Browse Items (3095 total)
Sort by:
-
Development of an AI Based Framework for Reverse Supply Chain of Pharmaceutical Products
The pharmaceutical reverse supply chain is an integral part of pharmaceutical industry. Due to the complex nature of the process and strict government regulations, it is important to use different AI technologies to increase the efficiency of the reverse supply chain. This research aims to design an AI driven framework for reverse supply chain of pharmaceutical products which would increase efficiency, speed, automate processes and enhance trust among the stakeholders. The framework consists of five modules namely Collection and Sorting Centre, Return Management, Real-time Inventory Management, Disposal Centre, and Data Analytics. In each module different AI technologies have been embedded to increase the efficiency of the system. The proposed framework offers a holistic approach that not only aligns with stringent pharmaceutical standards but also contributes to a more robust, transparent, and environmentally sustainable reverse supply chain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Machine Learning Approach to Crude Oil Price Prediction Using Support Vector Machine (SVM)
Crude oil is one of the most important energy sources, and fluctuations in its international prices affect all aspects of the economy. The price of crude oil is influenced by several variables, and the length of time that each component has an effect differs giving an increase in non-linear oil price features. Although it is a complex task, identifying the most essential factor influencing for precise predicting, crude oil prices are essential. Therefore, this study aims to employ a machine learning model to address the intricate relationships among different factors. Primarily, it gathers data regarding West Texas Intermediate (WTI) and Brent crude oil prices as well as macroeconomic variables. Secondly, the data is normalized to prepare it for further analysis. Finally, a crude oil prediction model is constructed using Support Vector Machine (SVM) to predict future international crude oil prices. The daily, weekly, and monthly prices are used to confirm the models efficacy developed using WTI and Brent oil. The models performance is also evaluated by incorporating various combinations of macroeconomic variables to find the most influential factor. Results from experiments show indicates the benchmark model was much exceeded by the developed model and performed very well in terms of prediction accuracy. The findings reveal that selecting the appropriate variables can greatly enhance prediction accuracy. This model has the potential to provide valuable insights for traders, investors, and energy-related enterprises, offering beneficial guidance for decision-making purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhancing Crude Oil Price Prediction with Neural Network Models
A nations economic, social, and national security are all severely affected by variations in crude oil prices, which is a basic energy source. Research on accurately forecasting price changes for crude oil is always progressing. This research presents a forecasting strategy for crude oil pricing using artificial neural networks. The presented model uses standardization techniques to prepare the historical data for the subsequent processes. It is possible to predict future prices by using a Feed Forward Neural Network (FFNN) with four layers. West Texas Intermediate (WTI) and Brent crude oil prices are utilized on a daily, weekly, and monthly basis to demonstration and confirmation. Directional statistic, accuracy of prediction, the model is evaluated using root mean square error and mean absolute error expressed as percentages. Empirical findings confirm that the suggested approach performs better than any of the previous approaches. Additionally, it is noted that the presented method achieved higher prediction in contrast to other methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Prophesying Credit Card Frauds Using Predictive and Deep Transfer Learning: A Comprehensive Experimental Perspective
Credit card fraud has become a major issue in the online financial environment, requiring the implementation of smart and automated tools for real-time detection of frauds. Machine Learning (ML) has been an important asset in this area because of its capability to discover underlying patterns, learn new fraud methods, and offer scalable solutions. This study investigates the usage of different classical machine learning and deep transfer learning based on predictive models for credit card fraud detection with a focus on their comparative performance on six important parameters: time elapsed, accuracy, precision, recall, TNR and F1 score. The investigation makes use of a PCA transformed benchmark dataset with a total of 2,84,807 credit card transactions to train models. In depth experimentation is performed using five classical ML models named Random Forest, Logistic Regression, Linear SVM; Non-Linear SVM; XGBoost and four classical Deep Learned models named MLP, Shallow ANN, ID CNN, and LSTM. To enhance experimental validity, prediction capability of four GNN based CNN models such as Boosting-GNN, Jump-Attentive GNN, GNN and PC-GNN are also tested. Deep learning based neural network models are analysed using seven different activation functions and each model is fit using 10 epochs of batch size 512. Testing results point out that overall best performance in classical ML models is shown by Non-Linear SVM with best recall score depicted by ANN on RBG kernel and GPU. In the ensemble category, Random Forest model exhibits overall best performance with best recall for XGBoost. Precision, accuracy and F1 score of random forest and XG boost are highest. Results have shown that in case of Random Forest the accuracy, precision, recall and F1score are 99.9%, 97.7%, 81.9% and 89.12% respectively whereas for XG boost the values for accuracy, precision and F1 score are 99.96%, 92.63%, 83.81% and 88% respectively. Deep Learned models showed high accuracies, however they were significantly utilized computational resources in respect to elapsed time. The study provides a roadmap to financial institutions for efficient model selection while deciding on implementing automated and trustworthy fraud detection systems and helps shape the dynamic world of intelligent financial security solutions to reduce financial losses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
QuiltCraft: A Computer Vision Framework for Sustainable Upcycling Waste Textiles into Artistic Quilts
QuiltCraft represents a pioneering approach where we aim to mitigate the pervasive childrens cloth crisis by ingeniously repurposing discarded cloth into captivating and artistic quilts. By harnessing the power of computer vision techniques, predominantly facilitated by using OpenCV and various Python libraries, our innovative system streamlines the intricate process of identifying cloth sizes, segmenting them into manageable pieces, and orchestrating their arrangement into visually stunning quilt designs. This paper explores the methodology, algorithms, and intricate implementation details underpinning the Quilt Craft framework, supplemented by empirical evidence attesting to its remarkable effectiveness. Through the seamless transformation of childrens clothes into captivating works of art, QuiltCraft not only champions the cause of waste reduction but also catalyzes advancing sustainable practices within the fashion industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Novel Machine Learning Ensemble Approach for Corrupt Data Packet Identification
In contemporary network infrastructures, ensuring the fidelity of data transmission is paramount for robust communication and security. The intrusion of corrupted data packets can severely degrade network efficiency, resulting in critical data loss, exploitable security gaps, and suboptimal resource allocation. This paper indicates the significantly increase detection accuracy and system resilience by synergistically using the predictive capability of many machine learning paradigms especially. This paper employs sophisticated feature engineering to extract discriminative attributes from network packet headers and payloads, followed by a refined ensemble learning strategy that leverages both stacking and boosting techniques for optimal classification performance. Compared to conventional single-model techniques, evaluated on real-world network traffic datasets our model shows a significant increase in key performance measures. Here a pioneering hybrid machine learning ensemble framework designed for the precise identification and mitigation of corrupted data packets. Notably, the ensemble framework excels in minimizing false positives, enabling real-time packet analysis and bolstering network security. This study contributes to the evolution of intelligent, adaptive network defense mechanisms, providing a scalable and high-performance solution for safeguarding data integrity and mitigating the deleterious effects of corrupted data packets in modern, high-throughput communication environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Financial Literacy, Inclusion, and Empowerment of Rural Women Entrepreneurs: A Bibliometric Review on the Role of Incubation Centers
In the last twenty years, efforts have been made to address how financial literacy and inclusion can empower rural women entrepreneurs. However, one essential factor tends to be left behindthe contribution of incubation centers to enabling these women to succeed. This research examines the academic landscape in closer detail through a bibliometric analysis of 2,012 peer-reviewed articles between 2005 and mid-2025 based on the Scopus database. Using tools such as Biblioshiny and VOSviewer, we delve into how the discourse on financial capability, women entrepreneurship, and economic empowerment has progressed throughout time. The research indicates a meteoric increase in interest following 2020, fueled primarily by vulnerabilities uncovered with the COVID-19 pandemic. Despite expansion, however, something is certain: there remains relatively little research linking incubation assistance to rural women directly or financial literacy programs. Our analyses of keywords and co-authorships indicate a splintered discipline with ample space for richer, more holistic research. This article not only charts what is known but also reveals what is lacking, presenting new directions for researchers and policymakers dedicated to building stronger safety nets for rural women entrepreneurs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
BlockE: Securing Cryptos Future with Smart Identity and Tracking
BlockE is established to provide a regulated space within which one can solve the concerns related to the crypto space about its illegitimate features. The block that is built for BlockE must be the Virtual Identity (Virtual ID) system to enable user transactions to control monitoring without compromising trust and safety. Another paper with more detailed information about BlockE, which describes the concept, structure, as well as realization aimed at linking decentralization and regulation. It further outlines some of the functions of BeToken, the native cryptocurrency token, and emphasizes scaling, security, and transparency within the fast-growing crypto ecosystem. Its a cool platform equipped with mind-blowing features like the Chat with Wallet, where people can communicate with each other privately and securely, thereby gaining access only to the users who use the channel, and the BlockE AI Chatbot, a virtual assistant on a sophisticated linguistic model. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhancing Regional Language Proficiency in Large Language Models Through Translated Datasets
Although Large Language Models (LLMs) have made significant progress in Natural Language Processing the lack of high-quality training data frequently limits their ability to perform well in regional languages. To improve LLM competency this study methodically translates an English dataset into the low-resource language of Bhojpuri. On this new dataset we apply a structured translation methodology and then refine an LLM that has already been trained. The models capacity to produce contextually relevant and culturally appropriate responses in Bhojpuri has significantly improved according to a comparison of its performance before and after fine-tuning. Our findings show that this translation-centric approach provides a practical and affordable way to enhance the usefulness and inclusivity of LLMs increasing the effectiveness and accessibility of these potent AI tools for underrepresented linguistic groups globally. For linguistic groups that are marginalized globally. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Explainable AI in Healthcare: A Hybrid CNN-ViT Approach for Pneumonia Detection Using SHAP
The adoption of Artificial Intelligence (AI) in healthcare has improved diagnostic accuracy, particularly in medical imaging. However, the opaque nature of deep learning models raises concerns about interpretability in high-stakes applications such as pneumonia diagnosis. This study proposes an Explainable AI (XAI) framework that integrates a Hybrid CNN-ViT architecture with SHAP (SHapley Additive Explanations) for pneumonia detection from chest X-rays. Our approach achieves competitive diagnostic performance (94% accuracy) while enhancing transparency by highlighting clinically relevant features such as lobar consolidations and ground-glass opacities. By grounding explanations in established radiological findings, the framework supports clinical trust and regulatory compliance. This work contributes to bridging the gap between AI performance and medical accountability, positioning explainable deep learning as a trustworthy tool for real-world healthcare deployment. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An Eco-Friendly Antenna for 6G Communication Enabling Sustainable Infrastructure
This research presents the development of an antenna on an organic substrate for 6G infrastructure utilizing sustainable materials. The substrate is developed from 75% used tea powder and 25% carbon sourced from used batteries, combined with polyvinyl alcohol (PVA), molded, and thermally processed to create a 5mm thick organic substrate. The developed patch antenna operates within the frequency range of 14.8 to 17.3GHz, encompassing the new lower 6G candidate band (14.8 to 15.3GHz). The antenna exhibits 2.5GHz bandwidth with a resonant frequency of 15.7GHz. This antenna highlights the potential for integrating eco-friendly materials into modern telecommunications technology, promoting sustainability, and supporting the circular economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Interpretable Breast Cancer Risk Stratification Using Statistical Feature Engineering on Thermal Images
This research solves the black box problem of AI implementation in imaging by introducing a transparent, statistically grounded approach to breast cancer risk stratification via infrared thermography without compromising performance. Using the public DMR-IR dataset, statistical feature engineering was applied to training data by extracting first- and second-order statistical features. After ensuring non-normality with a Shapiro-Wilk test, feature significance was established with the Mann-Whitney U test. LASSO regularization selected the five most predictive features: mean, standard deviation, kurtosis, correlation, and energy. To counteract class imbalance, SMOTE was applied, and two machine learning modelslogistic regression and random forest (classifier)were trained on the balanced data and then evaluated on an unseen test dataset. Reporting an AUC of 0.98 over logistic regressions 0.96 reflects stringent statistical feature engineering and great generalization, creating a strong and interpretable model for breast cancer diagnosis in thermal images, and instills more clinical confidence in AI-based diagnostic systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Comparative Analysis of Disaster Recovery, Encryption, and Database Migration Methods in Cloud Environments
This research conducts comparative analysis and performance evaluation on disaster recovery approaches, encryption strategies, and database migration methods in cloud environments. The study highlights deeper technical insights encryption techniques and demonstrates superior performance compared to the other encryption methods in securing non-data files. This approach enhances protection against insider threats while avoiding reliance on existing Oracle wallet features, ultimately leading to a reduction in licensing expenses. This study also evaluates various database migration solutions, specifically AWS DMS, Google DMS, Azure DMS, and IBMS. Notably, IBMS stands out for its proficiency in producing cross-region data copies while achieving a 75% reduction in infrastructure costs. A comparative analysis was conducted on various disaster recovery strategies, including Standard DR, Pilot Light, Warm Standby, Hot Standby, Semi Replication, and DDI. Among these, the DDI is being observed as noteworthy since it excels in decision making capabilities and auto replication role switching advantages of standby databases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Optimizing Algorithmic Trading Through DRL: A Comparative Analysis of Single-Agent and Multi-Agent Models
This work investigates how Deep Reinforcement Learning (DRL) can elevate algorithmic tradingespecially in fast-paced, high-frequency markets. We propose a full-fledged framework to compare different setups, from solo agents to multi-agent systems, applying DRL methods like Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C), along with combinations of these. We trained on hourly stock data from 24 firms over two years (Jan 1, 2020Jan 1, 2022) and tested performance over the next year (Jan 1, 2022Jan 1, 2023). We evaluated key factorsreturns, risk control, and how well these models adapt to changing markets. The single-agent PPO model stood out, achieving a remarkable profit factor of 28.07 on BIDU and keeping peak drawdowns frequently under 1%. This demonstrates both solid capital protection and high risk-adjusted performance. Ensemble models showed balanced performance in both single-agent and multi-agent setups, achieving a Sharpe ratio of 0.75 and Sortino ratios up to 7.7, outperforming existing benchmarks. Comparative analyses revealed that ensemble strategies enhance market responsiveness and improve both stability and profitability in volatile environments. Sensitivity analysis confirmed the robustness of model performance across various hyperparameter settings. Overall, the proposed DRL-based ensemble framework demonstrates strong potential to improve real-world HFT systems by delivering more adaptive, stable, and efficient algorithmic trading solutions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Analysis of Blockchain Transaction Patterns and Risk Assessment in the Open Metaverse
This research explores the use of an ensemble comprising Random Forest, XG-Boost, and Isolation Forest algorithms for anomaly detection and risk analysis of blockchain transactions in the Open Metaverse. We created a multi-phase strategy using temporal feature engineering, risk assessment frameworks, and behavioral pattern analysis using an extensive dataset of 78,600 transactions. The ensemble model achieved 99% classification accuracy and zero false positives for valid transactions, while effectively detecting phishing (84% precision, 68% recall) and scam activities (81% precision, 91% recall). With 56.19% of the categorization, risk assessment measures were the most important predictive indicators, followed by transaction patterns and behavioral attributes. Our cross-validation analysis resulted exceptional stability in performance, with standard deviation of 0.0004 across diverse transaction scenarios. This confirms that our methodology can reliably detect intricate risk patterns, playing a crucial role in strengthening transaction security as the Open Metaverse continues to evolve. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Advancements in Astronaut Health Monitoring Technologies
Astronautics also outlines unique biological and cognitive obstacles that demand advancements in health monitoring and techniques for risk prevention for space travellers. This study investigates the consequences of microgravity, space radiation and persistent confinement on astronaut well-being, focusing on cardiovascular, musculoskeletal, neurological and immune system vulnerability. Cardiovascular ailment, a major concern, is monitored using clinical prediction models (CPMs) that combine traditional risk factors, biomarkers and machine learning techniques. Additionally, AI-powered methods consisting of GPT-based models and time series transformers are required for real-time health monitoring and analytical assessment. Test-based outcomes illustrate that models such as logistic regression, random forest and support vector machines attain high designation accuracy in defining astronauts health hazards from non-astronaut data. Furthermore, wearable medical trackers and space-sourced clinical techniques are detected as an alternative solution for both space missions and terrestrial well-being. The study also highlights the need for perpetual advancements in zero gravity to protect astronauts well-being and enhance medicinal solutions for upcoming space travels. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
SentimentViz: Leveraging RoBERTa in Python for Advanced Sentiment Analysis and Decision-Making for a Famous Indian FMCG (Ayurvedic) Brand
Indian consumer preferences for Ayurvedic brands increasingly turn to the marketplace for well-being. Ayurveda has a deep-rooted history in emerging economies like India, and its increasing role in health, wellness, and exports contributes to Indias economic development. The consumption and changing lifestyle patterns significantly contribute to achieving the United Nations Sustainable Development Goals (SDGs). The primary objective of this study is to explore consumer sentiment that includes perceptions, feelings, and attitudes toward these natural healthcare products contributing to specific SDG targets, leading to good health and well-being. In a data-driven world where governments and businesses seek insights from vast amounts of unstructured text data, sentiment analysis plays a pivotal role in decision-making. Sentiment analysis helps analyze different aspects of unstructured data, including customer experience and insights generated in terms of usage, challenges, and preferences and ultimately helps manage customer engagement. The sentiment analysis requires understanding the context, grouping similar words, removing unrelated content, and then gauging the sentiment of the text. There has always been a challenge to contextualize and gauze the deeper sentiments and create scalable solutions. To build on this need for deeper sentiment understanding and scalable solutions, SentimentViz is a proposed accelerator as part of this paper that leverages Python and chooses the best methodology for text-mining problems. It enables real-time analysis with robust visualization capabilities: In this study, the SentimentViz accelerator is leveraged to estimate the sentiment of 9 products using a robust data science framework and best-of-the-class ML techniques. The detailed consumer sentiment analysis helped to develop a deeper understanding of the value of FMCG (Ayurvedic products) for an emerging economy like India. This will help marketers build targeted marketing campaigns, brand health monitoring, and customer retention strategies through informed decisions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
NLP and Topic Modeling in Healthcare: Identifying Diseases from Patient Histories
Topic modeling and Natural Language Processing (NLP) have demonstrated significant prospects in the healthcare industry for extracting insightful information from unstructured patient histories that can help diagnose diseases and enhance clinical decisions. In this study, patient histories are grouped into ten different clusters using advanced K-Means clustering, with the Dunn Index being used to validate the clustering performance. After the clusters are formed, each cluster is subjected to topic modeling approaches. Four topic modeling approaches are examined in this study, Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), Latent Semantic Indexing (LSI), and Non-negative Matrix Factorization (NMF). These techniques are used to find disease-related terms from patient histories. Coherence scores, which show the semantic significance of the terms produced, and execution times, which show the computational efficiency needed for real-time healthcare applications, are used to evaluate the models. According to experimental findings forthe USMLE Step 2 Clinical Skills exam dataset, NMF and HDP generated the most cohesive terms, with NMFs faster execution time (1.67s) making it appropriate for widespread healthcare applications. Whereas, a reasonable balance between coherence and computational demands is offered by LDA and LSI. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Mul-Sensis: Multilingual Sentiment Analysis Framework for Emotion Detection
Sentiment analysis is a pivotal Natural Language Processing (NLP) task that enables the extraction of actionable insights from textual data, particularly social media. With the rise of public discourse on platforms like Twitter, analyzing sentiment trends has become crucial for decision-making in domains such as policy implementation, feedback evaluation, and public opinion monitoring. Mul-Sensis employs a hybrid approach combining transformer-based models with classical machine learning algorithms to enhance sentiment classification. The system integrates advanced preprocessing techniques to address linguistic complexities like sarcasm, idiomatic expressions, and domain-specific nuances. A robust hybrid annotation approach, incorporating both human expertise and machine-assisted methods, ensures high-quality, bias-free sentiment labeling. This study contributes a scalable, interpretable, and domain-agnostic framework for sentiment analysis, offering valuable insights for policymakers, researchers, and industries relying on textual data analytics. The findings highlight the transformative potential of hybrid and ensemble-based NLP approaches for understanding public sentiment across diverse cultural and linguistic contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Intelligent Analytical Framework to Improve Customer Retention in the SaaS Industry
In the software as a service (SaaS) sector, churn is a crucial indicator as it directly affects a businesss earnings, prospects for expansion, and viability over time. Because SaaS companies mostly rely on recurring income from subscriptions, high churn rates can be detrimental to their operations. Customer retention is crucial for SaaS companies as it is frequently more profitable and cost-effective than bringing on new customers. Retention expenses and efforts can be decreased by focusing on an appropriate set of customers. This study focuses on an intelligent analytical framework that uses machine learning and artificial intelligence techniques to find the ideal group of customers for a SaaS-based organization to retain. The previous papers concentrated on either classification or survival analysis to determine the probabilities of churn. A few studies used explainable AI models to improve the predictability of the model predictions. Not having a holistic prediction model and retention strategies provides the research gap for this study. The proposed methodology used feature selection models to identify the most significant drivers of churn, and the most popular predictive models, like logistic regression, random forest, support vector machine, and neural networks, are applied to the training set. The likelihood of churn is calculated by using classification models. The Kaplan-Meier estimate is used for survival analysis to determine the odds of survival based on the tenure of each account. Lastly, the prediction models interpretability is enhanced by using explainable AI models like SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The neural networks model gave the best accuracy of 71% for the classification model, which provided the probability of churn and the likelihood of survival, has been predicted by Survival Analysis. Explainable AI models have identified the most important features that the model considers when arriving at the probability. This enabled the company to segment the data based on the probabilities of churn and survival, and the feature importance and respective retention strategies have been planned for each segment. By implementing the suggested analytical methodology, the business may determine which customers are most important to target with customer retention strategies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.