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Bamboo Trade Dynamics: A Hybrid ARIMALSTM Forecasting Approach for Indias ExportImport Trends (20172025/26)
Bamboo plays a dual role in Indias economy, serving as both an ecological safeguard and a driver of rural livelihoods. This paper examines bamboo exportimport flows between 2017 and 2025/26 using official trade statistics. A hybrid ARIMALSTM forecasting model is implemented to capture both linear and nonlinear patterns. Results demonstrate rising exports, stable imports, and higher predictive accuracy compared to ARIMA alone, confirming bamboos growing role in sustainable trade. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Alcohol-Attributable Liver Disease in India, 20002021: Comparative Analysis Across Alcohol Policy Regimes Using GBD 2021
Alcohol use is ranked among the leading causes of liver disease in the world, and the most dreadful consequences of this condition are cirrhosis and hepatic cellular carcinoma (HCC). India has an eclectic policy environment, with bans, regulation, liberal paradigms, and the influence of such policies on the epidemiological process is inadequately studied. Based on the Global Burden of Disease (GBD) 2021 data of nine states (20002021), this study focuses on disability-adjusted life years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) due to alcohol-related cirrhosis and HCC. States were classified as prohibited (Bihar, Gujarat, Nagaland), regulated (Karnataka, Kerala, Tamil Nadu), and liberal (Goa, Punjab, Sikkim). Liberal states had the highest burdens, with Sikkim leading by approximately (410 per 100,000), followed by Goa (360 per 100,000) and Punjab (290 per 100,000), all above prohibited state averages. In Bihar, there was 27% reduction of DALY, whereas Kerala had the highest increase of 44%. More than 90% of total variation in DALYs was attributed to YLLs, with men also experiencing larger overall burden, ranging between 45 and 811 times during midlife. The panel regression displayed low cohort-level variance (R2= 0.41) but strong state-level effects (R2= 0.98), that signify a high level of heterogeneity. These results show that in addition to policies, variations in implementation, fiscal priorities, and social contexts determine the burden experienced in India, which further points to the need to implement evidence-supported, targeted interventions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Insights into the Publication Trends of Pharmaceutical Reverse Supply Chain Using Data Mining Approach
Due to the non-profitable nature of reverse supply chain of pharmaceutical products, researchers and companies have not shown much interest in this field. Due to stringent regulatory compliances pharmaceutical companies and hospitals are mandated for proper disposal of pharmaceutical wastes. This research aims to highlight the publication trends of pharmaceutical reverse supply chain using data mining approach. The metadata of published literature was extracted from Scopus and analysis was done for the title and abstracts of the articles. It was found that there is limited published literature on this topic. Co-occurrence map of text-based data, time graph of co-occurrence map of text, trigrams word cloud, keywords plus word cloud and unigrams word cloud were formed to get insights into the publication trend. A model had been proposed from the consumers end for pharmaceutical reverse supply chain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Automated Diabetic Retinopathy Diagnosis Using Ensemble Approach
Diabetic Retinopathy is a major reason of vision impairment among diabetic patients, early and accurate diagnosis is crucial. This research focuses on developing a machine learning-based classification system to detect different stages of DR using Support Vector Machine (SVM), Random Forest (RF) and ensemble model. The dataset is divided into five categories: Healthy, Mild, Moderate, Proliferative and Severe DR. Performance evaluation using various metrics, including Accuracy, F1-score, RMSE and AUC-ROC, indicates that the ensemble model achieves the best results, with an accuracy of 77.66% and an AUC-ROC of 0.9015. The confusion matrices show that existing models struggle with certain misclassifications, the ensemble approach enhances overall predictive capability. Future improvements can include integrating deep learning models such as convolutional Neural Networks leveraging larger and more diverse datasets and incorporating image preprocessing techniques to enhance feature extraction. This system can help ophthalmologists to detect early and treatment planning, ultimately decrease the risk of blindness in diabetic patients. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Comparative Evaluation of Standalone LLMs and Retrieval-Augmented Generation Models Using Hypothetical Gemini Systems
This study assesses the efficacy of two theoretical language models; Gemini Standalone LLM and Gemini RAG (Retrieval Augmented Generation) across diverse natural language inquiries. The assessment centers on three principal metrics: precision, pertinence, and inference duration. The experiment utilizes a controlled simulation to illustrate the benefits and drawbacks of independent language creation versus retrieval augmented generation strategies. The results demonstrate that RAG at trains superior accuracy and relevance by integrating retrieved context, albeit it incurs longer inference durations. This comparative analysis seeks to assist researchers in comprehending the ramifications of including retrieval methods into big language models. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Bibliometric Analysis of Gifts in the Era of E-Commerce: A Data Mining Approach
Gifting is a universal phenomenon. It is deeply connected with history, human culture, social interactions, and economic activities. This study aims to look at the work of various researchers on online gifting. The keywords (gift OR gifts) AND (online OR electronic OR e-commerce OR virtual) were used on the Scopus Database. Bibliometric analysis was conducted on 397 relevant publications, which were filtered and selected from the list of 1398 documents. Analysis through Term co-occurrence map, Network visualization map of terms in title/abstract fields, and Topic trends, among others, was done. Four primary clusters were found in the Term co-occurrence map as well as Network Visualization Map Most of the research was from the USA and China. The multi-disciplinary element of gifting is visible in the analysis. Some of the emerging topics were virtual reality, live streaming, social networking, advertising, and online shopping. The impact of gifts in promotions and marketing showed the potential of gifts as a major tool for marketers. The study was limited to only the Scopus database and gives insights into the evolution of online gifting behaviour. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Comparative Analysis of GANs and Diffusion Models for Hyperspectral Image Classification
Hyperspectral imaging, which is obtained across numerous spectral bands, presents difficulties in classification due to its high dimensionality and intricate nature. This study provides a comparison of Generative Adversarial Networks (GANs) and Diffusion models regarding the classification of the Indian Pines, Pavia University, and Salinas Datasets, utilizing Multi-Layer Perceptron and Random Forest classifiers. The findings indicate the GANs combined with Random Forest outperform Diffusion models, attaining accuracies of 88%, 96% and 95% respectively. This approach may not outperform the top models, such as HTD-2D-3D-PCNN, but is simpler in structure and more computational efficient. Key recommendations would be real-time processing, edge device optimization, and applications customized to agriculture and urban planning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Hybrid Machine Learning Models for Crowdfunding Success Prediction
Crowdfunding has become a viable option for founders and entrepreneurs as an alternative source of funding, where individuals can access a large pool of supporters to fill the funding gap. There are a variety of reasons why it is difficult to predict if crowdfunding project will be successful. As such, types of projects, duration of the campaign, target funding goal, and overall supporter activity are constantly changing. This research, therefore, aims to explore, the use of machine learning for predictive models that quantitatively leverage the historical records of projects on kickstarter, in order to find the success probability. In order to analyze the predictive ability for campaign success, was used a combination of machine learning models - Logistic Regression, Random Forest, XGBoost, LightGBM, and Decision Trees - the models that had the greatest precision were Decision Tree (99.91% acc), and LightGBM (99.90% acc) hence why they were selected. In addition, this research demonstrates how feature selection coupled with ensemble learning can significantly increase predictive potential by providing valuable information for campaign builders, platform operators, and investors who are undertaking crowdfunding projects. These findings indicate that predictive modelling can support campaign design, promote investor trust, and enhance credibility for crowdfunding platforms, through uncovering fraud. Additional measures measuring social media interaction or sentiment analysis could be incorporated to provide information for better predictive models. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
IoT-Enabled Smart Breath Analyzer for Real-Time Monitoring of Ammonia, Alcohol, and VOC Biomarkers for Early Disease Detection
Real-time monitoring of breath biomarkers such as ammonia, alcohol, and volatile organic compounds (VOCs) is important for early disease diagnosis of metabolic and organ-related diseases. Traditional disease diagnosis techniques are invasive, time-consuming, expensive and not suitable for using in remote areas. To overcome these limitations, this paper proposes the design and development of an IoT-based Smart Breath Analyzer for real-time monitoring and analysis of ammonia, alcohol, and volatile organic compounds (VOC) concentration present in exhaled human breath. The system consists of different kinds of gas sensors connected to an ESP32 microcontroller for measuring gas concentration, which is processed and sent to Blynk application via Wi-Fi for visualization and disease prediction. A trained machine learning model is used in the system which classifies biomarker patterns that may be associated with conditions such as kidney disorders, respiratory issues, or alcohol influence, based on literature-derived thresholds. The system is presented as a proof-of-concept screening tool rather than a clinical diagnostic solution. The results are showed on an OLED display and accessed via a mobile app developed using the Blynk IoT platform. This non-invasive, affordable, and scalable solution improves continuous health monitoring and early diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Machine Learning Approach to Modelling Sales Performance Based on Social Media Analysis
By address the strategic challenges faced while maximising the advertising effectiveness, this paper examines the predictive relationship between different advertising sales platform and the brand sales performance. Four different supervised machine learning models - Random Forest, XGBoost, LightGBM, and a voting classifier ensemble model are applied to categorise the brand sales performance. The models are trained on a multi-platform advertising dataset. This research focuses on both the accuracy and interpretability of the data to make it easier identifying which platform affects the brand sales the most, which is quite different from the existing approaches that concentrates on finding predictive accuracy. This study aims to help marketers and businesses to make better and well informed data-driven decisions for marketing strategies by finding the most effective advertising platform. Each of the models were evaluated using Classification metrics which includes Accuracy, Precision, Recall and F1 score, along with confusion matrix. Future scope of the paper includes merging the models in real time systems and also by expanding it to carefully examine different time periods and customer groups. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Metaheuristic Optimization of Deep Learning Models for Land Cover Classification Using Remote Sensing Data
Deep learning techniques have greatly advanced land-cover classification from remote sensing imagery, but their performance depends critically on choosing optimal hyperparameters. Manually tuning hyperparameters (e.g., learning rate, network depth, dropout rate) is time-consuming and often suboptimal. Metaheuristic algorithms offer an automated approach to this problem. In this work, we compare five metaheuristic optimizersParticle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), African Vulture Optimization Algorithm (AVOA), and an Enhanced Dipper Throat Optimization Algorithm (EDTOA)for hyperparameter tuning of convolutional neural networks (CNNs), a ResNet-50, and a U-Net. We evaluate these methods on two benchmark land-cover datasets: EuroSAT (patch-level multispectral image classification) and DeepGlobe (pixel-wise satellite image segmentation). Our data preprocessing includes normalization, data augmentation, and computing spectral indices (e.g., NDVI) to enrich the feature set. Each metaheuristic searches the hyperparameter space to maximize validation accuracy (for EuroSAT) or mean Intersection-over Union (mIoU) (for DeepGlobe). In addition to predictive performance, we analyze the computational cost (wall-clock time, epochs to convergence, GPU usage) of each optimizer to assess the trade-off between efficiency and accuracy. AVOA and EDTOA achieve the best results on both datasets (e.g., up to 98.5% accuracy on EuroSAT and 56% mIoU on DeepGlobe), outperforming the PSO, GA, and DE baselines while offering favorable cost-performance balance. These findings demonstrate that advanced metaheuristics can significantly improve deep model performance in land-cover classification. Our contributions include a comprehensive experimental comparison of five optimizers, a detailed methodology integrating spectral index features, a cost performance analysis, and reference results to guide future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Synthetic Image Generation for Crop Disease Classification Using Generative Adversarial Networks
Due to biological diversity and unstructured surroundings, agricultural image analysis strives for optimal model performance to better accomplish visual identification objectives. Large-scale, balanced, and ground-truthed image datasets are very helpful, but they are frequently hard to come by, which restricts the creation of very effective models. The identification of plant diseases has benefited enormously from the continuous advancement of deep learning (DL) techniques, which provide a robust tool with incredibly accurate results. However, the efficiency of deep learning models is dependent on the quantity and caliber of labeled data used for training. Precise classification of crop diseases is important for precision agriculture. These models suffer from limited and imbalance datasets especially for rare diseases. The study suggests a framework using Generative Adversarial Network (GAN) for image generation to enhance the classification of diseases. The study employs conditional GAN trained on a PlantVillage and New plant diseases datasets to generate synthetic images of diseased leaves. The images are evaluated using Structural similarity index (SSIM). Then the augmented images are integrated with the CNN classifier to measure the accuracy of disease prediction using synthetic dataset to validate the efficiency of image generation. The Author(s) 2026. -
Forecasting Market Turbulence: A Multi-model Study Using GARCH, Random Forest, and LSTM in the Indian Stock Market
The dynamic and unpredictable nature of the Indian stock market presents significant challenges in forecasting return behavior and managing financial risk. This study explores market turbulence through a comparative analysis of three distinct modeling approaches: the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, Random Forest, and Long Short-Term Memory (LSTM) networks. By analyzing historical return data from Indian Nifty indices, the research captures both linear dependencies and complex nonlinear patterns associated with market volatility. The results highlight the GARCH models strength in modeling conditional volatility, while the machine learning and deep learning techniquesRandom Forest and LSTMexhibit enhanced predictive power in capturing intricate fluctuations in stock returns. The findings suggest that integrating traditional econometric methods with data-driven approaches offers a more comprehensive and accurate understanding of market dynamics. This multi-model framework is valuable for investors, financial analysts, and policymakers aiming to anticipate and navigate periods of heightened market uncertainty. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Blockchain Node Intelligence Based onDecentralized Framework
Blockchain enhances transparency, transaction speed, and governance reliability for organizations such as manufacturing and supply chain organizations by operating in decentralized environments. Supply chain traceability involves tracking products from their origin to customers, requiring transparency, authenticity, and high efficiency. This paper tries to address the performance gain and challenges in blockchain-based supply chain by making efficient use of on-prem and cloud environments in the blockchain network. As the volume of data being generated in blockchain network continues to grow, data security and performance become increasingly critical. Many existing big data security systems rely on controlled third-party providers, making them vulnerable to various security risks. Blockchain technology offers a promising solution by addressing key challenges such as scalability, immutability, trust, data governance, and transparency, thereby enhancing the protection of personal information. This work focuses on assessment of blockchain processing performance through on-prem, distributed, and decentralized environments and possibility of blockchain nodes to have intelligence, optimized processing capabilities by gaining appropriate infrastructure. We analyze the key challenges of blockchain when execution happens in a standalone system than in scalable ones. We tested a few popular mining processes on cloud platforms and a local system to assess execution speed and discuss a suitable platform to host blockchain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Compact Polarization Insensitive Triple-Band Bandstop Frequency Selective Surface forTerahertz Applications
In this article, a compact triple-band bandstop frequency selective surface (FSS) is designed for terahertz applications. The proposed FSS is a single-layer structure. The unit cell consists of cross-dipoles featuring three distinct arms. The designed FSS offers three bandstop responses centered at 0.34, 0.39, and 0.43 THz with 10-dB stopband bandwidths of 12, 5, and 14GHz, respectively. The filter operation is elucidated using surface current densities and an equivalent circuit model. The unit cell exhibits a surface area of 0.374?00.374?0, where ?0 is free space wavelength at lower resonance frequency. The proposed FSS exhibits excellent stability in incidence angles up to 40? and 80? for TE and TM polarization, respectively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Design ofSIRW-Based Self-diplexing Slot Antenna withLow Cross-Polarization Level forMillimeter-Wave Applications
This article presents a miniaturized self-diplexing slot antenna utilizing a substrate-integrated rectangular waveguide (SIRW) for millimeter-wave applications. The proposed antenna is built using a rectangular cavity, a slot on the top conductor, and two microstrip-feed lines. The rectangular cavity is realized for operation at TE110 mode (26.5GHz), which is perturbed for radiation at two distinct frequency bands by loading a slot on the top conductor. Two microstrip-feed lines are employed for excitation of the slot, which generates two operating bands. For better understanding of radiation phenomena, the electric field distribution, equivalent circuit model, and parametric analysis are provided. To further validate the proposed design, a millimeter-wave self-diplexing antenna operating at 22 and 28GHz is built and full-wave simulated. The proposed antenna occupies a smaller footprint area of 0.396?g2 with an isolation greater than 20 dB. Additionally, the suggested antenna offers low cross-polarization level better than ?30 dB. The realized gain and efficiency of the proposed antenna at lower (upper) frequency bands are 5.5 dBi (5.4 dBi) and 90.6% (94%), respectively. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Optimizing Base Station Placement toMinimize Interference forSatellite Terrestrial Networks (STN)
The rapid advancement of 5G and 6G technologies has spurred the development of Satellite-Terrestrial Networks (STNs), integrating terrestrial infrastructure with Low Earth Orbit (LEO) satellites to enable seamless global connectivity. Efficient spectrum allocation and interference management remain major challenges due to limited resources and the dynamic behavior of satellites. This study addresses these challenges by optimizing base station (BS) deployment to enhance spectral efficiency and reduce interference in STN environments. Delaunay Triangulation (DT) is employed to establish initial spatial separation between BSs, followed by gradient descent (GD) for fine-tuned optimization. Simulation results demonstrate that the optimized scenario substantially reduces interference and improves key performance metrics, including SINR, INR, CI Ratio, and received power, with gains ranging from 30% to 400%. These findings, derived from small-scale simulations, indicate the frameworks potential for enhancing STN performance in dense and interference-prone environments and provide a foundation for future research on interference-resilient STN architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhancing Smart Home Security Through Dynamic Authentication for Voice-Activated Commands
The proliferation of voice-activated IoT devices in smart environments has introduced significant security challenges, particularly around unauthorized access and command spoofing. In response, this paper proposes a unique multi-factor authentication framework that combines dynamically generated code words with smartphone-based verification to ensure secure voice command execution. Unlike conventional fixed-passphrase systems, our method generates unique session-based code words and requires real-time confirmation via a trusted mobile device, offering increased resistance against replay and impersonation attacks. The proposed framework is designed to integrate seamlessly with existing virtual assistants and IoT ecosystems, and its applicability extends beyond smart homes to include connected vehicles and industrial IoT systems, offering a scalable and secure authentication solution. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Hybrid Deep Learning Model Using U-Net and Vision Transformer for Artificial Intelligence Powered Cervical Stenosis Diagnosis
This study presents a deep learning-based approach for the classification of cervical stenosis using MRI spine images, integrating multiple phases such as preprocessing, segmentation, feature extraction, and classification. A U-Net-based segmentation model effectively delineates key anatomical structures, including the spinal canal, intervertebral discs (IVDs), and neural foramen, improving feature extraction and classification accuracy. Furthermore, ResNet-50 is employed for feature map generation, leveraging deep hierarchical representations to extract meaningful spatial patterns from MRI slices. For classification, a Vision Transformer (ViT)-based model is utilized, taking advantage of its self-attention mechanism to capture both local and global dependencies within MRI images. Unlike conventional CNN-based models, ViT processes MRI scans as patches, enabling a more context-aware analysis of stenotic regions. The model is trained using an 80%20% train-test split and evaluated using standard performance metrics, achieving an accuracy of 92.60%, precision of 90.16%, recall of 95.43%, and an F1-score of 91.56%. These results indicate that the ViT model outperforms traditional CNN-based classifiers in cervical stenosis detection, ensuring higher sensitivity and specificity in real-world clinical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Attention-Powered Deep Learning for Employee Analytics: A Multi-Model Approach
In the ever-evolving field of human resources analytics, there is the integration of the latest techniques of machine learning that can strongly enhance decision-making. This paper introduces a revolutionary architecture for multi-model neural networks that integrate disparate networks in analyzing the background, development, performance, and engagement of an employee for all key elements of this employee. Each of the processes with attention fine-tunes the importance of features and therefore largely improves the concentration and interpretability of results. These networks are thus ensured of thorough analysis in the form of in-depth evaluation, which enables classification to be discrete and into clear performance categories. Preparation of raw data was also done with much care; we used the Employee/HR Dataset from Kaggle in order to process this raw data before its use in deep learning application. Our proposed architecture outperformed by accurately classifying the employee performance categories, with result showing a high classification accuracy of 86.49% on the test set. This study, therefore, establishes that customized neural network architectures are applicable in supporting organizations in realizing their data driven culture and in making human resource operations more efficient. 2026, Springer Science and Business Media Deutschland GmbH. All rights reserved.