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Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlighting the role of pharmacogenomics in developing better predictive models. We analyzed 379 CRC patients receiving fluorouracil-based chemotherapy, collecting data on fluorouracil metabolism-related SNPs (TYMS, MTHFR, DPYD, RRM1), blood inflammatory markers, and clinical status. Six machine learning models-K-nearest neighbors, support vector machine, gradient boosting decision trees (GBDT), eXtreme Gradient Boosting (XGBoost), LightGBM, and random forest-were compared against multivariate logistic regression and a deep learning model (i.e., multilayer perceptron, MLP). Feature importance analysis highlighted seven predictors: histological grade, N and M staging, monocyte count, platelet-to-lymphocyte ratio, MTHFR rs1801131, and RRM1 rs11030918. In a five-fold cross-validation, XGBoost and GBDT exhibited superior performance, with Area Under Curve (AUC) of 0.88 ± 0.02. XGBoost excelled in identifying favorable prognosis (recall = 0.939). GBDT demonstrated balance in recognizing both categories, with a recall for favorable prognosis of 0.908 and a precision for unfavorable prognosis of 0.863. MLP had a similar AUC (0.87) with high precision for favorable prognosis (recall = 0.946). In external validation, XGBoost model achieved an accuracy of 0.79. An online prognostic tool based on XGBoost was developed, integrating metabolism-related SNPs and inflammatory markers, enhancing CRC treatment precision and supporting tailored chemotherapy., Competing Interests: Data Availability  The data that support the findings of this study are openly available in Github at https://github.com/xxxxljyf/SNPs-and-Blood-Inflammatory-Marker-Featured-Machine-Learning-Models-in-CRC., (© 2024. The Author(s).)

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 ("Westernized Millennial", n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 ("Healthy", n = 10616) consisted of healthy adults; Cluster 3 ("Mediterranean Young Adult", n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 ("Pre-morbid", n = 600) was characterized by healthy adults with declined mood; Cluster 5 ("Pro-morbid", n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses ("lifemetabotypes"). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention., (© 2024. The Author(s).)

Frailty is a state that is closely associated with adverse health outcomes in the aging process. The frailty index (FI), which measures frailty in terms of cumulative deficits, has been widely used for frailty assessment in elderly people, and its advantage of self-reported information collection makes it applicable to a broader group of elderly people. Our study aims to simplify the Frailty Index Assessment Scale, while maintaining its reliability and accuracy, to easily and quickly assess frailty in elderly people. In this study, participants (age ≥ 65 years) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which had 13,339, 372 and 1214 participants in 2008, 2011, and 2014, respectively, were used. The 2008 dataset was split into 80% for training and 20% for internal validation, and the data from 2011 to 2014 as external validation. In order to obtain effective predictors, we used Lasso regression, Boruta algorithm and random forest classifier score for feature selection. We used six models for predictive model construction and evaluated the models in the validation dataset. Model performance was measured by area under the curve (AUC), accuracy and F1 score. Logistic regression was found to be the best performing and most interpretable algorithm with AUC, accuracy and F1 of 0.974, 0.932 and 0.880 for the validation dataset, respectively. The AUCs for the external independent validation dataset were 0.963 and 0.977, respectively. Subgroup analysis showed that the model had good predictive power in both males and females. The predictive power was stronger among the elderly people over 80 years old, with AUC, accuracy and F1 of 0.973,0.914, and 0.893, respectively. The model also obtained good predictive power in the case of FI measured by different indicators. The model showed good robustness in the follow-up assessment of frailty status in elderly people, with the AUC remaining above 0.95 and accuracy above 0.9 over the long-term follow-up. Using machine learning techniques, we have successfully developed a simple frailty assessment prediction model based on 10 key features to shorten the frailty assessment scale with near full-scale accuracy. A user-friendly website was created to facilitate the application of this prediction model ( https://healthy-aging.shinyapps.io/Frailty_Assessment/ )., (© 2024. The Author(s).)

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with "small world" network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research., (© 2024. The Author(s).)

In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system's ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system's classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field's capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease., (© 2024. The Author(s).)

Rigorous comparisons of human and machine learning algorithm performance on the same task help to support accurate claims about algorithm success rates and advances understanding of their performance relative to that of human performers. In turn, these comparisons are critical for supporting advances in artificial intelligence. However, the machine learning community has lacked a standardized, consensus framework for performing the evaluations of human performance necessary for comparison. We demonstrate common pitfalls in a designing the human performance evaluation and propose a framework for the evaluation of human performance, illustrating guiding principles for a successful comparison. These principles are first, to design the human evaluation with an understanding of the differences between human and algorithm cognition; second, to match trials between human participants and the algorithm evaluation, and third, to employ best practices for psychology research studies, such as the collection and analysis of supplementary and subjective data and adhering to ethical review protocols. We demonstrate our framework's utility for designing a study to evaluate human performance on a one-shot learning task. Adoption of this common framework may provide a standard approach to evaluate algorithm performance and aid in the reproducibility of comparisons between human and machine learning algorithm performance., (© 2022. The Author(s).)

Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to help reveal central nervous system alterations characteristic of ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets (with further improvement to 93% and 90% after supervised domain adaptation). The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellar biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD., (© 2022. The Author(s).)

This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a huge population worldwide. As cells become less responsive to insulin or fail to produce it adequately, blood sugar levels rise. This has the potential to cause severe health complications including kidney disease, vision impairment and heart conditions. Early diagnosis is paramount in mitigating the risk and severity of diabetes-related complications. To tackle this, we proposed a robust framework for diabetes prediction using Synthetic Minority Over-sampling Technique (SMOTE) with ensemble machine learning techniques. Our approach incorporates strategies such as imputation of missing values, outlier rejection, feature selection using correlation analysis and class distribution balancing using SMOTE. The extensive experimentation shows that the proposed combination of AdaBoost and XGBoost shows exceptional performance, with an impressive AUC of 0.968+/-0.015. This outperforms not only alternative methodologies presented in our study but also surpasses current state-of-the-art results. We anticipate that our model will significantly improve diabetes prediction, offering a promising avenue for improved healthcare outcomes in diabetes management., Competing Interests: Declarations. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Machine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. The brain is the most pivotal organ in the human body. A brain stroke is generally caused by a blockage in the brain arteries. A brain stroke is one of the primary reasons for death. Therefore, early prediction of diseases like brain stroke, heart attack can significantly help in making decisions for doctors. The research study aims to find a robust and potential technique for the early prediction of brain stroke, Alzheimer's, heart attack, cancer, Parkinson's and potentially reducing the incidence of severe post complications of the mentioned diseases. By considering the five datasets as input, machine learning models have been trained for the research study. Early prediction of brain stroke has been done using eight individual classifiers along with 56 other models which are designed by merging the pairs of individual models using soft and hard voting for brain stroke and eight individual classifiers have been used for early prediction of heart attack, cancer, Alzheimer and Parkinson's. After analyzing the results of each classifier for each disease, the proposed method, which is a pair of random forest and decision tree using a hard voting method for early brain stroke prediction, achieves the highest accuracy of 99%, which is better than all classifiers. Along with accuracy, the proposed method attained a value of 98% in precision, an outstanding 100% in recall, and 99% in F1 score. XGBoost performed best for cancer, Parkinson's, Alzeihmer's and Bernoulli naive bayes performed best in case of Heart attack .Upon comparing the values of these performance metrics, they outshine all the other model's values., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics approval The research meets all applicable standards concerning the ethics of experimentation and research integrity, and the following is being certified/declared true. As an expert scientist and along with co-authors of the concerned field, the paper has been submitted with full responsibility, following the due ethical procedure, and there is no duplicate publication, fraud, plagiarism, or concerns about animal or human experimentation., (© 2024. The Author(s).)

The management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply chain provides the highly crucial facilities necessary to maintain the quality and safety of the product. The storage temperature is the most vital factor in maintaining both the quality and shelf-life of a perishable food. Adequate storage temperature control ensures that perishable foods are transported to the end-users in good quality and safe to consume. This paper presents perishable food storage temperature control through mathematical optimal control model where the storage temperature is regarded as the control variable and the deterioration of the perishable food's quality follows the first-order reaction. The optimal storage temperature for a single perishable food is determined by applying the Pontryagin's maximum principle to solve the optimal control model problem. For multi-temperature commodities supply chain, an unsupervised machine learning (ML) method, called k-means clustering technique is used to determine the temperature clusters for a range of perishables. Based on descriptive analysis, it is observed that the k-means clustering technique is effective in identifying the best suitable storage temperature clusters for quality control of multi-commodity supply chain., (© 2024. The Author(s).)

Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers., (© 2024. The Author(s).)

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare., (© 2024. The Author(s).)

Orthopedic diseases are widespread worldwide, impacting the body's musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy., (© 2024. The Author(s).)

Biological fluid stains can be instrumental in solving crimes. Identification of semen can help reconstruct events in sexual assault cases and identify suspects via DNA profiling. Current methods for semen identification suffer from limitations, including destruction of the sample and potential false positives. One of the main unsolved issues is the elimination of underlying substrate interference. In this paper, chemometric approaches were developed to isolate and identify a biofluid stain on interfering substrates using Raman spectroscopy. The first approach, called Multivariate Curve Resolution with the Addition Method, combines the standard addition method with multivariate curve resolution. The second one uses a criterion based on reducing the spectrum complexity when a spectral component is removed from a Raman spectrum of a multi-component sample entirely. The results demonstrate the superiority of the first approach relative to the second for both small volume fraction of the fluid stain compared to the substrate and random noise., (© 2024. The Author(s).)

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs' solubility in supercritical carbon dioxide (SCCO 2 ) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289., (© 2024. The Author(s).)

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA's operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis., (© 2024. The Author(s).)

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution., (© 2024. The Author(s).)

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil's forecasts, we employed COVID-19 data from several European and U.S. states. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms conventional data-centric approaches, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants., (© 2024. The Author(s).)

This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023., (© 2024. The Author(s).)

Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction., (© 2024. The Author(s).)

Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast magnetic resonance imaging (PC-MRI) signal formed by the velocity of flowing CSF are represented as a set of velocity-encoded images or maps, which can be thought of as signal data in the context of medical imaging, enabling the evaluation of pulsatile patterns throughout a cardiac cycle. However, automatic segmentation of the CSF region in a PC-MRI image is challenging, and implementing an explained ML method using pulsatile data as a feature remains unexplored. This paper presents lightweight machine learning (ML) algorithms to perform CSF lumen segmentation in spinal, utilizing sets of velocity-encoded images or maps as a feature. The Dataset contains 57 PC-MRI slabs by 3T MRI scanner from control and idiopathic scoliosis participants are involved to collect data. The ML models are trained with 2176 time series images. Different cardiac periods image (frame) numbers of PC-MRIs are interpolated in the preprocessing step to align to features of equal size. The fivefold cross-validation procedure is used to estimate the success of the ML models. Additionally, the study focusses on enhancing the interpretability of the highest-accuracy eXtreme gradient boosting (XGB) model by applying the shapley additive explanations (SHAP) technique. The XGB algorithm presented its highest accuracy, with an average fivefold accuracy of 0.99% precision, 0.95% recall, and 0.97% F1 score. We evaluated the significance of each pulsatile feature's contribution to predictions, offering a more profound understanding of the model's behavior in distinguishing CSF lumen pixels with SHAP. Introducing a novel approach in the field, develop ML models offer comprehension into feature extraction and selection from PC-MRI pulsatile data. Moreover, the explained ML model offers novel and valuable insights to domain experts, contributing to an enhanced scholarly understanding of CSF dynamics., (© 2024. The Author(s).)

Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive evaluations, various machine learning models, including WeightedEnsemble, CatBoost, and RandomForest, demonstrated high potential in accurately diagnosing early gastric cancer. The study emphasizes the importance of model explainability in medical diagnostics, showing how transparent, explainable models can increase trust and clinical acceptance, thereby improving diagnostic accuracy and patient outcomes. This research not only highlights key biomarkers and clinical features critical for early detection but also presents a versatile approach that could be applied to other medical diagnostics, promoting broader adoption of machine learning in clinical settings., (© 2024. The Author(s).)

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly., (© 2024. The Author(s).)

The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students' performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students' performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features., (© 2024. The Author(s).)

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death., (© 2024. The Author(s).)

Comprehending digital content written in natural language online is vital for many aspects of life, including learning, professional tasks, and decision-making. However, facing comprehension difficulties can have negative consequences for learning outcomes, critical thinking skills, decision-making, error rate, and productivity. This paper introduces an innovative approach to predict comprehension difficulties at the local content level (e.g., paragraphs). Using affordable wearable devices, we acquire physiological responses non-intrusively from the autonomous nervous system, specifically pulse rate variability, and electrodermal activity. Additionally, we integrate data from a cost-effective eye-tracker. Our machine learning algorithms identify 'hotspots' within the content and regions corresponding to a high cognitive load. These hotspots represent real-time predictors of comprehension difficulties. By integrating physiological data with contextual information (such as the levels of experience of individuals), our approach achieves an accuracy of 72.11% ± 2.21, a precision of 0.77, a recall of 0.70, and an f1 score of 0.73. This study opens possibilities for developing intelligent, cognitive-aware interfaces. Such interfaces can provide immediate contextual support, mitigating comprehension challenges within content. Whether through translation, content generation, or content summarization using available Large Language Models, this approach has the potential to enhance language comprehension., (© 2024. The Author(s).)

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products., (© 2024. The Author(s).)

The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of features from an image or signals, which are presented in tabular form. The possibility of high-precision and high-speed processing of such large volumes of medical data requires the use of artificial intelligence tools. A linear machine learning model cannot accurately analyze such data, and traditional bagging, boosting, or stacking ensembles typically require significant computing power and time to implement. In this paper, the authors proposed a method for the analysis of large sets of medical data, based on a designed linear ensemble method with a non-iterative learning algorithm. The basic node of the new ensemble is an extended-input SGTM neural-like structure, which provides high-speed data processing at each level of the ensemble. Increasing prediction accuracy is ensured by dividing the large dataset into parts, the analysis of which is carried out in each node of the ensemble structure and taking into account the output signal from the previous level of the ensemble as an additional attribute on the next one. Such a design of a new ensemble structure provides both a significant increase in the prediction accuracy for large sets of medical data analysis and a significant reduction in the duration of the training procedure. Experimental studies on a large medical dataset, as well as a comparison with existing machine learning methods, confirmed the high efficiency of using the developed ensemble structure when solving the prediction task., (© 2024. The Author(s).)

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes., (© 2024. The Author(s).)

Knowledge distillation is an effective approach for training robust multi-modal machine learning models when synchronous multimodal data are unavailable. However, traditional knowledge distillation techniques have limitations in comprehensively transferring knowledge across modalities and models. This paper proposes a multiscale knowledge distillation framework to address these limitations. Specifically, we introduce a multiscale semantic graph mapping (SGM) loss function to enable more comprehensive knowledge transfer between teacher and student networks at multiple feature scales. We also design a fusion and tuning (FT) module to fully utilize correlations within and between different data types of the same modality when training teacher networks. Furthermore, we adopt transformer-based backbones to improve feature learning compared to traditional convolutional neural networks. We apply the proposed techniques to multimodal human activity recognition and compared with the baseline method, it improved by 2.31% and 0.29% on the MMAct and UTD-MHAD datasets. Ablation studies validate the necessity of each component., (© 2024. The Author(s).)

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36)., (© 2024. The Author(s).)

An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such an ensemble can outperform the single best classifier. If so, what form of ensemble learning system (also known as multiple classifier learning systems) yields the most significant benefits in the size or diversity of the ensemble? In this paper, the ability of ensemble learning to predict and identify factors that influence or contribute to autism spectrum disorder therapy (ASDT) for intervention purposes is investigated. Given that most interventions are typically short-term in nature, henceforth, developing a robotic system that will provide the best outcome and measurement of ASDT therapy has never been so critical. In this paper, the performance of five single classifiers against several multiple classifier learning systems in exploring and predicting ASDT is investigated using a dataset of behavioural data and robot-enhanced therapy against standard human treatment based on 3000 sessions and 300 h, recorded from 61 autistic children. Experimental results show statistically significant differences in performance among the single classifiers for ASDT prediction with decision trees as the more accurate classifier. The results further show multiple classifier learning systems (MCLS) achieving better performance for ASDT prediction (especially those ensembles with three core classifiers). Additionally, the results show bagging and boosting ensemble learning as robust when predicting ASDT with multi-stage design as the most dominant architecture. It also appears that eye contact and social interaction are the most critical contributing factors to the ASDT problem among children., (© 2023. The Author(s).)

Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like Nigeria, particularly due to the high poverty rate. Many individuals have fallen victim to these scams, resulting in significant financial losses. Despite efforts to detect Ponzi schemes using various methods, including machine learning (ML), current techniques still face challenges, such as deficient datasets, reliance on transaction records, and limited accuracy. To address the negative impact of Ponzi schemes, this paper proposes a novel approach focusing on detecting Ponzi schemes on Ethereum using ML algorithms like random forest (RF), neural network (NN), and K-nearest neighbor (KNN). Over 20,000 datasets related to Ethereum transaction networks were gathered from Kaggle and preprocessed for training the ML models. After evaluating and comparing the three models, RF demonstrated the best performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667. Comparative evaluations with previous models indicate that our model achieves high accuracy. Moreover, this innovative work successfully detects key fraud features within the Ponzi scheme dataset, reducing the number of features from 70 to only 10 while maintaining a high level of accuracy. The main strength of this proposed method lies in its ability to detect clever Ponzi schemes from their inception, offering valuable insights to combat these financial threats effectively., (© 2023. The Author(s).)

In the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in patients with advanced urothelial cancer (aUC). Discovering biomarkers using genomic profiling to understand potential patient heterogeneity is essential to help improve patient care with precision medicine. For the JAVELIN Bladder 100 trial, it is unclear which variable selection methods can most reliably identify biomarkers to inform patient care because the dataset is characterized by high collinearity and low signal. The aim of this paper was to evaluate available selection methods and their ability to discover prognostic and predictive biomarkers in patients with aUC receiving first-line maintenance therapy. A simulation study evaluated the performance of popular variable selection approaches for high-dimensional data, including penalized regression models, random survival forests, and Bayesian variable selection methods. For Bayesian variable selection methods, a modified Bayesian Information Criterion (BIC) thresholding rule was proposed in addition to the traditional BIC thresholding rule. These methods were applied to the JAVELIN Bladder 100 dataset to investigate potential biomarkers associated with survival benefit. Results from the simulations demonstrated the strengths and limitations of the different methods. The variable selection methods demonstrated low false discovery rates under different conditions. However, their performance declined in the presence of high collinearity. Using the JAVELIN Bladder 100 data, we identified some potentially significant biomarkers across multiple models. Several lasso-related methods were able to identify potentially biologically meaningful variables in the trial. Some variable selection methods (such as stochastic search variable selection and random survival forest) may not be well suited to this type of data due to the presence of extreme collinearity and low signal. Future research should explore novel variable selection methods that may be more suitable for identifying prognostic and predictive biomarkers in this population.Trial registration: ClinicalTrials.gov Identifier: NCT02603432., (© 2023. The Author(s).)

A recent surge of patent applications among public hospitals in China has aroused significant research interest. A country's healthcare innovation capacity can be measured by its number of patents. This paper explores the link between the number of patents and ten independent variables. Multicollinearity was carefully detected and removed by using the variable selection method and LASSO regression, respectively. The Poisson model and the negative binomial model were proposed to analyze the patent data. Three goodness of fit tests, the Pearson test, the deviance test, and the DHARMa non-parametric dispersion test, were conducted to investigate if the model has a good fit. After discovering four clusters by conducting agglomerative hierarchical clustering, these two models were replaced by the negative binomial mixed model. The likelihood ratio test was used to determine which model is more appropriate and the results reveal that the negative binomial mixed model outperforms both the Poisson model and the negative binomial model. Three variables, number of health technicians per 10,000 people, financial expenditure on science and technology as well as number of patent applications per 10,000 health personnel, have a significantly positive relationship with the number of patents in Chinese tertiary public hospitals., (© 2023. Springer Nature Limited.)

The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through silicon vias (TSVs) in terms of automated and time-efficient analysis. In this paper, we develop a fully automated high-efficient End-to-End Convolutional Neural Network (CNN) model, utilizing two sequentially linked CNN architectures, suitable to classify and locate thousands of TSVs as well as provide statistical information. In particular, we generate interference patterns of the TSVs by conducting a unique concept of Scanning Acoustic Microscopy (SAM) imaging. Scanning Electron Microscopy (SEM) is used to validate and also disclose the characteristic pattern in the SAM C-scan images. By comparing the model with semi-automated machine learning approaches its outstanding performance is illustrated, indicating a localisation and classification accuracy of 100% and greater than 96%, respectively. The approach is not limited to SAM-image data and presents an important step towards zero defect strategies., (© 2023. The Author(s).)

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics., (© 2023. The Author(s).)

Due to the increasing prevalence of chronic kidney disease and its high mortality rate, study of risk factors affecting the progression of the disease is of great importance. Here in this work, we aim to develop a framework for using machine learning methods to identify factors affecting kidney function. To this end classification methods are trained to predict the serum creatinine level based on numerical values of other blood test parameters in one of the three classes representing different ranges of the variable values. Models are trained using the data from blood test results of healthy and patient subjects including 46 different blood test parameters. The best developed models are random forest and LightGBM. Interpretation of the resulting model reveals a direct relationship between vitamin D and blood creatinine level. The detected analogy between these two parameters is reliable, regarding the relatively high predictive accuracy of the random forest model reaching the AUC of 0.90 and the accuracy of 0.74. Moreover, in this paper we develop a Bayesian network to infer the direct relationships between blood test parameters which have consistent results with the classification models. The proposed framework uses an inclusive set of advanced imputation methods to deal with the main challenge of working with electronic health data, missing values. Hence it can be applied to similar clinical studies to investigate and discover the relationships between the factors under study., (© 2022. The Author(s).)

The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables., (© 2022. The Author(s).)

Ground subsidence caused by natural factors, including groundwater, has been extensively researched. However, there have been few studies on ground sink caused mainly by artifacts, including underground pipelines in urban areas. This paper proposes a method of predicting ground sink susceptibility caused by underground pipelines. Underground pipeline data, drilling data, and 77 points of ground sink occurrence were collected for five 1 × 1 km urban areas. Furthermore, three ground sink conditioning factors (GSCFs) (pipe deterioration, diameter, and length) were identified by correlation analysis. Pipe deterioration showed the highest correlation with ground sink occurrence, followed by pipe length and pipe diameter in that order. Next, four machine learning methods [multinomial logistic regression (MLR), decision tree (DT) classifier, random forest (RF) classifier, and gradient boosting (GB) classifier] were applied. The results show that GB classifier had the highest accuracy of 0.7432, whereas the accuracy of RF classifier was 0.7407; thus, GB classifier was not significantly more accurate. RF classifier showed the highest reliability (0.84, 0.70, 0.87) according to the area under the receiver operating characteristic (AUC-ROC) curve. Ground sink susceptibility maps (GSSMs) of the five regions in an urban area were created using RF classifier, which performed the best overall., (© 2022. The Author(s).)

In recent years, the omnipresence of cardiac problems has been recognized as an epidemic. With the correct and quick diagnosis, both mortality and morbidity from cardiac disorders can be dramatically reduced. However, frequent medical check-ups are pricey and out of reach for a large number of people, particularly those living in low-income areas. In this paper, certain time-honored statistical techniques are used to determine the factors that lead to heart disease. Also, the findings were validated using various promising machine learning tools. Feature importance approach was employed to rank the clinical parameters of the patients based on the correlation of heart disease. In the case of statistical investigations, nonparametric tests such as the Mann Whitney U test and the Chi square test, as well as correlation analysis with Pearson correlation and Spearman Correlation were used. For additional validation, seven of the potential feature important based ML algorithms were applied. Moreover, Borda count was implemented to acknowledge the combined observation of those ML models. On top of that, SHAP value was calculated as a feature importance technique and for detailed evaluation. This research reveals two aspects of heart disease diagnosis.We found that eight clinical traits are sufficient to diagnose cardiac disorders, in which three traits are the most important sign of heart disease. One of the discoveries of this investigation uncovered chest pain, number of major blood vessels, thalassemia, age, maximum heart rate, cholesterol, oldpeak, and sex as sufficient clinical signs of individuals for the diagnosis of cardiac disorders. Over the above, considering the findings of all three approaches, chest pain, the number of major blood vessels, and thalassemia were identified as the prime factors of heart disease. The research also found, fasting blood sugar does not have a direct impact on cardiac disease. These findings will have the potency to be incredibly useful in clinical investigations as well as risk assessment for patients. Limiting the most critical features can have a significant impact on the diagnosis of heart disease and reduce the severity of health risks and death of patients., (© 2022. The Author(s).)

The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified into the directed percolation (DP) universality class, the model of PCPD has been controversially discussed since its infancy. To our best knowledge, there is so far no consensus on whether the phase transition of the PCPD falls into the unknown university classes or else conveys a new kind of non-equilibrium phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original configurations of the model and provide reasonable estimates of thresholds. Therefore, no matter whether the non-equilibrium lattice model is a random process of unitary (for instance the DP) or binary (for instance the PCP), or whether it contains the diffusion motion of particles, unsupervised learning can capture the essential, hidden information. Beyond that, supervised learning is also applied to learning the PCPD at different diffusion rates. We proposed a more accurate numerical method to determine the spatial correlation exponent [Formula: see text], which, to a large degree, avoids the uncertainty of data collapses through naked eyes., (© 2022. The Author(s).)

Learning discriminative visual patterns from image local salient regions is widely used for fine-grained visual classification (FGVC) tasks such as plant or animal species classification. A large number of complex networks have been designed for learning discriminative feature representations. In this paper, we propose a novel local structure information (LSI) learning method for FGVC. Firstly, we indicate that the existing FGVC methods have not properly considered how to extract LSI from an input image for FGVC. Then an LSI extraction technique is introduced which has the ability to properly depict the properties of different local structure features in images. Secondly, a novel LSI learning module is proposed to be added into a given backbone network for enhancing the ability of the network to find salient regions. Thirdly, extensive experiments show that our proposed method achieves better performance on six image datasets. Particularly, the proposed method performs far better on datasets with a limited number of images., (© 2022. The Author(s).)

Achieving accurate and reliable maize disease identification in complex environments is a huge challenge. This is because disease images obtained from natural environments are often in complex contexts that may contain elements similar to disease characteristics or symptoms. Based on cascade network and two-stage transformation learning, the new method is proposed in this paper and applied the improved method to the task of identification and classification of four maize leaf types in a complex environment. The proposed method has a cascade structure which consists of a Faster R-CNN leaf detector (denoted as LS-RCNN) and a CNN disease classifier, named CENet(Complex Environment Network). The LS-RCNN detector with an attention mechanism was used to detect maize leaves from the image, and the CENet model further classified the leaf images detected in the first stage into four categories: Cercospora leaf spot, Common rust, Northern Leaf Blight, and Healthy, which allowed image features to be extracted more efficiently. The subsequent use of a two-stage transfer learning strategy to train CENet models of disease images in complex contexts allows for faster training of the models while ensuring accuracy. The experimental results show that the proposed method is used to identify four types of maize leaves with an F1-score of 99.70%, which is better than some popular CNN models and others' methods, and has a more obvious advantage in terms of training speed. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification under complex backgrounds., (© 2022. The Author(s).)

Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple independent variables. There are relatively few adjustable hyperparameters in the standard random forest models, among them the minimum size of the terminal nodes on each tree. The usual stopping rule, as proposed by Breiman, stops tree expansion by limiting the size of the parent nodes, so that a node cannot be split if it has less than a specified number of observations. Recently an alternative stopping criterion has been proposed, stopping tree expansion so that all terminal nodes have at least a minimum number of observations. The present paper proposes three generalisations of this idea, limiting the growth in regression random forests, based on the variance, range, or inter-centile range. The new approaches are applied to diabetes data obtained from the National Health and Nutrition Examination Survey and four other datasets (Tasmanian Abalone data, Boston Housing crime rate data, Los Angeles ozone concentration data, MIT servo data). Empirical analysis presented herein demonstrate that the new stopping rules yield competitive mean square prediction error to standard random forest models. In general, use of the intercentile range statistic to control tree expansion yields much less variation in mean square prediction error, and mean square prediction error is also closer to the optimal. The Fortran code developed is provided in the Supplementary Material., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper, we investigate whether machine learning can acquire explainable knowledge without directly introducing problem-specific information such as explicit physical mechanisms. In particular, a potential of machine learning to obtain the capability to identify a part of material structures that critically affects a physical property without human prior knowledge is mainly discussed. The guide for constructing the machine learning framework adopted in this paper is to imitate human researchers' process of thinking in the interpretation and development of materials. Our framework was applied to the optimization of structures of artificial dual-phase steels in terms of a fracture property. A comparison of results of the framework with those of numerical simulation based on governing physical laws demonstrated the potential of our framework for the identification of a part of microstructures critically affecting the target property. Consequently, this implies that our framework can implicitly acquire an intuition in a similar way that human researchers empirically attain the general strategy for material design consistent with the physical background., (© 2022. The Author(s).)

Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has become one of the research priorities. In this paper, a new method, BIR-CNN, is proposed to classify of Android malware. It combines convolution neural network (CNN) with batch normalization and inception-residual (BIR) network modules by using 347-dim network traffic features. CNN combines inception-residual modules with a convolution layer that can enhance the learning ability of the model. Batch Normalization can speed up the training process and avoid over-fitting of the model. Finally, experiments are conducted on the publicly available network traffic dataset CICAndMal2017 and compared with three traditional machine learning algorithms and CNN. The accuracy of BIR-CNN is 99.73% in binary classification (2-classifier). Moreover, the BIR-CNN can classify malware by its category (4-classifier) and malicious family (35-classifier), with a classification accuracy of 99.53% and 94.38%, respectively. The experimental results show that the proposed model is an effective method for Android malware classification, especially in malware category and family classifier., (© 2022. The Author(s).)

The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2-8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration., (© 2022. Crown.)

Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press., (© 2022. The Author(s).)