Correlation-based damage detection method using convolutional neural network for civil infrastructure

SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

In a recent development, attention has shifted to the application of artificial intelligence for the optimization of wastewater treatment processes. This research compared the performances of the machine learning (ML) model: random forest, decision tree, support vector machine, artificial neural network, convolutional neural network, long-short term memory, and multiple linear regressors for optimization in effluent treatment. The training, testing, and validation datasets were obtained via the design of an experiment conducted on the removal of total dissolved solids (TDS) from pharmaceutical effluent. The breadfruit-activated carbon (BFAC) adsorbent was characterized using scanning electron microscopy and X-ray diffraction techniques. The predictive capacity of an ML algorithm, and neural network architecture implemented to optimize the treatment process using statistical metrics. The results showed that MSE ≤ 1.68, MAE ≤ 0.95, and predicted-R2 ≥ 0.9035 were recorded across all ML. The ML output with minimum error functions that satisfied the criterion for clean discharge was adopted. The predicted optimum conditions correspond to BFAC dosage, contact time, particle size, and pH of 2.5 mg/L, 10 min, 0.60 mm, and 6, respectively. The optimum transcends to a reduction in TDS concentration from 450 mg/L to a residual ≤ 40 mg/L and corresponds to 90% removal efficiency, indicating ± 1.01 standard deviation from the actual observation practicable. The findings established the ML model outperformed the neural network architecture and affirmed validation for the optimization of the adsorption treatment in the pharmaceutical effluent domain. Results demonstrated the reliability of the selected ML algorithm and the feasibility of BFAC for use in broad-scale effluent treatment.

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.

Customer attrition is especially an issue in industries such as retail, banking, and telecommunications where customer acquisition costs are significantly higher than the costs of retaining repeat customers. The customer lack of interest is now predictable through machine learning models, and deep learning has become instrumental in early intervention for retention. In order to assess the quality of churn prediction, the study tests six basic machine learning techniques: random forest, logistic regression, and the k-nearest neighbors method, as well as four deep learning techniques: long short term memory (LSTM), bidirectional LSTM, convolutional neural networks (CNN), and artificial neural networks (ANN). The performance of the model is then assessed via the evaluation matrices, including the accuracy, precision, recall, and F1-score from the customer's behavioral data after feature extraction from large datasets. The study reveals that DL models offer improved handling of the churn and non-churn customer classification and Random Forest as well as other ML models comparable accuracy. This research can conclude that LSTM and ANN models outshine in actual-world churn prediction circumstances, especially when long-term consumer behavior evaluation is required. To enhance the current outcomes of a given prediction model, this research focuses on data preprocessing and the utilization of bootstrapping, feature extraction, and the combination of multiple models. The implications of the study provide specific practical recommendations for firms to effectively manage customer churn and increase customer retention by employing data-dealing techniques.

Systematic Review of Machine Learning in Diagnosis of Myeloproliferative Neoplasia

Noninvasive assessment of skin structure using hyperspectral images has been intensively studied in recent years. Due to the high computational cost of the classical methods, such as the inverse Monte Carlo (IMC), much research has been done with the aim of using machine learning (ML) methods to reduce the time required for estimating parameters. This study aims to evaluate the accuracy and the estimation speed of the ML methods for this purpose and compare them to the traditionally used inverse adding-doubling (IAD) algorithm. We trained three models – an artificial neural network (ANN), a 1D convolutional neural network (CNN), and a random forests (RF) model – to predict seven skin parameters. The models were trained on simulated data computed using the adding-doubling algorithm. To improve predictive performance, we introduced a stacked dynamic weighting (SDW) model combining the predictions of all three individually trained models. SDW model was trained by using only a handful of real-world spectra on top of the ANN, CNN and RF models that were trained using simulated data. Models were evaluated based on the estimated parameters’ mean absolute error (MAE), considering the surface inclination angle and comparing skin spectra with spectra fitted by the IAD algorithm. On simulated data, the lowest MAE was achieved by the RF model (0.0030), while the SDW model achieved the lowest MAE on in vivo measured spectra (0.0113). The shortest time to estimate parameters for a single spectrum was 93.05 μs. Results suggest that ML algorithms can produce accurate estimates of human skin optical parameters in near real-time.

A urinary tract infection, or UTI, is caused when bacteria get into the urinary tract- kidneys, bladder, or urethra. UTIs cause more than 8.1 million visits to healthcare providers each year. About 60% of women and 12% of men get infected with UTI during their lifetime, therefore being more prominent in females. UTIs can be found by analyzing a urine sample. The urine is examined under a microscope for bacteria or white blood cells that show infection. Healthcare providers may also take a urine culture. This test examines urine to detect and identify bacteria and yeast, which may be causing a UTI. Several models have been proposed to predict urine culture positivity based on urinalysis. Applications of machine learning (ML) methods have been used extensively to solve various complex challenges in recent years in various application areas. ML methods are characterized by their ability to examine data and discover exciting relationships, provide interpretation, and identify patterns. ML can help enhance the reliability, performance, predictability, and accuracy of diagnostic systems for many diseases. This survey provides a comprehensive review of the use of ML in the diagnosis of Urinary Tract infection in human beings. To provide a reliable classification of results assistance of 27 algorithms was tested. Algorithms applied included Logistic regression, Decision tree, Random Forest, and Support Vector Machine. Each model was evaluated by F1-score, AUC-ROC, accuracy, sensitivity, and specificity. Baseline epidemiological factors, previous antimicrobial consumption, medical history, and previous culture results were included as features. Machine learning models such as the Artificial neural network have been used as well for the prediction of the presence of urinary infection. Some specific parameters have been selected with the help of the Analysis of variance technique which gave high accuracy. This survey provides a comprehensive review of the use of ML in the medical field highlighting standard technologies and how they affect medical diagnosis. It provides valuable references and guidance for researchers, practitioners, and decision-makers framing future research and development directions. It is found that Machine Learning models can improve the early prediction of urine culture positivity and UTI by combining automated urinalysis with other clinical information. Clinical utilization of the models can reduce the risk of delayed antimicrobial therapy in patients with nonspecific symptoms of UTI and classify patients with UTI who require further treatment and close monitoring. In conclusion, the paper provides a survey on the machine learning models used with the highest accuracy to detect UTI potential patients.

Though large amounts of work with artificial intelligence are used in typhoon rainfall forecasting, the predictive skills of existing models are unsatisfactory. To address this problem, this study aims to propose an improved cluster‐wise typhoon rainfall forecasting model that integrates the grid‐search cross‐validation method with machine learning and deep learning (DL) models including support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), convolutional neural network (CNN), and long short‐term memory (LSTM). Grid‐search cross‐validation is a modified parameterization technique that helps to find the best parameters for machine/DL models. In the first stage, a second‐order polynomial regression model was used to cluster the typhoon track; and in the second stage, cluster‐wise typhoon rainfall was recognized within a 500 km radius from each typhoon center. After that, a modified cluster‐wise typhoon rainfall forecasting model was proposed using cluster‐wise antecedent hourly typhoon rainfall within this distance for 1–6 hr lead time. Results show that the proposed model based on the SVM, RF, AdaBoost, CNN, and LSTM is capable of providing more accurate forecasts (the efficiency of the forecast is increased by 45%–90%) than the existing typhoon rainfall forecasting models that are based on SVM with a genetic algorithm, RF, artificial neural network, multilayer perceptron network, and deep neural network. Therefore, the current study recommends using cluster‐wise typhoon rainfall forecasting model with a grid‐search cross‐validation method for disaster prevention and mitigation.

Early Autism Spectrum Disorder (ASD) detection is important for early intervention.This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children.Using a publicly available dataset, five ML models were evaluated: Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Random Forest improved with Convolutional Filters (ConvRF).The models were trained and tested using a set of evaluation metrics, including accuracy, precision, recall, F1-score, and ROC Area Under the Curve (AUC).Among these, the ConvRF model attained superior performance, achieving a recall of 90% and an AUC of 88%, indicating its robustness in identifying ASD children.These results highlight the model's effectiveness in ensuring high sensitivity, which is critical for early ASD detection.This study shows the promise of combining ML and eye-tracking technology as accessible non-invasive tools for enhancing early ASD detection, resulting in timely and personalized interventions.Limitations and recommendations for future research are also included.

Commentary: Machine learning and cardiac surgery risk prediction

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.

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel CNN architecture is evaluated by conducted comprehensive experiments on two benchmark intrusion detection datasets. The designed CNN performance was compared with four other ML models such as logistic regression, k-nearest neighbors, decision trees, and random forest. The experimental results revealed that the proposed CNN architecture outperformed the traditional ML models with highest accuracy of 97.68% and 97.39% and highest F1-score of 97.68% and 97.40% for. UNSW-NB15 and CICIDS2017 datasets, respectively. The proposed CNN architecture serves as a promising alternative to traditional ML models, offering improved performance in identifying and mitigating network attacks.