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In this research, an upgraded and environmentally friendly process involving WO 3 /Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R 2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R 2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L -1 , 3.13, 117.65 min, and 0.19 g L -1 ) and (5 mg L -1 , 3, 120 min, and 0.19 g L -1 ), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment., (© 2024. The Author(s).)

Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R 2 and R 2 cv at 0.91 and 0.81, respectively. For external validation, the R 2 test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence., (© 2024. The Author(s).)

Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computational Fluid Dynamics (CFD) and Artificial Neural Networks (ANN). The research thoroughly examines the impact of various factors, such as inlet temperatures, velocities, channel heights, salt concentration, and membrane characteristics, on the process's efficiency, specifically calculating the water vapor flux. A rigorous validation of the CFD model aligns well with established studies, ensuring reliability. Subsequently, over 1000 data points reflecting variations in input factors are utilized to train and validate the ANN. The training phase demonstrated high accuracy, with near-zero mean squared errors and R 2 values close to one, indicating a strong predictive capability. Further analysis post-ANN training shed light on key relationships: higher membrane porosity boosts water vapor flux, whereas thicker membranes reduce it. Additionally, it was detailed how salt concentration, channel dimensions, inlet temperatures, and velocities significantly influence the distillation process. Finally, a mathematical model was proposed for water vapor flux as a function of key input factors. The results highlighted that salt mole fraction and hot water inlet temperature have the most effect on the water vapor flux. This comprehensive investigation contributes to the understanding of DCMD and emphasizes the potential of combining CFD and ANN for optimizing and innovating water desalination technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance., (© 2022 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

For economical and sustainable benefits, conventional retaining walls are being replaced by geosynthetic reinforced soil (GRS). However, for safety and quality assurance purposes, prior tests of pullout capacities of these materials need to be performed. Conventionally, these tests are conducted in a laboratory with heavy instruments. These tests are time-consuming, require hard labor, are prone to error, and are expensive as a special pullout machine is required to perform the tests and acquire the data by using a lot of sensors and data loggers. This paper proposes a data-driven machine learning architecture (MLA) to predict the pullout capacity of GRS in a diverse environment. The results from MLA are compared with actual laboratory pullout capacity tests. Various input variables are considered for training and testing the neural network. These input parameters include the soil physical conditions based on water content and external loading applied. The soil used is a locally available weathered granite soil. The input data included normal stress, soil saturation, displacement, and soil unit weight whereas the output data contains information about the pullout strength. The data used was obtained from an actual pullout capacity test performed in the laboratory. The laboratory test is performed according to American Society for Testing and Materials (ASTM) standard D 6706-01 with little modification. This research shows that by using machine learning, the same pullout resistance of a geosynthetic reinforced soil can be achieved as in laboratory testing, thus saving a lot of time, effort, and money. Feedforward backpropagation neural networks with a different number of neurons, algorithms, and hidden layers have been examined. The comparison of the Bayesian regularization learning algorithm with two hidden layers and 12 neurons each showed the minimum mean square error (MSE) of 3.02 × 10 -5 for both training and testing. The maximum coefficient of regression (R) for the testing set is 0.999 and the training set is 0.999 for the prediction interval of 99%.

Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal's angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low.

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse's internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types-namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)-with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.

Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective of this study was to develop a novel rice plant recognition sensor based on a tactile method which acquires tactile information through physical touch. The tactile sensor would be mounted on the paddy field weeder to provide identification information for the actuator. First, a flexible gasbag filled with air was developed, where vibration features produced by tactile and sliding feedback were acquired when this apparatus touched rice plants or weeds, allowing the subtle vibration data with identification features to be reflected through the voltage value of an air-pressured sensor mounted inside the gasbag. Second, voltage data were preprocessed by three algorithms to optimize recognition features, including dimensional feature, dimensionless feature, and fractal dimension. The three types of features were used to train and test a neural network classifier. To maximize classification accuracy, an optimum set of features (b (variance), f (kurtosis), h (waveform factor), l (box dimension), and m (Hurst exponent)) were selected using a genetic algorithm. Finally, the feature-optimized classifier was trained, and the actual performances of the sensor at different contact positions were tested. Experimental results showed that the recognition rates of the end, middle, and root of the sensor were 90.67%, 98%, and 96% respectively. A tactile-based method with intelligence could produce high accuracy for rice plant recognition, as demonstrated in this study.

Background: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using the protein-protein interaction (PPI) networks., Results: As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence of gliomas or, at the very least, that they are useful for tumor researchers., Conclusion: Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve our understanding of the biological functions involved in glioma growth., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. It is used to investigate some types of abnormal heart function including arrhythmias and conduction disturbance. In this paper the proposed method is used to classify the ECG signal by using classification technique. First the Input signal is preprocessed by using filtering method such as low pass, high pass and butter worth filter to remove the high frequency noise. Butter worth filter is to remove the excess noise in the signal. After preprocessing peak points are detected by using peak detection algorithm and extract the features for the signal are extracted using statistical parameters. Finally, extracted features are classified by using SVM, Adaboost, ANN and Naïve Bayes classifier to classify the ECG signal database into normal or abnormal ECG signal. Experimental result shows that the accuracy of the SVM, Adaboost, ANN and Naïve Bayes classifier is 87.5%, 93%, 94 and 99.7%. Compared to other classifier naïve bayes classifier accuracy is high.

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models' precision and their controllers' effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems. [ABSTRACT FROM AUTHOR]

Improving soil strength and reducing the anticipated settlement and construction cost is a great paradox for civil as well as geotechnical engineers. In this paper, these aspects and other suitable types of ground improvement are discussed based on the principles of using geosynthetics for soil reinforcement. A series of load-settlement tests were also performed to compare strength and settlement of the silty sand reinforced with lime and one layer of geotextile. The study finds the maximum insertions of geotextile at 0.2D (3.0 cm) beneath the square footing base, and the lime percentage of 5.0% increases the UBC substantially. The UBC of lime-treated and geotextile-reinforced silty sand was to an optimum of 1,360 kN/m2 that has shown an enhancement of 258% compared to that of untreated and unreinforced silty sand that is approximately 380 kN/m2. Furthermore, comparative analysis between two ANN models was performed to provide improved estimate of the UBC, namely artificial neural network (ANN) and extreme learning machine (ELM). The developed computational models were then compared with experiment data, which proved that such models are more economical and effective than the expensive and time consuming conventional techniques. Consequently, based on the results, it was further validated that ELM possesses better generalization capability compared to ANN for predictive efficiency and thereby proves the efficiency of the model in estimating the ultimate bearing capacity of square footings incorporated with geotextile and lime-treated silty sand. This places the ELM model as a useful tool in the initial conceptual as well as the design for improvement steps of soil reinforcement. [ABSTRACT FROM AUTHOR]

This study investigated the effects of various parameters on the SPL (Sound Pressure Level) levels of rod-airfoil configurations. An experimental study was performed to investigate the effects of the rod parameters, such as the configuration of the rod, the distance between the rod and the airfoil, the diameter effect of the rod, and the geometry of the rod, on the performance of the rod-airfoil configuration. An Artificial Neural Network (ANN) model was then developed and applied to accurately predict the SPL of rod-airfoil configurations. The results of the study revealed that the Levenberg-Marquardt (LM) algorithm with 2 hidden neurons produced the best performance in predicting the SPL level, with a training R-squared value of 0.9998 and a testing R-squared value of 0.998715. The findings also indicated that increasing rod diameter increases sound pressure level while reducing gap width increases SPL levels and decreases frequency values. This method offers a more precise and effective technique to forecast the SPL levels of rod-airfoil designs, allowing designers to enhance their creations and lower noise levels. The findings of this study can also be utilized to direct future research in this area and offer important information for a better understanding of the mechanism of rod-airfoil noise creation. To the best of the authors' knowledge, this is the first study to look into rod-airfoil design predictions made using machine learning approaches. [ABSTRACT FROM AUTHOR]

Mango is a highly diverse tropical fruit with numerous varieties that differ in flavor, texture, and chemical composition. Consequently, identifying fraudulent substitutions of mango varieties poses a significant challenge using traditional techniques. Therefore, there is an increasing need for new methods to discriminate between mango varieties. Hyperspectral imaging coupled with machine learning techniques presents a promising approach for varietal discrimination. In this study, mango samples of eleven varieties were collected from a germplasm bank, with four slices obtained from each sample. Hyperspectral images were acquired in the Vis–NIR and NIR ranges for each slice, and spectral profiles were extracted and pretreated. Three discrimination models, linear discriminant analysis, K-nearest neighbor, and artificial neural networks, were implemented and validated using relevant wavelengths selected through a covering array feature selection algorithm. The performance of these models was evaluated using precision, accuracy, and F-score metrics. The average spectral profiles of the studied varieties exhibited a similar behavior with slight differences, which could be used for classification within the evaluated ranges. The optimal number of variables selected to refine the models was 17 for the UV–Vis–NIR range and 21 for the NIR range, with an accuracy ranging between 0.752 and 0.972. This study concludes that hyperspectral imaging combined with machine learning techniques can effectively discriminate between different varieties of mango. [ABSTRACT FROM AUTHOR]

Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete. [ABSTRACT FROM AUTHOR]

Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes. [ABSTRACT FROM AUTHOR]

Predicting crop yields before harvest is important for making and carrying out policies about food safety, transportation costs, import-export, storage, and selling of agricultural goods. The weather is a key factor in crop growth and its development. Therefore, models that include meteorological variables can predict reliable forecasts for crop output; however, selecting the appropriate model for use in agricultural production forecasting can be challenging. This study investigates the development of wheat yield prediction models using various multivariate analysis techniques and weather indices derived from meteorological data collected over 22 years in Punjab, India. Five different modeling approaches, including stepwise multiple linear regression (SMLR), LASSO, elastic net (ELNET), artificial neural network (ANN), and ridge regression, were employed and compared for their effectiveness in predicting wheat yield. The models were calibrated using data from 17 years (2000–01 to 2016–17) and validated using data from the subsequent 5 years (2017–18 to 2021–22). Evaluation metrics such as R2, root mean square error (RMSE), normalized root mean square error (NRMSE), mean biased error (MBE), and modeling efficiency (EF) were utilized to assess model performance. The results indicate varying degrees of performance across districts and modeling techniques. ANN demonstrated the highest performance during both calibration and validation periods, followed closely by LASSO and ELNET. However, certain districts showed discrepancies in model fit, with some models performing better than others depending on the specific district. Overall, ANN emerged as the most reliable approach for wheat yield prediction in Punjab followed by ELNET and LASSO, offering valuable insights for agricultural planning and management. This comprehensive analysis provides valuable contributions to the field of crop yield prediction, enhancing understanding of the complex interactions between weather variables and agricultural outcomes. [ABSTRACT FROM AUTHOR]

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint. The machine learning method applied is for time series data. This study focuses on building process control models to enable semi-automation in small to medium-size factories in which the data are not as time dependent. Correlation coefficients were used to identify key process parameters that contribute to feed pH value and DE. Artificial neural network (ANN), support vector machine (SVM), and linear regression (LR) models were built to predict feed pH and DE. The results suggest (1) model accuracy ranges from 91% to 96%; (2) the ANN models yielded about 1% to 3% higher accuracy than the SVM and LR models and the prediction accuracy is robust even with as few as six data sets; (3) both the SVM and ANN models have noise tolerant properties, but ANN has a higher noise tolerance than SVM; (4) SVM performance can be hindered when using high-dimensional data sets; (5) the LR model yields higher variation in accuracy prediction than ANN and SVM; (6) distribution fitting is a good approach for generating data; however, fidelity of fitting can greatly impact accuracy; and (7) multi-stage models yield higher accuracy than single-stage models, but there are pros and cons to each approach. [ABSTRACT FROM AUTHOR]

This document is a list of references to research papers and articles that focus on machining processes, specifically the machining of superalloy Inconel 718. The papers cover a range of topics including tool wear reduction, surface integrity evaluation, optimization techniques, and prediction models using artificial intelligence and machine learning. The research explores different lubrication techniques, nanofluid additives, and cutting parameters to improve the machinability of Inconel 718. The papers utilize various methodologies such as neural networks, fuzzy logic, response surface methodology, and genetic algorithms to optimize machining parameters and predict tool wear, surface roughness, and material removal rate. [Extracted from the article]

The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operationbased intrusion detection system, proves highly effective in identifying potential threats.Invalid relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operationbased intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. [ABSTRACT FROM AUTHOR]

Cost estimation and prediction are crucial processes for the success of construction projects, especially for infrastructure development. This study analyzes historical data collected between 2011 and 2023 and investigates the relationship between construction elements and the final cost of highway construction projects in Iraq. Different cost analysis approaches, including statistical assessment and machine learning techniques, were applied to a dataset of 291 highway projects. Cost estimation is a time-consuming and risky process that requires many qualitative and quantitative parameters to be well analyzed. However, machine learning provides a comprehensive assessment tool to predict future costs. Four ANN-based models were investigated and precision was improved by combining RMSE and the correlation coefficient (R) as a controller. The results showed improvements in performance metrics, such as error reduction rate and correlation coefficient, for the models developed. The best performance was achieved at an R of 0.989. The proposed model can be effectively adapted to predict road construction costs. Despite the need for more data, the implication of the proposed model can ensure a sustainable application, saving the time and resources required by construction professionals to predict road project costs during the planning phase. [ABSTRACT FROM AUTHOR]

The primary design property necessary to ensure the longevity and durability of manufactured materials is the material hardness. The primary objective of this study was to investigate the effect of cutting parameters, namely feed rate, cutting speed, and depth of cut, on the surface hardness generated during the turning process of aluminum alloy 6061. The turning experiments were conducted using a Taguchi L27 orthogonal array arranged for three-level cutting parameters. The Analysis of Variance (ANOVA) was employed to determine the relative importance of each parameter on surface hardness. Additionally, an Artificial Nural Network (ANN) predictive model using the back-propagation learning algorithm was created to predict surface hardness levels at each level of the cutting parameters. The results revealed that increasing the values of all the turning parameters resulted in an increase in hardness, and it was concluded that the feed rate was the most critical factor (53.41%) in achieving high surface hardness, followed by the depth of cut (27.89%), whereas cutting speed had a lower impact (18.7%). This study also suggests a simple equation for estimating the surface hardness from the cutting parameters. The ANN model could accurately estimate the surface hardness with a coefficient of correlation (R) higher than 0.98 between the predicted and experimental values. The predicted values of hardness by ANN were more precise (R² =0.973839) than those predicted by ANOVA (R²=0.893). [ABSTRACT FROM AUTHOR]

In a changing environment, the increasing inconsistency of runoff series complicates the development of runoff forecasting models. Mutation tests have frequently been employed to assess runoff inconsistency, yet their integration with runoff forecasting is rare. In this study, we proposed a combination of machine learning models based on mutation tests to predict monthly runoff at the Pingshi Station, located in the Lechang Gorge Reservoir of the Pearl River in Guangdong Province, China. Specifically, the mutation points of the monthly runoff were assessed using the Mann-Kendall test and the Moving T-test (MTT). The development of member models, including Artificial Neural Networks (ANN) and Support Vector Machines (SVM), utilized both the original runoff series and the sub runoff series identified after the first mutation point. Tele-connected factors and historical monthly runoffs served as candidate input variables. These variables were selected through linear and nonlinear filter methods and further refined by a greedy search based on 10-fold cross-validation. The improvement in overall forecasting performance by combining the ANN, SVM, and the Simple Average Method (SAM) was analyzed. The results showed that, for the validation dataset, the root mean square errors of the combined models with MTT decreased by about 14–19% compared to the member models with MTT, and 10–12% compared to the combined models without mutation tests. The corresponding Nash-Sutcliffe efficiency coefficients increased by approximately 35–45% and 12–14%, respectively. This study highlights the effectiveness of integrating mutation tests, machine learning, and combining models to enhance the forecasting performance of inconsistent monthly runoff. [ABSTRACT FROM AUTHOR]

Compound channels play an important role in hydraulic and hydrological engineering. Many rivers and streams have naturally compound channels, which are important for both flood control and river ecosystems. When predicting discharge, interval predictions are usually more realistic and reliable than traditional point predictions made using ML models. In this study, the authors used three well-known machine learning techniques (Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Artificial Neural Networks (ANN)) to predict compound channel discharge. To accurately assess uncertainty, two resampling techniques were employed: Bootstrap (B) and Jackknife after Bootstrap (JB). The proposed approach reflects the impacts of statistical uncertainties using prediction confidence intervals and the reliability of point prediction. The JB resampling technique has proven to offer superior accuracy in terms of point prediction and prediction intervals when compared to Bootstrap. Bootstrapping did not yield superior outcomes compared with the standalone ML models. The JB-ANN and JB-ANFIS models outperformed the JB-SVM and Bootstrap ML models in the testing phase, as shown by various statistical measures. This study suggests that the JB-ANN approach is a reliable and robust tool for accurately predicting compound discharges. The findings reveal that when the models have low accuracy in point prediction, the level of uncertainty in predicting the interval also increases. The innovation of this research is enhancing the examination of uncertainties in compound channel discharge by incorporating two resampling techniques to analyze three different ML models. [ABSTRACT FROM AUTHOR]

Accurate streamflow simulation is crucial for effective hydrological management, especially in regions like the upper Baro watershed, Ethiopia, where data scarcity challenges conventional modeling approaches. This study evaluates the efficacy of three hydrological models: the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS), artificial neural network (ANN), and support vector regression (SVR) in predicting runoff. Using data from 2000 to 2016, the analysis focused on various performance metrics such as the Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R²). The results indicated that the ANN model significantly outper-formed the others, achieving an NSE of 0.98, RMSE of 24 m³/s, and R² of 0.99. In comparison, the HEC-HMS model yielded an NSE of 0.85, RMSE of 113.4 m³ /s, and R² of 0.89, while the SVR model displayed an NSE of 0.97, RMSE of 27 m³/s, and R2 of 0.99. These findings highlight the superior performance of ANN in regions with limited hydrological data, suggesting its potential as a reliable alternative to traditional physical models. By demonstrating the efficacy of machine learning models, this research facilitates the way for innovative approaches to water resource management, offering valuable insights for policymakers and practitioners. [ABSTRACT FROM AUTHOR]

This study introduces an integrated method for managing process risks in a business process reengineering (BPR) project using robust data envelopment analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA (severity, occurrence and detection (S-O-D)) and incorporating two additional factors (breakdown cost and breakdown duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine-learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an automotive sector company, and the results showed the impact of uncertainty on values by comparing different approaches, such as RPN, PFMEA-DEA and PFMEA-RDEA. A new reduced risk categorization was achieved, which allowed for decision makers to focus on the necessary actions for reengineering. [ABSTRACT FROM AUTHOR]

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model's performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions. [ABSTRACT FROM AUTHOR]

Liquefaction is one of the natural hazards that occurs due to earthquakes and has a significant impact on the loss of human lives and various civil infrastructures. In this study, metaheuristic ANN with optimization techniques (i.e., ANN-GWO, ANN-GTO, ANN-GAO, ANN-HHO, ANN-SSA, and ANN-SMA), machine learning techniques are used to predict the probability of liquefaction ( P L ) from the SPT-based dataset. A dataset of 834 case histories, including seven geotechnical and seismic parameters, was used for training and testing different metaheuristic algorithms. The performance of the proposed machine learning algorithm used at every stage of analysis includes statistical parameters evaluation, score analysis, actual vs. predicted curve, error matrix, Taylor diagram, OBJ criteria, DDR criteria, and AIC criteria. The ANN-GTO model has been found to be the best model for the prediction of the probability of liquefaction potential of soil. However, all proposed models can successfully predict the liquefaction potential of soil with reasonably good accuracy. The proposed models can be used as a key tool in the prediction of the liquefaction susceptibility of any soil deposit. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate. [ABSTRACT FROM AUTHOR]

This paper explores the implementation of machine learning-based algorithms for TL dose assessment. It focuses on the radiation field classification, performance quotient evaluation, and shallow and deep dose equivalent assessment of ANN and LGBM, in comparison to the traditional method of DT. We evaluate these algorithms based on the element response data measured by TLD. A data set was built for training, and the base element responses of test categories were amplified, and normalized to 1 mSv Cs-137 within the range of ±3 %. Both algorithms consist of five subset models for classifying radiation fields and identifying ratios of mixed fields. The LGBM showed the best accuracy in classifying considered radiation fields and the lowest performance quotients. By comparing the tolerance levels of deep dose and shallow dose equivalents among the three algorithms, the LGBM yields the smallest difference between the predicted and true dose equivalents. This smaller difference implies the LGBM offers the least bias and standard deviation in the expected value, giving higher accuracy and precision in dose assessment over the traditional DT method. The findings from this study further contribute to the adoption of ML-based algorithms for TL dose assessment, underscoring its importance in the field.

A hydrodynamic thrust bearing could be forced to operate in mixed lubrication regime under various circumstances. At this state, the tribological characteristics of the bearing could be affected significantly and the developed phenomena would have a severe impact on the performance of the mechanism. Until recently, researchers were modeling the hydrodynamic lubrication problem of the thrust bearings either with analytical or with numerical solutions. The analytical solutions are very simple and do not provide enough accuracy in describing the actual problem. To add to that, following only computational methodologies, can lead to time consuming and complex algorithms that need to be repeated every time the operating conditions change, in order to draw safe conclusions. Recent technological advances, especially on the field of computer science, have provided tools that enhance and accelerate the modeling of thrust bearings' operation. The aim of this study is to examine the application of Artificial Neural Networks as Machine Learning models, that are trained to predict the coefficient of friction for lubricated pad thrust bearings in mixed lubrication regime. The hydrodynamic analysis of the thrust bearing is performed by solving the Average 2-D Reynolds equation numerically. In order to describe the roughness of the profiles, both the flow factors suggested by N. Patir and H.S. Cheng (1978) and the model of J.A. Greenwood and J. H. Tripp (1970) are taken into consideration. Three lubricants, the SAE 0W30, the SAE 10W40 and the SAE 10W60, are tested and compared for a variety of operating velocities and applied coatings. The numerical analysis results are used as training datasets for the machine learning algorithms. Four different ML methods are applied in this investigation: Artificial Neural Networks (ANNs), Multi- Variable Quadratic Polynomial Regression, Quadratic SVM and Regression Trees. The coefficient of determination, R 2 is calculated and used to determine the most accurate ML method for the current study. The results showed that ANNs provide very good accuracy in the prediction of friction coefficient compared to the rest of the ML models discussed. [ABSTRACT FROM AUTHOR]

Accurate flood prediction models and effective flood preparedness rely on thoroughly understanding rainfall–runoff dynamics. Similarly, effective rainfall–runoff models account for multiple interrelated parameters for robust runoff prediction. Process-based physical models offer valuable insights into hydrological processes, but their effectiveness can be hindered by data limitations or difficulties in acquiring specific data. Motivated by the frequent flooding events and limited data availability in the East Branch DuPage watershed, Illinois, this study addresses a critical gap in research by investigating effective discharge prediction methods. In this study, two significant machine learning (ML) models, artificial neural network (ANN) and support vector machine (SVM), were employed for discharge prediction. Historical data spanning from 2006 to 2021 were utilized to assess the performance of the models. Hyperparameter tuning was performed on the models to optimize their performance, and root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and the normalized root mean squared error (NRMSE) were used as evaluation metrics. Although both machine learning models demonstrated strong performance, the analysis revealed that the ANN model emerged as the more reliable option for predicting discharge in the watershed. Crucially, the ANN model surpassed the SVM model’s performance, achieving superior accuracy in predicting peak discharge events within the study area. Our findings have the potential to assist decision-makers and communities in implementing more dependable flood mitigation strategies, particularly in regions where hydrology data are limited.

This study presents a comprehensive analysis of the morphology, mechanical properties, erosion resistance, and machine learning modeling of Si 3 N 4 +TiC + VC and CrNi based ceramic coatings deposited via plasma spraying. The cross-sectional morphology analysis revealed good mechanical interlocking between the coatings and the substrate, with pores predominantly observed in the Si 3 N 4 +TiC + VC coating. The coating thicknesses were measured as follows: 260 μm for Si 3 N 4 +TiC + VC, 243 μm for Si 3 N 4 +TiC + VC blended with CrNi, and 232 μm for pure CrNi coating. Mechanical characterization showed that microhardness values ranged from 1933 HV for Si 3 N 4 +TiC + VC coating to 499 HV for pure CrNi coatings. Slurry erosion tests demonstrated that pure Si 3 N 4 +TiC + VC coating exhibited superior erosion resistance at 90° impingement angle compared to other coatings. Additionally, a rigorous evaluation of over 20 machine learning regression models revealed that Gaussian Process Regressors and Artificial Neural Network (ANN) with a layer size of 20, displayed satisfactory performance in modeling mass loss prediction. The erosion mechanisms observed in the coatings provided valuable insights into their response to slurry erosion, highlighting the influence of coating composition and impingement angles. The findings underscore the potential of Si 3 N 4 +TiC + VC ceramic coatings to enhance the erosion resistance of stainless steel, offering promising applications in various industries. [Display omitted] [ABSTRACT FROM AUTHOR]

Recent meta-heuristics for optimization issues include particle swarm optimization (PSO) techniques. They take their cues from the coordinated movements of swarms of fish and birds, which exhibit collective behaviors. A sophisticated learning phase, like back-propagation, is needed for artificial neural networks (ANNs), which are thought of as a source of artificial intelligence (AI). This phase allows for the calculation of the error gradient for each neuron, from the final layer to the first. However, some qualities of the objective function are necessary (cost). This inspired us to experiment with meta-heuristics to streamline the training of ANNs to manage complex nonlinear systems. The objective of this research is to apply deep reinforcement learning (DRL) to automatically calculate the PSO algorithm's parameters while also optimizing the supervised learning process of ANN. After a number of case studies, our methodology consistently leads to the coefficients of the ideal ANN. [ABSTRACT FROM AUTHOR]

Early and accurate diagnosis of leukemia is crucial for effective treatment. Machine Learning (ML) offers promising tools for leukemia diagnosis classification, but the required high-dimensional datasets pose challenges. This study explores the effectiveness of ML algorithms for leukemia disease classification and investigates the impact of feature selection with the Minimum Redundancy Maximum Relevance (MRMR ) technique. MRMR was implemented to select informative features and evaluate four ML algorithms (Naïve Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs)) using feature subsets with varying levels of relevance based on MRMR scores. Our results demonstrate that MRMR effectively reduced dimensionality while maintaining and even improving classification accuracy. KNN and SVM achieved the highest accuracy (100% for 67, 30, and 24 feature subsets), suggesting the benefit of focusing on highly relevant features. NB exhibited consistent accuracy across all feature sets. [ABSTRACT FROM AUTHOR]

The power quality problem refers to the issues caused by the sudden rise of nonstandard voltage, current, or frequency. The problems that emerge from poor power quality due to non-linear loads are voltage sag, swell, interruptions, harmonics, and transients in distribution systems. Various compensation devices are used nowadays to improve power quality. The advances in power electronic technologies improve the reliability and functionality of power electronic based controllers, resulting in increased applications of FACTS devices like DSTATCOM and Dynamic Voltage Restorer (DVR) which are fast, flexible, and efficient solutions to the power quality problems. These devices are used to restore the source, load voltage, and current disturbances caused by different loads and faults. These devices were tested in a standard IEEE 14-bus system for Total Harmonic Distortion (THD) minimization while utilizing PI-based Artificial Neural Networks (ANNs) and Linear Regression (LR). The results were analyzed and compared. [ABSTRACT FROM AUTHOR]

The interlayer bonding condition in asphalt pavement significantly affects pavement performance. This study employed machine learning techniques to predict interlayer shear strength (ISS). Feed-forward artificial neural networks (ANN) and random forest (RF) models were developed and compared with traditional multiple linear regression (MLR). Utilizing 156 datasets, divided into 70% training and 30% testing, model performance was assessed using R-squared, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) was utilized for model interpretation. The results indicated that the ANN and RF models outperformed MLR, explaining over 95% of experimental data. RF exhibited superior performance with lowest MSE, RMSE, and MAE (0.0029, 0.0538, and 0.0376 MPa). SHAP analysis highlighted the significance of temperature, normal stress, shear deformation rate, and curing time as influential variables in ISS prediction. Elevated temperature adversely influenced ISS, while normal stress, shear deformation rate, and curing time positively contributed to ISS. [ABSTRACT FROM AUTHOR]

New advancements in deep learning issues, motivated by real-world use cases, frequently contribute to this growth. Still, it's not easy to recognize the speaker's emotions from what they want to say. The proposed technique combines a deep learning-based brain-inspired prediction-making artificial neural network (ANN) through social ski-driver (SSD) optimization techniques. When assessing speaker emotion recognition (SER), the recognition results are compared with the existing convolutional neural network (CNN) and long short-term memory (LSTM)-based emotion recognition methods. The proposed method for classification based on ANN decreases the computational costs. The SER algorithm allows for a more in-depth classification of different emotions because of its relationship to ANN and LSTM. The SER model is based on ANN and the recognition impact of the feature reduction. The SER in this proposed research work is based on the ANN emotion classification system. Speaker recognition accuracy values of 96.46%, recall values of 95.39%, precision values of 95.21%, and F-Score values of 96.10% are obtained in this proposed result, which is higher than the existing result. The average accuracy results by using the proposed ANN classification technique are 4.38% and 2.89%, better than the existing CNN and LSTM techniques, respectively. The average precision results by using the proposed ANN classification technique are 4.67% and 2.49%, better than the existing CNN and LSTM techniques, respectively. The average recall results by using the proposed ANN classification technique are 2.90% and 1.42%, better than the existing CNN and LSTM techniques, respectively. The average precision results using the proposed ANN classification technique are 3.80% and 3.10%, better than the existing CNN and LSTM techniques, respectively. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of four machine learning models for predicting house prices: Linear Regression (LR), Artificial Neural Networks (ANNs), Random Forest Regressor, and Support Vector Regression (SVR). As the demand for housing continues to rise due to population growth, the housing business has become one with high long-term potential. This is evidenced by an upward trend in house prices in Indonesia and other regions. For instance, in the United States, new single-family home sales climbed 8.8% to 693,000 units (SAAR) in March 2024, marking the fastest pace since September 2023 and an 8.3% year-over-year increase. Property values are influenced by various internal factors, such as area and number of rooms, as well as external factors like inflation rates and government policies. Linear Regression (LR) is known for its simplicity and ease of interpretation, providing high accuracy with low computational cost. In contrast, Artificial Neural Networks (ANNs) excel at capturing non-linear relationships, making them suitable for more complex datasets. Random Forest Regressor, an ensemble learning method, enhances predictive performance by averaging multiple decision trees and controlling overfitting, while SVR is effective in high-dimensional spaces and can model non-linear relationships using kernel functions. We employ the Boston housing dataset, a widely used benchmark for house price prediction tasks, to compare these models. The dataset is split into 70% for training and 30% for testing. The performance of the models is evaluated using Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE). The results show that Linear Regression achieves an MSE of 0.0106, R-squared of 0.67, and MAE of 0.075. The Artificial Neural Network achieves an MSE of 0.0046, R-squared of 0.86, and MAE of 0.047. The Random Forest Regressor achieves an MSE of 0.0060, R-squared of 0.81, and MAE of 0.050, while the SVR achieves an MSE of 0.0054, R-squared of 0.83, and MAE of 0.056. By comparing these models, we find that the Artificial Neural Network (ANN) achieves the highest accuracy, followed by SVR, Random Forest Regressor, and Linear Regression. These findings provide valuable insights for buyers, sellers, and developers in the housing market, highlighting the potential of advanced machine learning models in accurately predicting house prices. [ABSTRACT FROM AUTHOR]

The paper presents how can we predict multiple disease prediction by utilizing machine learning(ML) and deep learning(DL) techniques by analyzing different datasets consisting of various medical parameters our approach integrates ML algorithms such as Logistic regression, KNN,Random forest, support vector machine for initial classification and then sequentially used DL methodology ANN(Artificial Neural Networks) through this extensive experimentation and validation of real world medical datasets, our proposed framework gives the promising outcomes in achieving a remarkable performance of predicting various diseases such as breast cancer, lung cancer, diabetes, monkeypox, parkinson's. thus offering valuable insights to get early diagnosis. which helps the healthcare professionals to detect and diagnose the disease at preventive stages to improve the patient care. The interface we used for predicting diseases is Streamlit. [ABSTRACT FROM AUTHOR]

Borehole breakouts significantly influence drilling operations' efficiency and economics. Accurate evaluation of breakout size (angle and depth) can enhance drilling strategies and hold potential for in situ stress magnitude inversion. In this study, borehole breakout size is approached as a complex nonlinear problem with multiple inputs and outputs. Three hybrid multi-output models, integrating commonly used machine learning algorithms (artificial neural networks ANN, random forests RF, and Boost) with the Walrus optimization algorithm (WAOA) optimization techniques, are developed. Input features are determined through literature research (friction angle, cohesion, rock modulus, Poisson's ratio, mud pressure, borehole radius, in situ stress), and 501 related datasets are collected to construct the borehole breakout size dataset. Model performance is assessed using the Pearson Correlation Coefficient (R2), Mean Absolute Error (MAE), Variance Accounted For (VAF), and Root Mean Squared Error (RMSE). Results indicate that WAOA-ANN exhibits excellent and stable prediction performance, particularly on the test set, outperforming the single-output ANN model. Additionally, SHAP sensitivity analysis conducted on the WAOA-ANN model reveals that maximum horizontal principal stress ( σ H ) is the most influential parameter in predicting both the angle and depth of borehole breakout. Combining the results of the studies and analyses conducted, WAOA-ANN is considered to be an effective hybrid multi-output model in the prediction of borehole breakout size. [ABSTRACT FROM AUTHOR]

This study aims to develop and assess seven machine learning (ML) models, including an Artificial Neural Network (ANN), Gradient Boosting (GB), Extra Trees (ET), Lasso Regression (LASSO), Least Angle Regression (Lars), Random Forest (RF), and Adaptive Boosting (Adaboost), that are effective and simple in forecasting the roller length of a hydraulic jump in a rectangular horizontal canal. This study also proposes a novel approach for applying the Buckingham Pi theorem to ascertain four model inputs and one output, corresponding to five dimensionless Pi terms that have not previously been explored. The impact of channel surface roughness, fluid viscosity, channel geometry, and gravity on the hydraulic jump's roller length was considered to improve the model's performance. Furthermore, the importance of input features was determined, resulting in two model input scenarios. The scenario with four inputs containing the inflow Reynolds number Re1* surpasses the three-input scenario. Because of their high accuracy and efficiency, six ML models outperformed three empirical equations. The GB, ANN, and ET models showed the best performance in both validation and testing phases, whereas LASSO, Lars, RF, and Adaboost performed with lower precision, in descending order. Regarding the ANN model, the best has two hidden layers of sixteen neurons each. In the validation and testing phases, this model achieves the highest Nash–Sutcliffe efficiency of 0.992 and 0.933, respectively. As a result, this study recommends using the GB, ANN, and ET models to forecast the roller length of the hydraulic jump instead of empirical equations when the inflow Froude number, Fr1, varies between 2.4 and 15.9. [ABSTRACT FROM AUTHOR]

The lowest height at which a product can fall without suffering severe harm is known as the "critical drop height" for agricultural products. It is a crucial factor to take into account for crops like loquats that are prone to bruising or damage upon impact. By establishing the minimum altitude at which the product can be dropped without experiencing substantial harm, suitable processing procedures may be established from harvest to the end consumer, thereby preserving product quality and worth. The critical drop height can be ascertained through swift, affordable, non-destructive, and non-traditional methods, rather than time-consuming and expensive laboratory trials. In the study, we aimed to estimate the critical drop height for loquat fruit using machine learning methods. Three different machine learning methods with different operating principles were applied. R2, MAE, RMSE, and MAPE metrics were used to assess the models. There were no obvious differences in both the comparisons within the models, namely the training and test results and the mutual comparisons of the models. However, with a slight difference, the SVMs model performed better in the training data set, and the ETs model performed better in the test data set. Plots were drawn to visualize model performances, and the results obtained from the plots and metrics support each other. [ABSTRACT FROM AUTHOR]

Digital technologies sustain today's world. Every part of the world is working towards digital technologies, which none of us can eliminate. Enormous growth is achieved only by unexpected acceleration by digital technologies, including the Internet of Everything (IoE), Artificial Neural Networks (ANN), Machine Learning (ML), Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning (DL), and many more. These technologies started occupying all the engineering sectors, including manufacturing. This paper focuses on tribology analysis related to manufacturing concerning various digital manufacturing technologies. The paper narration includes Tribology using digital technologies wherein the journals and patent landscape analysis abet them. In trend, Tribology utilizes all these technologies today and envisages its growth with the predominant technological invention in the border view. The survey of various literature reveals that only three digital technologies, including AI, ML, and ANN, are used by tribologists around the globe. Other Technologies like Evolutionary Algorithm (EA), Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Interference Systems (ANFIS) are not used predominantly. [ABSTRACT FROM AUTHOR]