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Indian Stock Market Prediction Using Neural Networks: A Comparative Analysis
Predicting stock prices remains a challenging problem due to the highly dynamic and nonlinear nature of financial markets. Traditional statistical models like ARIMA and GARCH often fail to capture the complexities inherent in stock market data. This paper investigates the use of deep learning techniques, focusing on Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM ensemble model for stock price prediction in the Indian stock market. The CNN model efficiently extracts temporal patterns from sequential data, while the CNN-LSTM ensemble leverages temporal dependencies for improved long-term prediction accuracy. Historical data from Tata motors, spanning over two decades, was used to train and evaluate the models. Experimental results highlight the CNN-LSTM ensemble's superior performance in capturing volatile trends and long-term dependencies, with a notable decrease in test loss compared to standalone CNN. This study underscores the effectiveness of hybrid deep learning architectures in enhancing prediction reliability, paving the way for more adaptive and robust financial forecasting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Impact of the Internet on Human Life a Data-Driven Analysis Using Machine Learning and Statistical Correlations
These days Internet is became an essential part of human life and affects various domains which includes education, business, social interactions, mental health. It pushes the society ahead through increasing innovations, amplifying learning techniques, connecting people across the globe and access to vast resources which makes it a valuable tool in this modern society. But it comes with problems such as Internet addiction, sleeping disorders, health complications. This abstract discusses about dual impact of Internet uses, focusing on its significant benefits and possible dangers. Hence, there is need to manage use of Internet so one can make use of its benefits at the same time reducing the affects which are caused by Internet on human life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Review on Development and Properties of Ultra-High-Performance Concrete
This study presents a review of literature on ultra-high-performance concrete that brings in information regarding the preparation of UHPC, mix design of UHPC on the basis of various particle packing density models, microstructural analysis, durability studies, and strength characteristics. A data base is collected to study the performance, mechanical strength and durability of UHPC from various research works. UHPC can found to be a long-term solution for present day challenges that is faced in construction industry when conventional concrete is used and makes this concrete a novel concept in concrete technology. The advantages of UHPC are: less porosity, high abrasion resistance, greater mechanical properties, high packing density, and improvement in fatigue behavior though the cost of UHPC is high. The non-availability of a standard code for mix design of UHPC makes it difficult to arrive at consistent and comparable mix. The cost of UHPC can be controlled with the use of naturally available materials and utilizing agricultural and industrial waste materials in UHPC. From the data collected, it is observed that the binder content can be optimized and cement which is residual in UHPC can be replaced by industrial residue like fly ash, GGBS, glass powder, etc. and thereby brings down the cost without compromising on the strength and performance. The information shared in this paper will help the contractors, consultants, engineers, industry stakeholders and researchers to alleviate the confusions regarding the use of UHPC in construction industry and to encourage research to make this concrete construction-industry friendly. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A Comparative Study on IOT Security Using Machine Learning Techniques
This increased reliance on networks has made the security of IoT devices a highly burning issue. Among the sea of threats, the threats associated with DDoS attacks are at a high level since they do damage to the functionality of devices and unavailability of networks. Detection and mitigation of DDoS attacks will demand IoT environments based on powerful classification algorithms. This paper evaluates the performances of three prominent algorithms: Decision Tree, Random Forest, and Histogram-Based Gradient Boosting for the classification of DDoS attack traffic within IoT networks. An IoT-23 dataset comprising a subset of attacks, including DDoS, is used herein for the purpose of achieving high classification accuracy to ensure a reliable evaluation of attacks. The results clearly show that all three algorithms are pretty good in terms of detection performance, and Histogram-Based Gradient Boosting is the best in terms of generalization accuracy. These results open new perspectives for the implementation of machine learning, generally, and Histogram-Based Gradient Boosting, specifically, directed to improving security in IoT networks against DDoS attacks, which is an extremely promising result when working within the light of some insights for future research and development within this critical area of security. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation
Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, affecting individuals across all ages and sexes. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in the field of medical diagnostics, offering the potential to enhance the detection, diagnosis, and prediction of breast cancer. Despite these advancements, challenges remain, including the need for large, diverse datasets to train robust models, the integration of AI tools into clinical workflows, and addressing ethical concerns related to AI in healthcare. This paper explores the application of Large Language Models (LLMs) using embeddings in breast cancer management, focusing on its ability to analyze medical data, including imaging, histopathology datasets to identify patterns that may be imperceptible to human experts. Datasets from real-world setting have been secured for analysis across multiple models. Convolutional Neural Network (CNN) model and custom-built large language model are employed to demonstrate the precision and accuracy of Generative AI techniques and observed that custom-built LLM with 98.44% outperforms the traditional AI approaches such as CNN with 61.72%. Future studies can further establish how these models can assist in stratifying patients based on risk, thereby enabling personalized treatment plans that can reduce overtreatment and improve quality of life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction
This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed models user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Autoregressive Model with Students t-Errors
This paper examines a first-order autoregressive model that incorporates students t-distributed errors. The estimation procedure is developed using the maximum likelihood method, with the solutions demonstrated using a simulation approach. As the estimating equations were not in a closed-form expression, we obtained the parameter estimates using the NewtonRaphson method. For a finite sample size, a parametric bootstrap procedure for the unit root test has been illustrated. To demonstrate the applications of the proposed model, a time series of quarterly GDP percentage changes are analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Detection andDealing ofMulticollinearity Using aNew RidgeEstimator
The Ordinary Least Square (OLS) method is a widely used technique for estimating regression coefficients that describe the relationship between the independent variables and the dependent variable. The regression estimates obtained using this approach give a poor result in the presence of multicollinearity. Ridge regression is employed to address this problem. A ridge estimator, also called a biasing constant, plays a notable role in the parameter estimation of ridge regression. In this paper, we introduced a new ridge estimator and studied its properties. A comparative study of the proposed estimator with some of the existing ridge estimators under extreme multicollinearity has been illustrated by using a simulation technique. The estimator developed in this paper seems to perform better than all the other estimators because of the smaller ratio of average mean square error. The performance of the proposed estimator is analysed and verified using real-life data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Evaluating Technostress: Work-Life Balance and Well-Being in Varied Work Contexts
In the contemporary digital landscape, the phenomenon of technostress, defined as stress induced by technology usage, has emerged as a crucial factor influencing work-life balance and employee well-being. This study will explore the impact of technostress in varied work modes such as traditional office-based, remote, and hybrid models. Employing a quantitative approach, the researcher conducted surveys on a representative sample of employees across multiple industries. The results indicate that technostress adversely influences work-life balance and well-being, and the difference in various work modes is observable. Further, it has also been observed that there are significant differences in technostress and well-being concerning the various work modes; working from home comes out to be a positive option, which is related to lesser levels of technostress and higher outcomes of well-being. The present study shows how organisational interventions may be implemented in mitigating technostress-inducing effects: induction of digital literacy, instillation of appropriate communication policies, and embedding of supportive work culture. Essentially, an intervention could help organisations improve well-being 0f employees and achieve better work-life balance in a digital environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Mobilizing Automated Vehicles: Harmonizing the Intersection of Technological Innovations and Legal Regulations
There will be significant shifts in transportation with the introduction of autonomous vehicles (AVs), which will increase efficiency, safety, and environmental friendliness. But these technologies can only be used to their full potential if technological breakthroughs are seamlessly integrated with strong legal regulations. The article delves into the ways in which technological advances and legal frameworks meet, highlighting how important regulatory measures are for ensuring the secure use of AVs. In addition, the article delves into the current legal framework around AVs, drawing attention to the difficulties caused by inconsistent regulations and the necessity for flexible rules that can stay up with the fast-paced advancements in technology. The purpose of this article is to examine current policies and case studies to shed light on how to effectively integrate technology advancements with legal requirements to create conditions that are favorable to the broad use of autonomous vehicles. The results highlight the need for manufacturers, lawmakers, and the general public to work together for the sake of society's safety and well-being during the shift to autonomous transportation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique
Cardiovascular disease is the leading cause of death and mortality worldwide. Thus, early diagnosis of CVDs is crucial since the disease can be managed with optimal care. In the current study, we consider CardioAugmentNet, which is a CNN model augmented with data augmentation strategies for the classification of several cardiovascular pathologies in ECG images. A proposed method was designed to provide a robust algorithm for the detection of irregular heart rhythms, myocardial infarction and other cardiac diseases. The model is trained and tested on the dataset of ECG images from individuals with various prevalent cardiovascular diseases as well as normal hearts. Therefore, the CardioAugmentNet state-of-the-art model classifies different cardiac abnormalities with high accuracy, suggesting that it can be used in clinical practice. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Technological Interventions in Unpaid Care Work and Gender Dynamics
Unpaid care work, frequently centered on women, is an important yet neglected component of the world economy. This study strives to address the potential and flexibility that technology brings in curbing gender disparities, along with the bodily burden associated with unpaid care work. An examination is made for smart home devices, telehealth solutions, and caregiving applications that will further be evaluated for their effectiveness in minimizing the time and effort invested in unpaid care responsibilities. Through existing theoretical frameworks, empirical evidence, and case studies, this paper aims to determine how technological innovation can more effectively redistribute care work between genders and enhance the economic value of unpaid care to further improve gender equality. For instance, in Japan, smart home appliances such as automated pill dispensers and remote monitoring devices have become crucial solutions to a caregiving burden largely imposed on women. Telemedicine services similar to these have transported rural India from its unfavorable health care situation, thereby significantly shortening the time women spend on activities related to health care. Caregiver applications have assisted in the United States in achieving an equal distribution of caregiver responsibilities between male and female caregivers. Sophisticated robotic assistants in South Korea may fill gaps in the workforce while tending to older populations; thus, potentially minimizing housework hours for women. The education systems operating online across Sub-Saharan Africa enable girls to juggle learning with caring effectively and hence strike long-lasting gender parity. Such socio-economic advantages were purely garnered through wearable health monitors in Europe that eased family members burdens while experiencing economic benefits at a broader level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Efficacy of AI for Three-Dimensional Point Cloud Semantic Segmentation of Heritage Data for XR Environments
In heritage documentation, three-dimensional (3D) models created using Scan-to-BIM processes are essential for interpreting and presenting historic structures. Point cloud data derived from 3D laser scanning and photogrammetry facilitate realistic digital models used for immersive experiences. For this, raw point clouds, which are unstructured, are processed, semantically classified, and segmented to create parametric architectural objects in modeling platforms. Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS) refers to segmenting point clouds into classes like walls, columns, etc. Automating 3DPCSS using Artificial Intelligence (AI) has gained importance in current research activities because of its versatility and efficiency over manual segmentation. However, implementing it solely with AI presents various operational and conceptual challenges, particularly for XR models in digital heritage. Automated segmentation often fails to capture the unique characteristics and intricate geometries, leading to misrepresentations or oversimplifications. Selecting an appropriate algorithmic framework for automating 3DPCSS is essential to address this gap. This paper aims to understand the efficacy of AI algorithms in recent research for 3DPCSS, particularly those tailored for 3D modeling. A study of Dwarakadesh Haveli, Ahmedabad, India, highlights the workflow and challenges of integrating point clouds into 3D models. The findings indicate the need for a detailed approach tailored to the projects specific characteristics, emphasizing the importance of systematic algorithm ensemble experimentation to refine segmentation, leading to the development of 3D parametric objects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Managing Retail Credit Default Risk Using Artificial Intelligence and Machine Learning: Ethical Considerations and Challenges
Credit risk management is an integral aspect of financial stability of financial institutions. The determinants of credit default behavior help in identifying types of credit risk based upon customers habits or actions such as purchasing behavior, spending patterns, social interactions, and moral hazard. The awareness on such behavioral factors helps in getting a complete picture in analyzing the credit worthiness of any individual. The onset of artificial intelligence (AI) and machine learning (ML) has remodeled the methodologies of credit risk assessment. Training and developing learning models to predict the borrowers behavior evolved along with AI and ML, developing advanced mechanisms for managing credit default risks. Using risk modeling to determine behavioral scoring enables the banks to acquire creditworthy retail borrowers, alongside its inherent demerits of the models. This study is focused on the employment of AI and ML techniques along with qualitative determinants in managing credit risk, drawing attention to the advantages and disadvantages and applications and implications for the financial service providers. The paper examines contemporary literature on modern methods of credit risk management and sheds light on the technological accuracy of credit risk calculations and automated decision-making processes alongside methods to alleviate biases. The study also outlines the sequential phases of the conventional models along with the regulatory and ethical considerations of AI-ML-driven credit risk assessment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Development ofNew Fidelity Theorems inQuantum Communication
Fidelity estimates the closeness or degree of overlap between a pair of unknown quantum systems. In quantum information processing study, a primary task is to estimate the fidelity of a pure states pair in a 2-dimensional Hilbert space or between a mixed state, ?, and pure state existing in N dimensional Hilbert space while transmitting them through noisy environments. This paper suggests two theorems to estimate fidelity while proving and justifying them numerically. The first theorem computes fidelity between pure states and the second estimates fidelity between mixed and pure states. The mathematical basis of the theorems confirms accuracy in estimation while dealing with single- and multiqubit system environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Financial Inclusion as a Tool for Social Equity: A Focus on the Elderly and Underprivileged
Financial inclusion is a key element in reducing poverty and promoting economic growth, particularly for vulnerable groups such as the elderly and the poor. This article explores the multiple barriers that these groups face in accessing financial services, including economic constraints, discrimination, and lack of identification and credit history. The report highlights the special challenges that older people face due to limited mobility and digital literacy. The study emphasizes the importance of a multi-faceted policy approach to improving financial inclusion, including developing simplified financial products, expanding financial literacy programs, and working with NGOs. The paper focuses on effective strategies and proposes interventions aimed at empowering these populations and improving their access to financial resources, ultimately supporting economic stability and reducing inequality. The findings highlight the need for policymakers and financial institutions to work together to address systemic barriers that impede the financial autonomy of vulnerable groups. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging Machine Learning for Epidermal Ailment Detection
Skin disorders are common across the globe, often proving to be difficult to diagnose because of coexisting signs and symptoms. In this paper, we study the feasibility of using machine learning (ML) techniques for automatic skin disease detection. We look at the emerging patterns in fundamental studies within the scope of focus that deals with image processing for feature extraction and employing classification methods for disease detection. We focus on feature extraction and the classification of images. One of the major strengths is the ML-based approach with better access and usability and higher chances of them being detected at an early stage. In addition, we consider some of the drawbacks and problems of these methods, including biased data and lack of sufficient professional oversight. We also consider other aspects, whereby one of them is further analysis of the requirement in the case of the absence of the adequate data, standard models, and unambiguous explanations of the inner processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Harnessing Machine Learning toOptimize Social Media Marketing
Machine learning (ML) has transformed the way digital advertising is done and analyzed by social media data. This paper explores ML in targeted advertising, including techniques such as supervised learning, neural networks, and NLP. While ML improves campaign precision and consumer engagement, it also presents challenges such as algorithmic bias, data privacy concerns, and computational scalability. This study is a synthesis of existing research and explores real-world applications, providing a critical analysis of MLs capacity to optimize social media advertising. It argues that while ML may provide exceptional possibilities for customization and engagement, its success can only be ensured through appropriate ethical practices, transparency, and innovation in technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
The Quantum Shift inMachine Learning
Quantum machine learning (QML) leverages the power of quantum computing to extensively enhance the performance of simple ML algorithms. This kind of enhancement efficiently avoids complicated high-dimensional and computationally expensive applications. Following exciting discoveries of some key algorithms such as Grovers and Shors algorithm, QML came up with advanced models such as quantum neural networks (QNNs), quantum support vector machines (QSVMs), and quantum deep architecture. The discussed models give better results in data processing, pattern recognition, and optimization in comparison to their classical variants. This survey discusses the most important developments in QML with focus on Quantum Fourier Transform for encoding the data. Applications of QML in quantum chemistry, secure distributed computing, and high-dimensional data analysis are shown. It will present new breakthroughs and continue to challenge practical, scalable QML solutions toward mapping out the course toward quantum advantage in machine learning tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Deep Learning-Based Consolidated Disease Classification in Health Data Management
Healthcare data management is critical for ensuring comprehensive and high-quality medical treatment. Sensitive patient data management has a potential new option thanks to blockchain technology. However, existing blockchain-based healthcare data management systems face challenges in scalability, integration, and regulatory compliance. To address these issues, a novel blockchain-based healthcare data management system has been proposed to provide a secure, decentralized, and interoperable platform for managing sensitive patients medical information. Proposed approach involves collecting comprehensive health measurements from patients using wearable sensors and ensuring the security and integrity of patient data through robust user verification protocols. Artificial Neural Networks (ANNs) are employed to consolidate disease symptoms, enhancing the efficiency and accuracy of data analysis. The results and comparative analysis showcase the efficiency of the proposed method in terms of precision, accuracy, recall, search accuracy and F1-score. The accuracy of the proposed method is improved by 12.9%, 6.07%, and 14.28% when related to the existing ACTION-EHR, BSDMF, and BlockMedCare techniques respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.