Artificial Intelligence Approach in Aerospace for Error Mitigation

The technology known as cemented paste backfill (CPB) has gained considerable popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of CPB’s flowability or transportability. The minimal shear stress necessary to start the flow is known as Yield stress (YS), and it serves as an excellent measure of the strength of the particle-particle interaction. The traditional evaluation and measurement of YS performed by experimental tests are time-consuming and costly, which induces delays in construction projects. Moreover, the YS of CPB depends on numerous factors such as cement/tailing ratio, solid content and oxide content of tailing. Therefore, in order to simplify YS estimation and evaluation, the Artificial Intelligence (AI) approaches including eight Machine Learning techniques such as the Extreme Gradient Boosting algorithm, Gradient Boosting algorithm, Random Forest algorithm, Decision Trees, K-Nearest Neighbor, Support Vector Machine, Multivariate Adaptive Regression Splines and Gaussian Process are used to build the soft-computing model in predicting the YS of CPB. The performance of these models is evaluated by three metrics coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The 3 best models were found to predict the Yield Stress of CPB (Gradient Boosting (GB), Extreme Gradient Boosting (XGB) and Random Forest (RF), respectively) with the 3 metrics of the three models, respectively, GB {R2 = 0.9811, RMSE = 0.1327 MPa, MAE = 0.0896 MPa}, XGB {R2 = 0.9034, RMSE = 0.3004 MPa, MAE = 0.1696 MPa} and RF {R2 = 0.8534, RMSE = 0.3700 MPa, MAE = 0.1786 MPa}, for the testing dataset. Based on the best performance model including GB, XG and RF, the other AI techniques such as SHapley Additive exPlanations (SHAP), Permutation Importance, and Individual Conditional Expectation (ICE) are also used for evaluating the factor effect on the YS of CPB. The results of this investigation can help the engineers to accelerate the mixed design of CPB.

Earth system models (ESMs) are our key tools for analyzing the planet's existing state and predicting its evolution in the next continuing human-caused events. However, the use of artificial intelligence (AI) approaches to augment or even replace conventional ESM functions has expanded in recent years, raising hopes that AI will be able to overcome some of the major difficulties in climate research. We address the advantages and disadvantages of neural ESM neurons, as well as the unsolved question of whether AI will eventually replace ESMs. Dynamic geophysical events are the foundation of Earth and environmental studies. Given the widespread acceptance of AI and the growing amount of Earth data, the geoscientific community may wish to seriously explore using artificial intelligence (AI) approaches at a much deeper level. Although it is a tall ambition to integrate hybrid physics and AI approaches from a fresh perspective, geology has yet to figure out how to make such methods feasible. This research is an important step towards realising the concept of combining physics and artificial intelligence to address problems with the Earth's system.

Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study

Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis of different AI approaches used to analyze gait of stroke patients using a retrospective dataset of 120 individuals. The main objective is to evaluate the effectiveness, accuracy, and clinical relevance of machine learning (ML) and deep learning (DL) models in identifying gait abnormalities and predicting rehabilitation outcomes. Multiple AI techniques—including support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), and convolutional neural networks (CNN)—were trained and tested on time-series gait data with spatiotemporal parameters. Performance metrics such as accuracy, precision, recall, and area under the curve (AUC) were used to compare model results. Initial results indicate that DL models, particularly CNNs, outperform traditional ML methods in capturing complex gait patterns and providing reliable classification. However, simpler models showed advantages in interpretability and computational efficiency. This study highlights the potential and shortcomings of AI-based gait analysis tools in supporting clinical decision-making and planning personalized stroke rehabilitation.

LoRaWAN technology’s reliability is challenged by weather parameters, which can influence the communication channel design, especially when dealing with outdoor devices. We propose to analyze this effect by evaluating the relationship between the received signal strength indicator (RSSI) and different weather parameters, as well as its temporal changes. A rigorous statistical analysis of the RSSI sequences is conducted to assess if they could be represented by a specific statistical model. For this purpose, several models are investigated. The Artificial Intelligence (AI) algorithms cover machine learning (ML) and deep learning methods are appealing when dealing with time series forecasting. Nevertheless, the classical autoregressive integrated moving average (ARIMA) model can be an attractive alternative due to its simplicity. Therefore, this work proposes a comparative study of ARIMA, AI, and hybrid approaches to forecast the RSSI using weather parameters as regressors. The considered AI algorithms are the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM). Also, hybrid models are constructed, coupling the ARIMA with them. The models are evaluated in time series of RSSI, measured by eight different LoRaWAN transmitter nodes and considering the temperature, pressure, relative humidity, and rain as weather parameters. Our analysis reveals that temperature is the dominant factor among weather parameters, and negatively affects RSSI. The ARIMA model that uses only the temperature as a regressor provides consistently better fits than the ARIMA without regressors. Moreover, coupling the ARIMA with the temperature as a regressor and the ANN (ARIMA-ANN) is the best option among the pure AI and hybrid approaches. However, it provided accuracy measures very close to those obtained from the ARIMA model fitted in the first stage, with similar performance. Therefore, the ARIMA model considering the temperature is the most competitive alternative when analyzing RSSI measurements, with the advantage of being the most straightforward method. These results suggest that the RSSI from the analyzed LoRaWAN receiver nodes may not present nonlinear patterns and, considering several weather parameters, they are affected mainly by the outdoor temperature.

For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.

This study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is conducted using the AI approach. A dataset with 1,030 HPC experimental tests is used to train and validate the prediction model. Depending on the results of the experiments, the forecast outcomes of the IO-SVR model are of a much higher quality compared to the outcomes of other AI approaches. Additionally, because of the high-quality learning capabilities, the IO-SVR is highly recommended for calculating HPC strength.

The new trend toward modern earthquake-resistant design of buildings based on nonlinear dynamic analysis accounting for peak and energy demands requires efficient ground motion record selection procedures. For this reason, in the present work, two record selection strategies based on acceleration and input energy spectrum considering the spectral shape via Np are presented. Furthermore, both record selection strategies are optimized by means of four artificial intelligence (AI) techniques: Genetic Algorithms (GA), Harmony Search (HS), Particle Swarm Optimization (PSO) and Vibrating Particle System (VPS). In particular, the effectiveness of each AI approach toward the best set of ground motion records for nonlinear dynamic analysis is compared. For this aim, spectral acceleration and input energy design spectra were considered, as well as 1024 seismic records obtained from the Pacific Earthquake Engineering Research Center. For all the AI or meta-heuristic approaches, the fitness function used is focused on minimizing the difference between the average spectrum of eleven ground motion records and the design spectrum using the well-known parameter Np, which represents the spectral shape in a range of periods. In addition, a penalization is included for those spectra with very large or low demands. Thus, 24 sets of eleven seismic records that can be used for nonlinear dynamic analysis of structures with a fundamental period of vibration of 0.6, 1.2 and 1.8 seconds were obtained and purposes. The results demonstrate the ability of the two records selection strategies analyzed and the four meta-heuristic procedures, achieving results quickly and simply regardless of the type of demands, intensities and periods considered. Finally, it is concluded that the VPS algorithm is better in comparison with GA, HS, and PSO since it obtains superior results in almost all the selected cases.

Urban land use change is a complex and dynamic process. It is therefore important to understand the complexity involved with system dynamics and choose appropriate modelling approaches. For this purpose, this paper firstly reviews how artificial intelligence (AI) approaches provide solutions to aid urban land dynamics modelling. The three dimensions that are considered pivotal for the understanding of urban dynamic processes – urban land dynamics, planning support and AI infrastructure – are defined. Once these three dimensions are clarified, it is possible to propose the different solution spaces provided by AI approaches using a graphic representation of a cube and its associated mathematical formulation. It is therefore possible to understand and define the best data model to represent the complexity of different phenomena in urban systems.

The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers. Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models. The systematic literature review yielded nearly 30 articles meeting the predefined criteria. Analyses of these studies showed that AI methodologies consistently outperformed traditional statistical approaches. In the AI approaches, DL consistently produced the most precise results, while classical ML demonstrated varied performance but maintained high predictive accuracy. The sample size was the recurring factor that increased the accuracy of the predictions for models of the same type. AI and statistical approaches can detect PM developing among patients with gastrointestinal cancers. Therefore, if clinicians integrated these approaches into diagnostics and prognostics, they could better analyze and manage PM, enhancing clinical decision-making and patients' outcomes. Collaboration across multiple institutions would also help in standardizing methods for data collection and allowing consistent results.

Background: Despite major advances in diagnoses and treatments, cardiovascular disease (CVD) continues to be the leading cause of morbidity and mortality worldwide. To improve and optimize CVD results, AI techniques have the potential to radically revolutionize the way we practice cardiology, especially in imaging and provide with new tools to interpret data and make clinical decisions. Aim: Establishing strategies are necessary to improve the diagnosis and treatment of CVD in the future. Nowadays, artificial intelligence (AI) may have the potential to solve this problem. The application of AI in heart diseases aims to facilitate the detection of radiology patients. Methods: The machine learning algorithms used in this study are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Naive Bayes, and Decision Tree. In our study, a total of 600 patients, 300 female and 300 male, who were diagnosed with IHD as a result of the findings obtained from the reports of the patients who underwent CAG in the Fırat University Hospital were included in our study. Accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained by the classification. Results: Among the algorithms we have used, KNN had the highest success rate. It was followed by SVM in the second success rate. The success rate of RCA was 83% in KNN, and it was 75% in SVM. While the success rate of LCx in KNN was 76%, it was 68% in SVM. Similarly, the success rate of LAD in KNN was 73%, and it was 71% in SVM. Conclusion: The demand for CAG will be rising in the coming years, owing to an increase in HR. Therefore, new strategies will be sought to reduce the duration of CAG. We consider the application of AI in routine clinical practice. Keywords: Right coronary artery, Left coronary artery, Stenosis, Artificial intelligence

In recent years, the term Artificial Intelligence (AI) has gained recognition across various fields, such as healthcare, finance, agriculture, and many other sectors due to its versatility and capacity to improve outcome. Within the applications of AI, classification stands out as one of the most prevalent, aiding in optimizing decision-making and efficiently organizing data. In recent years, there has been an increase in the use of zebrafish (Danio rerio) in studies related to human dermatological diseases, such as vitiligo, which involves the autoimmune-mediated destruction of the melanocytes in the epidermal layer. Despite the current interest in studies related to this disease, no papers were found applying Machine Learning (ML) or Deep Learning (DL) models to classify the effects of the disease and its treatments. In this context, this paper uses the challenge of evaluating the effects of vitiligo in zebrafish to compare the performance of different AI approaches. The methodology employed in this paper includes image acquisition, dataset creation, preprocessing, model testing, and evaluation. The ML models applied in this study were Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), while the DL models included Visual Geometry Group 16 (VGG16) and GoogLeNet. Following the evaluation, SVM and GoogLeNet achieved the best results, correctly classifying 80% and 71% of the data, respectively. Moreover, the former accurately identified all samples in the healthy and treated classes, with misclassifications occurring only within the sick class. The models performed satisfactorily in relation to the objectives of this study and the results exhibited potential for future applications in treating vitiligo in humans.

Researchers are interested in genomic level cancer classification because it offers a thorough, precise, and reliable diagnosis for various cancer types. The gene expression data set is made up of hundreds of samples, each containing thousands of genes. As a result, accurately and effectively classifying such samples is difficult. To analyze gene expression patterns efficiently, recent studies have used artificial intelligence, data mining, and statistical techniques. Furthermore, machine learning methods have been extensively used to construct robust and accurate classification frameworks. In this paper, we examined the effectiveness of machine learning-based classification techniques such as Support Vector Machine, Logistic Regression, Naïve Bayes, Decision Tree, K-Nearest Neighbors, and Random Forest methods. In a total of 72 patients, 7129 distinct gene expressions and their degrees were evaluated to determine their relevance. Features have been normalized and PCA was used to decrease the dataset's dimensionality and complexity. The dataset was divided into training and test sets, with 90% of the occurrences in the training set and 10% in the test set. And finally, SMOTE was employed to oversample the minority class. In this study, gene expression data was utilized to differentiate Acute Myeloid Leukemia (AML) from Acute Lymphoblastic Leukemia (ALL). To evaluate the performance of the six machine learning models we used, we examined their associated confusion matrices and calculated nine performance evaluation metrics for each model's prediction on the test dataset. Results demonstrate that SVM, Logistic Regression, and Naïve Bayes outperformed the other ML techniques with an accuracy of 100%.

Predicting the fracture behavior of concrete using artificial intelligence approaches and closed-form solution

Assessing the robustness of clinical trials by estimating Jadad's score using artificial intelligence approaches