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This scientific paper explores the use of correlation coefficients of vibration signals and machine learning algorithms for structural damage assessment in beams under moving loads. The paper discusses the challenges of maintaining structural integrity and the importance of automated, nondestructive monitoring techniques. Preprocessing techniques, such as the random decrement technique (RDT), are highlighted for improving data analysis. Machine learning algorithms are identified as valuable tools for structural damage assessment. The paper concludes by emphasizing the potential of machine learning in safeguarding critical infrastructures. The text also discusses trigger points and the vibration response of a slender beam under a moving load. An artificial neural network (ANN) is proposed as a computational model for identifying non-linear features. Experimental testing on a simulated bridge girder using accelerometers collected data to identify and locate damage in the beam. The ANN achieved high accuracy in detecting damage appearance and location, but further research is needed to improve accuracy in real-world situations. [Extracted from the article]

In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among the various physical properties of fluid particles, are significant. These weights are used as input values for the learning algorithm, enabling relatively accurate isoline tracking. In addition, compared to existing learning models such as linear regression, LSTM (long short-term memory), and learning representation (1-layer) models, our method obtained superior surface tracking results without accumulating errors. By using our proposed network structure to track the fluid surface, it learns and predicts values derived from existing fluid simulation algorithms, eliminating the need for computational processes for level-set values and enabling real-time surface tracking. As the scale of the simulation increases, our method significantly reduces the time and resources consumed compared to traditional methods and can track the fluid surface without additional resource consumption. Furthermore, due to our method's simple network structure, the time consumed in the initial process of loading the model into memory is faster than models such as CNN and LSTM. Our proposed model occupies less than 30 kb of storage space, making it suitable for use in middleware. Lastly, to verify the generality of our method, we conducted tests in a total of five scenes, and in all test scenes, visually natural fluid isolines were represented. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the level of customer deposits on a sample of Tunisian commercial banks. Design/methodology/approach: Training and testing datasets are developed to evaluate the level of customer deposits of 15 Tunisian commercial banks over the 2002–2021 period. This study uses two predictive modeling techniques: the MLR using a FE model and ANN. In addition, it uses the mean absolute error (MAE), R-squared and mean square error (MSE) as performance metrics. Findings: The results prove that both methods have a high ability in predicting customer deposits of 15 Tunisian banks. However, the ANN method has a slightly higher performance compared to the MLR method by considering the MAE, R-squared and MSE. Practical implications: The findings of this paper will be very significant for banks to use additional management support to forecast the level of their customers' deposits. It will be also beneficial for investors to have knowledge about the capacity of banks to attract deposits. Originality/value: This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables. [ABSTRACT FROM AUTHOR]

Super-computing or HPC clusters are built to provide services to execute computationally complex applications. Generally, these HPC applications involve large scale IO (input/output) processing over the networked parallel file system disks. They are commonly developed on top of the C/C++ based MPI standard library. The HPC clusters MPI–IO performance significantly depends on the particular parameter value configurations, not generally considered when writing the algorithms or programs. Therefore, this leads to poor IO and overall program performance degradation. The IO is mostly left to individual practitioners to be optimised at code level. This usually leads to unexpected consequences due to IO bandwidth degradation which becomes inevitable as the file data scales in size to petabytes. To overcome the poor IO performance, this research paper presents an approach for auto-tuning of the configuration parameters by forecasting the MPI–IO bandwidth via artificial neural networks (ANNs), a machine learning (ML) technique. These parameters are related to MPI–IO library and lustre (parallel) file system. In addition to this, we have identified a number of common configurations out of numerous possibilities, selected in the auto-tuning process of READ/WRITE operations. These configurations caused an overall READ bandwidth improvement of 65.7% with almost 83% test cases improved. In addition, the overall WRITE bandwidth improved by 83% with number of test cases improved by almost 93%. This paper demonstrates that by using auto-tuning parameters via ANNs predictions, this can significantly impact overall IO bandwidth performance. [ABSTRACT FROM AUTHOR]

In the last decades, climate change has led to increasingly frequent drought events within the Mediterranean area, creating an urgent need of a more sustainable management of groundwater resources exploited for drinking and agricultural purposes. One of the most challenging issues is to provide reliable simulations and forecasts of karst spring discharges, whose reduced information, as well as the hydrological processes involving their feeding aquifers, is often a big issue for water service managers and researchers. In order to plan a sustainable water resource exploitation that could face future shortages, the groundwater availability should be assessed by continuously monitoring spring discharge during the hydrological year, using collected data to better understand the past behaviour and, possibly, forecast the future one in case of severe droughts. The aim of this paper is to understand the factors that govern different spring discharge patterns according to rainfall inputs and to present a model, based on artificial neural network (ANN) data training and cross-correlation analyses, to evaluate the discharge of some karst spring in the Umbria region (Central Italy). The model used is a fully connected neural network (FCNN) and has been used both for filling gaps in the spring discharge time series and for simulating the response of six springs to rainfall seasonal patterns from a 20-year continuous daily record, collected and provided by the Regional Environmental Protection Agency (ARPA) of the Umbria region. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

Sea surface gage height (SSGH) data are commonly measured at sparsely deployed tidal stations while coastal resources management requires spatially distributed SSGH data. This paper presents three different machine learning models, including a Random Forest (RF) model, an Extreme Gradient Boosting (XGBoost) model, and an Artificial Neural Network (ANN) model, for retrieving and mapping spatially distributed SSGH. The models were originally developed and independently validated using 10 years of VIIRS satellite remote sensing data and corresponding in-situ gage height (GH) data within the MATLAB ANN toolbox and RStudio. The paper made two major new contributions to the determination of SSGH, including a scientific contribution and a technical contribution. Scientifically, this paper has provided new insights into important factors affecting SSGH. The SSGH varies significantly with the location (GPS coordinates) and Julian Day with both factors affecting the tide and thus SSGH. SSGH is also affected by sea surface waves that affect the reflectance of light from different wavelengths of satellite sensors, which are described with the VIIRS remote sensing reflectance (Rrs) bands including Rrs 410, 443, 486, 551, and 671. Technically, the machine learning models, particularly the ANN model, are capable of providing the most accurate and spatially distributed SSGH data particularly for coastal waters, as characterized by the model performance metrics including the correlation coefficient R value of 0.94 for training, 0.94 for validation, and 0.93 for testing and the low root mean square error (RMSE) value of 0.113 m for training, 0.113 m for validation, and 0.118 m for testing. [ABSTRACT FROM AUTHOR]

The present review paper addresses the increasing interest in the application of machine learning (ML) algorithms in the assessment of the fatigue response of additively manufactured (AM) metal alloys. This review aims to systematically collect, categorize, and analyze relevant research papers in this domain. The most commonly used ML algorithms are presented, discussing their specific relevance to the fatigue modeling of AM metal alloys. A detailed analysis of the most relevant input features used in the literature to predict the main parameters related to the fatigue response is provided. Each work has been analyzed to highlight its strengths and peculiarities, thereby offering insights into novel methodologies and approaches for addressing critical challenges within this field. Particular attention is dedicated to the role of defects and the related size‐effect, as they strongly influence the fatigue response. In conclusion, this review not only synthesizes existing knowledge but also offers forward‐looking recommendations for future research directions, providing a valuable resource for researchers in the domain of ML‐assisted fatigue assessment for AM metal alloys. [ABSTRACT FROM AUTHOR]

Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations. [ABSTRACT FROM AUTHOR]

Along with seaports automation, there is growing interest in the automation of Grab-Type Ship Unloader (GTSU) that unloads coal and iron ore from bulk carriers. Autonomous unloading operations of GTSU offer the potential for significant productivity improvement and cost savings. In this paper, an AI model trained with manual operation data was designed for GTSU automation operation, and the AI model was verified through the equation-of-motion-based GTSU operation simulator. The operation data of hoist, grab, and trolley were predicted by training the designed AI model with the manual operation data of GTSU. Before applying the predicted data to the actual equipment, the predicted driving data was verified using the equation-of-motion-based GTSU operation simulator. The AI prediction model was designed using the Multi-Layer Perception network, a type of artificial neural network. The AI prediction model was evaluated with the Mean-Squared Error indicator, and the validation loss was found to be less than 0.02. In addition, verification of the prediction data was performed using the GTSU dynamics-based simulator. The Mean Relative Error was up to 0.50, and the R2 score value exceeded 0.92, indicating that the model is effective in predicting operation data. The proposed AI prediction model will be effectively utilized to implement a fully automated unloading system. [ABSTRACT FROM AUTHOR]

Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries. [ABSTRACT FROM AUTHOR]

In this paper, we propose an effective approach for identifying damages in the Z24 bridge, a large-scale bridge in Switzerland. The dataset of the Z24 bridge is evaluated as a benchmark, reflecting the behavior of a real structure that has been used for numerous studies. However, most of the previous studies have only addressed the issues of updating the model or estimating the location and severity of damages. The core idea behind our proposed approach is to leverage the strengths of two effective Machine Learning (ML) algorithms: K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), to assess both the location and severity of damages in the Z24 bridge. First, we employ KNN, an unsupervised learning algorithm, for pinpointing the damage location. This strategy proves highly efficient, significantly reducing computation time by circumventing the need for a loss function during KNN training. By adopting this approach, KNN effectively mitigates the risk of encountering local minima in the ANN optimization process. Subsequently, we deploy ANN to determine the damage severity. When compared to previous studies on the Z24 bridge, our proposed method (KNN-ANN) exhibits promising results. Furthermore, our results illustrate that KNN-ANN consistently outperforms traditional ANN methodologies. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are the future of networking. They represent one of the best technologies for automation due to their characteristics, such as cost-effectiveness and wireless nature. However, WSNs require explicit programming to perform certain tasks, lacking adaptability. To address this limitation, machine learning (ML) can be highly beneficial as it grants networks the ability to make decisions automatically. This capability can refine the accuracy and efficiency of WSNs. This paper aims to provide a detailed classification of machine learning algorithms, discussing various problems of traditional wireless sensor networks and the solutions that machine learning can offer. It also includes a brief discussion of problems associated with machine learning in WSNs. This review paper is written with the hope of being useful for researchers in a future era where the amalgamation of WSNs and machine learning algorithms will influence many aspects of human life. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

• A model to explore the multiphysics characteristic of semiconductors is presented. • The model determines the thermal and mechanical properties of complex patterns. • Results show a strong correlation between thermal and mechanical properties. • Dielectric bands asymmetrically affect thermal and mechanical behavior. • Machine learning can predict one property from another. As semiconductor performance improves through advanced package designs, it becomes important to consider both thermal and mechanical properties. Better understanding their relationship enhances system design and optimization. This study has introduced a numerical method that takes into account both thermal and mechanical fluxes to construct thermal and mechanical property maps for practical application in semiconductor engineering. Furthermore, this paper investigated the relationship between the two properties using the constructed property maps in commercially available semiconductor packages. Owing to the complexity of packaging design patterns, a coupled isoparametric mapping and machine learning (ML) method was introduced to investigate their effects. The model then determines and investigates the anisotropic equivalent thermal and mechanical properties of the commercialized semiconductor packages with complex pattern designs, according to the package materials, volume fractions of each material, and design patterns. This approach pursues more sophistication and practicality compared to simplified composite structure property evaluation models. Additionally, all processes in the algorithm are automated based on ML methods, making them practically applicable in the semiconductor industry. The study shows that there is a strong correlation between the thermal and mechanical characteristics in the complex package patterns. This relationship was able to form a fairly clear category based on the shape of the dielectric band because the dielectric band had significantly different effects on the thermal and mechanical fluxes. The study discussed the physical implications of the similarities and differences between the two behaviors and validated the results of the equivalent properties through finite element (FE) analysis. Finally, the study cross-validates the relationship by showing that information from one flux behavior can be used to predict the other based on the ML method. Based on the results, this paper can provide a better understanding of thermal and mechanical fluxes in semiconductor package patterns for package design and optimization. [ABSTRACT FROM AUTHOR]

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor's speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

With the rapidly evolving geo-political landscape and unceasing advancements in technology, sustainable energy security is a very important topic. Research indicates that electrochemical energy systems are quite promising to solve many of energy conversion, storage, and conservation challenges while offering high efficiencies and low pollution. The paper provides an overview of electrochemical energy devices and the various optimization techniques used to evaluate them. The optimization techniques include linear regression, factorial design, the Taguchi method, artificial neural networks, filters, and a combination of such methods to improve these systems. To support the growing interest in research, the bulk of this study focuses on a review of the most promising and highly researched electrochemical energy devices, such as fuel cells, batteries, and supercapacitors. The paper also provides modest commentary on hydrogen production technologies, electrochemical reactors, and membrane separation technologies, amongst other technologies. Building on a previous paper by the authors of this paper on artificial intelligence in hybrid renewable energy systems with fuel cells, this work provides a comparative review of optimization techniques for supercapacitors by highlighting key findings based on model accuracy. A summary of the advantages and disadvantages of the different major optimization techniques is presented. The paper concludes that a combination of optimization techniques is used to overcome the drawbacks of individual techniques, with adaptive filters being the most widely studied. This paper presents studies on the Design of Experiments (DoE) with the goal of building a better understanding of the relationships that exist between different operating variables in various electrochemical devices. • The electrochemical systems can be optimized for better performance with high accuracy. • The optimization techniques used are either model-based or data-driven. • Linear regression and artificial neural networks are the most common techniques studied. • Hybrid models could be applied to enhance systems' accuracy and minimize errors. [ABSTRACT FROM AUTHOR]

Abnormalities in normal blood flow from the heart cause several types of heart disease known as cardiovascular diseases. Over more than 17.5 million Global death occurs due to cardiovascular disease and 75% of death occurs from low- and middle-income countries. Early detection can save a lot of people’s lives. ECG signals reflect the electrical activity of the heart. Thus, heart rhythm disorders or alterations in the ECG waveform are evidence of underlying cardiovascular problems, such as arrhythmias. However, manual analysis of electrocardiogram (ECG) signals is a laborious and prone-to-error task, even for a specialist with many hours of experience. This work focuses on identifying the five diseases of a dataset with the help of an artificial neural network. We have used a sequential model in keras with 5 dense layers. Then the ANN keras model is trained from the selected parameters of dataset and then test dataset is used. The classification report tells us how well the model has identified the diseases. [ABSTRACT FROM AUTHOR]

Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach. [ABSTRACT FROM AUTHOR]

A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions. [ABSTRACT FROM AUTHOR]

Research background: Predicting the flow of inbound tourism accurately has always posed a significant challenge for all parties involved in the industry. The complex nature of the tourism product, which is directly and indirectly affected by various risks, disasters, and crises, further highlights its susceptibility to disruptions and fluctuations. As a result, there has been a growing interest in forecasting inbound tourism flows using contemporary data science methods and artificial neural network (ANN) methods. This paper, therefore, seeks to explore the AI forecasting techniques by employing a deep machine learning (DML) approach and comparing various Python libraries for time series forecasting within a Jupyter Notebook computing environment. The data from domestic and international tourism stays registered in Bulgaria for the period 2002 to 2023 has been utilized to construct an advanced deep neural network using multiple Python libraries. Purpose of the article: The purpose of the current paper is to establish which time series forecasting model - Exponential Smoothing, TBATS, Auto ARIMA, Theta or LSTM has better accuracy estimation and may be applied for similar tasks in the future by for research and practical economic purposes. Methods: The applied methodology was based on the classical scientific method. As to the main findings they could help in daily operation planning, managing and relocating resources in tourism on micro and macro level as well as with insights on the drawbacks and limitations on this research area related to analysis and novelty implications. Findings & Value added: What is more, the results obtained reaffirmed that ANN can be applied for an accurate forecasting, especially in the case of Bulgaria, where such models have not been applied yet neither by the tourism related academics nor by the business and the policymakers. [ABSTRACT FROM AUTHOR]

The evaluation of uterine contraction offers significant information regarding the progression of labour. The occurrence of deliveries before the expected dates leads to undesirable consequences for the mother and fetus. Electrohysterography (EHG) is a non-invasive monitoring scheme generally preferred to detect preterm delivery and reduce the hostile consequences. This paper proposed a novel term and preterm prediction method for predicting preterm deliveries by employing EHG signals. The proposed scheme involves three phases: pre-processing, feature extraction and prediction. Initially, the acquired EHG signals are pre-processed using a band pass filter and wavelet transform to remove noise and artefacts from the EHG signal. The second stage is feature extraction, where the representative features, including Shannon energy, median frequency, time-varying centroid frequency, etc., are extracted. At last, the prediction is carried out using an enhanced sheep flock optimized hybrid extreme artificial neural learning network (ESFHEANL). ESFHEANL is the combination of a hybrid extreme artificial neural learning network (HEANL) and enhanced sheep flock optimization (ESFO) algorithm. It assists in diagnosing the term and preterm birth more accurately. The proposed scheme is implemented in the Python platform and assessed the performance in terms of accuracy, recall, specificity and f-measure using the term-preterm EHG (TPEHG) database. Finally, the experimental outcomes evidenced that the proposed scheme achieved better performance and was employed to diagnose term and preterm births accurately. [ABSTRACT FROM AUTHOR]

This paper reports the prediction results of the ionospheric irregularities represented by the ROTI index close to the equatorial ionization anomaly's Northern peak. A feedforward backpropagation Neural Network (NN) approach is implemented using a time series Nonlinear Autoregressive approach with Exogenous inputs (NARX). We used the data from a dual frequency GPS-SCINDA station located at Helwan (geographic coordinates of 29.86°N, 31.32°E, and MLAT of 29.94°N) in Egypt. To allow the model to be independently tested during varying levels of solar activities, we collected a 5-minute resolution of ROTI data for the solar cycle 24 from 2009 to 2017. The factors that influence the development of ionospheric irregularities are involved in the established model representing the diurnal and seasonal variations as well as solar and geomagnetic activity parameters. To support learning, we included IRI-foF2 and IRI-hmF2 parameters in the input layer neurons to improve the model learning about the behavior of the ionospheric F layer. The results show that the NN-ROTI values precisely match the GPS-ROTI values with an RMSE of 0.106 TECU/min and a prediction efficiency of 95%. The predicted values are highly correlated with the originals, with a regression of 0.89. Furthermore, the irregularities were more prevalent during the equinox months than during the solstice months. It is also observed that predicted NN-ROTI values in all seasons have RMSEs less than 0.03 and 0.05 TECU/min for the low and high solar activity, respectively. The results also showed a clear correlation between solar activity and the occurrence percentage of ionospheric irregularities indicating a solar cycle dependency. The prediction values of the NN-ROTI occurrence % demonstrated a remarkable correlation with the observed GPS-ROTI occurrence % across SC24. [ABSTRACT FROM AUTHOR]

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%. [ABSTRACT FROM AUTHOR]

Current challenges in the field of luminescent materials are concerned with designing efficient material to meet the rapidly rising demands of industry. Luminescent material excitation and emission are highly complex phenomena driven by the combination of atomic-level properties such as valence electron, inter-atomic radius, ionic radius, etc., and physical properties such as crystal structure, symmetry, etc. The current research paper focuses on the development of a machine-learning algorithm based on simple luminescent materials to predict the excitation to the closest possible accuracy using easily accessible key attributes by the CatBoost regressor, multiple linear regression (MLR), and an artificial neural network (ANN) approach. These selected features likely correlate with the excitation of the material. In comparison, the ANN and MLR algorithms have higher mean absolute error values in both the training and test datasets. The CatBoost algorithm outperforms the other algorithms in terms of mean of the absolute percentage difference, achieving a value of 0.302136% in the training dataset. The CatBoost algorithm exhibits the lowest root mean squared error value of 1.680768 nm in the training dataset, indicating that its predictions have a smaller average deviation from the actual values. The style for studying the material property has the potential to reduce the cost and time involved in an Edisonian approach to the lengthy laboratory experiment to identify excitation. [ABSTRACT FROM AUTHOR]

Low-cost inertial measurement units (IMUs) are commonly used to determine the orientation of objects, such as unmanned aerial vehicles (UAVs) and smartphones. They calculate yaw by measuring Earth's magnetic field's horizontal components. However, in the presence of tilt (pitch or roll), a tilt-compensation operation is necessary. This is usually done by projecting measurements onto a horizontal plane. This method has limitations, particularly for large tilt angles and when the IMU is pointing toward the east or west directions. In this paper, we expose the shortcomings of this conventional approach and propose a novel machine learning–based solution employing an artificial neural network (ANN). This method eliminates the need to determine tilt angles and uses accelerometer and magnetometer measurements as its inputs. The dataset for training and testing the ANN was collected based on a 3D nonmagnetic scaled platform, using a low-cost IMU and a Raspberry Pi platform. On one hand, our method outperforms the conventional tilt-compensation technique and other complementary filters (Madgwick and Mahony) in terms of accuracy, as evidenced by the root mean square error (RMSE = 1.95°). However, this superiority comes at the expense of a more complex system that consumes more processing time. [ABSTRACT FROM AUTHOR]

In deep learning, the accuracy and generalization ability of the model largely depend on the optimization of the loss function. Up to now, dozens of optimization methods have been used in the deep learning models. Among them, stochastic gradient descent (SGD) is a popular and widely used method, and most of the other up-to-date optimization methods are variants or improvements of the original SGD. Among all the variations and improvements, the adaptive moment estimation (Adam) is one of the classics. However, Adam has also been pointed out to have non-convergence or error-convergence. Combining with the improvement points of the existing algorithms, this paper proposes an improved algorithm based on Adam, called NewAdam. NewAdam is modified from Adam in both search direction and learning rate. We perform a theoretical analysis on it and conduct numerical experiments on three data sets and two network architectures to illustrate the effectiveness of NewAdam. [ABSTRACT FROM AUTHOR]

This article used machine learning (ML) and artificial neural network (ANN) algorithms to predict the likelihood of a country being sanctioned by the Basel Institute on Governance for not adhering to anti-money laundering (AML) standards. Data for this paper came from the Basel AML Index and the World Bank. The results showed that the logistic regression and support vector machine (SVM) classifiers had the highest performance and balanced accuracy scores in sanction prediction. Additionally, these two algorithms also had the highest precision, specificity, and F1 scores, indicating that they were robust in their predictions of money laundering sanctions. In contrast to the ML classifiers, the ANN model had the highest sensitivity and receiver operating characteristic scores for money laundering sanctions. The strongest predictors of sanctions are financial transparency, political and legal risks, unemployment rate, and money laundering and terrorist financing risks. These findings reinforce the potential practical applications of ML and ANN models in predicting sanctions. [ABSTRACT FROM AUTHOR]

The development of fragility curves is an important step in seismic risk assessment within the scope of performance‐based earthquake engineering. The goal of this work is to use machine learning methods (regression‐based tools) to forecast the large‐span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large‐span precast parking structures, including: neural networks, genetic algorithm‐based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large‐span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization‐based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention. [ABSTRACT FROM AUTHOR]

The automotive S-rail plays a crucial role in frontal car crashes, aiming to absorb impact energy and reduce passenger injury. This paper innovatively presents an optimization approach to determine the optimal configuration of an S-rail featuring a tapered, multicell front section. The structural design of the S-rail is conceptualized within the design space of a heavy quadricycle vehicle and subsequently subjected to numerical investigation through LS-DYNA using nonlinear explicit dynamics analyses. The objective is to maximize energy absorption while minimizing the S-rail's initial peak force (IPF) and mass. An artificial neural network (ANN) is employed to construct surrogate models for the optimization process. The non dominated sorting genetic algorithm, integrated with the ANN, yields an optimal S-rail design. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, using the mean weighting method on the Pareto frontier optimal solution set, is appropriately applied to select the optimal solution. The optimized S-rail shows improved specific energy absorption compared to the baseline model while maintaining a low IPF. In conclusion, this study highlights the superior predictability of an ANN over conventional quadratic response surface methodology. The results confirm the effectiveness of the ANN-based optimization approach and the selection of a compromise solution using the TOPSIS technique. The proposed procedure has substantial potential to enhance the safety and performance of automotive S-rails. [ABSTRACT FROM AUTHOR]

Summary: The rapid advancement and growth of technology have rendered cloud computing services indispensable to our activities. Threats and intrusions have since multiplied exponentially across a range of industries. In such a scenario, the intrusion detection system, or simply the IDS, is deployed on the network to monitor and detect any attacks. The paper proposes a feed‐forward deep neural network (FFDNN) method based on deep learning methodology using a filter‐based feature selection model. The feature selection strategy aims to determine and select the most highly relevant subset of attributes from the feature importance score for training the deep learning model. Three benchmark data sets were used to assess the experiment: CIC‐IDS 2017, UNSW‐NB15, and NSL‐KDD. In order to justify the proposed technique, a comparison was done using other learning algorithms ranging from classical machine learning to ensemble learning methods that can detect various attacks. The experiments showed that the FFDNN model with reduced feature subsets gave the highest accuracy of 99.53% and 94.45% in the NSL‐KDD and UNSW‐NB15 data sets, while the ensemble‐based XGBoost model performed better in the CIC‐IDS 2017 data set. In addition, the results show that the overall accuracy, recall, and F1 score of the deep learning algorithm are generally better for all the data sets. [ABSTRACT FROM AUTHOR]

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. [ABSTRACT FROM AUTHOR]

The debate about the efficacy of mergers and acquisitions as a growth strategy in terms of ex-post value creation has been developing for decades. This paper aims to create an artificial neural network that examines trends in the financials and marks the potential sources of value creation in the high-tech industry mergers between 2011 and 2021. The findings demonstrate that ANN can be implemented as a highly efficient model for analyzing complex financial events due to its flexibility and lack of prior assumptions about the data. [ABSTRACT FROM AUTHOR]

Researchers are putting in a lot of time and energy into developing methods to use machine learning algorithms, a subfield of AI, to ‎diagnose disease in an individual patient. Extensive studies have been conducted on the potential benefits of using machine learning ‎techniques in the treatment of cardiovascular disease. For this preliminary study, the study have zeroed down on heart illness to get ‎as specific as possible about our methodology. In this paper, the authors investigate the differences in accuracy between different ‎machine learning approaches specifically, (SVM), (KNN), and a(ANN) when applied to the categorization of cardiovascular ‎illness. Our research makes use of approximately seventy thousand patient records from the Kaggle dataset, which focuses on ‎cardiovascular disease The Kaggle dataset comprises a limited number of variables per patient record, encompassing serum ‎cholesterol, diastolic and systolic blood pressure, relative blood glucose levels, and the presence or absence of angina.‎ The present study examines the Kaggle dataset and employs the (KNN), (SVM), and (ANN) methodologies. The results indicate ‎that the K-nearest neighbors (KNN) algorithm, specifically with a value of 9 for the number of neighbors, achieved an accuracy of ‎‎0.997. The SVM model using default hyperparameters achieved an accuracy of 0.9997. The Support Vector Machine (SVM) model ‎utilizing a Radial Basis Function (RBF) kernel and a C value of 100.0 exhibited an accuracy of 0.9998. Conversely, the SVM model ‎employing a linear kernel and a C value of 1000.0 achieved a perfect accuracy of 1.000. The Feedforward neural network achieved a ‎perfect accuracy of 1.000 using the Adam optimization algorithm after 10 epochs and 50 batches..In future research, the authors ‎intend to employ a deep neural network hypermodel in order to enhance the accuracy of their findings. [ABSTRACT FROM AUTHOR]

Variable selection is one of the most important contents of machine learning research and a large number of variable selection methods have been proposed at present. From deep learning perspective, we propose a new variable selection method based on multi-layer artificial neural networks in this paper. The method propagates errors gained form a trained network model through the network from output layer to input layer and the values gained by input layer give information about the relative contribution. The variables with big contribution value are selected, which we call neural inverse propagation (NIP). Specifically, after training a neural network, the gained errors between the actual network outputs and the desired outputs go through the network from top to down and arrive at the input layer. The absolute values gained by input units provide information about the relative importance (contribution) of input units, which is that the larger the value is, the more important the corresponding input variable is. We remove k variables corresponding to the first k minimum absolute value gained by input units to the predictive variables, to perform variable selection. The efficiency of the proposed method is illustrated through its application to classification and regression tasks on a number of synthetic datasets and a real-world image classification dataset. [ABSTRACT FROM AUTHOR]

Nonconvex penalties are utilized for regularization in high-dimensional statistical learning algorithms primarily because they yield unbiased or nearly unbiased estimators for the parameters in the model. Nonconvex penalties existing in the literature such as SCAD, MCP, Laplace and arctan have a singularity at origin which makes them useful also for variable selection. However, in several high-dimensional frameworks such as deep learning, variable selection is less of a concern. In this paper, we present a nonconvex penalty which is smooth at origin. The paper includes asymptotic results for ordinary least squares estimators regularized with the new penalty function, showing asymptotic bias that vanishes exponentially fast. We also conducted simulations to better understand the finite sample properties and conducted an empirical study employing deep neural network architecture on three datasets and convolutional neural network on four datasets. The empirical study based on artificial neural networks showed better performance for the new regularization approach in five out of the seven datasets. [ABSTRACT FROM AUTHOR]

• Analyzing reliability of a distribution company by data envelopment analysis. • Obtaining distribution companies' reliability targets using best past performance. • Estimation of reliability target level by machine learning methods. • Incorporation of outage characteristics for reliability target estimations. Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions. [ABSTRACT FROM AUTHOR]

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper's main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes. [Display omitted] • Classical PCA compression outweighs neural nets in image quality and operation time. • Appropriate learning algorithm is essential for the neural PCA compression. • Neural PCA compression can have an advantage in online data acquisition schemes. • Appropriate data types are important for compression ratio and image quality. [ABSTRACT FROM AUTHOR]

Pavement engineering has always relied on asphalt concrete as the primary material. As a result, increasing the quality of asphalt concrete for greater performance and longer durability has remained a focus of research. The present paper aims to assess the potential of soft computing-based models, such as Artificial neural networks, Support vector machines, Gaussian process, M5P tree, Random forest, and Random tree-based models, used for the prediction of Marshall Stability of carbon–fiber asphalt mix. Five different statistical indices are used to evaluate the performance of each model with different input variables, such as Bitumen content (BC), Carbon fiber (CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD), Specific gravity coarse aggregate (SG(CA)), Water absorption coarse aggregate (WA(CA)) and Specific Gravity of bitumen (SG(B)), to get the Marshall Stability (MS) as an output. Five statistical indices were used to assess the effectiveness of the applied models i.e., Coefficient of correlation (CC), Mean absolute error (MAE), Root mean square error (RMSE), Relative absolute error (RAE) and Root relative squared error (RRSE). According to performance evaluation results, it has been found that the ANN-based model has outperformed all the applied models for predicting the Marshall Stability of asphalt concrete using carbon fiber with CC value as 0.9287 and 0.9126, R2 value as 0.8624 and 0.8328, MAE value as 1.7527 and 1.8702, RMSE value as 2.3305 and 2.4438, RAE value as 32.51 and 39.64% and RRSE values as 37.30 and 43.59% for both the training and testing stages respectively. Taylor's diagram suggests that the ANN model outperforms the other applied models. Sensitivity analysis shows that the bitumen content (BC) is the more sensitive parameter in the carbon–fiber asphalt mix. Furthermore, carbon fiber is comparable to the sensitivity of the bitumen content which shows the significance of the carbon fiber in the asphalt mix in predicting the Marshall Stability. [ABSTRACT FROM AUTHOR]

Cold Atmospheric Pressure Plasma (CAP) is very potent and impactful technology implemented for both technological and biomedical applications. This paper focuses on the implementation of artificial neural network (ANN) for a novel double ring electrode based cold atmospheric pressure plasma which is to operated only in the glow discharge region for its application in biomedical field. ANN inherently helps in visualizing the effective output parameters such as peak discharge current, power consumed, jet length (with sleeve) and jet length (without sleeve) for given set of input parameters of supply voltage and supply frequency using machine learning model. The capability of the ANN model is demonstrated by predicting the output parameters of the CAP beyond the experimental range. Finally, the optimized settings of supply voltage and supply frequency will be determined using the composite desirability function approach to simultaneously maximize the peak discharge current, jet length (with sleeve) and jet length (without sleeve), and minimize the power consumption. [ABSTRACT FROM AUTHOR]

In the populated and developing countries, governments consider the regulation and protection of environment as a major task and should take into consideration the concept of Smart Environment Monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, data sets and other devices connected across the globe through a network. This system can basically help in monitoring air quality which is necessary in the field of meteorological studies and movement factors. Also, these factors contribute a lot in air pollution. The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3. In recent years, machine learning in most emerging technology for predicting on historical data with 99.99% of accuracy. To prognosticating air quality index of NCR, India in different aspects like stubble farming, motor vehicle emission, and open construction practices which result in polluting the air quality of NCR. The manuscript is helping to frame a structured view of air quality prediction methods in reader's mind. [ABSTRACT FROM AUTHOR]

Corrosion presents significant challenges in the marine, offshore, and oil and gas industries, resulting in annual economic losses amounting to billions of dollars. To address these losses and ensure the structural integrity of marine infrastructure, it is essential to implement effective corrosion monitoring techniques. In recent years, machine learning algorithms have gained prominence across various fields, offering innovative solutions to corrosion-related concerns. This paper provides a comprehensive critical review, primarily focusing on the two most prevalent machine learning algorithms: artificial neural networks and random forests. The review critically analyzes their applications, methodologies, and effectiveness in the realm of marine and offshore steel structures, oil and gas pipelines, as well as construction materials like Al alloys and Mg alloys and the analysis of corrosion coating behavior. Furthermore, this study explores the key findings and inherent limitations of these machine learning techniques, emphasizing their potential in corrosion prediction, detection, and the mitigation of corrosion issues in the marine, offshore, and oil and gas industries. By identifying existing research gaps and offering recommendations for future investigations, this paper emerges as an invaluable resource for researchers, engineers, and practitioners aiming to advance corrosion prevention and management strategies within these pivotal domains. • In-depth analysis of ANN and RF in maritime, offshore, and oil and gas corrosion research (2018-2023). • Critical evaluation of ML algorithms in maritime steel structures, pipelines, and construction materials. • Valuable review for cost-effective maritime corrosion maintenance strategies. • Provides concise corrosion management expertise to empower researchers and industry experts. [ABSTRACT FROM AUTHOR]

• Trapping indices stabilization is lagged as injection period is active. • Greater CO 2 cumulative volume occurs as reservoir pressure is far from caprock fracture pressure. • Larger closures will require pressure management-based brine production to reach theoretical storage capacity. • ML-based CO 2 storage modelling preliminary de-risked saline aquifers lacking numerical models. This paper presents a machine learning (ML) model designed to speed up the appraisal of geologic CO 2 storage sites by predicting the effectiveness in trapping and accommodating CO 2 in saline aquifers. Considering the urgency of de-risking as much geologic CO 2 storage resources as possible to help with CO 2 emission reduction Paris' goal, ML-based reservoir modelling has been documented as proper tool when a faster, good approximate, and less expensive approach is needed to surrogate multiple assessments of storage sites traditionally performed by long-timeframe and multi-stage geologic CO 2 storage numerical modelling approach. In this paper, a case study is presented. It consisted of a dataset comprised of six geologic aquifer parameters (CO 2 residual saturation, horizontal permeability, vertical to horizontal permeability ratio, porosity, brine salinity, and CO 2 flow rate) and elapsed time as input data, and as output data the CO 2 trapping mechanism indices (Solubility Trapping Index, Residual Trapping Index, and Structural Trapping Index) along with the dynamic storage capacity (CO 2 injected volume). Such dataset was used to train and test the artificial neural network (ANN) model. The dataset was generated from thousands of post-processed numerical realizations at several injection periods by applying design of experiment using a synthetic aquifer model derived from the Bunter Sandstone Closure 36 aquifer numerical model, from the Southern North Sea. The ANN architecture designed in Python consisted of 3 hidden layers and 40 nodes and its performance was assessed using the coefficient of determination (R2) and root mean squared error (RMSE). The ANN performance showed accuracies (R2) for training and testing with 96% and 95% of precision respectively. Practical application of the ANN model was successfully implemented to CO 2 storage aquifer sites selected from CO2Stored® database which lacking numerical models (Bunter Closure 3, 9, 35, and 40), obtaining at the end of 100-years injection case a Structural, Residual, and Solubility Trapping Index averaging 83%, 11%, and 6% respectively, with low variation coefficient indicating that trapping indices were predicted properly because aquifers selected for ANN model application have similar structures (dome-like shape) and reservoir properties. In addition, CO 2 injected volume predictions for 100-years injection case were ranging from 397 to 456 million ton (Mt) totalling 2.1 giga ton (Gt) of potential storage capacity which represents 70% of total theoretical volumetric capacity. These results show the significant impact to accelerate geologic CO 2 storage sites assessment by implementing ML-based modelling to preliminary de-risking groups of saline aquifers and reasonably consider them technically feasible CO 2 storage sites in UK. [ABSTRACT FROM AUTHOR]

Minimizing the detrimental effects of road transport greenhouse gas (GHG) emissions on climate change and global warming requires accurate emission forecasting. To forecast greenhouse gas emissions from industrial and civilian transportation on roads in China, we present new approaches that use data extraction and managed machine learning methods for regression and identification. Four methods are examined: decision tree, multinomial logistic regression, multivariate linear regression, and artificial neural network (ANN) multiple-layer perceptron. The findings suggest that the multiple-layer perceptron approach of ANN has superior prediction accuracy compared to other models. Ensemble modelling techniques, such as Bagging and Boosting, significantly improved the predictive performance of the developed multilayer perceptron system. The paper's conclusions are significant for transport policymakers, regulators, and international organizations in mitigating GHG emissions. [ABSTRACT FROM AUTHOR]

The prediction of wear in cutting tools is pivotal for boosting productivity and reducing manufacturing costs. Although current data-driven models in machine learning and deep learning have advanced predictive capabilities, they often lack generality and demand substantial data training. This paper presents a novel approach using Bayesian Regularized Artificial Neural Networks (BRANNs) to precisely forecast wear in milling tools. Unlike conventional machine learning models, BRANNs merge the strengths of artificial neural networks (ANNs) and Bayesian regularization, yielding a more robust and generalized predictive model. We utilized three open-access datasets from the literature alongside an in-house dataset generated by our milling setup. Initially, we assessed the model's predictive ability by training and testing it against individual open-access datasets. We investigated the impact of input features, training data size, hidden units, training algorithms, and transfer functions on the model's predictive capability. Subsequently, we trained the model using three open-access datasets and tested it against our in-house data. Our findings demonstrate that the developed model surpasses existing state-of-the-art models in accuracy and transferability [Display omitted] • Milling tool wear prediction using Bayesian Regularized Artificial Neural Networks. • Proposed BRANN model outperforms existing models in predicting tool wear. • Process parameters and monitoring signals are used as input to predict tool wear. • Training based on open-access data to predict wear for in-house experimental data. [ABSTRACT FROM AUTHOR]

[Display omitted] • UV/VIS Machine vision system to determine CQAs of fully white tablets. • Rapid evaluation of compression force impact and API content using UV/VIS imaging. • Accurate analysis of critical quality attributes with multivariate data techniques. • Rapid analysis suitable for both off-line and in-line use. The paper provides a demonstration of how UV/VIS imaging can be employed to evaluate the crushing strength, friability, disintegration time and dissolution profile of tablets comprised of solely white components. The samples were produced using different levels of compression force and API content of anhydrous caffeine. Images were acquired from both sides of the samples using UV illumination for the API content prediction, while the other parameters were assessed using VIS illumination. Based on the color histograms of the UV images, API content was predicted with 5.6 % relative error. Textural analysis of the VIS images yielded crushing strength predictions under 10 % relative error. Regarding friability, three groups were established according to the weight loss of the samples. Likewise, the evaluation of disintegration time led to the identification of three groups: <10 s, 11–35 s, and over 36 s. Successful classification of the samples was achieved with machine learning algorithms. Finally, immediate release dissolution profiles were accurately predicted under 5 % of RMSE with an artificial neural network. The 50 ms exposition time during image acquisition and the resulting outcomes underscore the practicality of machine vision for real-time quality control in solid dosage forms, regardless of the color of the API. [ABSTRACT FROM AUTHOR]