Artificial Neural Network Model Research Articles

Artificial intelligence prediction of the mechanical properties of banana peel-ash and bagasse blended geopolymer concrete

This research explores the application of Artificial Intelligence (AI) techniques to assess the mechanical properties of geopolymer concrete made from a blend of Banana Peel-Ash (BPA) and Sugarcane Bagasse Ash (SCBA), using a sodium silicate (Na2SiO3) to sodium hydroxide (NaOH) ratio ranging from 1.5 to 3. Utilizing three AI methodologies—Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP)—the study aims to enhance prediction accuracy for the mechanical properties of geopolymer concrete based on 104 datasets. By optimizing mix designs through varying proportions of BPA and SCBA, alkaline activator molarity, and aggregate-to-binder ratios, the research identified combinations that significantly enhance mechanical properties, demonstrating notable international relevance as it contributes to global efforts in sustainable construction by effectively utilizing industrial by-products. The experimental results demonstrated that increasing the molarity of the alkaline activator from 4 to 10 M significantly enhanced both the compressive and flexural strengths of the geopolymer concrete. Specifically, a mixture containing 52.5% SCBA and 47.5% BPA at a 10 M molarity achieved a maximum compressive strength of 33.17 MPa after 20 h of curing. In contrast, a mixture composed of 95% SCBA and 5% BPA at a 4 M molarity exhibited a substantially lower compressive strength of only 21.27 MPa. Additionally, the highest recorded flexural strength of 9.95 MPa (77.25% SCBA and 22.5 BPA) was observed at the 10 M molarity, while the flexural strength at 4 M was lowest, at 4.12 MPa (95% SCBA and 5% BPA). Microstructural analysis through Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (ED-SEM) revealed insights into the pore structure and elemental composition of the concrete, while Thermogravimetric Analysis (TGA) provided data on the material’s thermal stability and decomposition characteristics. Performance analysis of the AI models showed that the ANN model had an average MSE of 1.338, RMSE of 1.157, MAE of 3.104, and R2 of 0.989, while the ANFIS model outperformed with an MSE of 0.345, RMSE of 0.587, MAE of 1.409, and R2 of 0.998. The GEP model demonstrated an MSE of 1.233, RMSE of 1.110, MAE of 1.828, and R2 of 0.992, confirming that ANFIS is the most accurate model for predicting the mechanical and rheological properties of geopolymer concrete. This study highlights the potential of integrating AI with experimental data to optimize the formulation and performance of geopolymer concrete, advancing sustainable construction practices by effectively utilizing industrial by-products.

Effect of Architecture and Inference Parameters of Artificial Neural Network Models in the Detection Task on Energy Demand

Artificial neural network models for the task of detection are used in many fields and find various applications. Models of this kind require adequate computational resources and thus require adequate energy expenditure. The increase in the number of parameters, the complexity of architectures, and the need to process large data sets significantly increase energy consumption, which is becoming a key sustainability challenge. Optimization of computing and the development of energy-efficient hardware technologies are essential to reduce the energy footprint of these models. This article examines the effect of the type of model, as well as its parameters, on energy consumption during inference. For this purpose, sensors built into the graphics card were used, and software was developed to measure the energy demand of the graphics card for different architectures of YOLO models (v8, v9, v10), as well as for different batch and model sizes. This study showed that the increase in energy demand is not linearly dependent on batch size. After a certain level of batch size, the energy demand begins to decrease. This dependence does not occur only for n/t size models. Optimum utilization of computing power due to the number of processed images for the studied models occurs at the maximum studied batch size. In addition, tests were conducted on an embedded device.

Maximizing waste cooking oil biodiesel production employing novel Brotia costula derived catalyst through statistical and machine learning optimization techniques

The limitations of homogeneous catalysts associated with biodiesel production such as non-reusability, emulsification, and saponification have led to the increased cost of biodiesel synthesis. To overcome these limitations, the current work aims to investigate the applicability and effectiveness of green catalyst made from Brotia costula shells (BCS) for waste cooking oil biodiesel production. The catalyst was synthesized by calcinating BCS at 800℃−1000℃ for 4 h. Catalyst characterization showed that BC900 (BCS calcined at 900℃) contained 98.15 CaO wt% and exhibited the smallest grain size of 39.23 nm with the highest specific surface area of 14.28 m2/g and highest catalytic activity. Optimization of biodiesel synthesis was carried out using response surface methodology - Box-Behnken design (RSM-BBD), artificial neural network (ANN) coupled with genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) coupled with GA. Maximum experimental biodiesel yield of 97.78±0.59 % at optimum conditions of methanol: WCO molar ratio 9.73: 1, reaction time 2.12 h, catalyst dose wt% 7.59 and reaction temperature 69.3°C was obtained with ANFIS-GA optimization model. The prediction accuracy of the ANFIS model (R2 = 0.988) was better than those of the RSM-BBD (R2 = 0.982) and ANN (R2 = 0.955) models. The synthesized catalyst exhibited high catalytic activity up to four cycles of reusability (yield > 88 %). It is environment-friendly, inexpensive, renewable, and performs on par with catalysts synthesized from other mollusk shells. Using this catalyst for biodiesel production will contribute to the biorefinery's ability to explore sustainable raw materials and may lower overall biodiesel cost.

A dimensionless group-incorporating artificial neural network (DI-ANN) model for single-track depth prediction of SS316L for laser-directed energy deposition (L-DED)

A dimensionless group-incorporating artificial neural network (DI-ANN) model for single-track depth prediction of SS316L for laser-directed energy deposition (L-DED)

Implementation of Artificial Neural Networks as a Method for Early Detection of Tax Evasion Behavior in Indonesia

Research Aims: This study aims to analyze the ability of the Artificial Neural Network (ANN) model in early detection of tax evasion behavior in Indonesia. Design/methodology/approach: This study uses a qualitative method with a descriptive approach, the types of data used in this study are primary data and secondary data. Primary data is obtained from the results of interviews to obtain the information needed by the researcher. The interview informants came from the DGT sub-Directorate of Potential, Compliance and Tax Revenue with the initials K&H, tax consultants and taxpayers. while secondary data is collected through the DGT online website and through articles and journals on Artificial Intelligence (AI), Artificial Neural Networks (ANN), tax evasion, and financial information data on income, expenses, amount of assets, taxes payable) and behavioral indicators (tax compliance history, frequency of late reporting, significant changes in financial statements). The data analysis technique used is descriptive analysis, namely data collection, data reduction, data presentation and conclusion drawing Research Findings: The results of this study show that the use of a neural network model can be an effective tool to detect symptoms of tax evasion behavior early. This method allows tax authorities to monitor and identify suspicious activity more efficiently and effectively. Theoretical Contribution/Originality: This study makes a significant theoretical contribution by integrating the use of Artificial Neural Networks (ANN) in detecting tax evasion in Indonesia. The originality of this research lies in the application of ANN in the context of Indonesia's tax system, where the use of advanced technology such as ANN is still relatively new. The study also combines the pentagon's fraud theory, which adds elements of arrogance and competence in the analysis of tax evasion behavior, with a machine learning approach, creating a more holistic and innovative framework

Artificial Neural Network-based Model for Predicting Cardiologists’ Over-apron Dose in CATHLABs

Abstract Aim: The radiation dose that cardiologists receive in the catheterization laboratory is influenced by various factors. Handling high-stress tasks in interventional cardiology departments may cause physicians to overlook the use of dosimeters. Therefore, it is essential to develop a model for predicting cardiologists’ radiation exposure. Materials and Methods: This study developed an artificial neural network (ANN) model to predict the over-apron radiation dose received by cardiologists during catheterization procedures, using dose area product (DAP) values. Leveraging a validated Monte Carlo simulation program, we generated data from simulations with varying spectra (70, 81, and 90 kVp) and tube orientations, resulting in 125 unique scenarios. We then used these data to train a multilayer perceptron neural network with four input features: DAP, energy spectrum, tube angulation, and the resulting cardiologist’s dose. Results: The model demonstrated high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 μSv, outperforming a traditional linear regression model, which had an R-value of 0.48 and an RMSE of 18.15 μSv. This significant improvement highlights the effectiveness of advanced techniques such as ANNs in accurately predicting occupational radiation doses. Conclusion: This study underscores the potential of ANN models for accurate radiation dose prediction, enhancing safety protocols, and providing a reliable tool for real-time exposure assessment in clinical settings. Future research should focus on broader validation and integration into real-time monitoring systems.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

Financial Distress Prediction Using an Artificial Neural Network Integrating a Two-Stage Feature Selection Method

Financial distress prediction (FDP) is critical for companies, banks, and investors, and artificial neural networks (ANN) have been proven to be an efficient method for FDP. However, the “curse of dimensionality” in FDP not only increases the computational complexity, but also reduces the prediction accuracy. To solve this problem, this paper takes an ANN model as the basic classifier and presents a new two-stage feature selection method integrated with multiple filters and a wrapper method. The financial data of Chinese listed companies are applied for comparative analysis to verify the effectiveness of the constructed method. The results demonstrate that the proposed method achieves a smaller feature subset and better predictive effect than other methods, thus solving the “curse of dimensionality” more effectively and improving the accuracy. In addition, SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) are employed to investigate the relative importance of selected features. Their results increase the credibility of the proposed model, giving users more confidence in using this “black box” model.

Prediction of Rubber Fatigue Life Using an Assimilation‐based Learning Approach and Incremental Crack Propagation Model

ABSTRACTAccurately and efficiently predicting the fatigue life of rubber materials has been a long‐standing challenge due to limited understanding of the fatigue mechanism. In this study, a variational assimilation‐based machine learning method assisted with incremental crack propagation model is proposed to predict the fatigue life of rubber materials. Firstly, according to the fracture mechanics theory, a new rubber fatigue life prediction model based on incremental crack propagation and sparse experimental data is established, which owns higher accuracy than the classical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method is introduced to generate a dense fatigue life dataset of rubber materials with high accuracy. Finally, the artificial neural network model is trained, cross‐validated, and tested using the dense dataset, and the three‐dimensional variational assimilation model is employed to merge the predicted values of artificial neural network with experimental data. By comparing against the experimental data, the effectiveness of the proposed method was verified; thereby, we offer an accurate and efficient approach to predict the rubber fatigue life.

Comparative Analysis of Fall Detection Using Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) Algorithms

Fall detection is a critical area of research due to its implications for public health, particularly among older adults more vulnerable to fall-related injuries. This study explores the application of pervasive computing and machine learning algorithms in detecting falls using accelerometer and gyroscope sensors. Leveraging a dataset obtained from smartphone sensors, the study employs data processing techniques and classification algorithms, including K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN), to differentiate fall events from other activities. Evaluation metrics such as accuracy, precision, recall, and F1-score are utilized to assess the performance of the models. The results demonstrate the effectiveness of both the ANN and KNN models in accurately predicting fall detection with high precision and recall scores. The study highlights the potential of pervasive computing systems and machine learning methods to enhance fall detection capabilities, contributing to the development of proactive measures for ensuring the safety and well-being of individuals at risk of falls. Future research directions include exploring alternative algorithms and sensor modalities to improve fall detection systems further.

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Terrain parameter identification for wheeled mobile robots using deep neural networks

This paper introduces an innovative approach to soil parameter estimation using deep artificial neural networks (ANN) tailored to estimate a wide range of soil types. Previous research in this field has relied on limited datasets and oversimplified assumptions in semi-empirical models. In this work, the sensitivity of stress distribution is analyzed with respect to soil parameters, revealing significant errors resulting from the simplifications in semi-empirical models and emphasizing the need for more accurate estimation methods with minimal reliance on simplifying assumptions. Training an ANN requires a comprehensive dataset, which has been a challenge due to limited access to diverse soil samples. Addressing this issue, the paper conducts simulations of a single wheel’s movement across a wide range of soil parameter combinations to generate the required dataset. These simulations are done using a dynamic motion-compliant semi-empirical model detailed in the article. Subsequently, the network’s hyperparameters are fine-tuned through a grid search, followed by extensive training of the optimized ANN model. In the end, the ANN demonstrates promising performance in accurately estimating soil parameters, validated through simulation results of both single wheel and Mars rover motion.

Biological activities of Hypericum spectabile extract optimized using artificial neural network combined with genetic algorithm application

Optimizing extraction conditions can help maximize the efficiency and yield of the extraction process while minimizing negative impacts on the environment and human health. For the purpose of the current study, an artificial neural network (ANN) combined with a genetic algorithm (GA) was utilized for that the extraction conditions of Hypericum spectabile were optimized. In this particular investigation, the main objective was to get the highest possible levels of total antioxidant status (TAS) for the extracts that were obtained. In addition to this, conditions of the extract that exhibited the maximum activity have been determined and the biological activity of the extract that was obtained under these conditions was analyzed. TAS values were obtained from extracts obtained using extraction temperatures of 30–60 °C, extraction times of 4–10 h, and extract concentrations of 0.25-2 mg/mL. The best model selected from the established ANN models had a mean absolute percentage error (MAPE) value of 0.643%, a mean squared error (MSE) value of 0.004, and a correlation coefficient (R) value of 0.996, respectively. The genetic algorithm proposed optimal extraction conditions of an extraction temperature of 59.391 °C, an extraction time of 8.841 h, and an extraction concentration of 1.951 mg/mL. It was concluded that the integration of ANN-GA can successfully be used to optimize extraction parameters of Hypericum spectabile. The total antioxidant value of the extract obtained under optimum conditions was determined as 9.306 ± 0.080 mmol/L, total oxidant value as 13.065 ± 0.112 µmol/L, oxidative stress index as 0.140 ± 0.001. Total phenolic content (TPC) was 109.34 ± 1.29 mg/g, total flavonoid content (TFC) was measured as 148.34 ± 1.48 mg/g. Anti-AChE value was determined as 30.68 ± 0.77 µg/mL, anti-BChE value was determined as 41.30 ± 0.48 µg/mL. It was also observed that the extract exhibited strong antiproliferative activities depending on the increase in concentration. As a result of LC-MS/MS analysis of the extract produced under optimum conditions in terms of phenolic content. The presence of fumaric, gallic, protocatechuic, 4-hydroxybenzoic, caffeic, 2-hydoxycinamic acids, quercetin and kaempferol was detected. As a result, it was determined that the H. spectabile extract produced under optimum conditions had significant effects in terms of biological activity.

Effect of temperature and relative humidity on hydrolytic degradation of additively manufactured PLA: Characterization and artificial neural network modeling

Understanding the long-term durability of 3D-printed polymeric materials under varying temperature and humidity conditions is essential for expanding their industrial applications. Therefore, it is critical to assess the impact of degradation on mechanical properties such as tensile strength. In this study, we manufactured specimens with dual orientations by additive manufacturing-based 3D printing and subjected them to accelerated degradation under various temperature and humidity conditions to evaluate their durability in degradation environments. Mechanical properties significantly decreased under the most severe conditions, with a maximum reduction of 76.7 % observed in molecular weight. The deconvolution of the molecular weight distribution and its correlation with mechanical properties were thoroughly investigated. We derived an equation representing the relationship between the peaks obtained from deconvoluting the molecular weight distribution and the tensile strength. Furthermore, to expedite and simplify tensile strength assessment, we trained an artificial neural network (ANN) model using tensile test results to construct a predictive model. The ANN utilized temperature, humidity, printing angle, and time as input data, with tensile strength as the output. Validation of this model demonstrated the capability to predict tensile strength accurately under different temperature and humidity conditions.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

Biosorption of cobalt and chromium from wastewater using manganese dioxide and iron oxide nanoparticles loaded on cellulose-based biochar: Modeling and optimization with machine learning (artificial neural network)

In this study, two nanomaterials with excellent adsorption capacities were developed to remove heavy metals efficiently from wastewater. Manganese dioxide MnO2 nanoparticles and iron oxide Fe2O3 nanoparticles were successfully synthesized using cassava peel carbon and characterized by different techniques. The experimental tests for the adsorption process were done in a batch system, and the influence of various parameters such as temperature (from 5 to 60 °C), initial concentration (from 10 to 60 mg/L), pH (2 to 8), and contact time (5 to 180 min) on the biosorption of cobalt (II) and chromium (VI) were fully investigated. Furthermore, the Qmax were 546.32 mg/g and 349.59 mg/g for chromium (VI) and cobalt (II) respectively. The results fitted Langmuir with the pseudo-second-order model, revealing that chemisorption controls heavy metals removal, while the thermodynamic sorption was an endothermic and spontaneous reaction. Artificial Neural Network (ANN) model was developed to predict as well as to simulate the experimental results, for this purpose, the proposed model was based on five independent inputs or variables and one output or response which is the predicted adsorbed amount, the proposed ANN model showed an appreciable prediction accuracy with high optimization ability for chromium (VI) and cobalt (II) removal.

Unlocking the future: machine learning models for predicting in-hospital mortality after transcatheter aortic valve replacement: a single-arm meta-analysis

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

‘TLDetect’: AI-Based Application for Detection and Correction of Anomalous TLD Glow Curves

This research reviews a novel artificial intelligence (AI)-based application called TLDetect, which filters and classifies anomalous glow curves (GCs) of thermoluminescent dosimeters (TLDs). Until recently, GC review and correction in the lab were performed using an old in-house software, which uses the Microsoft Access database and allows the laboratory technician to manually review and correct almost all GCs without any filtering. The newly developed application TLDetect uses a modern SQL database and filters out only the necessary GCs for technician review. TLDetect first uses an artificial neural network (ANN) model to filter out all regular GCs. Afterwards, it automatically classifies the rest of the GCs into five different anomaly classes. These five classes are defined by the typical patterns of GCs, i.e., high noise at either low or high temperature channels, untypical GC width (either wide or narrow), shifted GCs whether to the low or to the high temperatures, spikes, and a last class that contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of the technician’s time spent reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, more accurate, and faster. Moreover, a database of the class anomalies distribution over time of GCs is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.

Advanced Prediction Models for Scouring Around Bridge Abutments: A Comparative Study of Empirical and AI Techniques

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Deep learning modeling to assess surface temperature and greenery impact on emergency room access

Abstract The escalating land surface temperature is a key environmental indicator with potential repercussions on human health. An artificial NN model was built to predict the number of ER accesses as a dependent variable, to establish the predictive power and interactions of land surface temperature and normalized differential vegetation index(NDVI), which is used to measure urban greenery.A map of Taranto was built by vector files on the Apulia region website and processed using R Studio 2023.06.2.The vector file containing the mapping of the city of Taranto was processed to obtain a 200mx200m grid of the entire surface.The Local Health Authority of Taranto provided indications of the residences of ER accesses, and coordinates were merged to gridded vector file to obtain the average of ER accesses and the socio demographic features per single grid unit. NDVI and surface temperature were averaged for July 2023 using images from Landsat 8(30m) and merged to the vector providing an average value of temperature and NDVI index per single quadrant. The dataset was divided into two db according to an 80/20 ratio respectively for the training and test dataset.A NN model was built on dichotomous value (above/below median) for each quadrant (6513 units) with hyperparameter tuning technique using the accuracy to select the best model.A MOC model was built to reduce the probability of NN detection by at least 40% in a randomly selected subject.A MOC model was built considering sociodemographic variables as fixed features and an epsilon of 0 for 175 generations and an ICE curves population initialization strategy. NNET accuracy value was 0.849 and 0.841 in training and testing, suggesting a good fitting of the model.Relative change plots were adopted to show the amount and the direction of generated counterfactuals, to reduce grid unit prediction to be over median.LST decreasing and NDVI score increasing were associated with a reduction of classification probability of grid unit. Key messages • Rising land surface temperature is a critical environmental indicator with potential impacts on human health. • Artificial neural network models perform well in predicting the number of ER accesses with respect to land surface temperature and NDVI, which is used to measure urban greenery.