Search Results (1,391)

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network’s utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method’s average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications. Full article

To investigate effective techniques for estimating rice production in hilly and mountainous areas, in this study, we collected yield data at the field level, agro-meteorological data, and Sentinel-2/MSI remote sensing data in Chongqing, China, between 2020 and 2023. The integral values of vegetation indicators from the rice greening up to heading–filling stages were determined using the Newton–trapezoidal integration method. Using correlation analysis and importance analysis of permutation features, the effects of agro-meteorological variables and vegetation index integrals on rice yield were assessed. The chosen characteristics were then combined with three machine learning techniques—random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR)—to create six rice yield estimate models. The results showed that combined vegetation indices were more effective than indices used in separate development phases. Specifically, the correlation coefficients between the integral values of eight vegetation indices from rice greening up to heading–filling stages and rice yield were all above 0.65. By introducing agro-meteorological factors as new independent variables and combining them with vegetation indices as input parameters, the predictive capability of the model was evaluated. The results showed that the performance of PLSR remained stable, while the prediction accuracies of SVM and RF improved by 13% to 21.5%. After feature selection, the inversion performance of all three machine learning models improved, with the RF model coupled with variables selected during permutation feature importance analysis achieving the optimal inversion effect, which was characterized by a coefficient of determination of 0.85, a root mean square error of 529.1 kg/hm², and a mean relative error of 5.63%. This study provides technical support for improving the accuracy of remote sensing-based crop yield estimation in hilly and mountainous regions, facilitating precise agricultural management and informing agrarian decision making. Full article

Reversible data-hiding in encrypted images (RDHEI) plays a pivotal role in preserving privacy within images stored on cloud platforms. Recently, Wang et al. introduced a dual-stage permutation encryption scheme, which is highly compatible with RDHEI techniques. In this study, we undertake an exhaustive examination of the characteristics inherent to the dual-stage permutation scheme and propose two cryptanalysis schemes leveraging a large-kernel convolutional neural network (LKCNN) and a known plaintext attack (KPA) scheme, respectively. Our experimental findings demonstrate the effectiveness of our cryptanalysis schemes in breaking the dual-stage permutation encryption scheme. Based on our investigation, we highlight significant security vulnerabilities in the dual-stage permutation encryption scheme, raising concerns about its suitability for secure image storage and privacy protection in cloud environments. Full article

Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet’s PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model’s. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r²: 0.61; RMSE: 0.29 kg.m⁻²). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers’ management decisions. Full article

This study harnesses RNA sequencing data from the Cancer Genome Atlas to unearth pivotal genetic markers linked to the progression of liver hepatocellular carcinoma (LIHC), a major contributor to cancer-related deaths worldwide, characterized by a dire prognosis and limited treatment avenues. We employ advanced feature selection techniques, including sure independence screening (SIS) combined with the least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD), information gain (IG), and permutation variable importance (VIMP) methods, to effectively navigate the challenges posed by ultra-high-dimensional data. Through these methods, we identify critical genes like MED8 as significant markers for LIHC. These markers are further analyzed using advanced survival analysis models, including the Cox proportional hazards model, survival tree, and random survival forests. Our findings reveal that SIS-Lasso demonstrates strong predictive accuracy, particularly in combination with the Cox proportional hazards model. However, when coupled with the random survival forests method, the SIS-VIMP approach achieves the highest overall performance. This comprehensive approach not only enhances the prediction of LIHC outcomes but also provides valuable insights into the genetic mechanisms underlying the disease, thereby paving the way for personalized treatment strategies and advancing the field of cancer genomics. Full article

With the outstanding growth of Internet of Things (IoT) devices, security and power efficiency of integrated circuits can no longer be overlooked. Current approved standards for cryptographic algorithms are not suitable for constrained environments. In this context, the National Institute of Standards and Technology (NIST) started a lightweight cryptography (LWC) competition to develop new algorithm standards that can be fit into small devices. In 2023, NIST has decided to standardize the Ascon family for LWC. This algorithm has been designed to be more resilient to side-channel and fault-based analysis. Nonetheless, hardware implementations of Ascon have been broken by multiple statistical fault analysis and power analysis. These attacks have underlined the necessity to develop adapted countermeasures to side-channel and perturbation-based attacks. However, existing countermeasures are power and area consuming. In this article, we propose a new countermeasure for the Ascon cipher that does not significantly increase the area and power consumption. Our architecture relies on the nonvolatile feature of the Magnetic Tunnel Junction (MTJ) that is the single element of the emerging Magnetic Random Access Memories (MRAM). The proposed circuit removes the bias exploited by statistical attacks. In addition, we have duplicated and complemented the permutation of Ascon to enhance the power analysis robustness of the circuit. Besides the security aspect, our circuit can save current manipulated data, ensuring energy saving from 11% to 32.5% in case of power failure. The area overhead, compared to an unprotected circuit, is ×2.43. Full article

In the realm of management decision-making, the selection of green suppliers has long been a complex issue. Companies must take a holistic approach, evaluating potential suppliers based on their capabilities, economic viability, and environmental impact. The decision-making process, fraught with intricacies and uncertainties, urgently demands the development of a scientifically sound and efficient method for guidance. Since the concept of Fermatean fuzzy sets (FFSs) was proposed, it has been proved to be an effective tool for solving multi-attribute decision-making (MADM) problems in complicated realistic situations. And the Power Bonferroni mean (PBM) operator, combining the strengths of the power average (PA) and Bonferroni mean (BM), excels in considering attribute interactions for a thorough evaluation. To ensure a comprehensive and sufficient evaluation framework for supplier selection, this paper introduces innovative aggregation operators that extend the PBM and integrate probabilistic information into Fermatean hesitant fuzzy sets (FHFSs) and Fermatean probabilistic hesitant fuzzy sets (FPHFSs). It successively proposes the Fermatean hesitant fuzzy power Bonferroni mean (FHFPBM), Fermatean hesitant fuzzy weighted power Bonferroni mean (FHFWPBM), and Fermatean hesitant fuzzy probabilistic weighted power Bonferroni mean (FHFPWPBM) operators, examining their key properties like idempotency, boundedness, and permutation invariance. By further integrating PBM with probabilistic information into FPHFSs, three new Fermatean probabilistic hesitant fuzzy power Bonferroni aggregation operators are developed: the Fermatean probabilistic hesitant fuzzy power Bonferroni mean (FPHFPBM), Fermatean probabilistic hesitant fuzzy weighted power Bonferroni mean (FPHFWPBM), and Fermatean probabilistic hesitant fuzzy probabilistic weighted power Bonferroni mean (FPHFPWPBM). Subsequently, a MADM method based on these operators is constructed. Finally, a numerical example concerning the selection of green suppliers is presented to demonstrate the applicability and effectiveness of this method using the FPHFPWPBM operator. Full article

Damage identification, both in terms of size and location, in bridges is important for timely maintenance and to avoid any catastrophic failure. An earlier experimental study showed that damage in a steel box girder orthotropic plate can be successfully detected using the measured vibration acceleration data. In this study, the wavelet packet decomposition (WPD) method is used to analyze the measured vibration acceleration responses and then the estimation of the permutation entropy (PE) on the re-constructed signals. A damage index is then defined based on the permutation entropy difference (PED) between the damaged and the healthy conditions to detect the location and size of the damage. The method is further validated through the finite element (FE) model of a steel box girder and the computed vibration acceleration responses when subjected to the sinusoidal excitations at different frequencies. In addition, the robustness of the methodology under different white noise interference conditions is also verified. The results show that the proposed methodology can effectively identify the location of human-made damage and accurately estimate the degree of damage under different frequencies of sinusoidal excitation. The method has shown a strong anti-noise property. Full article

While most studies focused on the impact of intensive agriculture on the barn owl’s diet, little is known about the effect of cropland abandonment. We compared the taxon composition/evenness and feeding guild structure of small mammal prey identified in pellets collected before (2004) and after (2012) the abandonment of 9% of cultivated fields within a cultural landscape. Data on prey abundance per pellet were analysed through non-metric multidimensional scaling and permutational, paired tests. Prey taxon evenness in 2012 was significantly lower than in 2004. That induced a shift in prey taxon composition as indicated by the significantly lower dietary similarity compared with the random expectation. The increasing and declining abundance of Murinae and Crocidurinae, respectively, had the largest contribution to the differentiation of the diet spectrum. Insectivorous prey was significantly more abundant in 2004 compared to 2012, while the opposite was true for omnivorous prey. Our results suggest that even a small fraction of abandoned crops in the landscape might induce a detectable shift in the barn owl’s food niche. The dietary effects are similar to those observed after agricultural intensification, that is, an increase in the abundance of generalists to the detriment of specialist mammal prey. Full article

The Hotelling T2 statistic is used to compare the mean vectors of two independent multivariate Gaussian distributions. Nevertheless, this statistic is highly sensitive to outliers and is not suitable for high-dimensional datasets where the number of variables exceeds the sample size. This study introduces a robust permutation test based on the minimum regularized covariance determinant (MRCD) estimator to address these limitations of the two-sample Hotelling T2 statistic. Simulation studies were performed to evaluate the proposed test’s empirical size, power, and robustness. Additionally, the test was applied to both uncontaminated and contaminated Alzheimer’s Disease datasets. The findings from the simulations and real data examples provide clues that the proposed test can be effectively used with high-dimensional data without being impacted by outliers. Finally, an R function within the “MVTests” package was developed to implement the proposed test statistic on real-world data. Full article

Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients’ data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years (‘older adults’), males, current smokers, and BMI classified as ‘overweight’ and ‘obese’ were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models’ interpretability were from the ‘sociodemographic characteristics’, ‘pre-hospital comorbidities’, and ‘medications’ categories. However, ‘pre-hospital comorbidities’ played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients’ conditions when urgent treatment plans are needed during the surge of patients during the pandemic. Full article

With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to sort or rearrange the markers in a predetermined order by swapping them out at the vertices of a tree in the fewest possible swaps. Only certain classes of transposition trees, like path, star, and broom, have computationally efficient algorithms for sorting permutations. In this paper, we examine the so-called n−broom transposition trees. A single broom or simply a broom is a spanning tree formed by joining the center of the star graph with one end of the path graph. A generalized version of a broom known as an n−broom is created by joining the ends of n brooms to one vertex, known as the n−broom center. By using the idea of clear path markers, we present a novel algorithm for sorting permutations on an n−broom for n>2 that reduces to a novel 2−broom algorithm and that further reduces to two instances of a 1−broom algorithm. Our single-broom algorithm is similar to that of Kawahara et al.; however, our proof of optimality for the same is simpler. Full article

Today, permutation tests represent a powerful and increasingly widespread tool of statistical inference for hypothesis-testing problems. To the best of our knowledge, a review of the application of permutation tests for complex data in practical data analysis for hypothesis testing is missing. In particular, it is essential to review the application of permutation tests in two-sample or multi-sample problems and in regression analysis. The aim of this paper is to consider the main scientific contributions on the subject of permutation methods for hypothesis testing in the mentioned fields. Notes on their use to address the problem of missing data and, in particular, right-censored data, will also be included. This review also tries to highlight the limits and advantages of the works cited with a critical eye and also to provide practical indications to researchers and practitioners who need to identify flexible and distribution-free solutions for the most disparate hypothesis-testing problems. Full article

Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed graph neural network (GNN) approach to construct models that predict four distinct material properties. Our graph model represents materials as element graphs, with chemical formulas serving as the only input. This approach ensures permutation invariance, offering a robust solution to prior limitations. By employing bootstrap methods to train this individual GNN, we further enhance the reliability and accuracy of our predictions. With multi-task learning, we harness the power of extensive datasets to boost the performance of smaller ones. We introduce the inaugural version of the Materials Properties Prediction (MAPP) framework, empowering the prediction of material properties solely based on chemical formulas. Full article

Global warming poses a significant threat to our planet, with the food sector contributing up to 37% of total greenhouse gas emissions. This study aimed to assess the climate change impact and healthiness of menus in a long-term care facility in Italy. We analyzed two 28-day cyclical menus using the carbon footprint (CF) and the Modified EAT-Lancet Diet Score (MELDS) to evaluate adherence to the Planetary Health Diet (PHD). Monte Carlo simulations were employed to explore 20,000 daily menu permutations. Results showed that the mean GHGEs of spring/summer and autumn/winter daily menus were 2.64 and 2.82 kg of CO₂eq, respectively, with 99% of menus exceeding the 2.03 kg of CO₂eq benchmark. Only 22% of menus were adherent to the PHD, with MELDSs ranging from 12 to 29. A strong inverse association between the CF and adherence to the PHD was observed. These findings suggest significant potential for reducing the CFs of meals served in nursing homes while promoting adherence to a planetary diet, presenting an opportunity to set new standards in caregiving and environmental sustainability. Full article