A Comprehensive Study Toward Women Safety Using Machine Learning Along with Android App Development

The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

BackgroundEarly identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed to establish whether machine learning (ML) models could predict HFNC outcome, early in the course of treatment, with greater accuracy than currently used clinical indices.MethodsWe developed ML models trained using measurements made within the first 2 h of treatment from 184 AHRF patients (37% HFNC failures) treated at the respiratory ICU of the University Hospital of Modena between 2018 and 2023. For external validation, we used a dataset on 567 AHRF patients (22% failures) comprising 510 patients from the recent RENOVATE trial in Brazil and 57 from the MIMIC-IV and eICU databases in the US. Predictive performance of the ML models was benchmarked against optimized thresholds of the following clinical indices: respiratory rate oxygenation index (ROX) and variants, heart rate to saturation of pulse oxygen (SpO2) ratio, SpO2/FiO2 ratio, PaO2/FiO2 ratio, sequential organ failure assessment and heart rate, acidosis, consciousness, oxygenation and respiratory rate scores.ResultsInternal and external predictive performance of a Support Vector Machine (SVM) ML model was superior to all clinical indices across all scenarios tested. In external validation on the 567-patient dataset, a SVM model trained on non-invasive measurements had an accuracy of 73%, sensitivity of 73%, specificity of 73%, and AUC of 0.79. The ROX index had an accuracy of 64%, sensitivity of 79%, specificity of 60%, and AUC of 0.74. When arterial blood gasses (ABG’s) were also used for model training, the SVM model had an accuracy of 83%, sensitivity of 84%, specificity of 82%, and AUC of 0.82 in external validation on the MIMIC-IV/eICU dataset. The modified ROX index, which requires PaO2, achieved 70% accuracy, 63% sensitivity, 74% specificity, and AUC of 0.65.ConclusionsDecision support tools based on SVM models could provide clinicians with more accurate early predictions of HFNC outcome than currently available clinical indices. If available, ABG measurements could improve the capability to accurately identify patients at risk of failing HFNC therapy.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Improving autocoding performance of rare categories in injury classification: Is more training data or filtering the solution?

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.

This dissertation addresses two significant issues in the effective application of machine learning algorithms and models for the physical and engineering sciences. The first is the broad challenge of automated modeling of data across different processes in a physical system. The second is the dilemma of obtaining insightful interpretations on the relationships between the inputs and outcome of a system as inferred from complex, black box machine learning models.Automated Geometric Shape Deviation Modeling for Additive Manufacturing SystemsAdditive manufacturing systems possess an intrinsic capability for one-of-a-kind manufacturing of a vast variety of shapes across a wide spectrum of processes. One major issue in AM systems is geometric accuracy control for the inevitable shape deviations that arise in AM processes. Current effective approaches for shape deviation control in AM involve the specification of statistical or machine learning deviation models for additively manufactured products. However, this task is challenging due to the constraints on the number of test shapes that can be manufactured in practice, and limitations on user efforts that can be devoted for learning deviation models across different shape classes and processes in an AM system. We develop an automated, Bayesian neural network methodology for comprehensive shape deviation modeling in an AM system. A fundamental innovation in this machine learning method is our new and connectable neural network structures that facilitate the transfer of prior knowledge and models on deviations across different shape classes and AM processes. Several case studies on in-plane and out-of-plane deviations, regular and free-form shapes, and different settings of lurking variables serve to validate the power and broad scope of our methodology, and its potential to advance high-quality manufacturing in an AM system.Interpretable Machine LearningMachine learning algorithms and models constitute the dominant set of predictive methods for a wide range of complex, real-world processes. However, interpreting what such methods effectively infer from data is difficult in general. This is because their typical black box natures possess a limited ability to directly yield insights on the underlying relationships between inputs and the outcome for a process. We develop methodologies based on new predictive comparison estimands that effectively enable one to ``mine’’ machine learning models, in the sense of (a) interpreting their inferred associations between inputs and/or functional forms of inputs with the outcome, (b) identifying the inputs that they effectively consider relevant, and (c) interpreting the inferred conditional and two-way associations of the inputs with the outcome. We establish Fisher consistent estimators, and their corresponding standard errors, for our new estimands under a condition on the inputs' distributions. The significance of our predictive comparison methodology is demonstrated with a wide range of simulation and case studies that involve Bayesian additive regression trees, neural networks, and support vector machines. Our extended study of interpretable machine learning for AM systems demonstrates how our method can contribute to smarter advanced manufacturing systems, especially as current machine learning methods for AM are lacking in their ability to yield meaningful engineering knowledge on AM processes.

Road crash prediction has proven to be an effective means of improving highway safety. In recent years, machine learning (ML) models have been embraced as efficient for the prediction of road accident frequency. This study applied two machine learning models for the prediction of road accident occurrence on selected South-West highway in Nigeria. Accident data were obtained for the period of 10 years from 2013 to 2022 from the Federal Road Safety Commission (FRSC) of Nigeria on the road under study and the traffic operations were determined on site using manual counting and stopwatch approach. Machine Learning (ML) models including Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were also used as the statistical model for safety prediction of the highway with consideration to identified contributing factors. The performance of the models was compared for both training and testing dataset using coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The study showed consistency and effective performance in both ML models with R2 of 0.99 in SVM, 0.97 and XGBoost for training data, also 0.93 in SVM and 0.76 in XGBoost testing data. ML models are also easy and fast to implement. The result of this study supports the use of ML as a predictive tool for road safety evaluation. The knowledge attained from this study will benefit transportation planners, engineers, and policymakers to implement effective measures aimed at reducing the crash occurrence, thereby enhancing overall transportation safety and efficiency.

The EGNOS NOTAM proposals provision is a key enabler for the publication of RNP approaches down to LPV minima in Europe, as it provides the different AIS (Aeronautical Information Service) the required information to establish a NOTAM service covering this type of procedures, in line with the ICAO standards. Currently, more than 400 APV-I and 150 LPV-200 SBAS procedures are published in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovak Republic, Spain, Sweden, Switzerland, UK and the Bailiwick of Guernsey. The EURONOTAM tool is responsible for generating EGNOS NOTAM proposals, implementing an analytical service volume that propagates the orbits of the satellites (based on the GPS almanacs) and computes the satellites in view for each configured airport. Once the satellite visibility is computed, the prediction module estimates the residual errors for each Line Of Sight (LoS), taking into account the number of EGNOS Ranging Integrity Monitoring Stations (RIMS) in view (for the User Differential Range Error —UDRE— computation) and the number of Ionospheric Pierce Points surrounding the neighbouring Ionospheric Grid Points (for the Grid Ionospheric Vertical Error —GIVE— estimation). With these estimations, the tool calculates the Protection Levels for each airport and determines if a new NOTAM proposal is to be issued in case the service is not available to support APV-I or LPV-200 operations in a specific location. Thus, the accuracy of the UDRE and GIVE estimations is essential in order to precisely predict the EGNOS availability events. Taking this into account, the present study is aimed at analysing the feasibility and the potential improvement that could be obtained, by using Machine Learning (ML) or Deep Learning (DL) techniques to enhance or replace the current analytical model. More specifically, the different “classic” ML models which will be addressed are decision trees, random forests, support vector machines and extreme gradient boosting (XGBoost). On top of these ML models, DL techniques are also covered by this study. The outputs of these models will be used to estimate the Protection Levels, and its applicability to the current EURONOTAM software will be discussed. The study will start with the analysis of the input dataset used to train the model. Aimed at analysing the impact of EGNOS station outages on the system performance and the capacity of the model to predict such impact, the input dataset will be composed of a minimum of 41 scenarios, including single and multiple station outages. For the UDRE estimation, more than 1.1 million samples, including the following features, will be considered: satellite coordinates, satellite dilution of precision (DOP) (based on the number of station in view), number of RIMS-A and RIMS-C on sight, satellite family, slot number and RIMS status. Similar features will be considered for the GIVE, with more than 3.5 million samples, including as well information on the number and distribution of the IPPs surrounding each IGP. Feature importance will be evaluated using SHAP values. SHAP values are calculated for each input feature and for each sample, and they measure the contribution of that feature to the predicted value for the sample. Summing the absolute SHAP values for all samples gives a measure of how the output is driven by that feature globally. This will be used to identify which are the most important features (and eventually, consider updating EURONOTAM model to consider these features – e.g. DOP) and discard the unimportant ones from the ML model. The real UDREi and GIVEi values used for the model training and testing will be obtained from the real EGNOS messages downloaded from the EDAS FTP. After preparing the input data set and splitting it into the training and testing datasets, the model hyperparameters will be tuned through a grid search with 5 fold cross-validation (for each set of values, 4/5 of the original training set will be used for actually training the model and the remaining 1/5 will be used for assessing the performance). The parameters resulting in the best averaged model performance will be selected. The resulting model will be evaluated with the testing dataset, using the following metrics: - Binary Accuracy: ratio between number of samples correctly classified as monitored or not-monitored vs the total number of samples - Total Accuracy: ratio between the number of correctly classified samples (considering the 16 possible labels) and the total number of samples. - Matthews correlation coefficient (MCC): A coefficient of +1 represents a perfect prediction and 0 represents an average random prediction. - Mean & RMS error on UDRE or GIVE value (UDREi / GIVEi converted to UDRE /GIVE for the assessment). The results obtained so far seem to indicate that Machine learning models can significantly improve the estimates obtained from the current analytical model (geometric-based). For instance, for one of the ML models tested, the system is able to correctly classify the 86% of the UDREi labels versus the 76% obtained with the current model, meaning a 13% improvement. In terms of binary accuracy, the ML model properly estimates the monitored / not-monitored status for the 99% of the samples tested. The RMS and mean absolute error are decreased by a 45% and 55% respectively. For the GIVE estimation, the improvement in the classification problem is more remarkable, with a 30% enhancement with respect to the current value. The RMS and mean absolute errors decrease as well by a 36% and a 50% factor respectively. The study will determine if Machine learning models can significantly improve the estimates obtained from the current analytical model (geometric-based). The study will also evaluate Protection Levels predicted with the usage of ML-estimated UDRE and GIVE values, where an important improvement in terms of prediction accuracy is expected. The analysis will be completed with a brief discussion on the available software libraries enabling the embedding of ML trained models in existing code and the capability of the model to predict situations that are very different from the ones actually trained. This is particularly challenging in this study, due to the lack of a full set of samples covering all the non-nominal conditions, since potential future situations might have never happened before and cannot be therefore, trained. Future steps will focus on this topic, and aim at finding more robust machine learning models that are able to deal with non-nominal conditions while still outperforming traditional methods under nominal conditions.

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study

Simple SummaryEffective models for predicting non-functioning pituitary neuro-endocrine tumours (NF PitNET) recurrence and regrowth following surgical intervention remain elusive. Previous studies have identified conflicting risk factors in predictions of tumour progression for patients receiving surgery for NF PitNET. The aim of this study was to develop machine learning (ML) models to improve prediction of post-operative NF PitNET progression up to 15 years following surgery. ML models were shown to be effective for predicting tumour remission, stability, and regrowth, but were non-performant when predicting tumour recurrence or reduction in size. The extent of surgical resection was shown to have the strongest influence in the performant models, with lesser influence from age, tumour volume, and the use of post-operative radiotherapy, and with no influence shown from pre- or post-operative endocrine function.Post-operative tumour progression in patients with non-functioning pituitary neuroendocrine tumours is variable. The aim of this study was to use machine learning (ML) models to improve the prediction of post-operative outcomes in patients with NF PitNET. We studied data from 383 patients who underwent surgery with or without radiotherapy, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree, showed superior performance in predicting tumour progression when compared with parametric statistical modelling using logistic regression, with SVM achieving the highest performance. The strongest predictor of tumour progression was the extent of surgical resection, with patient age, tumour volume, and the use of radiotherapy also showing influence. No features showed an association with tumour recurrence following a complete resection. In conclusion, this study demonstrates the potential of ML models in predicting post-operative outcomes for patients with NF PitNET. Future work should look to include additional, more granular, multicentre data, including incorporating imaging and operative video data.

In recent years, a significant amount of research has focused on analyzing the effectiveness of machine learning (ML) models for malware detection. These approaches have ranged from methods such as decision trees and clustering to more complex approaches like support vector machine (SVM) and deep neural networks. In particular, neural networks have proven to be very effective in detecting complex and advanced malware. This, however, comes with a caveat. Neural networks are notoriously complex. Therefore, the decisions that they make are often just accepted without questioning why the model made that specific decision. The black box characteristic of neural networks has challenged researchers to explore methods to explain black-box models such as SVM and neural networks and their decision-making process. Transparency and explainability give the experts and malware analysts assurance and trustworthiness about the ML models’ decisions. In addition, it helps in generating comprehensive reports that can be used to enhance cyber threat intelligence sharing. As such, this much needed analysis drives our work in this paper to explore the explainability and interpretability of ML models in the field of online malware detection. In this paper, we used the Shapley Additive exPlanations (SHAP) explainability technique to achieve efficient performance in interpreting the outcome of different ML models such as SVM Linear, SVM-RBF (Radial Basis Function), Random Forest (RF), Feed-Forward Neural Net (FFNN), and Convolutional Neural Network (CNN) models trained on an online malware dataset. To explain the output of these models, explainability techniques such as KernalSHAP, TreeSHAP, and DeepSHAP are applied to the obtained results.

Integrated STEM projects and experiments promote interdisciplinary learning, enhance critical thinking, and prepare students for real-world problem-solving by bridging science, technology, engineering, and mathematics. While numerous research studies have explored the STEM education domain, few have focused on supporting educators in identifying and designing effective integrated STEM projects and experiments. This research addresses this gap by introducing a novel system that leverages machine learning (ML) and large language models (LLMs) to assist educators in classifying and creating integrated STEM projects.We created and labeled a dataset of 1,071 project descriptions in this research work, mapped to one or more STEM areas (Science, Technology, Engineering, Mathematics). The dataset was developed using manually curated records, harvesting online sources, and synthetic data generation. Natural Language Processing (NLP) techniques were applied to prepare project descriptions for analysis. Various ML models, including Random Forest, Support Vector Machines (SVM), Logistic Regression, and Naive Bayes, were used for multi-label classification. The Random Forest model achieved the best performance, with a Precision of 91.99%, Recall of 94.59%, F1 Score of 93.27%, and Hamming Loss of 6.74%.The system operates in two phases: In Phase 1, the instructor proposes a project title with a description, and in response, the ML model identifies one or more STEM areas as the most relevant. In Phase 2, ChatGPT, specifically GPT-4o, assists educators by defining experiment objectives, generating ideas, designing procedures, and suggesting tools and resources. This human-in-the-loop system enhances collaboration between educators, ML models, and LLMs, streamlining the design of effective integrated STEM projects.This work's implications extend to improving the accessibility and effectiveness of integrated STEM education by reducing the barriers educators face in designing interdisciplinary projects. It provides a scalable framework for integrating advanced AI techniques into educational practices and fostering classroom innovation.

Improving Clinical Decisions in IR: Interpretable Machine Learning Models for Predicting Ascites Improvement after Transjugular Intrahepatic Portosystemic Shunt Procedures

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.