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This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15–3 (CA 15–3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor demonstrated selective detection of CA 15–3 in serum and MUC-1 in tissue homogenates, with limits of detection (LODs) of 0.07 and 0.11 U mL−1, respectively. The nanoscale dimensions of the GAgNPs endowed them with electrochemically active surface areas, facilitating sensitive biomarker detection. Experimental studies targeted CA 15–3 and MUC-1 biomarkers in clinical samples, and the biosensor exhibited ease of use and good selectivity. Furthermore, ML algorithms were employed to analyze the electrochemical data and predict biomarker concentrations, enhancing the diagnostic accuracy. The Random Forest algorithm achieved 98% accuracy in tumor presence prediction, while an Artificial Neural Network attained 76% accuracy in CA 15–3-based tumor grade classification. The integration of ML techniques with the GAgNPs-based biosensor offers a promising approach for noninvasive, accurate, and early detection of canine mammary tumors, potentially revolutionizing veterinary diagnostics. This multilayered strategy, combining eco-friendly nanomaterials, electrochemical sensing, and ML algorithms, holds significant potential for advancing both biomedical research and clinical practice in the field of canine mammary tumor diagnostics. [ABSTRACT FROM AUTHOR]

Faced with the terrible spread of electronic attacks and intrusions, which tend to increase, securing computer systems has become an urgent necessity for enterprises. In this work, we are interested in a very specific security problem, which is the identity theft of business experts that causes major problems for the company, such as loss of money, fraud, information leakage, customer distrust of using the enterprise's services, etc. The implementation of an implicit authentication system based on the study of the expert behavior is a very effective way to fight this problem. The objective of this work is to propose a system dedicated to business experts, which will be able to detect impostors and false profiles during the authentication phase and during the negotiation phase in a continuous manner. The proposed system relies on machine learning algorithms, behavioral authentication, the keystroke dynamics of real experts and their behaviors during the negotiation phase, as well as their ways of interacting with the application, in order to prevent imposters to infiltrate the system by trying to login with the account of a real expert. So, we followed the following steps: Data acquisition, Criteria extraction, Model creation and Storage. We Have used three machine learning algorithms, which are: K Nearest Neighbor, Random Forest and Logistic Regression. [ABSTRACT FROM AUTHOR]

The primary goal of the present research work is to build machine learning based classification model for classifying an individual with different degrees of autism that can yield better performance than traditional autism assessment scale used in India. The study aimed to find how significant are the selected additional 6 attributes for better identification and understanding of Autism spectrum disorder (ASD). This work focuses on a unique screening tool developed for the Indian population –Indian Scale for assessment of Autism(ISAA) to build the classification model. For better performance few additional significant features are also considered following proper procedure. Selective machine learning models are built and finally performance evaluation is done on all classifier results. As an alternative approach, a binary classifier multi-layer perceptron (MLP) model has also been trained on both the data sets to distinguish between people with autism who need less attention and those who need more attention for assessment and further intervention. Overall performance of 3 best classifiers (KNN, Random Forest, SVM) suggests that after considering additional 6 attributes SVM produced better efficacy in identification of degree of autism over conventional ISAA parameters with higher weighted F1 score of 85.7% and accuracy of 90% respectively. It is also reconfirmed with the results of the MLP model. The basic model converges after 71 epochs with training accuracy 97.78% wherein the extended model converges after 61 epochs with training accuracy 98.89%. The best model functions better than conventional scale of assessment which also made the screening task unbiased, accurate and time efficient. [ABSTRACT FROM AUTHOR]

In the era of advanced cyber developments, intrusions becomes a common event in any network. Although there are research studies and developers found ways to improve the detection models, there is some problem that persists in the intrusion models such as extracting key features from a large dataset, and delayed detection is a critical issue that needs to be addressed. Hence the proposed study aimed to develop a model that could extract key features from the dataset and use them effectively in the detection of threats. The study incorporates two approaches, one is feature extraction by the K-Nearest Neighbourhood, and feature selection by the K-Best approach. And the other is the balanced Gini-Entropy approach for the Random Forest (RF) classifier. This combined approach by KNN, K-best, and RF is referred to as (KK-RF). This combined approach of feature extraction, selection, and classification results in an effective threat detection model with high accuracy of about 99.61%. Moreover the proposed model has achieved precision and the recall rates of 97.3 and 96.6% respectively. Concurrently, the model attained markable F1-score of 96.6 respectively. Also, from the comparison results, it is observed that the proposed model had higher performance. [ABSTRACT FROM AUTHOR]

Multiple prognostic scores have been developed to predict morbidity and mortality in patients with spontaneous intracerebral hemorrhage(sICH). Since the advent of machine learning(ML), different ML models have also been developed for sICH prognostication. There is however a need to verify the validity of these ML models in diverse patient populations. We aim to create machine learning models for prognostication purposes in the Qatari population. By incorporating inpatient variables into model development, we aim to leverage more information. 1501 consecutive patients with acute sICH admitted to Hamad General Hospital(HGH) between 2013 and 2023 were included. We trained, evaluated, and compared several ML models to predict 90-day mortality and functional outcomes. For our dataset, we randomly selected 80% patients for model training and 20% for validation and used k-fold cross validation to train our models. The ML workflow included imbalanced class correction and dimensionality reduction in order to evaluate the effect of each. Evaluation metrics such as sensitivity, specificity, F-1 score were calculated for each prognostic model. Mean age was 50.8(SD 13.1) years and 1257(83.7%) were male. Median ICH volume was 7.5 ml(IQR 12.6). 222(14.8%) died while 897(59.7%) achieved good functional outcome at 90 days. For 90-day mortality, random forest(RF) achieved highest AUC(0.906) whereas for 90-day functional outcomes, logistic regression(LR) achieved highest AUC(0.888). Ensembling provided similar results to the best performing models, namely RF and LR, obtaining an AUC of 0.904 for mortality and 0.883 for functional outcomes. Random Forest achieved the highest AUC for 90-day mortality, and LR achieved the highest AUC for 90-day functional outcomes. Comparing ML models, there is minimal difference between their performance. By creating an ensemble of our best performing individual models we maintained maximum accuracy and decreased variance of functional outcome and mortality prediction when compared with individual models. Key points: • 1st Key Point: We are comparing different machine learning models for ICH prognostication. Random Forest was the most accurate model for mortality, and logistic regression was the most accurate model for functional outcomes. • 2nd Key Point: Ensembling models maintained accuracy and decreased variance. [ABSTRACT FROM AUTHOR]

The majority of people are using connecting media in recent times, like Facebook, Twitter, LinkedIn, etc. People like to communicate on social websites as compared to direct conversation due to the lack of time, fast communication, minimizing long distance, etc. but more fraud such as fake accounts, cloned attacks, etc. are generated due to growing data on social connecting media. In our proposed approach, we are using the Bernoulli RBM_RF Classifier (BRRC) to detect cloned attacks in connecting media. This model is based on deep learning algorithms and more research is under process but still, there is no such kind of model to provide high accuracy. In this model, we have used multiple algorithms Artificial Neural Networks (ANN), Logistic Regression, Decision Trees, K-Nearest Neighbour Classification, Random Forest, and Bernoulli RBM with extra add-on features of the random forest we have created a Bernoulli pipeline also known as BRRC. Our goal is to establish a clear neural network connection between our target attributes. Finally, we created a restricted Boltzmann machine using a RBM Bernoulli pipeline with additional add-on random forest features after using random forest, where we employed n number of decision trees for classification and provided the same in depth. Our model can extract higher-level features from the given data and helps in the dimensionality Reduction of the input data by learning a compressed representation. Accuracy can be improved by using a big dataset with particular connecting media attributes that are either not currently available or are only sparingly present. We can conclude that Bernoulli Restricted Boltzmann Machine is the best fit and suitable for our model with 93% accuracy. [ABSTRACT FROM AUTHOR]

The ever-growing 1.15 million new cases of cancer on a yearly basis alone in India is a major cause of concern for the experts and researchers working in various biomedical organizations. The advent of modern text engineering strategies and NLP techniques can play a crucial role in the discovery and analysis of pre-existing knowledge present in the cancer related biomedical archives. The available 10 Cancer hallmarks can provide key insights and make significant impact in the ongoing cancer research. It is extremely important to identify and classify required information due to time and resource crunch which needs to be quickly accessed. This article introduces a novel machine learning framework called Cancer Hallmark Classification and Topic Modeling (CHCTM), designed for supervised learning. The CHCTM framework is capable of semantically learning, categorizing, and extracting significant topics and their combinations related to the hallmarks of cancer (HoC) from a dataset comprising 1499 PubMed documents. The key contributions of this research include the creation of an innovative ensemble classification model using a meta-classifier based on Random Forest (RF). Additionally, it introduces an Enhanced Latent Dirichlet Allocation (ELDA) topic modeling strategy to generate relevant mixtures of topics. The performance of the CHCTM framework is evaluated using precision, recall, accuracy, and F-score parameters. Comparative analysis with other biomedical baseline methods reveals an 8% improvement in F-score. The coherence values acquired for ELDA are tallied and weighted against PLSA and LDA models to demonstrate the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of "never seen" shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages. [ABSTRACT FROM AUTHOR]

Understanding the intricate pathways of protein secretion in bacteria is crucial for advancing research on bacterial diseases and their potential treatments, particularly in the case of non-classical protein secretion pathways. These pathways pose unique challenges due to the complex signaling mechanisms involved. To address this, we employed advanced machine learning techniques and gathered physical–chemical characteristics of amino acids from the AA index site. Through a meticulous six-step methodology, we curated a comprehensive dataset by filtering raw genome data and juxtaposing it with a positive dataset comprising 141 proteins from authoritative literature sources. Leveraging a conventional Random Forest machine learning algorithm, we achieved an impressive accuracy rate of approximately 91% in classifying non-classical secreted proteins. This validation was conducted on a dataset of 14 positive and 92 negative proteins, resulting in a sensitivity of 91% and a specificity of 86%. Notably, our study distinguishes itself by its rapid execution of non-classical secretion pathway analyses, rendering it particularly suitable for large datasets. This speed does not compromise accuracy, allowing for comprehensive Omics analyses. Consequently, our research underscores the significance of carefully selecting appropriate descriptors and constructing a robust training dataset to enhance Omics analyses of bacterial protein secretion via non-classical pathways. For further details, please refer to the complete study available at https://github.com/santosardr/non-CSPs. [ABSTRACT FROM AUTHOR]

Polycystic Ovary Syndrome (PCOS) is a hormonal state that affects women within the reproductive age group. Early and accurate diagnosis of medical conditions is crucial for the successful implementation of medication and prevention of significant health problems. In recent times, there has been an increasing amount of evidence suggesting that machine learning (ML) methods have significant potential in facilitating the diagnosis and classification of PCOS. The objective of this research is to evaluate the viability of using several machine learning algorithms, such as Random Forests, Decision Trees, Artificial Neural Networks, and XGBoost, for the purpose of classifying PCOS using clinical and laboratory features. Furthermore, the use of the Sine Cosine Algorithm was implemented for the purpose of selecting features in order to improve the accuracy and efficiency of the model. A sample of 500 individuals was selected for the purpose of training and assessing the models, with half of the participants (250) diagnosed with PCOS and the other half not exhibiting the disease. The findings suggest that the classification algorithms had strong performance in classifying PCOS, with XGBoost having the best level of performance. In addition, the use of the Sine Cosine Algorithm resulted in enhanced model precision and effectiveness via the identification of the most pertinent features for the classification objective. The results of this study emphasise the significant capabilities of machine learning methods, namely XGBoost and the Sine Cosine Algorithm, in aiding the diagnosis and classification of PCOS in a medical setting. Additional investigation is required in order to substantiate the models and prove their clinical significance in the provision of patient care. [ABSTRACT FROM AUTHOR]

Intestinal microbiota imbalance plays an important role in the progression of obstructive sleep apnea (OSA), and is considered to be the main mediator that triggers metabolic comorbidities. Here, we analyzed the changes in intestinal microbiota in patients with different severities of OSA based on apnea hypopnea index (AHI) classification, and explored the role of intestinal microbiota in the severity of OSA. This study included 19 healthy volunteers and 45 patients with OSA [5 ≤ AHI < 15 (n = 14), 15 ≤ AHI < 30 (n = 13), AHI ≥ 30 (n = 18)]. Relevant sleep monitoring data and medical history data were collected, and microbial composition was analyzed using 16S rRNA high-throughput sequencing technology. The diversity analysis of intestinal microbiota among different groups of people was conducted, including alpha diversity, beta diversity, species diversity, and marker species as well as differential functional metabolic pathway prediction analysis. With the increase of AHI classification, the alpha diversity in patients with OSA significantly decreased. The results revealed that the severity of OSA is associated with differences in the structure and composition of the intestinal microbiota. The abundance of bacteria producing short-chain fatty acids (such as Bacteroides, Ruminococcacea, and Faecalibacterium) in severe OSA is significantly reduced and a higher ratio of Firmicutes to Bacteroidetes. Random forest analysis showed that Parabacteroides was a biomarker genus with important discriminatory significance. The differential metabolic pathway prediction function shows that the main function of maintaining intestinal microbiota homeostasis is biosynthetic function. Our results show that the differences in the composition of intestinal microbiota in patients with different severities of OSA are mainly related to short-chain fatty acid-producing bacteria. These changes may play a pathological role in OSA combined with metabolic comorbidities. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has seen rapid expansion, but this growth brings significant privacy challenges due to the large amounts of data generated by myriad IoT devices. To address these challenges, this study introduces Smart Anonymity, a method that determines the optimal data anonymization algorithm for a dataset by assessing its unique features. The solution leverages OWL ontologies grounded in description logic (DL), which facilitates inconsistency checks and the discovery of new facts for data validation. Additionally, machine learning (ML) is incorporated to improve the accuracy of these classifications. ML is also instrumental in recommending suitable anonymization algorithms, with the random forest algorithm being employed explicitly for this purpose. The findings from this research indicate that Smart Anonymity effectively improves user privacy. [ABSTRACT FROM AUTHOR]

For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results' accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization. [ABSTRACT FROM AUTHOR]

In recent years, the application of machine learning methods in the prediction of hydrological processes such as precipitation has been widely considered. These methods can analyze large volumes of data and detect the existing trends and patterns. Therefore, in the present study, machine learning methods, including random forests (RF), Kstar algorithm and Gaussian process regression (GPR), were used to predict the precipitation of Sindh River basin in India during period of 1901 to 2020. In the next step, three distinct input scenarios include (i) using monthly precipitation data and considering the memory of time series up to 5 months delay, (ii) adding periodic term to the first scenario inputs and (iii) decomposing the data using the Daubechies 4 wavelet function and creating hybrid wavelet-learning machine (W-ML) models, were prepared and introduced to the models. The performance of each method was evaluated using the root mean square error (RMSE), mean absolute error (MAE), Kling–Gupta efficiency score (KGE) and Willmott index (WI). The results showed that single models with the first scenario inputs (without taking into account the periodicity of the data) did not have good accuracy, but by adding the periodicity, the performance of these models was significantly improved, and the average value of KGE index for all studied stations increased from 0.466 to 0.672. It was also found that the GPR model for all stations could not have good performance and RF and Kstar models are the most appropriate methods for predicting precipitation in the Sindh River basin, respectively. With the application of the third scenario and the development of W-ML hybrid models, the accuracy of precipitation forecasting was significantly improved, especially the maximum precipitation values were estimated with higher accuracy than standalone models. [ABSTRACT FROM AUTHOR]

Venture capital (VC) significantly contributes to the development of regional economies and fosters innovation. Analyzing the factors that influence VC investments holds key importance. This study employs two methods to ascertain the relative significance of different factors at the city level: the Lindeman, Merenda, and Gold (LMG) approach in multiple linear regression (MLR) and variable importance in random forest (RF) machine learning. The findings reveal that several factors, including economy, finance, innovation, location, and policy, significantly influence VC investments. Both the MLR and RF models highlight the preeminence of economic and financial variables, followed closely by the city's potential for innovation. Moreover, spatial heterogeneity exists in the importance of these variables. In the economically developed and densely populated eastern regions of China, the financial environment of cities emerges as the most crucial, whereas in the central and western regions, the economy and innovation, respectively, take precedence. This research contributes to a deeper understanding of the distribution of VC investments and offers valuable insights for the development of regional policies. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to explore the feasibility of using machine learning approaches to objectively differentiate the mobilization patterns, measured via accelerometer sensors, of patients pre- and post-intervention. Methods: The intervention tested the implementation of a Frailty Care Bundle to improve mobilization, nutrition and cognition in older orthopedic patients. The study recruited 120 participants, a sub-group analysis was undertaken on 113 patients with accelerometer data (57 pre-intervention and 56 post-intervention), the median age was 78 years and the majority were female. Physical activity data from an ankle-worn accelerometer (StepWatch 4) was collected for each patient during their hospital stay. These data contained daily aggregated gait variables. Data preprocessing included the standardization of step counts and feature computation. Subsequently, a binary classification model was trained. A systematic hyperparameter optimization approach was applied, and feature selection was performed. Two classifier models, logistic regression and Random Forest, were investigated and Shapley values were used to explain model predictions. Results: The Random Forest classifier demonstrated an average balanced accuracy of 82.3% (± 1.7%) during training and 74.7% (± 8.2%) for the test set. In comparison, the logistic regression classifier achieved a training accuracy of 79.7% (± 1.9%) and a test accuracy of 77.6% (± 5.5%). The logistic regression model demonstrated less overfitting compared to the Random Forest model and better performance on the hold-out test set. Stride length was consistently chosen as a key feature in all iterations for both models, along with features related to stride velocity, gait speed, and Lyapunov exponent, indicating their significance in the classification. Conclusion: The best performing classifier was able to distinguish between patients pre- and post-intervention with greater than 75% accuracy. The intervention showed a correlation with higher gait speed and reduced stride length. However, the question of whether these alterations are part of an adaptive process that leads to improved outcomes over time remains. [ABSTRACT FROM AUTHOR]

Word recognition has garnered significant attention due to its wide-ranging real-time applications, including postal automation, signature verification, bank check processing and writer identification. Handwritten word recognition poses a formidable challenge in computer vision, primarily due to the diverse writing styles exhibited by individuals. Presently, researchers are increasingly focused on developing holistic word recognition approaches. This holistic approach treats entire words as single, indivisible entities for the purpose of feature extraction and recognition, rendering it well-suited for identifying words with overlapping characters. In this study, authors have proposed a holistic approach for recognizing offline handwritten words in the Devanagari script, employing VGG16 (Visual Geometry Group) as a versatile feature extractor. Three classifiers, namely Gaussian Naive Bayes (Gaussian NB), eXtreme Gradient Boosting (XGBoost) and Random Forest (RF), are considered for classifying these word images. Based on the extracted features and the aforementioned classifiers, words are categorized into one of 50 distinct word classes. Various performance evaluation metrics, including Accuracy, Precision, Recall, F1-score and Area Under the Curve (AUC), are employed to assess the effectiveness of the proposed system. It is worth noting that the Random Forest (RF) based classification technique achieved exceptional accuracy, with impressive recall, high precision and a remarkable F1-score. Additionally, the XGBoost based classification exhibited comparable performance, achieving the highest possible AUC in this study. Furthermore, to underscore the significance of this work, authors have presented a comparative analysis of the proposed approach with existing state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

Recently, every human being has been busy keeping pace with fast changes and is unable to put his attention on a healthy lifestyle. This unhealthy lifestyle leads to several fatal diseases like heart disease (HD), cancer disease, allergies, asthma, breast cancer, etc. These diseases are increasing the morbidity and mortality rate every year, and out of these HD is more fetal. Earlier/Timely prediction of HD is regarded as one of the most crucial tasks in the medical field. In the healthcare industry, a big collection of datasets is available free of cost. Motivated by existing challenges, the authors proposed two intelligent models i.e., HyOPTRF (Model 1), and HyOPTXGBoost Classifier (Model 2), implemented on the Statlog HD dataset with default and hyper-tuned parameters. Both the models recorded maximum Accuracy of 92.59%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 93.93% F1 Score 93.75%, Training Time 0.02s, and Testing Time 0.005s for the HyOPTRF on Trial no. 2nd and Accuracy of 96.30%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 96.82%, F1 Score 96.77%, Training Time 0.009s, and Testing Time 0.002s for the HyOPTXGBoost Classifier on Trial no. 33rd. The proposed models were validated by the Stratify Kfold Cross-Validation technique and compared with the other existing models. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) is widely used in medical decision support systems because of its similarity to human reasoning. Despite the effectiveness of the latter, its performance can be improved when reinforced by other automatic learning techniques. This is what induced us to propose a hybrid model 3FS-CBR-IRF (Three feature selection- Case based reasoning- Improved random forest) that combines case-based reasoning and random forests. Firstly, a feature selection step is used by three techniques such as: Correlation, Dropping Constant Features and Chi-square to select the relevant features. Secondly, we have modeled the retrieval phase of the CBR system by the modified random forests; in this step two pruning methods (Pre-pruning and Post-pruning) are applied only to the highly ranked features to improve the performance of the CBR system. Thirdly, the adaptation phase of the retrieval cases based on the adaptation rules is proposed. The developed approach is evaluated on 13 medical databases using the precision and several other performance criteria, the results obtained are satisfactory with an average precision of 91% for the first model (CBR-FS-RF) which improves the CBR system by feature selection techniques with classic random forest, 97% for the model (CBR-FS-IRF) which improve the CBR system by feature selection techniques with improved random forests and 98% for the second model (CBR-FS-IRF) with adaptation. The results showed that the proposed pruning method selects the best tree in less time and with better precision than hybridization with classical random forest that means an improvement of the performance of the CBR system. [ABSTRACT FROM AUTHOR]

Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge. [ABSTRACT FROM AUTHOR]

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between −2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making. [ABSTRACT FROM AUTHOR]

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients. [ABSTRACT FROM AUTHOR]

This study examines the impact of internet usage frequency and behavior on social status discordance using CSS2021 data, and adopts a causal random forest model to test the heterogeneity of the impact of internet usage on social status discordance. The results reveal a clear inconsistency between objective and subjective social status among Chinese residents. In addition, increasing internet usage frequency can make actors more inclined to underestimate their social status. Moreover, capital enhancement through the internet significantly reduces the likelihood of status inflation. Finally, individuals with higher social status are less likely to have an upward bias in status cognition than those with lower social status when they use the internet frequently and use it for capital enhancement. These findings advance our understanding of social status identification among Chinese residents and provide different perspectives for studying social stratification in the digital age. [ABSTRACT FROM AUTHOR]

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond. [ABSTRACT FROM AUTHOR]

Urban heat islands, exacerbated by climate change, have become a pressing issue as summer temperatures rise. This study uses data mining techniques to classify the thermal impact of tree shade on building façades in the urban area of a tropical city. Our objective was to develop models to assist stakeholders and policymakers in forecasting the thermal impact of solar orientation and tree shade on building façades in the urban areas of a tropical city. Minimum and maximum infrared surface temperatures were registered in health clinics' façades in Teresina, Brazil. Random forest methodology was applied to develop classifying models. This technique, known for its robust classification and prediction of categorical variables, offers a significant advantage over other modeling methods. Key input variables included façade infrared surface temperature, solar orientation, environmental temperature, relative humidity, and the extent of tree shade. Critical attributes were identified as solar orientation (North, South, East, and West), tree shade, and façade temperature (maximum and minimum). Two tree-ensemble models were selected for an accuracy rate of 88% and Kappa (κ) = 0.86. The models indicate that tree-ensemble methods can accurately classify and predict the thermal impact of tree shade on building façades. Additionally, the method effectively identified and ranked the factors influencing thermal impact, providing users with reliable predictive capabilities. [ABSTRACT FROM AUTHOR]

This research investigates the complex phenomenon of nanofluid flow boiling and its associated heat transfer characteristics. Employing advanced numerical simulations and machine learning techniques, we explore the behavior of dimensionless heat transfer correlations in subcooled flow boiling scenarios using water and SiO2/water nanofluids. The study begins with an adaptive design of experiment, yielding a comprehensive dataset of nearly 250 simulations. In the numerical simulation phase, the extracted data are examined, and the grid independence of the modeling process is confirmed, ensuring robustness and reliability. The data, validated against experimental results, provides critical insights into the intricate interplay of dimensionless numbers influencing Nusselt number behavior. Subsequently, the extracted dataset from the numerical simulations underwent a two-stage feature selection process, incorporating Pearson correlation and iterative techniques, to identify the most influential dimensionless parameters for the calculation of the Nusselt number. Later, the data are randomly split into training and testing sets (70–30%), and predictive models are developed using Python, leveraging libraries such as Pandas, NumPy, scikit-learn, and Keras. A tenfold cross-validation approach is employed to ensure model stability and accuracy. Through response surface methodology (RSM), we establish regression equations for average and local Nusselt numbers, achieving minimal mean absolute error (MAE) and high R-squared ( R 2 ) values, demonstrating the effectiveness of our approach. Further enhancing predictive capabilities, we explore random forest, support vector machine, and artificial neural network (ANN) models. The ANN model emerges as the top performer, offering exceptional accuracy with MAE below 2.21% and R 2 above 0.95 for both average and local Nusselt numbers. Notably, the machine learning algorithms, from data preprocessing to the final model evaluation, required only about 10% of the time invested in the numerical simulations. [ABSTRACT FROM AUTHOR]

Purpose: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data. Methods: This retrospective study utilized a dataset of 1908 blastocyst embryos. The dataset includes ploidy status, morphokinetic features, morphology grades, and 11 clinical variables. Six machine learning (ML) models including Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), AdaBoost (ADA), and Light Gradient-Boosting Machine (LGBM) were trained to predict ploidy status probabilities across three distinct datasets: high-grade embryos (HGE, n = 1107), low-grade embryos (LGE, n = 364), and all-grade embryos (AGE, n = 1471). The model's performance was interpreted using XAI, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques. Results: The mean maternal age was 38.5 ± 3.85 years. The Random Forest (RF) model exhibited superior performance compared to the other five ML models, achieving an accuracy of 0.749 and an AUC of 0.808 for AGE. In the external test set, the RF model achieved an accuracy of 0.714 and an AUC of 0.750 (95% CI, 0.702–0.796). SHAP's feature impact analysis highlighted that maternal age, paternal age, time to blastocyst (tB), and day 5 morphology grade significantly impacted the predictive model. In addition, LIME offered specific case-ploidy prediction probabilities, revealing the model's assigned values for each variable within a finite range. Conclusion: The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process. [ABSTRACT FROM AUTHOR]

Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we mainly consider the local kernel estimators for regression models with a binary response and the predictors including spherical variables. We apply the random forests kernel to nonparametric binary regression models with spherical predictors. Simulation experiments and real examples are used to validate the performance of the new models. Compared with the classical von Mises–Fisher kernel and the linear-spherical kernel, the random forests kernel has better fitting effect and faster computation speed. Compared with other classifiers, the models proposed in this paper have better classification performance in both low and high dimensional cases. [ABSTRACT FROM AUTHOR]

Background: We aimed to develop novel artificial intelligence (AI) models based on early pregnancy features to forecast the likelihood of recurrent gestational diabetes mellitus (GDM) before 14 weeks of gestation in subsequent pregnancies. Methods: This study involved a cohort of 588 women who had two consecutive singleton deliveries and were diagnosed with GDM during the index pregnancy. The least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. 5 AI algorithms, namely support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting (LGB), decision tree classifier (DTC), and random forest (RF) classifier, and traditional multivariate logistic regression (LR) model, were employed to construct predictive models for recurrent GDM. Results: 326 (55.4%) experienced GDM recurrence in subsequent pregnancy. In the training set (67% of the study sample), 13 features were selected for AI models construction. In the testing set (33% of the study sample), the AI models (LGB, RF, and XGB) exhibited outstanding discrimination, with AUROC values of 0.942, 0.936, and 0.924, respectively. The traditional LR model showed moderate discrimination (AUROC = 0.696). LGB, RF, and XGB models also demonstrated excellent calibration, while other models indicated a lack of fit. All AI models showed superior overall net benefits, with LGB, RF, and XGB outperforming the others. Conclusions: The proposed LGB model demonstrated exceptional accuracy, excellent calibration, and superior overall net benefits. These advancements have the potential to assist healthcare professionals in advising women with a history of GDM and in developing preventive strategies to mitigate the adverse effects on maternal and fetal well-being. [ABSTRACT FROM AUTHOR]

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min–Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635–0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC. [ABSTRACT FROM AUTHOR]

Rainfall-triggered landslides pose a significant threat to both infrastructure and human lives, making it crucial to comprehend the factors that contribute to their occurrence. Specifically, understanding the relationship between these factors and the amount of rain that is necessary for triggering such events is essential for effective prediction and mitigation strategies. To address this issue, our study proposes a statistical modelling approach using machine learning, specifically the Random Forest algorithm, to investigate the connection between landslide predisposing factors and the daily rainfall intensity threshold necessary for the initiation of shallow landslides in Portugal. By leveraging a comprehensive dataset comprising historical landslide events, associated critical rainfall, and ten distinct landslide predisposing factors, we developed several models and used cross-validation technique to evaluate their performance. Our findings demonstrate that the Random Forest model effectively captures a relationship among landslide predisposing factors, critical daily rainfall intensity, and landslide occurrences. The models exhibit a satisfactory accuracy in assessing the spatial variation of critical daily rainfall intensity based on the predisposing factors, with a mean absolute percentage error (MAPE) of around 17%. Furthermore, the models provide valuable insights into the relative importance of various predisposing factors in landslide triggering, highlighting the significance of each factor. It was found that it takes higher rainfall intensity to trigger shallow landslides in the north region of Portugal when considering critical rainfall events of 3 and 13 days. Slope aspect, slope angle, and clay content in the soil are among the main predisposing factors used for defining the spatial variation of the daily rainfall intensity threshold. [ABSTRACT FROM AUTHOR]

Most data-driven landslide-susceptibility assessment models heavily rely on statistical analyses based on geological and environmental similarity principles. These models often struggle to establish connections with landslide destruction processes and mechanisms effectively. In response to this challenge, this study introduces a hybrid approach that combines the transient rainfall infiltration, regional slope-stability physics–based model (TRIGRS) and the random forest (RF) model. Initially, to calculate the safety coefficients of the study area, the TRIGRS model was employed, and appropriate non-landslide samples were selected based on these coefficients. Subsequently, to enable learning and fitting of the nonlinear relationships between sample points and geological environmental factors, historical landslide data and safety coefficient-filtered non-landslide point data were input into the RF model, ultimately generating landslide probability values that represent the magnitude of landslide occurrences. The coupled model demonstrated excellent predictive performance using the landslides induced by the 2019 "Lekima" typhoon in Yongjia, Zhejiang Province, as a case study. The results indicated that the evaluation effect of the TRIGRS and RF coupled model was satisfactory, achieving an accuracy (ACC) rate of 77.6% and an area under the curve (AUC) of 0.873. Furthermore, the ACC and AUC of the TRIGRS and RF coupled model increased by 8.22% and 9.20%, respectively, compared with those of the traditional buffering sampling method. Therefore, the TRIGRS and RF coupled model better evaluates regional landslide susceptibility than the traditional buffering sampling method. [ABSTRACT FROM AUTHOR]

The eastern Himalayan syntaxis (EHS) region is characterized by steep topography, strong tectonic activity, and strong disaster activity, and the disaster activity has a temporal correlation with the change of seasonal climate factors. Different from other disaster-prone areas, the disasters in the EHS area are more destructive and more susceptible to changes in climate factors. However, previous studies on disaster susceptibility in the region mainly focused on geohazard samples, regional topography, and geological conditions and seldom considered the characteristics of disaster activity and the characteristics of seasonal temperature and rainfall changes, which often led to an underestimation of disaster susceptibility in this region. Therefore, in this study, SBAS-InSAR technology and the GEE (Google Earth Engine) platform were used to identify 317 geological disasters and construct the spatial–temporal motion field and spatial-temporal background information of disasters in the last 5 years. Based on the random forest and optimized evaluation data model, the disaster susceptibility in the EHS area was quantitatively evaluated. The AUC value of the model was 0.89, indicating that the results have high reliability. The results showed that the high-susceptibility areas were mainly distributed in the riverbank and high-altitude areas. The SHAP model revealed that the factors of temperature, precipitation, slope, and elevation in the EHS area had a great negative influence on the disaster. The quantitative results of the influence of seasonal temperature and precipitation on disaster susceptibility showed that the area of susceptibility in summer was significantly higher than that in winter, and the area of high and very high susceptibility increased by about 13%. Finally, the dynamic failure process of typical landslide and glacier disasters in highly susceptible areas was quantitatively predicted, which provides a scientific basis for disaster prevention. [ABSTRACT FROM AUTHOR]

Dam failure may lead to significant casualties among downstream residents. Therefore, it is crucial to study a reliable method to quantitatively assess the loss of life (LOL) caused by dam failure for emergency response to dam failure incidents. Based on a statistical analysis of typical dam failure accidents in China and the research on the formation mechanism of LOL, the study quantified the factors influencing LOL using fuzzy theory and constructed a quantitative database for the LOL. Then, it proposed an innovative algorithm integrating the grey wolf optimization (GWO) algorithm and the random forest (RF) model. Finally, a data-driven assessment model for the LOL caused by dam failure was developed by combining the gray correlation analysis of the factors. The performance of the GWO-RF model was validated using a dataset of the LOL caused. The proposed model was used to assess the LOL in typical dam failure events. The results indicate that the model has higher accuracy, with an average absolute error of approximately 945 persons, significantly lower than 2529 persons in the Graham method. Thus, it can effectively estimate the LOL caused by dam failure. This study developed a novel method for quantitatively assessing the LOL caused by dam failure, which could also serve as a reference for modeling disaster consequences in other fields. [ABSTRACT FROM AUTHOR]

China's unsustainable agricultural production activities have caused severe agricultural non-point source pollution (ANPSP), particularly nitrogen source pollution (NSP), which impacts farmland ecological environments. Understanding NSP is important for managing ANPSP and improving environmental water quality. Thus, inventory analysis methods were used to assess county-level total nitrogen (TN) loads in large-scale agricultural areas, analyze spatial and temporal variations in TN loads, examine the major sources of TN loads and identify the driving factors of DITN using Pearson correlation and random forest (RF) methods. The results showed that (1) TN loads and discharge intensity decreased by 4.59% and 12.7%, respectively, increasing and then decreasing via market regulations, policy incentives, and policy constraints. The eastern and northern Henan Province plain areas were highly discharged. (2) The major pollution sources varied, with the total contributions of mineral fertilizers and livestock and poultry breeding to TN being 73.53% and 81.78%, respectively. Farmland solid waste contributed minimally but increased yearly from 10.84 to 19.74%. (3) The discharge intensity of TN was highly significantly negatively correlated with the urbanization rate, economic development and slope; neither was significantly correlated with arable land. The RF results showed that total mineral fertilizer consumption and total livestock and poultry production were the major factors controlling the NSP in Henan Province. China's agricultural policies have slightly reduced pollutant discharge intensities, but the situation remains grim. This study provides a scientific basis for the development of differentiated TN control measures in Henan Province, which is important for improving pollution prevention and control capabilities. [ABSTRACT FROM AUTHOR]

The grain protein concentration (GPC) and yield are intimately and negatively correlated in wheat. However, how carbon (C) and nitrogen (N) supplies influence the wheat growth that underlies yield and quality formation has not been fully explored. Two wheat varieties with high GPCs (ZM578) and low GPCs (LY502) were grown under low and optimum N supplies in the field, and their growth, yields, and GPCs were closely investigated. The grain yield and total N uptake of the wheat variety ZM578 were comparable to those of LY502, but the GPCs increased by 12.4–16.1% on average. Compared with LY502, ZM578 had lower maximum stem numbers but similar spike numbers at maturity due to a lower rate of tiller mortality, which partly contributed to its grain yield. The grain N and C assimilation rates strongly and linearly correlated with each other, while steeper slopes were observed for the optimum- vs. low-N treatments, as well as for the high- vs. low-GPC treatments. Similarly, the C to N ratios in the developing grains of ZM578 were lower than those in the developing grains of LY502, regardless of N availability, which indicated that more N was transported to the grains per unit C in high-GPC ZM578. The random forest model revealed that the grain N uptake and leaf senescence (SPAD during grain filling) were the major controlling factors of yields and GPCs in cultivar ZM578. Overall, this study demonstrated that high yields and high quality can be simultaneously achieved by optimizing N management and selecting superior varieties. [ABSTRACT FROM AUTHOR]

Background: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS) holds critical importance for both surgical education and patient safety. This study introduces machine learning (ML) techniques using features derived from electroencephalogram (EEG) and eye-tracking data to identify surgical subtasks and classify skill levels. Method: The efficacy of this approach was assessed using a comprehensive dataset encompassing nine distinct classes, each representing a unique combination of three surgical subtasks executed by surgeons while performing operations on pigs. Four ML models, logistic regression, random forest, gradient boosting, and extreme gradient boosting (XGB) were used for multi-class classification. To develop the models, 20% of data samples were randomly allocated to a test set, with the remaining 80% used for training and validation. Hyperparameters were optimized through grid search, using fivefold stratified cross-validation repeated five times. Model reliability was ensured by performing train-test split over 30 iterations, with average measurements reported. Results: The findings revealed that the proposed approach outperformed existing methods for classifying RAS subtasks and skills; the XGB and random forest models yielded high accuracy rates (88.49% and 88.56%, respectively) that were not significantly different (two-sample t-test; P-value = 0.9). Conclusion: These results underscore the potential of ML models to augment the objectivity and precision of RAS subtask and skill evaluation. Future research should consider exploring ways to optimize these models, particularly focusing on the classes identified as challenging in this study. Ultimately, this study marks a significant step towards a more refined, objective, and standardized approach to RAS training and competency assessment. [ABSTRACT FROM AUTHOR]

Long-term trends in fisheries catch are useful to monitor effects of fishing on wild populations. However, fisheries catch data are often aggregated in multi-species complexes, complicating assessments of individual species. Non-target species are often grouped together in this way, but this becomes problematic when increasingly common shifts toward targeting incidental species demand closer management focus at the species level. Species distribution models (SDMs) offer an under-utilised tool to allocate aggregated catch data among species for individual assessments. Here, we present a case study of two shovel-nosed lobsters (Thenus spp.), previously caught incidentally and recorded together in logbook records, to illustrate the design and use of catch allocation SDMs to untangle multi-species data for stock assessments of individual species. We demonstrate how catch allocation SDMs reveal previously masked species-specific catch trends from aggregated data and can identify shifts in fishing behaviour, e.g., changes in target species. Finally, we review key assumptions and limitations of this approach that may arise when applied across a broad geographic or taxonomic scope. Our aim is to provide a template to assist researchers and managers seeking to assess stocks of individual species using aggregated multi-species data. [ABSTRACT FROM AUTHOR]

Background: Multicenter early diuretic response (DR) analysis of single furosemide dosing following neonatal cardiac surgery is lacking to inform whether early DR predicts adverse clinical outcomes. Methods: We performed a retrospective cohort study utilizing data from the NEPHRON registry. Random forest machine learning generated receiver operating characteristic-area under the curve (ROC-AUC) and odds ratios for mechanical ventilation (MV) and respiratory support (RS). Prolonged MV and RS were defined using ≥ 90th percentile of observed/expected ratios. Secondary outcomes were prolonged CICU and hospital length of stay (LOS) and kidney failure (stage III acute kidney injury (AKI), peritoneal dialysis, and/or continuous kidney replacement therapy on postoperative day three) assessed using covariate-adjusted ROC-AUC curves. Results: A total of 782 children were included. Cumulative urine output (UOP) metrics were lower in prolonged MV and RS patients, but DR poorly predicted prolonged MV (highest AUC 0.611, OR 0.98, sensitivity 0.67, specificity 0.53, p = 0.006, 95% OR CI 0.96–0.99 for cumulative 6-h UOP) and RS (highest AUC 0.674, OR 0.94, sensitivity 0.75, specificity 0.54, p < 0.001, 95% CI 0.91–0.97 UOP between 3 and 6 h). Secondary outcome results were similar. DR had fair discrimination for kidney failure (AUC 0.703, OR 0.94, sensitivity 0.63, specificity 0.71, 95% OR CI 0.91–0.98, p < 0.001, cumulative 6-h UOP). Conclusions: Early DR poorly discriminated patients with prolonged MV, RS, and LOS in this cohort, though it may identify severe postoperative AKI phenotype. Future work is warranted to determine if early DR or late postoperative DR later, in combination with other AKI metrics, may identify a higher-risk phenotype. [ABSTRACT FROM AUTHOR]

The influence of ionic liquid (IL) characteristics, lignocellulosic biomass (LCB) properties, and process conditions on LCB pretreatment is not well understood. In this study, a total of 129 experimental data on LCB (grass, agricultural, and forest residues) pretreatment using imidazolium, triethylamine, and choline-amino acid ILs were compiled to develop machine learning (ML) models for cellulose, hemicellulose, lignin, and solid recovery. Following data imputation, a bilayer artificial neural network (ANN) and random forest (RF) regression, the two most widely adopted ML models, were developed. The full-featured ANN following Bayesian hyperparameter (HP) optimisation offered excellent fit on training (R2: 0.936–0.994), though cross-validation (R2CV) performance remained marginally poor, i.e. between 0.547 and 0.761. The fitness of HP-optimised RF models varied between 0.824 and 0.939 for regression, and between 0.383 and 0.831 in cross-validation. Temperature and pretreatment time had been the most important predictors, except for hemicellulose recovery. Bayesian predictor selection combined with HP optimisation improved the R2CV boundary for ANN (0.555–0.825), as well as for RF models (0.474–0.824). As predictive performance of the models varied depending on target response, use of a larger homogeneous dataset may be warranted. The predictive modelling framework for LCB pretreatment, developed in this study, can be extended to similar biochemical process systems. [ABSTRACT FROM AUTHOR]

In forensic practice, determining the postmortem submersion interval (PMSI) and cause-of-death of cadavers in aquatic ecosystems has always been challenging task. Traditional approaches are not yet able to address these issues effectively and adequately. Our previous study proposed novel models to predict the PMSI and cause-of-death based on metabolites of blood from rats immersed in freshwater. However, with the advance of putrefaction, it is hardly to obtain blood samples beyond 3 days postmortem. To further assess the feasibility of PMSI estimation and drowning diagnosis in the later postmortem phase, gastrocnemius, the more degradation-resistant tissue, was collected from drowned rats and postmortem submersion model in freshwater immediately after death, and at 1 day, 3 days, 5 days, 7 days, and 10 days postmortem respectively. Then the samples were analyzed with liquid chromatography–tandem mass spectrometry (LC–MS/MS) to investigate the dynamic changes of the metabolites. A total of 924 metabolites were identified. Similar chronological changes of gastrocnemius metabolites were observed in the drowning and postmortem submersion groups. The difference in metabolic profiles between drowning and postmortem submersion groups was only evident in the initial 1 day postmortem, which was faded as the PMSI extension. Nineteen metabolites representing temporally-dynamic patterns were selected as biomarkers for PMSI estimation. A regression model was built based on these biomarkers with random forest algorithm, which yielded a mean absolute error (± SE) of 5.856 (± 1.296) h on validation samples from an independent experiment. These findings added to our knowledge of chronological changes in muscle metabolites from submerged vertebrate remains during decomposition, which provided a new perspective for PMSI estimation. [ABSTRACT FROM AUTHOR]

To date South African forensic anthropologists are only able to successfully apply a metric approach to estimate population affinity when constructing a biological profile from skeletal remains. While a non-metric, or macromorphoscopic approach exists, limited research has been conducted to explore its use in a South African population. This study aimed to explore 17 cranial macromorphoscopic traits to develop improved methodology for the estimation of population affinity among black, white and coloured South Africans and for the method to be compliant with standards of best practice. The trait frequency distributions revealed substantial group variation and overlap, and not a single trait can be considered characteristic of any one population group. Kruskal-Wallis and Dunn's tests demonstrated significant population differences for 13 of the 17 traits. Random forest modelling was used to develop classification models to assess the reliability and accuracy of the traits in identifying population affinity. Overall, the model including all traits obtained a classification accuracy of 79% when assessing population affinity, which is comparable to current craniometric methods. The variable importance indicates that all the traits contributed some information to the model, with the inferior nasal margin, nasal bone contour, and nasal aperture shape ranked the most useful for classification. Thus, this study validates the use of macromorphoscopic traits in a South African sample, and the population-specific data from this study can potentially be incorporated into forensic casework and skeletal analyses in South Africa to improve population affinity estimates. [ABSTRACT FROM AUTHOR]

Background: To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors. Materials and Methods: In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancy maximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach. Results: A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original_shape_Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method. Conclusions: The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance. [ABSTRACT FROM AUTHOR]

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

Identification of landslide susceptible zones is the preliminary step to plan mitigation measures in landslide-prone mountainous terrains. The use of various machine learning (ML) algorithms has proven their superiority in terms of enhancing the success rate in susceptibility studies. Therefore, the present study focuses on spatial prediction of landslides using integrated supervised and unsupervised machine learning (ML) techniques with reference to Bhagirathi Valley, NW Himalaya. A landslide inventory of 514 landslides and 14 viable causative factors of landslides in the study area have been selected for the analysis. Three efficient supervised ML techniques, i.e., random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbour (KNN), have been integrated with an unsupervised ISODATA cluster classification technique to prepare the landslide susceptible maps (LSM) of the study area. All the models depict that the greater part of the high and very high landslide hazard zones lie in the Main Central Thrust zone and its vicinity in the Bhagirathi Valley. The accuracy of each model was determined and compared using several statistical signifiers like sensitivity, specificity, area under curve, accuracy, and Kappa index. The results show that XGBoost and RF models exhibit higher performance accuracy than KNN. The quantitative assessment of prepared LSMs of the study area was also done using frequency ratio (FR) and frequency density (FD). The results indicate the consistency of each model in the prediction of landslide zones in the study area as FR and FD both increase with the increase of landslide susceptibility levels from very low to very high in all the models. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive comparison between traditional hydrological models and advanced machine learning (ML) techniques in predicting streamflow dynamics. Traditional models, namely the Soil and Water Assessment Tool (SWAT) and Génie Rural à 4 Paramètres Journalier (GR4J), are juxtaposed against ML models, including Random Forest (RF), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Both SWAT and GR4J demonstrated commendable performance, with GR4J displaying marginally superior predictive accuracy, evidenced by its tighter RMSE values. In the realm of ML, RF exhibited exceptional prowess in integrating diverse climatic features, especially in a scenario integrating comprehensive meteorological data. ANN showcased consistent performance across different input scenarios, emphasising its robustness. LSTM and BiLSTM, tailored for time series data, underscored the importance of precipitation's temporal dynamics in streamflow predictions. A notable revelation is the significance of choosing appropriate input data, with certain scenarios outperforming others based on the amalgamation of meteorological parameters. The flow duration curve (FDC) analysis further highlighted the model capabilities, with RF and BiLSTM excelling in capturing extreme flows, while traditional models resonated more with medium flow regimes. This research offers vital insights for hydrologists and decision-makers, aiding in informed model selection for streamflow predictions. [ABSTRACT FROM AUTHOR]

Objectives: Aim of this study was to assess the value of virtual non-contrast (VNC) reconstructions in differentiating between adrenal adenomas and metastases on a photon-counting detector CT (PCD-CT). Material and methods: Patients with adrenal masses and contrast-enhanced CT scans in portal venous phase were included. Image reconstructions were performed, including conventional VNC (VNCConv) and PureCalcium VNC (VNCPC), as well as virtual monochromatic images (VMI, 40–90 keV) and iodine maps. We analyzed images using semi-automatic segmentation of adrenal lesions and extracted quantitative data. Logistic regression models, non-parametric tests, Bland–Altman plots, and a random forest classifier were used for statistical analyses. Results: The final study cohort consisted of 90 patients (36 female, mean age 67.8 years [range 39–87]) with adrenal lesions (45 adenomas, 45 metastases). Compared to metastases, adrenal adenomas showed significantly lower CT-values in VNCConv and VNCPC (p = 0.007). Mean difference between VNC and true non-contrast (TNC) was 17.67 for VNCConv and 14.85 for VNCPC. Random forest classifier and logistic regression models both identified VNCConv and VNCPC as the best discriminators. When using 26 HU as the threshold in VNCConv reconstructions, adenomas could be discriminated from metastases with a sensitivity of 86.7% and a specificity of 75.6%. Conclusion: VNC algorithms overestimate CT values compared to TNC in the assessment of adrenal lesions. However, they allow a reliable discrimination between adrenal adenomas and metastases and could be used in clinical routine in near future with an increased threshold (e.g., 26 HU). Further (multi-center) studies with larger patient cohorts and standardized protocols are required. Clinical relevance statement: VNC reconstructions overestimate CT values compared to TNC. Using a different threshold (e.g., 26 HU compared to the established 10 HU), VNC has a high diagnostic accuracy for the discrimination between adrenal adenomas and metastases. Key Points: • Virtual non-contrast reconstructions may be promising tools to differentiate adrenal lesions and might save further diagnostic tests. • The conventional and a new calcium-preserving virtual non-contrast algorithm tend to systematically overestimate CT-values compared to true non-contrast images. • Therefore, increasing the established threshold for true non-contrast images (e.g., 10HU) may help to differentiate between adrenal adenomas and metastases on contrast-enhanced CT. [ABSTRACT FROM AUTHOR]

After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience. [ABSTRACT FROM AUTHOR]

To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git. [ABSTRACT FROM AUTHOR]

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings. [ABSTRACT FROM AUTHOR]