes, has led to a paradigm shift in how health care is provided. The integration of the emerging technologies in Industry 4.0, such as Internet of Things, Artificial Intelligence, Big Data Analytics and Advanced Robots, are revolutionizing patient care, improving resource allocation and shaping research's landscape. To learn more about the ever-evolving relationship between Industry 4.0 and health care, this research paper begins with a bibliographic analysis. In this interdisciplinary convergence, our bibliometric analyses serve as a lens through which we can see the key trends, research areas and influential players. The review of literature highlights the profound impact of Industry 4.0 on health care, revealing that Internet of Things technologies for real-time patient tracking, proliferation of artificial intelligence in medical diagnosis and transforming power of big data Analytics are changing health care decision making. Methodologically, we leverage bibliometrics as a quantitative analytical tool, drawing on citation counts, bibliographic coupling, and keyword co-occurrence analysis. The data for this analysis, which covered the period 20152023, was carefully collected from Scopus database. The analysis of the information reveals that, particularly from 2018 onwards, there has been a significant increase in publications concerning Industry 4.0 and health care. In this research landscape India has emerged as a strong contributor, with countries such as the United States and Italy making significant progress. Publication trends and bibliographic coupling among countries and sources shed light on collaborative networks and research focus. The emergence of machine learning, artificial intelligence and data analysis as important themes is illustrated by a co-occurrence analysis of keywords that elucidates evolving research interests. In the complicated terrain of health care converging with Industry 4.0, this research paper serves as a compass. The report highlights this convergence's transformative potential, highlighting the pivotal role that bibliometrics analysis must play in determining future research areas in adopting Industry 4.0 in the health-care sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
On k-Facile Perfect Numbers
For a positive integer n, let ?(n) denote the sum of all positive divisors of n. Then n is said to be a k-facile perfect number if ?(n) = 2n + d1d2 dk, where 1 < d1, d2,, dk < n are distinct divisors of n. This paper characterizes k-facile perfect numbers and establishes their relationships with other special numbers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Measuring the Impact of Chatbot Attributes in Enhancing Consumer Satisfaction and Brand Loyalty Among Centennials: PLS-SEM Analysis
PurposeThis study investigates the potential impact of consumers views about chatbots dynamic, behavioral, and cognitive features on their satisfaction and brand loyalty. Design/methodology/approachData were collected using a survey comprising questionnaires from a sample of Indian centennial customers. Purposive sampling was the technique utilized. After that, the data was analyzed using the partial least squares algorithm with the help of smartPLS for structured modelling. FindingsThe results demonstrated that chatbots affective, behavioral, and cognitive characteristics significantly impacted consumer satisfaction and enhanced brand loyalty. Practical implicationsChatbots can improve brand loyalty by taking into account the affective, behavioral, and cognitive traits of their e-agents. Originality/valueThis work aims to contribute and enhance the expanding corpus of research on chatbots effects on increasing brand love. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Assessing the Impact of ESG Factors on Firm Performance: Empirical Evidence from CRIP Sector
In recent times, managers and politicians have been impacted by key variables such as ethics,corporate governance,and ESG, leading to investment choices and the establishment of strong rules. There is an increasing focus on the understanding of environmental stability and the socio-economic growth of nation-states, which has led to the priority of sustainable and responsible investment methods. Nevertheless, there remains a void in investigating the CSR- and associated corporate characteristics that impact business performance The main purpose of the current research is to explore the influence of ESG factors on company performance, with a special emphasis on the Infrastructure,Construction, Real Estate, Infrastructure, and Project (CRIP) sectors. The research employed the Crisil ESG database, providing comprehensive financial data andESG ratings of 42 organizations. Fixed effect panel regression was performed to evaluate the impact of ESG disclosure on company performance. The data revealed that the combined ESG score has a positive and large effect on the (WACC) Weighted Average Cost of Capital. The findings from the study are meant to aid varied stakeholders for policy-making and strategic decision-making in the Indian CRIP business. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Performance Analysis of Distribution Network Under EV Penetration with Different Types of Load Models
Electric Vehicles (EVs), the booming technology in the transport sector, are the eco-friendly solution for existing environmental issues like global warming. In this scenario, analyzing the variation in power flow and voltage profile at certain nodal junctions in a distribution system when an Electric Vehicle has been connected as a load is considered in this paper. The voltage stability analysis on a distribution system with an Electric Vehicle as a load and the other types of loads has been studied. The results provide that the parameters of total active power loss, total reactive power, minimum Voltage at the node, and the voltage stability index vary indefinitely. The load flow analysis is performed using the Backward/Forward Sweep (BFS) method on the IEEE-85 test system taking various loads such as constant power, residential, commercial, industrial, and composite types with Electric Vehicles load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Corrected Score Estimation of Regression with Autocorrelation Under Measurement Errors
In regression models with autocorrelated errors, measurement errors in the covariates can lead to biased and inconsistent estimation of the regression coefficients. Measurement error refers to the discrepancy between the observed values of a variable and its true values. It occurs during the data collection process due to various factors such as inaccuracies in measurement instruments, limitations in the measuring process, human errors, or environmental influences. These errors can introduce bias into collected data, impacting the reliability and validity of statistical analyses and model outcomes. Recognizing the importance of time dependencies, our study extends to time series regression models in the presence of measurement error. A score function is the derivative of the log-likelihood function with respect to the parameters. In this paper, a correction for score function in regression with autocorrelated errors is considered to account for the impact of measurement errors on parameter estimation, and it attempts to provide a two-step estimation procedure to resolve the bias caused by both these challenges. Further, the efficiency of these estimates is compared with least square estimates by carrying out a simulation study for finite samples, and we conclude the proposed methodology provides more accurate results. The applicability of the proposed model has been illustrated using the Phillips Curve dataset and it was found that in the presence of measurement error, corrected score estimation gives more accurate estimates than OLS estimates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
AI Enhanced Global Economic Resilience: Predicting and Mitigating Financial Crises
Global economic resilience relies on our ability to predict and mitigate financial crises, especially for small and medium-sized enterprises (SMEs)vital drivers of economic growth. These SMEs are particularly susceptible to market fluctuations in business-to-business or consumer-focused sectors. Organizations integration of big data technologies has revolutionized global financial data management, enhancing our resilience. In our interconnected world, the timely identification of impending financial crises is crucial. It's the linchpin to prevent catastrophic collapses that could send shockwaves through the global economy and societies. To address this challenge, we introduce the Nature-inspired Red-optimized Stochastic Artificial Neural Network (NRFO-SANN), a powerful instrument for detecting global financial crises and anomalies. Our approach leverages a diverse array of financial data collected worldwide. Employing Minmax normalization, we meticulously pre-process the data, ensuring its readiness for analysis. Principal Component Analysis (PCA) extracts the core features crucial for crisis identification. These insights fuel the implementation of the NRFO-SANN method, unlocking the potential of AI-driven prediction. The results are remarkable. Our NRFO-SANN model not only outperforms its peers but does so resoundingly. With an impressive 96% accuracy rate, it operates efficiently, taking just 1s for computations. It boasts an F-score of 96.5%, a sensitivity of 94% and a specificity of 95%. This model equips us with a robust tool for anticipating and responding to global financial crises, ultimately reinforcing the stability and resilience of the global economy and societies. In this era of AI-empowered global economic resilience, we possess enhanced capabilities to navigate the intricacies of our interconnected world. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Wheat Grain Identification Using Explainable Artificial Intelligence
This research delves into the innovative application of Explainable Artificial Intelligence (XAI) within wheat grain identification. Employing sophisticated machine learning models augmented by XAI techniques, the study aims to enhance the transparency and comprehensibility of decision-making processes associated with classifying wheat grains. Key objectives include refining model accuracy, imparting insights into critical identification-influencing characteristics, and developing an intuitive user interface tailored for end users, particularly farmers. Through a methodical analysis, the research underscores the significance of XAI in detecting flaws and fine-tuning the model, ultimately bolstering its reliability. The findings of this investigation carry implications for advancing agricultural practices, fostering stakeholder trust, and adapting to the ever-evolving dynamics of the environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Road Safety and Efficiency Through IoT-Enabled Car-to-Car Communication
Today's vehicles have been revolutionized by integrating Internet of Things (IoT) technology, which facilitates communication between cars, known as car-to-car (C2C) communication. This paper explores the potential of IoT-enabled C2C communication systems to improve security and efficiency by creating dynamic, real-time data exchange between vehicles. Through a comprehensive review of existing literature and technological advances, this study leads to an understanding of how IoT-based C2C communication can reduce incidents, reduce traffic accidents, and create a more peaceful driving environment. It also highlights the potential impact of C2C communications on transportation and policy development. This paper highlights the potential of IoT-enabled C2C communications as a revolutionary technology for the automotive industry, promoting road safety and better vehicle management. The findings highlight the importance of regulatory frameworks, data processing, and stakeholder collaboration for the successful deployment of communications systems of IoT-based C2C networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Smart AI Tool for Accident Damage Detection
Accidents and fatalities from motor vehicle accidents are major concerns despite substantial advancements in safety technology. Because of this, the industry has made significant investments in creating new safety features, like cutting-edge driver assistance systems, and raising public awareness of safe driving habits. In general, car accidents can result in severe damage to the vehicles involved, and assessing and repairing that damage can be time-consuming and expensive. Manual inspection of vehicles is prone to errors and often requires trained professionals to identify the extent and location of the damage. Therefore, there is a need to develop an automated system that can detect and assess the damage caused to vehicles using AI and deep learning techniques. An image-based processing technique, YOLOv3, is proposed in this work to automate damage detection on automobiles. In the work, we used CNN to create a Mask R-convolutional neural Networks model to identify the location of damage on a car. The damaged area is precisely marked in the images. The base weights from the Mask R-CNN COCO dataset are used to train the model. 21 epochs are used to process the images. The surface of the damage is highlighted in the final image using a color splash technique after processing. Auto insurance firms, vehicle rental companies, and repair shops would all benefit from this automated method of determining the degree of exterior vehicle damage and then calculating the severity of that damage. The value of fraudulent auto insurance claims can also be reduced. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Development of a Comprehensive Technology for Analyzing Data on the Used Car Market
In today's information society, organizations face a huge amount of data that requires analysis and intelligent technologies to make informed decisions. In this paper, the authors consider the problem of analyzing the used car market using big and open data technologies. The used car market has characteristics characterized by heterogeneity and dynamic demand depending on the region. This problem is relevant and important not only for companies involved in producing and selling cars but also for potential buyers. The authors developed a comprehensive data analysis technique based on the Python programming language and the K-means clustering algorithm in the research process. In the article, the authors described a comprehensive technology for analyzing the used car market, including various analysis methods, such as prices, offers, and competition. The proposed comprehensive technology includes various tools and programs for collecting, processing, and analyzing data. These methods can be combined into a single system, providing a more complete picture of the market and making more informed decisions. The structure of the study reflects an independent approach to the topic under study based on open data and research by Russian and foreign scientists. It should be noted that the study is based on a large amount of analytical data obtained from reliable sources and tools that confirm the conclusions formulated in this study. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.