Search Results (1,649)

Fraudulent online payment operations represent a persistent challenge in digital commerce, particularly in sectors like air travel, where credit and debit card payments dominate. This study presents a novel fraud detection framework tailored to airline ticket purchases, combining a synthetic dataset generator with a modular, customizable feature engineering process. These are two machine learning models—support vector machines (SVMs) and the light gradient boosting machine (LightGBM)—for real-time fraud detection. A synthetic dataset was generated, including a rich set of engineered features reflecting realistic user, transaction, and flight-related attributes. While both models were evaluated using classification-evaluation metrics, LightGBM outperformed SVMs in terms of overall performance with an accuracy of 94.2% and a recall of 71.3% for fraudulent cases. The main contribution of this study is the design of a reusable, customizable feature engineering framework for fraud detection in the airline sector, along with the development of a lightweight, adaptable fraud detection system for merchants, especially small and medium-sized enterprises. These findings support the use of advanced machine learning methods to enhance security in digital airline transactions. Full article

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions. Full article

This study investigates the impact of employee well-being, work flexibility, and innovative work behavior on job performance among full-time and gig workers in digital businesses. A comparative analysis was conducted to examine potential differences between the two groups. A structured questionnaire was administered to 201 full-time employees in digital business system development and 199 gig workers from the IT Support Thailand group on Facebook using convenience sampling. The data were analyzed using multiple group structural equation modeling (MG-SEM) via partial least squares (PLS). The findings reveal that work flexibility boosts innovative work behavior, with gig workers showing greater adaptability than full-time employees. Innovative work behavior is positively linked to job performance, underscoring creativity’s role in organizational success. However, employee well-being and work flexibility did not demonstrate a significant direct effect on job performance. This study employed a sample of full-time and gig workers in Thai digital businesses, which may limit the generalizability of our findings to other industries or sectors. To enhance external validity, future research is recommended, including comparative studies across diverse employment forms and industries. Moreover, the adoption of a mixed-methods approach is encouraged to provide a more comprehensive understanding and broaden the scope of inquiry across multiple national contexts. Our findings underscore the need for policies that promote flexibility, well-being, and innovation to boost job performance. Digital business managers should foster adaptability, creativity, and support for both full-time and gig workers. An inclusive, balanced work environment can enhance performance, innovation, and satisfaction, helping organizations stay competitive in fast-changing markets. This study contributes to digital business research by examining the interplay between employee well-being, work flexibility, and innovative work behavior in determining job performance across different employment types. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

Inverted (p-i-n) CsPbI_xBr_3−x (x = 0~3) perovskite solar cells (PSCs) are of growing interest due to their excellent thermal stability and optoelectronic performance. However, they suffer from severe energy level mismatch and significant interfacial energy losses at the bottom hole transport layers (HTLs). Herein, we propose a strategy to simultaneously enhance the crystallinity of CsPbI_2.85Br_0.15 and facilitate hole extraction at the HTL/CsPbI_2.85Br_0.15 interface by incorporating semiconducting single-walled carbon nanotubes (SWCNTs) onto [2-(3,6-dimethoxy-9H-carbazol-9-yl)ethyl] phosphonic acid (MeO-2PACz) HTL. The unique electrical properties of SWCNTs enable the MeO-2PACz/SWCNT HTL to achieve high conductivity, optimal energy level alignment, and an adaptable surface. Consequently, the defect density is reduced, hole extraction is accelerated, and interfacial charge recombination is effectively suppressed. As a result, these synergistic benefits boost the power conversion efficiency (PCE) from 15.74% to 18.78%. Moreover, unencapsulated devices retained 92.35% of their initial PCE after 150 h of storage in ambient air and 89.03% after accelerated aging at 85 °C for 10 h. These findings highlight the strong potential of SWCNTs as an effective interlayer for inverted CsPbI_2.85Br_0.15 PSCs and provide a promising strategy for designing high-performance HTLs by integrating SWCNTs with self-assembled monolayers (SAMs). Full article

The construction sector is a major contributor to global waste output, with construction and demolition waste (CDW) producing substantial environmental, economic, and logistical challenges. Traditional methods for handling waste in developing countries have failed to implement sustainability concepts successfully, resulting in inefficient resource consumption and increasing landfill reliance. This study develops an Analytic Hierarchy Process (AHP) framework to integrate circular economy (CE) principles into construction waste management (CWM). The framework evaluates four criteria under economic, environmental, social, and technological categorization and applies expert-based pairwise comparisons to prioritize alternative strategies. To ensure reliability, the results were further validated through sensitivity analysis and cross-validation using complementary MCDM methods, including the TOPSIS, WSM, and WPM. The research attempted to determine the most successful waste management approach by examining critical economic, social, technical, and environmental issues in the setting of Sri Lanka as a case study. A hierarchical model was built, and expert views were gathered using pairwise comparisons to assess the relative importance of each criterion. The results showed that environmental considerations had the greatest relative importance (41.6%), followed by economic (38.4%), technical (12.6%), and social aspects (7.4%). On-site waste segregation appeared as the most suitable method owing to its immediate contribution to sustainability, while off-site treatment, prefabrication, modular construction, and waste-to-energy conversion followed. The research underlines the significance of organized decision-making in waste management and advises incorporating real-time data analytics and artificial intelligence to boost adaptable and sustainable construction practices. Full article

Amid the health-conscious consumption trend, functional foods rich in γ-aminobutyric acid (GABA) and vitamin B9 are gaining prominence. Foxtail millet, a traditional grain naturally abundant in these nutrients, faces quality assessment challenges due to the time-consuming and destructive nature of conventional methods, hindering large-scale screening. This study pioneers the systematic application of hyperspectral imaging (HSI) for nondestructive detection of GABA and vitamin B9 in millet. Utilizing spectral data from 190 samples across 19 varieties, we developed an innovative “coarse-fine” feature wavelength selection strategy. First, interval-based algorithms (iRF, iVISSA) screened highly correlated wavelength subsets. Second, model population analysis (MPA) algorithms (CARS, BOSS) identified optimal core wavelengths, boosting model efficiency and robustness. Based on this, a stacked BiLSTM–Adaboost model was built, integrating bidirectional long short-term memory networks for sequence dependency and adaptive boosting for enhanced generalization. This enables efficient, rapid, nondestructive, and precise nutrient detection. This interdisciplinary breakthrough establishes a novel pathway for millet nutritional assessment, deepens fundamental research, and provides core support for industrial upgrading, breeding, quality control, and functional food development, supporting national health. Full article

This study aims to address the challenge of balancing historical preservation and sustainable material selection in ancient building renovations, particularly in regions with unique climatic conditions like Hunan Province. The research proposes a hybrid model integrating Genetic Algorithm-optimized Extreme Gradient Boosting (GA-XGBoost) and Long Short-Term Memory (LSTM) networks. The GA-XGBoost component optimizes hyperparameters to predict material performance, while the LSTM network captures temporal dependencies in environmental and material degradation data. A multi-objective optimization framework is developed to simultaneously prioritize preservation integrity and green performance. The methodology is validated through a case study on an ancient architectural complex in Rucheng, Hunan Province. Key results demonstrate that the hybrid model achieves superior accuracy in material selection, with an 18–23% reduction in embodied energy (compared to conventional AHP-TOPSIS methods) and a 21.9% improvement in prediction accuracy (versus standalone XGBoost with default hyperparameters). A multi-objective optimization framework is developed to simultaneously prioritize preservation integrity and green performance, with Pareto-optimal solutions identifying material combinations that balance historical authenticity (achieving 92% substrate compatibility) with substantial sustainability gains (18–23% embodied energy reduction). The model also identifies optimal material combinations, such as lime-pozzolan mortars with rice husk ash additives, which enhance moisture buffering capacity by 28% (relative to traditional lime mortar benchmarks) while maintaining 92% compatibility with original substrates (based on ASTM C270 compatibility tests). The findings highlight the model’s effectiveness in bridging heritage conservation and modern sustainability requirements. The study contributes a scalable and interpretable framework for green material selection, offering practical implications for cultural heritage projects worldwide. Future research directions include expanding the model’s applicability to other climate zones and integrating circular economy principles for broader sustainability impact. Preliminary analysis indicates the framework’s adaptability to other climate zones through adjustment of key material property weightings. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article

Wetlands are an important part of coastal ecosystems but are under increasing pressure from climate change-induced sea-level rise and flooding, in addition to development pressures associated with increasing human populations. The change in tidal events and their intensity due to sea-level rise is also reshaping and challenging the vitality of existing wetland systems, requiring more intensive localized studies to identify future-focused restoration and conservation strategies. To support this endeavor, this study utilizes tide gauge datasets from the Australian Bureau of Meteorology (BOM) for maximum sea-level (Hmax) prediction and Landsat Collection surface reflectance datasets obtained from the United States Geological Survey (USGS) database to detect and project patterns of change in the Maroochy River floodplain of Queensland, Australia. This study developed an efficient hybrid deep learning model combining a Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNNBiLSTM) architecture for the prediction of maximum sea-level and tidal events. The proposed model significantly outperformed three benchmark models (Multiple Linear Regression (MLR), Support Vector Regression (SVR), and CatBoost) in achieving a high correlation coefficient (r = 0.9748) for maximum sea-level prediction. To further address the increasing frequency and intensity of tidal events linked to sea-level rise, a CNNBiLSTM classification model was also developed, achieving 96.72% accuracy in predicting extreme tidal occurrences. This study identified a significant positive linear increase in sea-level rise of 0.016 m/year between 2014 and 2024. Wetland change detection using Landsat imagery along the Maroochy River floodplain also identified a substantial vegetation loss of 395.64 hectares from 2009 to 2023. These findings highlight the strong potential of integrating deep learning and remote sensing for improved prediction and assessment of sea-level extremes and coastal ecosystem changes. The study outcomes provide valuable insights for informing not only conservation and restoration activities but also for providing localized projections of future change necessary for the progression of effective climate adaptation and mitigation strategies. Full article

With the continuous growth of network threats, intrusion detection systems need to have robustness and adaptability to effectively identify malicious behaviors. However, factors such as noise interference, class imbalance, and complex attack pattern recognition have posed significant challenges to traditional systems. To address these issues, this paper proposes a dynamic preprocessing-enhanced DyP-CNX framework. The framework designs a sliding window dynamic interquartile range (IQR) standardization mechanism to effectively suppress the temporal non-stationarity interference of network traffic. It also combines a random undersampling strategy to mitigate the class imbalance problem. The model architecture adopts a CNN-XGBoost collaborative learning framework, combining a dual-channel convolutional neural network (CNN) and two-stage extreme gradient boosting (XGBoost) to integrate the original statistical features and deep semantic features. On the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the method achieved F1 values of 91.57% and 99.34%, respectively. The experimental results show that the DyP-CNX method has the potential to handle the feature drift and pattern confusion problems in complex network environments, providing a new technical solution for adaptive intrusion detection systems. Full article

Background: Breast cancer remains a critical public health problem worldwide and is a leading cause of cancer-related mortality. Optimizing clinical outcomes is contingent upon the early and precise detection of malignancies. Advances in medical imaging and artificial intelligence (AI), particularly in the fields of radiomics and deep learning (DL), have contributed to improvements in early detection methodologies. Nonetheless, persistent challenges, including limited data availability, model overfitting, and restricted generalization, continue to hinder performance. Methods: This study aims to overcome existing challenges by improving model accuracy and robustness through enhanced data augmentation and the integration of radiomics and deep learning features from the CBIS-DDSM dataset. To mitigate overfitting and improve model generalization, data augmentation techniques were applied. The PyRadiomics library was used to extract radiomics features, while transfer learning models were employed to derive deep learning features from the augmented training dataset. For radiomics feature selection, we compared multiple supervised feature selection methods, including RFE with random forest and logistic regression, ANOVA F-test, LASSO, and mutual information. Embedded methods with XGBoost, LightGBM, and CatBoost for GPUs were also explored. Finally, we integrated radiomics and deep features to build a unified multimodal feature space for improved classification performance. Based on this integrated set of radiomics and deep learning features, 13 pre-trained transfer learning models were trained and evaluated, including various versions of ResNet (50, 50V2, 101, 101V2, 152, 152V2), DenseNet (121, 169, 201), InceptionV3, MobileNet, and VGG (16, 19). Results: Among the evaluated models, ResNet152 achieved the highest classification accuracy of 97%, demonstrating the potential of this approach to enhance diagnostic precision. Other models, including VGG19, ResNet101V2, and ResNet101, achieved 96% accuracy, emphasizing the importance of the selected feature set in achieving robust detection. Conclusions: Future research could build on this work by incorporating Vision Transformer (ViT) architectures and leveraging multimodal data (e.g., clinical data, genomic information, and patient history). This could improve predictive performance and make the model more robust and adaptable to diverse data types. Ultimately, this approach has the potential to transform breast cancer detection, making it more accurate and interpretable. Full article

To mitigate the computational complexity inherent in permanent magnet synchronous motor (PMSM) control systems, this paper presents a dual-vector model predictive current control (DV-MPCC) strategy integrated with an improved exponential reaching law-based sliding mode controller (IEAL-SMC). A voltage phase angle decision-making mechanism is introduced to alleviate computational load and enhance the accuracy of voltage vector selection: this mechanism enables rapid determination of optimal control sectors and facilitates efficient screening of candidate vectors within the finite control set (FCS). To further boost the system’s disturbance rejection capability, a modified SMC scheme employing a softsign function-based exponential reaching law is developed for the speed loop. By adaptively tuning the smoothing parameters, this modified SMC achieves a well-balanced trade-off between fast dynamic response and effective chattering suppression—two key performance metrics in PMSM control. Experimental validations indicate that, in comparison with the conventional DV-MPCC approach, the proposed strategy not only improves the efficiency of voltage vector selection but also demonstrates superior steady-state precision and dynamic responsiveness across a broad range of operating conditions. Full article

UAV airborne network intrusion detection faces challenges due to highly imbalanced datasets, where normal samples significantly outnumber intrusion instances. This paper proposes an improved stratified sampling and ensemble learning (ISSEL) method to address this issue. The method improves upon traditional stratified sampling by clustering normal samples and performing distance-based sampling from cluster centers to ensure better feature space representation. Subsequently, five tree models, namely, decision tree, extra tree, random forest, gradient boosting tree, and XGBoost, are utilized to train each subset. The model prediction results are then integrated using an adaptive weighting strategy based on the F1 score. The experimental results on the MIL-STD-1553B data bus demonstrated that the ISSEL method maintained a high accuracy rate of 99.42% while significantly enhancing the recognition ability for minority-class attacks. The precision, recall, and F1 score reached 98.94%, 97.62%, and 98.28%, respectively. These results validate the effectiveness of the ISSEL method in handling imbalanced datasets, highlighting its potential application in the field of airborne network intrusion detection. Full article