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Background Purpose Methods Results Conclusions Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast‐enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams.The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast‐enhanced abdomen CT examinations.Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast‐enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations.The best classifier was found to be a random forest, with a precision of 0.892 in the held‐out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU.The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time‐consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast‐enhanced CT examinations. [ABSTRACT FROM AUTHOR]

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, a random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction performance was measured on 28 drugs from the Comprehensive In Vitro Proarrhythmia Assay initiative. Leave-one-drug-out cross-validation provided an unbiased estimation of model performance. Stratified bootstrap revealed the uncertainty in the asymptotic model prediction. Our study validated the utility of machine learning approaches in predicting drug-induced TdP risks from preclinical data. Our methods can be extended to other preclinical protocols and serve as a supplementary evaluation in drug safety assessment. [ABSTRACT FROM AUTHOR]

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto‐temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto‐temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism. [ABSTRACT FROM AUTHOR]

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model's simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry. [ABSTRACT FROM AUTHOR]

Background: Involuntary psychiatric hospitalisation occurs under different legal premises. According to German law, detention under the Mental Health Act (MHA) is possible in cases of imminent danger of self-harm or harm to others, while detention according to the legal guardianship legislation (LGL) serves to prevent self-harm if there is considerable but not necessarily imminent danger. This study aims to compare clinical, sociodemographic and environmental socioeconomic differences and similarities between patients hospitalised under either the MHA or LGL. Methods: We conducted a retrospective health records analysis of all involuntarily hospitalised cases in the four psychiatric hospitals of the city of Cologne, Germany, in 2011. Of the 1,773 cases, 87.3% were detained under the MHA of the federal state of North Rhine-Westphalia and 6.4% were hospitalised according to the federal LGL. Another 6.3% of the cases were originally admitted under the MHA, but the legal basis of detention was converted to LGL during the inpatient psychiatric stay (MHA→LGL cases). We compared sociodemographic, clinical, systemic and environmental socioeconomic (ESED) variables of the three groups by means of descriptive statistics. We also trained and tested a machine learning-based algorithm to predict class membership of the involuntary modes of psychiatric inpatient care. Results: Cases with an admission under the premises of LGL lived less often on their own, and they were more often retired compared to MHA cases. They more often had received previous outpatient or inpatient treatment than MHA cases, they were more often diagnosed with a psychotic disorder and they lived in neighbourhoods that were on average more socially advantaged. MHA→LGL cases were on average older and more often retired than MHA cases. More often, they had a main diagnosis of an organic mental disorder compared to both MHA and LGL cases. Also, they less often received previous psychiatric inpatient treatment compared to LGL cases. The reason for detention (self-harm or harm to others) did not differ between the three groups. The proportion of LGL and MHA cases differed between the four hospitals. Effect sizes were mostly small and the balanced accuracy of the Random Forest was low. Conclusion: We found some plausible differences in patient characteristics depending on the legal foundation of the involuntary psychiatric hospitalisation. The differences relate to clinical, sociodemographic and socioeconomical issues. However, the low effect sizes and the limited accuracy of the machine learning models indicate that the investigated variables do not sufficiently explain the respective choice of the legal framework. In addition, we found some indication for possibly different interpretation and handling of the premises of the law in practice. Our findings pose the need for further research in this field. [ABSTRACT FROM AUTHOR]

Background: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and anesthesiologists to prepare pre-emptive prevention when predicting PPH occurrence in advance. However, current works on PPH prediction focus on whether PPH occurs rather than assessing PPH amount. To this end, this work studies quantitative PPH prediction with machine learning (ML). Methods: The study cohort in this paper was selected from individuals with PPH who were hospitalized at Shijiazhuang Obstetrics and Gynecology Hospital from 2020 to 2022. In this study cohort, we built a dataset with 6,144 subjects covering clinical parameters, anesthesia operation records, laboratory examination results, and other information in the electronic medical record system. Based on our built dataset, we exploit six different ML models, including logistic regression, linear regression, gradient boosting, XGBoost, multilayer perceptron, and random forest, to automatically predict the amount of bleeding during cesarean section. Eighty percent of the dataset was used as model training, and 20 % was used for verification. Those ML models are constantly verified and improved by root mean squared error(RMSE) and mean absolute error(MAE). Moreover, we also leverage the importance of permutation and partial dependence plot (PDP) to discuss their feasibility. Result: The experiment results show that random forest obtains the highest accuracy for PPH amount prediction compared to other ML methods. Random forest reaches the mean absolute error of 21.7, less than 5.4 % prediction error. It also gains the root mean squared error of 33.75, less than 9.3 % prediction error. On the other hand, the experimental results also disclose indicators that contributed most to PPH prediction, including Ca, hemoglobin, white blood cells, platelets, Na, and K. Conclusion: It effectively predicts the amount of PPH during a cesarean section by ML methods, especially random forest. With the above insight, ML predicting PPH amounts provides early warning for clinicians, thus reducing complications and improving cesarean sections' safety. Furthermore, the importance of ML and permutation, complemented by incorporating PDP, promises to provide clinicians with a transparent indication of individual risk prediction. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs. [ABSTRACT FROM AUTHOR]

Summary The arc fault characteristics of certain loads lack significance, making it difficult to efficiently detect the line current characteristics. This research presents a novel approach for detecting arc faults using a combination of principalc analysis (PCA) and Random Forest (RF) based on voltage measurements. The time‐domain eigenvalues of the load terminal voltages of single and mixed loads are initially extracted during both arc fault and normal operation. Principal component analysis is then conducted on a subset of these eigenvalues. The skewness and magnitude features of the resulting principal components and load terminal voltages are utilized as inputs for the Random Forest algorithm. After training the model, classification results are obtained. Ultimately, it is contrasted with techniques such as rime optimization algorithm‐multilayer perceptron (RIME‐MLP), convolutional neural network‐gated recurrent unit‐SE attention (CNN‐GRU‐SE), and Kepler optimization algorithm‐support vector machine (KOA‐SVM). The results demonstrated that the approach exhibits superior accuracy and a reduced false alarm rate. [ABSTRACT FROM AUTHOR]

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO 2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H 2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications. [Display omitted] • Effective connectionist tools are used to assess H 2 storage with MOF. • LightGBM model exhibits a better prediction accuracy, compared to RF. • H 2 uptake in MOFs is most sensitive to changes in adsorption enthalpy. • Increased surface area and pressure lead to higher H 2 uptake. • Data pre-processing steps considerably improve the model predictive performance. [ABSTRACT FROM AUTHOR]

Forest topographic survey is a problem that photogrammetry has not solved for a long time. Forest canopy height is a crucial forest biophysical parameter which is used to derive essential information about forest ecosystems. In order to construct a canopy height model in forest areas, this study extracts spectral feature factors from digital orthophoto map and geometric feature factors from digital surface model, which are generated through aerial photogrammetry and LiDAR (light detection and ranging). The maximum information coefficient, Pearson, Kendall, Spearman correlation coefficients, and a new proposed index of relative importance are employed to assess the correlation between each feature factor and forest vertical heights. Gradient boosting decision tree regression is introduced and utilised to construct a canopy height model, which enables the prediction of unknown canopy height in forest areas. Two additional machine learning techniques, namely random forest regression and support vector machine regression, are employed to construct canopy height model for comparative analysis. The data sets from two study areas have been processed for model training and prediction, yielding encouraging experimental results that demonstrate the potential of canopy height model to achieve prediction accuracies of 0.3 m in forested areas with 50% vegetation coverage and 0.8 m in areas with 99% vegetation coverage, even when only a mere 10% of the available data sets are selected as model training data. The above approaches present techniques for modelling canopy height in forested areas with varying conditions, which have been shown to be both feasible and reliable. [ABSTRACT FROM AUTHOR]

Land deformation is a severe environmental problem that is often caused by groundwater overexploitation. Traditional approaches, such as those based on ground leveling, are used as standard for monitoring land deformation, but they cannot collect enough information for land-deformation mapping. In this study, the time-series Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) was used as an improved method to identify land deformation in Cangzhou after the initiation of China's South-to-North Water Diversion Project (SNWDP). Machine learning (ML) models, including random forest and k-nearest neighbor, were used to determine the relationship between groundwater pressure and land deformation. The results showed that from 2018 to 2022, the deformation rate was up to –115 mm/year in Nanpi and Dongguang and varied between –57 and –26 mm/year in Qingxian and Cangxian. Land deformation after the SNWDP implementation was less than before. The ML models' results show that the accuracy of the random forest and k-nearest neighbor methods were 85 and 77%, respectively. Evaluation of the groundwater-level trend measured in six wells showed that after the SNWDP implementation, the groundwater pressure started to recover in Cangzhou, but a decline has been observed recently, particularly in 2022. The mean decrease in impurity (MDI) values demonstrates that aquifers IV and III contribute the most to land deformation in Cangzhou, with the highest MDI values of 33 and 26%, respectively. The study provides new insights into the evolution of regional land deformation, and the methods employed in this research can be adopted in other regions with similar conditions. [ABSTRACT FROM AUTHOR]

The classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view. [ABSTRACT FROM AUTHOR]

The reliance on networks and systems has grown rapidly in contemporary times, leading to increased vulnerability to cyber assaults. The Distributed Denial-of-Service (Distributed Denial of Service) attack, a threat that can cause great financial liabilities and reputation damage. To address this problem, Machine Learning (ML) algorithms have gained huge attention, enabling the detection and prevention of DDOS (Distributed Denial of Service) Attacks. In this study, we proposed a novel security mechanism to avoid Distributed Denial of Service attacks. Using an ensemble learning methodology aims to it also can differentiate between normal network traffic and the malicious flood of Distributed Denial of Service attack traffic. The study also evaluates the performance of two well-known ML algorithms, namely, the decision tree and random forest, which were used to execute the proposed method. Tree in defending against Distributed Denial of Service (DDoS) attacks. We test the models using a publicly available dataset called TIME SERIES DATASET FOR DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION. We compare the performance of models using a list of evaluation metrics developing the Model. This step involves fetching the data, preprocessing it, and splitting it into training and testing subgroups, model selection, and validation. When applied to a database of nearly 11,000 time series; in some cases, the proposed approach manifested promising results and reached an Accuracy (ACC) of up to 100 % in the dataset. Ultimately, this proposed method detects and mitigates distributed denial of service. The solution to securing communication systems from this increasing cyber threat is this: preventing attacks from being successful. [ABSTRACT FROM AUTHOR]

In the framework of postcolonial Englishes and their evolutions, Hong Kong English represents an extraordinary case in that the political independence of Hong Kong from the British in 1997 did not result—as in most other cases—in a self-governing political body, but a formal re-integration of Hong Kong into China. As the handover of Hong Kong also marks a drastic linguistic re-orientation, this short-term diachronic paper studies newspaper data from before and after the 1997 handover. With a view to the structural object of investigation, particle placement, the exploration of relevant predictors and their effects in Hong Kong English sheds light on whether and, if so, how the end of British colonial rule has left linguistic traces in particle-placement choices. Using a sample of 322 particle verbs extracted from the South China Morning Post Corpus (SCMPC) and employing random forests, the study reports that pre- and post-handover constructional choices of particle placement are largely compatible with one another. Said choices are mainly guided by principles related to cognition and eurhythmy that—at least in the period observed—appear unlikely to be affected fundamentally by language-external political change. [ABSTRACT FROM AUTHOR]

Statistical analysis was used to analyze the influences of meteorological factors such as temperature and precipitation on the sugar content of sugarcane. Random forest and forward stepwise selection algorithms were utilized to screen the key meteorological factors. Based on the generalized additivity model and so on, six sugar content simulation models were constructed to simulate the sugar content of sugarcane at the maturity stage with the key meteorological factors as independent variables. Evaluate the simulation effects and quantify the influences of key meteorological factors on the sugar content of sugarcane by comparing and analyzing the simulated and measured values. The results showed that the meteorological factors in different regions and time scales affected the sugar content of sugarcane. The key meteorological factors include diurnal variation of temperature, minimum ground temperature, sunshine hours, maximum temperature, average air pressure, precipitation, and average relative humidity. The influence of diurnal variation of temperature on the sugar content of sugarcane showed a nearly stable linear trend, while the influences of the other key meteorological factors on the sugar content of sugarcane showed a complex fluctuation trend. Based on the key meteorological factors, six models were established to simulate the sugar content of sugarcane. The generalized additive model had the best simulation effect, with the fit R2 up to 0.97, the absolute error was 0.12 percent points, the relative error was 0.86%, and the root mean square error was 0.15 in the model validation. It indicates that the simulated values have good consistency and correlation with the measured values, and the model can realize the prediction of the sugar content of sugarcane in Guangxi. Meanwhile, quantifying the effects of key meteorological factors on the sugar content of sugarcane also provides a basis for regulating the ecosystem. [ABSTRACT FROM AUTHOR]

The complexity raised due to the exponential growth of renewable power generators in modern power system evokes the necessity of detecting the potential power quality threats. With this context, the article proposes a novel power quality (PQ) detection method in PV and wind generator integrated distribution networks. In the first stage, the variational mode decomposition (VMD) technique is utilized to pull out the characteristic modes out of the voltage signal for nine different PQ events. In the later stage, a set of features are extracted from the decomposed signal to train a novel Feature Enabled Random Forest Classifier (FERFC). The feature enabling capability of Random Forest classifier is reckoned with the help of a single decision tree. It has been proved that the proposed novel VMD-FERFC has the ability to accurately identify and classify the PQ disturbances in the PV-wind-based distribution network. The performance of the suggested technique is verified in presence of noise. Further the supremacy of the method is established by comparing with other competitive reported PQ detection methods. [ABSTRACT FROM AUTHOR]

As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing. [ABSTRACT FROM AUTHOR]

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes. [ABSTRACT FROM AUTHOR]

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput. [ABSTRACT FROM AUTHOR]

Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded. [ABSTRACT FROM AUTHOR]

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China's high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R2 of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R2 of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting. [ABSTRACT FROM AUTHOR]

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning's potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method. [ABSTRACT FROM AUTHOR]

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R2 = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R2 = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) are widely used in digital terrain analysis, global change research, digital Earth applications, and studies concerning natural disasters. In this investigation, a thorough examination and comparison of five open-source DEMs (ALOS PALSAR, SRTM1 DEM, SRTM3 DEM, NASADEM, and ASTER GDEM V3) was carried out, with a focus on the Chongqing region as a specific case study. By utilizing ICESat-2 ATL08 data for validation and employing a random forest model to refine terrain variables such as slope, aspect, land cover, and landform type, a study was undertaken to assess the precision of DEM data. Research indicates that spatial resolution significantly impacts the accuracy of DEMs. ALOS PALSAR demonstrated satisfactory performance, reducing the corrected root mean square error (RMSE) from 13.29 m to 9.15 m. The implementation of the random forest model resulted in a significant improvement in the accuracy of the 30 m resolution NASADEM product. This improvement was supported by a decrease in the RMSE from 38.24 m to 9.77 m, demonstrating a significant 74.45% enhancement in accuracy. Consequently, the ALOS PALSAR and NASADEM datasets are considered the preferred data sources for mountainous urban areas. Furthermore, the study established a clear relationship between the precision of DEMs and slope, demonstrating a consistent decline in precision as slope steepness increases. The influence of aspect on accuracy was considered to be relatively minor, while vegetated areas and medium-to-high-relief mountainous terrains were identified as the main challenges in attaining accuracy in the DEMs. This study offers valuable insights into selecting DEM datasets for complex terrains in mountainous urban areas, highlighting the critical importance of choosing the appropriate DEM data for scientific research. [ABSTRACT FROM AUTHOR]

The European Mediterranean Basin (Euro-Med), a region particularly vulnerable to global warming, notably lacks research aimed at assessing and enhancing the widely used remote climate detection products known as General Circulation Models (GCMs). In this study, the proficiency of GCMs in replicating reanalyzed 1981–2010 temperature data sourced from the ERA5 Land was assessed. Initially, the least data-modifying interpolation method for achieving a resolution match of 0.1° was ascertained. Subsequently, a pixel-by-pixel evaluation was conducted, employing five goodness-of-fit metrics. From these metrics, we compiled a Comprehensive Rating Index (CRI). A Multi-Model Ensemble using Random Forest was constructed and projected across three emission scenarios (SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5) and timeframes (2026–2050, 2051–2075, and 2076–2100). Empirical Bayesian Kriging, selected for its minimal data alteration, supersedes the commonly employed Bilinear Interpolation. The evaluation results underscore MPI-ESM1-2-HR, GFDL-ESM4, CNRM-CM6-1, MRI-ESM2-0, CNRM-ESM2-1, and IPSL-CM6A-LR as top-performing models. Noteworthy geospatial disparities in model performance were observed. The projection outcomes, notably divergent from IPCC forecasts, revealed a warming trend of 1 to over 2 °C less than anticipated for spring and winter over the medium–long term, juxtaposed with heightened warming in mountainous/elevated regions. These findings could substantially refine temperature projections for the Euro-Med, facilitating the implementation of policy strategies to mitigate the effects of global warming in vulnerable regions worldwide. [ABSTRACT FROM AUTHOR]

The prediction of the interaction between a drug and a target is the most critical issue in the fields of drug development and repurposing. However, there are still two challenges in current deep learning research: (i) the structural information of drug molecules is not fully explored in most drug target studies, and the previous drug SMILES does not correspond well to effective drug molecules and (ii) exploration of the potential relationship between drugs and targets is in need of improvement. In this work, we use a new and better representation of the effective molecular graph structure, SELFIES. We propose a hybrid mechanism framework based on convolutional neural network and graph attention network to capture multi‐view feature information of drug and target molecular structures, and we aim to enhance the ability to capture interaction sites between a drug and a target. In this study, our experiments using two different datasets show that the GCARDTI model outperforms a variety of different model algorithms on different metrics. We also demonstrate the accuracy of our model through two case studies. [ABSTRACT FROM AUTHOR]

In recent years, machine learning algorithms have been used in the mass appraisal of real estate. In this study, 5 machine learning algorithms are used for residential type real estate. Machine learning algorithms used for mass appraisal in this study are Artificial Neural Networks (ANN), Random Forest (RO), Multiple Regression Analysis (MRA), K-Nearest Neighborhood (k-nn), Support Vector Regression (SVR). To test the study, real estate data collected from the central districts of Ankara, were used. The main purpose of this study is to find out which machine learning algorithm gives the best results for the mass appraisal of real estates and to reveal the most important variables that affect the prices of real estate. According to the results obtained for the city of Ankara, it was observed that the best algorithm for mass appraisal is RF in residential-type real estates, followed by the ANN, k-nn, and linear regression algorithms, respectively. According to the results obtained from the residential real estate, it was concluded that heating and distances to places of importance had the greatest effect on the value. [ABSTRACT FROM AUTHOR]

The development of fragility curves is an important step in seismic risk assessment within the scope of performance‐based earthquake engineering. The goal of this work is to use machine learning methods (regression‐based tools) to forecast the large‐span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large‐span precast parking structures, including: neural networks, genetic algorithm‐based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large‐span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization‐based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention. [ABSTRACT FROM AUTHOR]

Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns. [ABSTRACT FROM AUTHOR]

The removal of reactive red M-2BE dye textile from aqueous solution was performed using multi-walled carbon nanotubes (MWCN) and powdered activated carbon (PAC). Kinetic adsorption modeling has been performed using machine learning (ML) algorithms of artificial neural networks, adaptive-neuro fuzzy inference system (ANFIS), random forest, gradient boosting, and support vector machine. Although ML models are more accurate, they often fail to interpret the reasoning behind predictions. Therefore, the SHapley Additive exPlanations (SHAP) were used to understand the effect of each feature on the adsorption capacity. The ANFIS has presented the best statistical metrics with R = 0.9993 , R M S E = 0.0214 , and S A E = 7.1172 . A higher adsorption capacity was observed for MWCN compared to PAC; while the first peaked at 300 mg L−1, the second approached 230 mg L−1. Temperature was found to have the smallest contribution in describing adsorption capacity. This novel application of ML with SHAP can provide important insights for adsorption researchers. [ABSTRACT FROM AUTHOR]

At present, the majority of techniques for assessing image quality are limited to extracting features from an image in a single space. This paper proposes a new dual-space multi-feature fusion based method for full-reference image quality assessment. This method involves simultaneously extracting features from both the YIQ and L*a*b* color spaces. First, we extract the luminance, slope, chroma, and gradient features in the spatial domain of the image to describe the salient differences in the image. Second, based on contrast sensitivity characteristics, we extract spatial frequency features in the spatial domain of the image to represent frequency differences in the image. Next, merge the features extracted in the dual space to construct a quality perception feature vector. Finally, the feature vector is input into the Random Forest model for regression prediction to obtain the predicted score of the image. Many experiments have been carried out on the four public datasets, and contrasted with other methods. The experimental confirm that the proposed method predicts image quality more accurately. The MATLAB source code and dataset of this paper will be published on GitHub, and the corresponding author can be contacted if necessary. [ABSTRACT FROM AUTHOR]

Execution of a marine operation (MO) requires coordinated actions of several vessels conducting simultaneous and sequential offshore activities. These activities have their operational limits given in terms of environmental parameters. Wave parameters are important because of their high energetic level. During the execution of a MO, forecast wave spectral parameters, i.e., significant wave height (Hs), peak period (Tp), and peak direction, are used to make an on-board decision. For critical operations, the use of forecasts can be complemented with buoy measurements. This paper proposes to use synthetic statistics of vessel dynamic responses to predict "real-time" wave spectral parameters using multi-output machine learning (ML) regression algorithms. For a case study of a vessel with no forward speed, it is observed that the random forest model predicts accurate Hs and Tp parameters. The prediction of wave direction is not very accurate but it can be corrected with on-board observations. The random forest model has good performance; it is efficient, useful for practical purposes, and comparable with other deep learning models reported in the scientific literature. Findings from this research can be valuable for real-time assessment of wave spectral parameters, which are necessary to support decision-making during the execution of MOs. [ABSTRACT FROM AUTHOR]

Terahertz waves hold significant potential for applications in wood identification, owing to their good penetration and distinctive fingerprints in wood. This study focuses on wood samples from five different Guibourtia species as the research objects. The terahertz time-domain spectroscopy (THz-TDS) is employed to acquire the spectroscopic signals of the wood samples and to extract their optical parameter data. The THz refractive indices are dimensionally reduced through principal component analysis (PCA), and three machine learning models, namely partial least squares-discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM), are employed to classify the wood of five different Guibourtia species. Time delays of the wood samples from five different Guibourtia species are concentrated in the range of 60–62 ps and exhibit different amplitudes in the frequency domain. Refractive indices showed significant variations within the THz band. PCA for dimensionality reduction of terahertz time-domain spectral data significantly improves the recognition rate of machine learning models. Applying PCA to the refractive index data, the RF model achieves a highest recognition rate of 96.9 % and an overall classification accuracy of 98 %. Current results demonstrate that THz-TDS enables rapid, accurate, and non-destructive classification and identification of wood from the Guibourtia species. [ABSTRACT FROM AUTHOR]

The early diagnosis of NASH disease can decrease the risk of proceeding elements and treatment costs for patients. This study aims to present an optimal combination of intelligent algorithms using advanced machine learning methods, including different feature selections and classifications based on clinical data and blood factors. In this work, collected data were from 176 patients to investigate NASH disease, and 19 features were extracted. We then sought to find the best combination of features based on different feature selection algorithms such as Feature Forward Selection (FFS), Minimum Redundancy Maximum Relevance (MRMR), and Mutual Information (MI). Finally, we used nine classifier frameworks with different mathematical mechanisms, including random forest (RF), logistic regression (LR), Linear Discriminant Analysis (LDA), AdaBoost, K nearest neighbors (KNN), multilayer perceptron model (MLP), support vector machine (SVM), and decision tree (DT) to estimate NASH disease. Our investigation revealed that the combination of dominant features, namely body mass index (BMI), glutamic pyruvic transaminase (GPT), total cholesterol (TC), high-density lipoprotein (HDL), Ezetimibe, lipoprotein level Lp(a), Loge(Lp(a)), total triglyceride (TG), Creatinine (Cre), HbA1c, Fibrate, and Sex, selected by the MRMR algorithm and classified by the RF method can provide the most appropriate performance based on less computation effort and maximum performance with accuracy, AUC, precision, and recall indices, which are 81.51 ± 9.35 , 82.53 ± 11.24 , 85.28 ± 9.68 , and 89.49 ± 7.92 , respectively. This study investigated the configuration of feature selection and classifier that is most suitable for classifying NASH disease based on clinical data and blood factors. The proposed intelligent algorithm based on MRMR and RF classifier can automatically diagnose NASH disease with appropriate performance and present an initial report without any further invasive methods. It also clarifies the diagnostic process and, as a result, the continuation of their prevention and treatment cycle. [ABSTRACT FROM AUTHOR]

Large-scale digital soil maps are essential for rational and sustainable land management as well as accurate fertilizer application. This study focuses on digital mapping of soil properties, namely soil organic carbon (SOC), pH, nitrogen (N), phosphorus (P), and potassium (K) in Chernozem topsoil and subsoil. The study was conducted on two arable fields in the Cis-Ural forest-steppe zone of the Republic of Bashkortostan (Russia). The random forest algorithm in combination with terrain attributes and Sentinel-2A satellite data was applied for spatial prediction of soil properties. The root-mean-square error (RMSE) and coefficient of determination (R2) were used to determine the model performance. According to the Pearson correlation, a significant positive relationship between SOC and N content was found at all sites and depths (R = 0.76–0.92). A cross-validation revealed that SOC (R2 = 0.22–0.62, RMSE = 0.35–0.89%) and N (R2 = 0.16–0.60, RMSE = 21.11–36.6 mg kg−1) were best predicted among other properties at all depths using remote sensing data, whereas the performance of predictive models decreased with depth. However, a relationship between the content of some soil properties and their spatial distribution at study depths was observed, which can be used as an additional explanatory variable. We suppose that digital mapping of soil properties at the arable field scale should not be limited to topographic and remote sensing variables. Based on this information, the use of auxiliary variables, such as collocated soil information in combination with relief and remote sensing data can be effective in more accurately estimating the spatial distribution of properties across arable fields at different depths. Overall, this study provides valuable insights into spatial modelling of the vertical distribution of soil properties, highlighting the significance of remote sensing data at the arable field scale. The findings can be valuable for land managers, agronomists, and policymakers seeking sustainable land management practices and efficient fertilizer application, as well as for developing further mapping procedures for arable fields. [ABSTRACT FROM AUTHOR]

Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R2: 0.47 to 0.66, RMSE: 325 to 399 kg ha−1). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R2 of 0.46 (RMSE = 730 kg ha−1), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds). [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI waveform metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI waveform, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI waveform to produce more accurate canopy height or volume estimates. [ABSTRACT FROM AUTHOR]

This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds for survival times with censored data. We build on recent work by Candès et al. (2023) , whose approach first subsets the data to discard any data points with early censoring times and then uses a reweighting technique, namely, weighted conformal inference (Tibshirani et al. 2019), to correct for the distribution shift introduced by this subsetting procedure. For our new method, instead of constraining to a fixed threshold for the censoring time when subsetting the data, we allow for a covariate-dependent and data-adaptive subsetting step, which is better able to capture the heterogeneity of the censoring mechanism. As a result, our method can lead to lower predictive bounds that are less conservative and give more accurate information. We show that in the Type-I right-censoring setting, if either the censoring mechanism or the conditional quantile of the survival time is well estimated, our proposed procedure achieves nearly exact marginal coverage, where in the latter case we additionally have approximate conditional coverage. We evaluate the validity and efficiency of our proposed algorithm in numerical experiments, illustrating its advantage when compared with other competing methods. Finally, our method is applied to a real dataset to generate lower predictive bounds for users' active times on a mobile app. [ABSTRACT FROM AUTHOR]

Background: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. Methods: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography–mass spectrometry (LC–MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. Results: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16–18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). Conclusions: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis. [ABSTRACT FROM AUTHOR]

We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset. Our estimators use either the bootstrap samples or the original sample in the estimation. Also, they are either based on a quantile plug-in procedure (the R-estimators) or on a direct minimization (the Q-estimators). This leads to 8 different estimators which are compared on simulations. From these simulations, it seems that the estimation method based on a direct minimization is better than the one plugging the quantile. This is a significant result because the method with direct minimization requires only one sample and could therefore be preferred. [ABSTRACT FROM AUTHOR]

Optimizing production–living–ecological space (PLES) can benefit territorial space planning and sustainable development in China. Thus, spatiotemporal evolution characteristics and influence factor analyses are vital. Laiwu, China, was selected as the study area due to its complex terrain and development history. A multitemporal land-use and land-cover classification extracted from Landsat remote-sensing images for 2001–2018 and the self-organizing map method were combined to identify the area's multitemporal PLES. The PLES evolution trajectories were extracted based on the multitemporal PLES. The main factors influencing the PLES spatiotemporal evolution differences were revealed using the random-forest-based factor importance evaluation method. The results showed that changes in the PLES trajectories occupied ∼ 18% of the study area. The ecological space recovery type was mainly observed in previously stable ecological space areas. Changes in the production space expansion (PSE) and living space expansion were mainly agglomerated in the fringe areas of previously stable production–ecological and living space lands. Distance from early built-up areas was the main factor influencing PLES expansion. Ecological space was mainly distributed far away from built-up areas, and living and production spaces were mainly distributed near the built-up areas. Additionally, the slope of the terrain affected the spatial distribution of PLES expansion to a certain extent, and the gross domestic product clearly affected the distribution of PSE. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The development of the smart grid (SG) offers a way to improve the generation of electrical energy as well as the corresponding transmission and distribution. Due to the versatile nature, the installing of SG consumes less area and time when compared to traditional grids. The major aim of SG is to provide controllability of assets and grid observability and enhance the security and performance of power system. An integration of internet of things (IoT) in smart grid ensures intelligent features with reduced human intervention, cost-effectiveness, and reliability. In order to communicate the useful information with the internet and other web applications, IoT-enabled sensors are widely utilized in the power grid system, which enables better grid management. A photovoltaic (PV) system and a high-gain integrated Luo converter are included in the IoT-based power monitoring system for the smart grid that is recommended as a result of these variables. The maximum solar power is tracked with a help of grey wolf optimized artificial neural network (GWO-ANN) which aids in enhanced operation of converter. The obtained converter output is applied to 3 ϕ grid through a 3 ϕ voltage source inverter (VSI). The parameters like temperature, intensity, converter voltage and current, and grid voltage and current are monitored by sensors and further stored in IoT webpage. The random forest (RF) classifier is used for the prediction of shortest path through which the monitored data are transmitted. The whole set-up contributes an improved monitoring of smart grid parameters which is proved by the generated results. [ABSTRACT FROM AUTHOR]

Hyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at each pixel. This data can be used in numerous remote sensing (RS) applications, along with mineral mapping. Mineral mapping is an important component of geological mapping, which helps in investigating the mineralization potential of an area. This work can be completed effectively by applying machine learning (ML) techniques to RS data. This paper proposes a stacked ensemble-based framework for mineral mapping using the dataset obtained by the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG). The study area is situated in Jahazpur, Rajasthan, India. The purpose of this stacked ensemble-based model is to enhance the performance of ML-based mineral mapping. The proposed stacked ensemble model consists of two major elements: a base learner (Naïve Bayes, KNN, artificial neural network, decision tree, and support vector machine) and a stacked learner (random forest). The results of the experiments show that the stacked ensemble-based model has a lot of potential for accurately mapping the minerals talc, montmorillonite, kaolionite, and kaosmec. The proposed model has obtained an overall accuracy of 98.96%, an average accuracy of 98.21%, and a Kappa coefficient of 0.9628. Research highlights: A stacked ensemble-based model for mineral mapping is proposed. The well-known five conventional machine learning models (called base models) are investigated for mineral mapping. The performance of the proposed model is evaluated on the AVIRIG–NG dataset. The study area is situated in Jahazpur, Rajasthan, India. The proposed method outperformed all base models. [ABSTRACT FROM AUTHOR]

In order to estimate the likelihood of 1, 3, 6 and 12 month mortality in patients with hip fractures, we applied a variety of machine learning methods using readily available, preoperative data. We used prospectively collected data from a single university hospital in Copenhagen, Denmark for consecutive patients with hip fractures, aged 60 years and older, treated between September 2008 to September 2010 (n = 1186). Preoperative biochemical and anamnestic data were used as predictors and outcome was survival at 1, 3, 6 and 12 months after the fracture. After feature selection for each timepoint a stratified split was done (70/30) before training and validating Random Forest models, extreme gradient boosting (XGB) and Generalized Linear Models. We evaluated and compared each model using receiver operator characteristic (ROC), calibration slope and intercept, Spiegelhalter's z- test and Decision Curve Analysis. Using combinations of between 10 and 13 anamnestic and biochemical parameters we were able to successfully estimate the likelihood of mortality with an area under the curve on ROC curves of 0.79, 0.80, 0.79 and 0.81 for 1, 3, 6 and 12 month, respectively. The XGB was the overall best calibrated and most promising model. The XGB model most successfully estimated the likelihood of mortality postoperatively. An easy-to-use model could be helpful in perioperative decisions concerning level of care, focused research and information to patients. External validation is necessary before widespread use and is currently underway, an online tool has been developed for educational/experimental purposes (https://hipfx.shinyapps.io/hipfx/). [ABSTRACT FROM AUTHOR]

This paper tackles the problem of constructing a nonparametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the regression function as well as a partial recovery of the missing values. The imputation is done through iterative assignation of the missing values to the tree's cells, maximising the CART criterion. A proof of the consistency of the random forest estimator is given in the case where each latent variable is missing completely at random (MCAR). [ABSTRACT FROM AUTHOR]

Background: Life satisfaction is a key component of students' subjective well‐being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches. Objective: Using ML algorithms, the current study predicts secondary students' life satisfaction from individual‐level variables. Method: Two supervised ML models, random forest (RF) and k‐nearest neighbours (KNN), were developed based on the UK data and the Japan data in PISA 2018. Results: Findings show that (1) both models yielded better performance on the UK data than on the Japanese data; (2) the RF model outperformed the KNN model in predicting students' life satisfaction; (3) meaning in life, student competition, teacher support, exposure to bullying and ICT resources at home and at school played important roles in predicting students' life satisfaction. Conclusions: Theoretically, this study highlights the multi‐dimensional nature of life satisfaction and identifies several key predictors. Methodologically, this study is the first to use ML to explore the predictors of life satisfaction. Practically, it serves as a reference for improving secondary students' life satisfaction. [ABSTRACT FROM AUTHOR]