Search

In this research paper, we explored the predictive capabilities of three different models of Polynomial Regression (PR), Extreme Gradient Boosting (XGB), and LASSO to estimate the density of supercritical carbon dioxide (SC-CO2) and the solubility of niflumic acid as functions of the input variables of temperature and pressure. The optimization of hyperparameters for these models is achieved using the innovative Barnacles Mating Optimizer (BMO) algorithm. For SC-CO2 density estimation, PR exhibits remarkable accuracy, showing an R-squared value of 0.99207 for data fitting. XGB performs admirably with an R2 of 0.92673, while LASSO model demonstrates good predictive ability, showing an R2 of 0.81917. Furthermore, we assess the models' performance in predicting the solubility of niflumic acid. PR exhibits excellent predictive capabilities with an R2 of 0.96949. XGB also delivers strong performance, yielding an R-squared score of 0.92961. LASSO performs well, achieving an R-squared score of 0.82094. The results indicated promising performance of machine learning models and optimizer in estimating drug solubility in supercritical CO2 as the solvent applicable for pharmaceutical industry. [ABSTRACT FROM AUTHOR]

Rock abrasivity plays an important role in the machine design, construction scheduling, and budgeting of TBM projects. Establishing several faster and simpler estimation equations for the Cerchar Abrasivity Index (CAI) of rocks is, therefore, very important. This study investigated the correlation between the CAI and mechanical properties of rock, rock mass classification parameters, and machine performance. A TBM construction database including 159 tunnel sections is established. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis (0.66 < R2 < 0.76). In this process, a normalized specific energy is proposed to evaluate the machine performance. The results show that the rock compressive strength and brittleness index are the most dependent parameters to explain CAI, and the estimated rock mass strength also indicates a close correlation. In addition, the contribution of a rock mass classification system and machine performance index for estimating CAI cannot be ignored. Finally, the estimation performance of the developed equations is compared and evaluated, and a new method for selecting an optimal model based on ranking is proposed. Since the input parameters of the proposed equations can be readily available at the project planning stage, they are very practical for TBM designers, tunnel designers, and contractors. Highlights: The correlations between the CAI and rock mechanical properties, rock mass classification parameters, and machine performance are investigated. A normalized specific energy index is proposed for assessing the performance of rocks excavation by TBMs. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis. A new method for selecting optimal estimation model based on ranking is presented. [ABSTRACT FROM AUTHOR]

Currently, there are demands for machine tools with higher dynamic performance as a result of their high machining accuracy and efficiency in designing complex parts in the manufacturing industry. The motion delays in each of the motion axes cause dynamic tracking errors or tool tracking errors which greatly affect the surface quality of machined parts; hence, tuning the dynamic parameters like the position gains of the motion axis causing the defect is as good as eliminating the error source rather than tuning the parameters of all the motion axes. This work proposes a decoupling method to identify the particular motion axis that greatly affects or causes the tool tracking error by using the ISO BK3 kinematic test, and this involves orientation contour error estimation and motion axis error computation based on inverse kinematics of the machine tool transformation. An experiment was carried out on an industrial machine tool with a tilting rotary table to verify the simulation results; the feed servo system model of the motion axes was constructed using MATLAB Simulink tools. The results obtained show that the tool tracking errors are greatly influenced by some dynamic parameters like the feedrate and the position gains emanating from a particular motion axis with dynamic deficiency, and the proposed decoupling method robustly identifies the individual motion axes greatly affecting the dynamic tracking error which makes it very relevant to tune only the dynamic parameters of those particular motion axes instead tuning all. Moreover, this method is simple and robust, and its implementation can help improve the dynamic performance of machine tools in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Hyperparameters play a critical role in analyzing predictive performance in machine learning models. They serve to strike a balance between overfitting and underfitting of research-independent features to prevent extremes. Manual tuning and automated techniques are employed to identify the optimal combination and permutation to achieve the best model performance. This study explores the pursuit of the best fit through various hyperparameters. Following Logistic Regression analysis, this research compared Random Forest, Randomized search, Grid search, Genetic, Bayesian, and Optuna machine learning model tuning for the best accuracy of prediction the student The model accuracy was further assessed using confusion matrices and Receiver Operating Characteristic—Area Under the Curve (ROC-AUC) curves for student grade classification. The genetic algorithm's recommended hyperparameter tuning yielded the highest accuracy (82.5%) and AUC-ROC score (90%) for student result classification. Manual tuning with an estimator of 300, criterion entropy, max features of sqrt, and a minimum sample leaf of 10 achieved an accuracy of 81.1%, which closely resembled the performance randomized search cross-validation algorithm. The default random forest model scored the least accuracy (78%). However, this manual tuning process took a lesser time (3.66 s) to fit the model while grid search CV tuned 941.5 s. Hence, this research made significant contributions to optimizing various machine learning models using a range of hyperparameters for grade classification. [ABSTRACT FROM AUTHOR]

This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of accurate moisture content prediction models. The study compares the performance of various machine learning models, and XGBoost emerges as the most promising model, demonstrating superior accuracy in predicting moisture content based on factors like soil properties, cement content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive exPlanations) to understand how these features influence the model's predictions. UPV is the most significant factor affecting predicted moisture content, followed by cement content and soil properties like uniformity coefficient. Also, the study explores the possibility of using a reduced set of features for moisture content prediction. They demonstrate that a combination of UPV, cement content, and uniformity coefficient can achieve good accuracy, highlighting the potential for practical applications where obtaining all data points might be challenging. [ABSTRACT FROM AUTHOR]

Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes. The prediction task of customer churn becomes more challenging when there is class overlap in the data training. In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers that churn. We used five different customer churn datasets from an open platform. CTGAN is a tabular GAN-based oversampling to address class imbalance but has a class overlap problem. We combined CTGAN with the ENN under-sampling technique to overcome the class overlap. CTGAN-ENN reduced the number of class overlaps by each feature in all datasets. We investigated how effective CTGAN-ENN is in each machine learning technique. Based on our experiments, CTGAN-ENN achieved satisfactory results in KNN, GBM, XGB and LGB machine learning performance for customer churn predictions. We compared CTGAN-ENN with common over-sampling and hybrid sampling methods, and CTGAN-ENN achieved outperform results compared with other sampling methods and algorithm-level methods with cost-sensitive learning in several machine learning algorithms. We provide a time consumption algorithm between CTGAN and CTGAN-ENN. CTGAN-ENN achieved less time consumption than CTGAN. Our research work provides a new framework to handle customer churn prediction problems with several types of imbalanced datasets and can be useful in real-world data from customer churn prediction. [ABSTRACT FROM AUTHOR]

Communication-based train control (CBTC) system is a typical cyber-physical system with open wireless communication that is vulnerable to attacks. To protect the security of wireless communication in the CBTC system, machine learning-based intrusion detection system (IDS) has been extensively researched. However, the performance of a machine learning-based IDS highly depends on feature design, and the spatial and temporal correlation of network data attributes makes it difficult to design features manually. Meanwhile, this type of IDS can only detect known attacks that are contained in the training dataset and fail to detect new attacks (i.e., zero-day attacks). To cope with the above issue, we propose a novel IDS based on transfer learning for the CBTC system. The proposed IDS leverages an optimized one-dimensional convolutional neural network block and long short-term memory to automatically extract spatial and temporal features from the original data. Furthermore, a knowledge transfer method is utilized to transfer the features to enable zero-day attack detection. We evaluate the proposed IDS on a dataset representing the CBTC system network data. The results show that the proposed IDS can achieve 99.32% accuracy for known attacks and 93.21% average F1-Score for zero-day attacks. [ABSTRACT FROM AUTHOR]

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts' recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew's efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT. [ABSTRACT FROM AUTHOR]

Label propagation can rapidly predict the labels of unlabeled objects as the correct answers from a small amount of given label information, which can enhance the performance of subsequent machine learning tasks. Most existing label propagation methods are proposed for static data. However, in many applications, real datasets including multiple feature value types and massive unlabeled objects vary dynamically over time, whereas applying these label propagation methods for dynamic partially labeled hybrid data will be a huge drain due to recalculating from scratch when the data changes every time. To improve efficiency, a novel incremental label propagation algorithm based on neighborhood relation (ILPN) is developed in this paper. Specifically, we first construct graph structures by utilizing neighborhood relations to eliminate unnecessary label information. Then, a new label propagation strategy is designed in consideration of the weights assigned to each class so that it does not rely on a probabilistic transition matrix to fix the structure for propagation. On this basis, a new label propagation algorithm called neighborhood relation-based label propagation (LPN) is developed. For the dynamic partially labeled hybrid data, we integrate incremental learning into LPN and develop an updating mechanism that allows incremental label propagation over previous label propagation results and graph structures, rather than recalculating from scratch. Finally, extensive experiments on UCI datasets validate that our proposed algorithm LPN can outperform other label propagation algorithms in speed on the premise of ensuring accuracy. Especially for simulated dynamic data, the incremental algorithm ILPN is more efficient than other non-incremental methods with the variation of the partially labeled hybrid data. [ABSTRACT FROM AUTHOR]

Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction. [ABSTRACT FROM AUTHOR]

Optimization techniques play a crucial role in improving the performance of machine learning applications. However, traditional techniques may not be suitable for addressing complex problems that involve numerous parameters requiring adaptation. This paper presents a novel variant of the gradient-based optimizer (GBO) and investigates the effectiveness of integrating the harmony search (HS) algorithm to enhance the GBO algorithm. The HS algorithm offers several advantages, including fast convergence and fewer adjustable parameters. The proposed method, called GBOHS, is evaluated through three experiments. The first experiment focuses on solving global optimization problems, while the second experiment assesses its ability to select the most relevant features using sixteen benchmark feature selection datasets. The final experiment applies the GBOHS method to solve five real engineering problems. The results are compared against those of well-known optimization algorithms using various performance measures, including fitness function value and classification accuracy. The findings demonstrate that the proposed GBOHS method achieves high accuracy and consistently outperforms the compared methods across all the experiments, demonstrating its promising effectiveness. [ABSTRACT FROM AUTHOR]

In the field of data engineering in machine learning (ML), a crucial component is the process of scaling, normalization, and standardization. This process involves transforming data to make it more compatible with modeling techniques. In particular, this transformation is essential to ensure the suitability of the data for subsequent analysis. Despite the application of many conventional and relatively new approaches to ML, there remains a conspicuous lack of research, particularly in the geotechnical discipline. In this study, ML-based prediction models (i.e., RF, SVR, Cubist, and SGB) were developed to estimate the undrained shear strength (UDSS) of cohesive soil from the perspective of a wide range of data-scaling and transformation methods. Therefore, this work presents a novel ML framework based on data engineering approaches and the Cubist regression method to predict the UDSS of cohesive soil. A dataset including six different features and one target variable were used for building prediction models. The performance of ML models was examined considering the impact of the data pre-processing issue. For that purpose, data scaling and transformation methods, namely Range, Z-Score, Log Transformation, Box-Cox, and Yeo-Johnson, were used to generate the models. The results were then systematically compared using different sampling ratios to understand how model performance varies as various data scaling/transformation methods and ML algorithms were combined. It was observed that data transformation or data sampling methods had considerable or limited effects on the UDSS model performance depending on the algorithm type and the sampling ratio. Compared to RF, SVR, and SGB models, Cubist models provided higher performance metrics after applying the data pre-processing steps. The Box-Cox transformed Cubist model yielded the best prediction performance among the other models with an R2 of 0.87 for the 90% training set. Also, the UDSS prediction model generally yielded the best performance metrics when it was used with the transformed-based models (i.e., Box-Cox, Log, and Yeo-Johnson) than that of scaled-based (i.e., Range and Z-Score) models. The results show that the Cubist model has a higher potential for UDSS prediction, and data pre-processing methods have impacts on the predictive capacity of the evaluated regression models. [ABSTRACT FROM AUTHOR]

In pulsed electrochemical machining (PECM), it is difficult to control machining stability and surface quality numerous due to the interrelated variables with highly nonlinear and complex characteristics. Thus, how to determine the optimal process parameters to ensure optimal machining performance became a hot research. In this paper, we investigate the recurrence behavior of Ti–48Al–2Cr–2Nb alloy in PECM by recurrence plots and the quantitative analysis. Besides, the Technique for Order Preference by Similarity to Ideal Solution with Criteria Importance through Intercriteria Correlation (CRITIC-TOPSIS) is employed to determine the optimal process parameters for PECM. The results demonstrate that the recurrence plots exhibit periodic characteristics at the macro level, and effectively identify current density changes in different dissolution stages of the alloy at the micro level. Each pulse stage exhibits an inherent oscillation frequency, and the recurrence plots display a similar block structure with evident fractal features. The optimal result show that the optimal process parameters for the potential, the duty cycle, electrolyte pressure and the feed velocity is 16 V, 40%, 0.4 MPa and 0.9 mm/min, respectively. After optimization, the machining stability and the surface morphology uniformity improves by 29.82 and 72.78%, respectively, and the surface roughness decreases by 24.09%, providing a basis for establishing the quantitative relationship between the machining stability and the surface roughness. [ABSTRACT FROM AUTHOR]

In recent years, healthcare monitoring systems (HMS) have increasingly integrated the Internet of Medical Things (IoMT) with cloud computing, leading to challenges related to data latency and efficient processing. This paper addresses these issues by introducing a Machine Learning-based Medical Data Segmentation (ML-MDS) approach that employs a k-fold random forest technique for efficient health data classification and latency reduction in a fog-cloud environment. Our method significantly improves latency issues, enhancing the Quality of Service (QoS) in healthcare systems and demonstrating its adaptability in heterogeneous network scenarios. We specifically employ the Random Forest algorithm to mitigate the common problem of overfitting in machine learning models, ensuring broader applicability across various healthcare contexts. Additionally, by optimizing data processing in fog computing layers, we achieve a substantial reduction in overall latency between healthcare sensors and cloud servers. This improvement is evidenced through a comparative performance analysis with existing models. The proposed framework not only ensures secure and scalable management of IoMT health data but also incorporates a stochastic approach to mathematically formulate performance indicators for the HMS queuing model. This model effectively predicts system response times and assesses the computing resources required under varying workload conditions. Our simulation results show a classification accuracy of 92%, a 56% reduction in latency compared to existing models, and an overall enhancement in e-healthcare service quality. [ABSTRACT FROM AUTHOR]

Nickel-based superalloys have gained widespread use in various industries like aerospace, power generation, chemical processing, and marine engineering due to their exceptional mechanical properties. However, machining these superalloys poses significant challenges due to their hardness. To address these issues and improve machining performance, selecting suitable cutting tool materials and coatings is crucial. This state-of-the-art review aims to provide an in-depth understanding of the latest research on cutting tool materials and coatings in enhancing the tool performance in machining the superior nickel-based superalloys. It begins by examining the applications and current challenges associated with machining these alloys. Different types of cutting tool materials, including cemented carbides, ceramic-based tools, and super-hard materials like polycrystalline cubic boron nitride, are analyzed, with a particular focus on tool geometry and failure. The advantages, limitations, and specific applications of each tool material are highlighted. Additionally, the role of coatings in enhancing cutting tool performance is explored. Various coating types, including titanium nitride (TiN), titanium carbonitride (TiCN), and aluminum oxide (Al2O3), are evaluated based on their effectiveness in machining nickel-based superalloys. The review also covers aspects such as tool wear, cutting forces, the surface roughness of machined parts, heat generation, and temperature measurement techniques employed by researchers to assess cutting tool performance. It underscores the importance of tailoring the selection of cutting tool materials and coatings to specific machining conditions for optimal results. The review concludes by summarizing key findings and suggesting potential areas for future research. [ABSTRACT FROM AUTHOR]

There are multiple scenarios in machine learning where the data used presents a heavy bias towards one of the classes. Evaluating the performance of machine learning models in such imbalanced scenarios proves to be difficult and challenging, as one of the classes is poorly represented in the data, and this class is often more relevant to the end-user. An abundance of performance metrics have been devised and commonly used in order to solve these specific problems, however, there is often a lack of common agreement on which metric is best and which to use in specific imbalanced scenarios. In this study, we experimentally study the impact of choosing one metric over another in the evaluation of a classifier for binary classification, as well as the effect of data characteristics such as class imbalance and noise on those metrics. Based on our extensive empirical analysis, we provide a set of easy-to-follow guidelines for which performance metric is best to use depending on the context of the problem. Specifically, we highlight the importance of using multiple different metrics which are fundamentally different in imbalanced domains, we also display results on why the usage of Davis' interpolation of the area under the precision-recall curve and the Matthews Correlation Coefficient metrics should be preferred over other similar metrics, as well as why the usage of geometric mean and F 1 score should be avoided in scenarios likely to present noise on the labels. [ABSTRACT FROM AUTHOR]

Athletes' performance evaluation is a critical step for the assessment of the skills and the quality of training of the athletes. This is even more important in team sports such as football, in which different roles, and hence different skills, are required. In light of this, the presented work aims at forecasting above team-average performance of football players by using supervised machine learning algorithms. Such algorithms were trained and tested on four biometric parameters as input and seven performance indicators as labels. The algorithms have been optimized using three optimization techniques: the gridsearch, and two versions of the whale-optimization algorithm, the standard one and another, proposed by us, in which Euclidean distance is used. The analyses were conducted by dividing the players by their role: the strikers, the midfielders and the defenders, to take into account the different skills required for each task. The obtained results show that the Random Forest, trained with specific combinations of biometric parameters is capable of predicting the selected performance indicators with an accuracy higher than 90%, for all the considered players' roles and that the proposed optimization technique outperforms the existing ones. In light of these results, the proposed method could be used profitably by football teams' staff to monitor their players, offering the possibility to take tactical decisions, design customized training sessions and orient market choices. [ABSTRACT FROM AUTHOR]

Wire-electrical discharge machining (wire-EDM) is gaining wider acceptance for producing components of Al-matrix composites (Al-MCs) that are hard to machine by traditional methodologies. The related research is primarily limited to ex-situ Al-MCs commonly reinforced with ceramic particles; however, Al-MCs reinforced with in-situ ordered intermetallics have evolved as superior composites nowadays. This research has focused on wire-EDM of in-situ Al/Al3Fe composites developed by the reactive stir-casting route. The influence of three machining variables (pulse-on-time, servo voltage, and peak-current) and one material parameter (vol% of reinforcement) have been studied following the L27 Taguchi design. The integrity of the machined surface has been characterized via measurements of surface roughness (SR) and the alteration of surface chemistry (ASC, ΣCu + Zn + O), in addition to the evaluation of kerf width (KW) as a machining performance indicator. It has been established that all four control factors are significant for KW, while ASC is influenced by all factors except vol% of reinforcement; however, only pulse-on-time is substantial for SR. Analytical models of individual responses are developed while the desirability approach helps to accomplish the multi-response optimization; several confirmation experiments establish the authenticity of these predictions with an error < 8%. Characterizations of machined surfaces and wire electrodes by FESEM and EDS techniques reveal that the surface integrity of in-situ Al/Al3Fe composites varies significantly with machining conditions. [ABSTRACT FROM AUTHOR]

The potential substitution of traditional cutting fluids with green Metalworking fluids (MWFs) is frequently explored because of their non-toxic and environmentally friendly characteristics. These green MWFs have often been evaluated for their tribological behavior and machining performance under various operating conditions. However, very few studies have analyzed the green metalworking fluids' ecological and eco-toxicological characteristics. With an aim to explore the potential impact and the related concerns of the developed green MWFs on the environment and eco-life, ecological and eco-toxicological experimental studies were performed in this research. This research study offers a comprehensive analysis of the biodegradability, soil eco-toxicity, cell viability, corrosion responsiveness, and reusability characteristics of the in-house developed nanoparticles-modified vegetable oil MWFs (MVO-MWFs). The findings from biodegradability studies demonstrate the inherent and readily biodegradable nature of the developed MWFs. Subsequently, soil eco-toxicity tests reveal the non-harmful nature of developed MWFs to the ecological organisms at the tested concentrations. Furthermore, during the cell viability assays, no evidence of cell death was observed in normal and cancer-affected cells under tested conditions. Additionally, the reusability studies on MQL imply that a considerable amount of MWF can be collected and reused to enhance the sustainability of the MWF system. This study established a decision-making approach to identify the optimal NP-based MWF combination by correlating multivariate data. Based on this work, a step-by-step methodology is recommended with four decision points to conduct a series of ecological and eco-toxicological protocols for the new or existing NP-based MWFs. This study contributes to the manufacturing research community and industry by shedding light on hitherto unexplored aspects of ecological and eco-toxicological investigations on the NP-based MVO-MWFs. [ABSTRACT FROM AUTHOR]

The high-dimensional nature of medical data frequently results in suboptimal performance of machine learning models. Applying feature selection before classification is necessary to improve the performance of classifiers. Although evolutionary-based wrapper feature selection methods are acknowledged for their superior performance in exploring optimal feature subsets, they have been demonstrated to carry the risk of overfitting and a potential loss of efficient search capability in the later stages of evolution. To address these issues, we propose a generalized wrapper feature selection method called Two Populations Based Feature Selection (TPBFS), which incorporates dual populations evolving in reverse directions to improve convergence speed. It introduces a probability-based crossover operation to mitigate overfitting and a record list to systematically track and replace optimal individuals, which helps to avoid getting stuck in local optima during later stages of evaluation. The experimental results demonstrate that TPBFS is effective in reducing the dimensionality of various medical datasets while guaranteeing the performance of classifiers. [ABSTRACT FROM AUTHOR]

The current investigation examines the influence of footing shape, the base area of footing (A), the mass of footing-machine assembly (m), and eccentric force settings (mee) on the dynamic response and performance of machine foundation systems. Five different footing configurations are employed to perform field block vibration tests involving three square, one circular and one rectangular footing. The experiments are performed at the geotechnical field laboratory of IIT Kanpur, India (N26°30′59.0892″, E80°13′51.6888″). The accuracy and reliability of the experimental results are endorsed by the results obtained from the mass-spring-dashpot (MSD) analysis. In addition, an artificial neural network (ANN) model is created to anticipate the dynamic behaviour of the soil-foundation system. A thorough parametric study demonstrates the efficacy of the developed ANN model. It is revealed from the investigation that the stiffness (k) and the damping ratio (D) of the soil for square foundations increase by 7% and 3%, respectively, with a 40% increase in A. Similarly, the circular foundation exhibits 7 and 3% higher k and 4 and 3% higher D than those obtained for square and rectangular foundations, respectively. For square foundations, a 24% enhancement in m leads to a 42 and 4% increase in k and D, respectively. In contrast, for circular and rectangular foundations, a 13% increase in m results in a 27 and 19% increase in k and D, respectively. In this study, experimental testing, analytical validation, and ANN modelling provide insight into the response of machine foundations under various operating conditions. The results of this study can be utilized to optimize the design of machine foundations. [ABSTRACT FROM AUTHOR]

Cues from multiple modalities have been successfully applied in several fields of natural language processing including machine translation (MT). However, the application of multimodal cues in low-resource MT (LRMT) is still an open research problem. The main challenge of LRMT is the lack of abundant parallel data which makes it difficult to build MT systems for a reasonable output. Using multimodal cues can provide additional context and information that can help to mitigate this challenge. To address this challenge, we present a multimodal machine translation (MMT) dataset of low-resource languages. The dataset consists of images, audio and corresponding parallel text for a low-resource language pair that is Manipuri–English. The text dataset is collected from the news articles of local daily newspapers and subsequently translated into the target language by translators of the native speakers. The audio version by native speakers for the Manipuri text is recorded for the experiments. The study also investigates whether the correlated audio-visual cues enhance the performance of the machine translation system. Several experiments are conducted for a systematic evaluation of the effectiveness utilizing multiple modalities. With the help of automatic metrics and human evaluation, a detailed analysis of the MT systems trained with text-only and multimodal inputs is carried out. Experimental results attest that the MT systems in low-resource settings could be significantly improved up to +2.7 BLEU score by incorporating correlated modalities. The human evaluation reveals that the type of correlated auxiliary modality affects the adequacy and fluency performance in the MMT systems. Our results emphasize the potential of using cues from auxiliary modalities to enhance machine translation systems, particularly in situations with limited resources. [ABSTRACT FROM AUTHOR]

The sensitive attribute selection requires a well-trained machine-learning model to avoid unauthorized access to sensitive data. A new hybrid approach Boosted Neural Sensitive Attribute selection (BNSAD) method is used to extract sensitive attributes from the authorized data. This method is used in various access control methods mainly our proposed Hierarchical Attribute Based Access Control (HABAC) system. The HABAC gives data privacy by ensuring authorized individuals or groups only access the data. The main aim of the proposed system is to give a new hybrid machine-learning algorithm for data privacy. The BNSAD algorithm identifies the patterns from data and gives the boosted trained model as an input to the neural network to predict the sensitive attribute. The performance of seven machine learning algorithms was identified initially. The top three performance metrics of the algorithms taken for the new algorithm construction. The 94%, 92%, and 90% accuracy of the BNSAD algorithm for three datasets is higher than the existing three selected top three algorithms. The algorithm is implemented on BLP, RBAC, ABAC, and HABAC access control models. The final model accuracy of 98%, 98%, and 95% is achieved by BNSAD with HABAC for three medical datasets to identify the sensitive attributes for enhanced privacy of the data. The performance metrics for the proposed HABAC system are high with the existing models. [ABSTRACT FROM AUTHOR]

With the fast advancement of AI technology in recent years, many excellent Data Augmentation (DA) approaches have been investigated to increase data efficiency in Natural Language Processing (NLP). The reliance on a large amount of data prohibits NLP models from performing tasks such as labelling enormous amounts of textual data, which require a substantial amount of time, money, and human resources; hence, a better model requires more data. Text DA technique rectifies the data by extending it, enhancing the model's accuracy and resilience. A novel lexical-based matching approach is the cornerstone of this work; it is used to improve the quality of the Machine Translation (MT) system. This study includes resource-rich Indic (i.e., Indo-Aryan and Dravidian language families) to examine the proposed techniques. Extensive experiments on a range of language pairs depict that the proposed method significantly improves scores in the enhanced dataset compared to the baseline system's BLEU, METEOR and ROUGE evaluation scores. [ABSTRACT FROM AUTHOR]

The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors. [ABSTRACT FROM AUTHOR]

Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs' performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties. [ABSTRACT FROM AUTHOR]

With the increasing volume of data on the web, distributed file systems such as HDFS are widely used to provide efficient, fast and reliable access to the ever-growing volumes of data. Parameter tuning, a core performance management task, directly affects the performance of storage systems. However, large, non-linear, discrete and non-digital storage system configuration parameters significantly increase the storage system parameter tuning burden. Traditional manual tuning methods rely on a lot of a priori knowledge and have high tuning costs. In this paper, we propose a Smart gambler (SG) algorithm based on a simulated environment and Hill Climbing to reduce the probability of falling into a local optimum. In comparative experiments on publicly available datasets, SG achieves an average improvement of 3.14 % and 5.02 % in the highest tuning value over search algorithms such as Genetic Algorithm and an average time saving of 32.1 % and 50.6 % in tuning time for both workloads, respectively. [ABSTRACT FROM AUTHOR]

The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. Overfitting of the model occurs as a result of imbalanced datasets, resulting in poor performance. In this study, we compare different oversampling techniques like synthetic minority oversampling technique (SMOTE), support vector machine SMOTE (SVM-SMOTE), Border-line SMOTE, K-means SMOTE, and adaptive synthetic (ADASYN) oversampling to address the issue of imbalanced datasets and enhance the performance of machine learning models. Preprocessing significantly enhances the quality of input data by reducing noise, redundant data, and unnecessary data. This enables the machines to identify crucial patterns that facilitate the extraction of significant and pertinent information from the preprocessed data. This study preprocesses the data using various top-level preprocessing steps. Furthermore, two imbalanced Twitter datasets are used to compare the performance of oversampling techniques with six machine learning models including random forest (RF), SVM, K-nearest neighbor (KNN), AdaBoost (ADA), logistic regression (LR), and decision tree (DT). In addition, the bag of words (BoW) and term frequency and inverse document frequency (TF-IDF) features extraction approaches are used to extract features from the tweets. The experiments indicate that SMOTE and ADASYN perform much better than other techniques thus providing higher accuracy. Additionally, overall results show that SVM with 'linear' kernel tends to attain the highest accuracy and recall score of 99.67% and 1.00% on ADASYN oversampled datasets and 99.57% accuracy on SMOTE oversampled dataset with TF-IDF features. The SVM model using 10-fold cross-validation experiments achieved 97.40 mean accuracy with a 0.008 standard deviation. Our approach achieved 2.62% greater accuracy as compared to other current methods. [ABSTRACT FROM AUTHOR]

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique's performance through hyperparameter tuning on the Arabic cyberbullying data set. [ABSTRACT FROM AUTHOR]

Code smells are poorly designed code structures indicating that the code may need to be refactored. Recognizing code smells in practice is complex, and researchers strive to develop automatic code smell detectors. An obstacle to developing these solutions is the datasets' limitations. Manually labeled datasets were collected to investigate the developers' perceptions of code smells. They are characterized by a high label disagreement that hurts the performance of Machine Learning (ML) models trained using them. Furthermore, all large, manually labeled datasets are developed for Java. We recently created a novel dataset for C# to alleviate these issues. This paper evaluates ML code smell detection approaches on our novel dataset. We consider two feature representations to train ML models: (1) code metrics and (2) CodeT5 embeddings. This study is the first to consider the CodeT5 state-of-the-art neural source code embedding for code smell detection in C#. To prove the effectiveness of ML, we consider multiple metrics-based heuristics as alternatives. In our experiments, the best-performing approach was the ML classifier trained on code metrics (F-measure of 0.87 for Long Method and 0.91 for Large Class detection). However, the performance improvement over CodeT5 features is negligible if we consider the advantages of automatically inferring features. Finally, our ML model surpassed less experienced annotators and nearly matched the most experienced annotator, suggesting it can assist less experienced developers under tight deadlines. To the best of our knowledge, this is the first study to compare the performance of automatic smell detectors against human performance. [ABSTRACT FROM AUTHOR]

Wildfire frequency and the total area burned in Brazil have steadily increased over time. However, in the Federal District, wildfire hazards have risen due to wildfires reaching several of its protected areas. Wildfire occurrence/incidence risk refers to the likelihood or probability of a wildfire event occurring in a particular geographic area. The risk is typically determined by factors such as weather conditions, topography, vegetation, and human activities and often measured through analyses of historical wildfire data and predictive models that estimate the likelihood of future wildfire events. High wildfire occurrence/incidence risk areas are those more likely to experience a wildfire incident, whereas low-risk ones are less susceptible to it. This study compares 8 machine learning models that predict wildfire occurrence risk worldwide so that they can be adopted in Brazil. They considered correlations among climate conditions, spatial location, topographic features, anthropogenic explanatory features, and fire occurrence and a dataset enriched with Brazilian governmental open data was comprised of observations on 16 climate features of 5 monitoring stations and satellite data on fires occurred over the past two decades and topographic, hydrographic, and anthropogenic features, such as Normalized Difference Vegetation Index (NDVI), urbanization index, and distance to rivers/roads. According to the results, fire risk can be predicted with 0.99 accuracy and the models showed more sensitive to NDVI, atmospheric pressure, and relative humidity, as demonstrated by a study on the impact of features. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a Cross-domain Recommendation (CDR) model targeting heterogeneous domains. Previous studies have mainly focused on homogeneous domains and pose limitations when applied to heterogeneous domains without common users, items, and metadata. To overcome this challenge, we propose a heterogeneous CDR model called latent features cross-domain recommendation (LFCDR). Our model leverages latent features (LF), which construct the correlations between user and item features based on domain categories, where a category represents the domain attributes. By extracting the LF of each domain, we find similar domain latent features and improve the performance of the sparsity domain through transfer learning. We performed experiments on latent features recommendation (LFR), a recommendation system using LF, and LFCDR, a CDR using LF of heterogeneous domains, using three heterogeneous domain datasets, and compared their performances with a factorization machine (FM). Our results illustrated that the performance of the LFR improved by up to 1.65, as measured by mean absolute error (MAE), compared to the FM. Additionally, the performance of the LFCDR improved by up to 1.66, depending on the relevance of the domain's category. [ABSTRACT FROM AUTHOR]

Range-Doppler images represent one of the most informative radar data forms, providing range and frequency information. This study explores the performance of machine learning and deep learning techniques in classifying human activities behind walls using Range-Doppler images. Therefore, we input the HOG features of Range-Doppler images into various machine-learning approaches. Although the HOG feature enhances the performance of machine learning methods, we observe the superior performance of Convolutional Neural Network (CNN) architectures in a more complex scenario in which the number of target activity classes is higher. To obtain sufficient data for the CNN architecture, we combine the Uniform Linear Array (ULA) and stepped-frequency continuous-wave (SFCW) structures, enabling the acquisition of multi-channel data. The experimental results demonstrate both the improvement of the machine learning accuracy from 95.33% to 98.67% through the HOG + Range-Doppler approach and an approximately 6% enhancement in CNN performance achieved through the SFCW-ULA combination. [ABSTRACT FROM AUTHOR]

Nowadays, there is such an outbreak of corona in the whole world that people are taking many precautions to avoid it, but according to the Guidelines of WHO, wearing a mask and maintaining distance is the best way to escape from this epidemic. It has been said that those who do not wear facemasks are at risk of infection. In technology, computer vision-based facemask detection uses a different learning algorithm to recognize face masks in real time. This research compares the performance of seven machine learning, deep learning, and transfer learning algorithms, such as K-NN, SGD, SVM, CNN, MobileNetV2, InceptionV3, and VGG-19, to discover the best algorithm for detecting who is wearing a masked face in a real-time environment. In addition, we got a higher accuracy of 100% in the InceptionV3 algorithm, that InceptionV3 tends to be faster when compared to another algorithms. [ABSTRACT FROM AUTHOR]

The advance rate (AR) of tunnel boring machines (TBMs) plays a pivotal role in evaluating their efficiency in tunnel engineering projects. This study focuses on the development of precise prediction models for TBM performance employing advanced algorithms, including gene expression programming, time series analysis, multivariate regression, artificial neural networks, particle aggregation algorithms, genetic algorithms, adaptive neural fuzzy inference systems, and support vector machines. The AR, serving as a performance metric, becomes the specific target for prediction models. A test database comprising 3597 datasets was curated from a tunneling project at the Sar Pol Zahab, Bazi Daraz water transfer tunnel. Utilizing 21 parameters as input variables, intelligent AR models were formulated based on comprehensive training and testing patterns, incorporating geological features and the key machine parameters influencing AR. Quantitative evaluation of the models involved statistical indicators such as root mean square error (RMSE), coefficient of determination (R2), and variance calculation. Comparative analysis based on RMSE, MAE, MAPE, VAF, and R2 superior gene expression function models showed that the gene expression algorithm with 1.41, 0.66, 6.33, 98.88, and 0.95 ahead of the nose is better than other approaches. These results underscore the efficacy of the gene expression programming-based model, suggesting its potential to yield a novel functional equation for accurate TBM performance prediction. [ABSTRACT FROM AUTHOR]

Clarifying the relationship between the man–machine environment and its impact on the tunnel wall drilling task performance (TWDTP) is crucial for enhancing the task performance. Based on a questionnaire survey, indicators of the man–machine environment that affect the TWDTP were proposed in this study, and exploratory factor analysis and a structural equation model were employed to examine the potential factors influencing the task performance and their degrees of influence. By comparing the discrepancy between the perceived performance and importance, the satisfaction of potential factors was evaluated, and the priority order for optimizing these factors was determined by considering the degree of influence and dissatisfaction. The results of survey data analysis based on actual tunnel drilling operation scenarios indicated that tools had the greatest impact on the TWDTP, followed by the quality of the physical environment, while human factors had the least influence on the task performance. Convenient functional maintenance is the key to improving the TWDTP, along with enhancing the quality of the working environment. Once these main aspects are optimized, it is important to consider additional factors such as availability of spare tools, efficient personnel organization, man–tool matching, and safety and health assurance. This research approach provides significant guidance in understanding the relationships between the man–machine environmental factors affecting the performance of complex engineering tasks and identifying key influencing factors, thus providing essential insights for optimizing the TWDTP. [ABSTRACT FROM AUTHOR]

Academic performance (AP) is crucial for lifelong success. Unfortunately, many students fail to meet expected academic benchmarks, leading to altered career paths or university dropouts. This issue is particularly pronounced in the early stages of higher education, highlighting the need for the instructors of these foundational courses to have access to simple yet effective tools for the early identification of students at high risk of academic failure. In this study, we propose a streamlined conceptual model inspired by the Model of Human Behavior (MHB) to which we have incorporated two dimensions: capacity and willingness. These dimensions are assessed through the definition of three variables: Prior Academic Performance (PAP), Personality and Academic Engagement, whose measurements can easily be obtained by the instructors. Furthermore, we outline a Machine Learning (ML) process that higher education instructors can use to create their own tailored models in order to predict AP and identify risk groups with high levels of transparency and interpretability. The application of our approach to a sample of 322 Spanish undergraduates studying two mathematical subjects at a Spanish university demonstrates its potential to detect failure early in the semester with a precision that is comparable with that of more complex models found in literature. Our tailored model identified that capacity was the primary predictor of AP, with a gain-to-baseline improvement of 21%, and the willingness variables increasing this to 27%. This approach is consistent over time. Implications for instructors are discussed and an open prediction and analysis tool is developed. [ABSTRACT FROM AUTHOR]

Superalloy Inconel718 is an important material for aircraft preparation because of its excellent performance at high temperatures. However, when cutting Inconel718, a large amount of cutting heat will be generated, resulting in excessive tool temperature and serious wear, which accelerates the tool failure. In order to solve this problem, the influence of tool angle on the process of thermal aided machining was studied by simulation model combined with thermal aided machining technology. During the cutting process, the workpiece preheating temperature rises from room temperature 20° C to 500° C, the front tool angle range is − 5° to 10°, and the rear tool angle range is 4° to 16°. By analyzing various parameters, it was found that a smaller tool rake angle can effectively reduce the tool temperature. Additionally, a flank angle of around 12° was found to decrease the maximum wear area of the tool by approximately 10.5%. Moreover, it was observed that implementing heat-assisted machining can result in a significant reduction of tool temperature by 11.1%, as well as a decrease in cutting force ranging from 18 to 22%, particularly at temperatures exceeding 500 °C. [ABSTRACT FROM AUTHOR]

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model's robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model's predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system's sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are becoming more popular due to their superior performance like high power density, long lifespan, broad operating range of temperatures, quick charging capabilities, and low self-discharge. The implementation of a Battery Management System (BMS) is crucial in order to guarantee the secure and optimal functioning of electric vehicle batteries. BMS monitors, controls, and maintains the health and performance of rechargeable batteries by monitoring the State of Charge (SoC), State of Health (SoH), and State of Temperature (SoT). SoC quantifies the amount of energy stored in a battery at a certain moment, and it is employed to approximate the remaining distance that may be covered. SoH describe the overall health or state of a rechargeable battery. It reveals how well a battery performs in comparison to its original state. SoT estimation keeps the battery temperature within a safe range, allowing it to reach a higher age and safety, which is critical for battery reliability. In this paper SoC, SoH, SoT estimation models for a lithium-ion battery have been developed using an improved EP-based R110-BLSTM approach. The Emperor Penguin based Residual Network-110 incorporated Bidirectional Long-Short Term Memory (EP-based R110-BLSTM) is ideal for estimating SoC, SoH, and SoT and features great accuracy, a quick estimation speed, and strong generalization capabilities. However, Extreme Learning Machine performance is heavily reliant on proper feature extraction. In order to enhance estimate performance by extracting the best features, Simulated-Annealing-based Golden Eagle optimization is utilized. Electric vehicle drive cycles are used to test the model's resilience to temperature changes. MATLAB 2018b software is used in the execution of this research. The results demonstrate that the proposed model performs better in terms of accuracy and has lower SoC, SoH, and SoT error rates than existing models. A thorough comparison between the recommended model and existing methods is also made, further demonstrating the proposed model's superiority. [ABSTRACT FROM AUTHOR]

Induction machines are widely utilized in the industry due to their sturdy construction nature and relatively simple maintenance. For the design, control, optimization, and failure analysis procedures of induction machines, equivalent circuit parameters are required. These parameters can be estimated using a variety of techniques, such as traditional testing, experimental research, analytical methods, programming, and machine learning algorithms. However, the aforementioned methods have limitations; experimental methods are laborious and time-consuming, analytical methods require complicated and iterative computations for each machine, and programming algorithms involve writing of lengthy command sequences. The article concentrates on a high precision estimation method based on neural network algorithms to avoid complex calculations, thus, minimizing time and effort, and eliminating errors. In this context, it is aimed to provide a greater reliability compared to studies using small amounts of datasets and to investigate earlier methods, which are not studied vastly, for estimating the parameters of induction machines. In this regard, three distinct artificial neural networks were applied to a large dataset consisting of 1164 machines with power output ranging from 4 to 900 kW and belonging to 7 different manufacturers. RMSE values of 0.0125 were attained in parameter estimation, and artificial neural networks produced encouraging results. [ABSTRACT FROM AUTHOR]

The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks. Evolution processes of complex networked systems in biology and social sciences, and their underlying mechanisms, still need better understanding. The authors propose a machine learning approach to reconstruct the evolution history of complex networks. [ABSTRACT FROM AUTHOR]

Wire electrical discharge machining (WEDM) has become the most important advanced production technology compared to other methods. The major goal of the current experiment is to use WEDM to ascertain the machining characteristics of the aluminum alloy EN AW-6082 T6, which has better corrosion resistance. The trials were planned using the design of experiments—response surface methodology. In total, 32 sWEDM experiments with molybdenum wire were conducted. The methods were used to reduce surface roughness (SR) and dimensional deviation (DD) by influencing machining parameters such as pulse on time (Ton), pulse off time (Toff), spark gap voltage (GV), peak current, and wire tension to help industrialists manufacture cost-effective industrial components. After machining, the significance of each parameter was determined using an analysis of variance (ANOVA), which revealed that for SR, Ton is the influencing factor and for DD, GV is the dominant factor. The measured output was optimized using desirability function analysis. The confirmatory tests were carried out, which indicate that the desired consistency with individual parameters was achieved, ensuring the validity of this investigation. [ABSTRACT FROM AUTHOR]

Aerodynamic test rigs are necessary for experimental testing of turbomachines and investigation of possible ways to improve machine performance. Existing installations give higher losses and do not work efficiently at off-design operating modes. An operating part with an adjustable radial diffuser was designed in order to determine the characteristics of turbine annular cascades. Experimental studies and computational verifications showed satisfactory results at various operating modes. The regulated backwall of the radial diffuser ensured supersonic velocity values behind the cascade and overall stable operation in a wide range of Mach numbers up to 1.3 when using compressors of comparatively small capacity. The optimum positions of the regulated backwall were determined, which provided a deep vacuum behind the cascade, as well as 1.5 times Mach number increase compared to the turbine cascade without a diffuser. Changing the inlet channel geometry at supersonic modes leads to an increase in the diffuser efficiency. Additionally, it was determined that the use of the turbine vane cascade in the test rig flow path is not necessary during calculation studies, but instead an axisymmetric vaneless converging area can be applied and give satisfactory results as well as reduce the time spent on calculations. The computational model can be used to optimize the design of an aerodynamic tunnel outlet area. [ABSTRACT FROM AUTHOR]

Purpose: Every industry needs an effective predictive strategy to optimise resource management and enhance plant economy by minimising wasteful expenditures and enhancing safety. Over 90 percent of machines have rolling element bearings and failure of such bearings might cause the machine to fail. Bearings are responsible for a significant amount of failures in manufacturing operations. Methods: The prime focus of this review is to investigate and evaluate several defect diagnosis approaches utilised in rolling element bearings including the conventional method, machine learning methods and the deep learning techniques. Results: It may help with a better comprehension of the methods and procedures applied in all varieties of defect detection approaches and offer new insights for future research that might enhance the performance of rolling element bearings. Conclusion: Machine performance will be significantly enhanced by discovering a technique that can give a significant level of precision and accuracy in early diagnosis. Early and precise diagnosis of the problem is crucial since it can assist to prevent equipment wear and tear. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been made in the field of non-autoregressive machine translations. However, the accuracy of non-autoregressive models still lags behind their autoregressive counterparts. This discrepancy can be attributed to the abundance of repetitive tokens in the target sequences generated by non-autoregressive models. In this study, we delve into this phenomenon and propose a novel approach to train a non-autoregressive model using unlikelihood loss. We evaluate our method on three widely used benchmark tasks. The experimental results demonstrating that our proposed approach significantly reduces the number of repetitive tokens while improving the overall performance of non-autoregressive machine translations. Compared to the baseline model "Mask-Predict", the average number of repetitions on IWSLT 14 DE → EN valid set is reduced from 0.48 to 0.17, resulting in a remarkable 62% decrease. [ABSTRACT FROM AUTHOR]

Accurate prediction of ground settlement is vital to guarantee the safety and efficiency of subway tunnel digging. In this study, to accurately predict the tunneling-induced ground settlement, the extreme gradient boosting (XGBoost) model is optimized using a combination of three optimization algorithms and the principal component analysis (PCA), based on a dataset of 288 tunnel case histories and 12 indicators representing the tunnel's spatial morphology, stratum properties, and excavation state. PCA is utilized to pre-process the sample dataset, further eliminate the correlation between indicators and reduce the dimension. Seven linearly independent principal components are obtained, constituting the model's input variables. Thereafter, particle swarm optimization (PSO), Bayesian optimization (BO), and sparrow search algorithm (SSA) are applied to optimize four important hyper-parameters of the XGBoost model, thus improving its performance effectively. The prediction performance of XGBoost, PSO-XGBoost, BO-XGBoost, SSA-XGBoost, PSO-support vector machine, PSO-random forest, grid search-back propagation neural network and grid search-deep neural network models are compared to determine the optimal model. The study results showed that the Pearson correlation coefficient, root mean square error, and mean absolute error of the proposed BO-XGBoost model on the training dataset are 0.9299, 1.2563, and 0.3015, whereas those on the testing dataset are 0.9154, 1.3114 and 0.9928, respectively. These results prove its reliability and superiority against other alternatives in predicting tunneling-induced ground settlement. [ABSTRACT FROM AUTHOR]

Purpose: Condition monitoring and fault diagnosis of Rolling Element Bearings (REBs) is a crucial and time-consuming task. The advantage of automating fault diagnosis using intelligent techniques is the reduced prerequisite of experienced and skilled personnel. The accuracy of fault detection methods is influenced by selecting the most discriminative, higher ranked features from the original high-dimensional feature space. So, it is fairly significant to explore different feature ranking techniques and investigate their impact on the performance of Machine Learning (ML) techniques in diagnosing the bearing faults. Method: This paper presents the influence of One-way ANOVA and Kruskal–Wallis feature ranking techniques on the performance of four ML classifiers, namely, Decision Tree, SVM, KNN, and ANN for fault classification in a ball bearing. For this, two open data sources of Case Western Reserve University (CWRU) and Paderborn University (PU) are employed. The influence of feature ranking on fault diagnostic performance of the classifiers is investigated through Classification Accuracy, Training Time, Prediction Speed, Recall, Precision, and F1-Score. Results and Conclusion: It is observed that selecting distinctive features through feature ranking significantly influences the performance of ML models. The maximum accuracy of fault classification is achieved with top 15 features ranked by the Kruskal–Wallis test for all the classifiers. The accuracies attained are 98.6, 99.4, 96.9, and 97.8% for Decision Tree, SVM, KNN, and ANN, respectively, with CWRU dataset. Similar trend is obtained through PU dataset giving corresponding maximum accuracies of 95.0, 97.8, 97.2, and 95.0%. The Kruskal–Wallis test outperforms the One-way ANOVA for both the data sources. [ABSTRACT FROM AUTHOR]

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs. [ABSTRACT FROM AUTHOR]

Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage–involving feature selection, covariate correction, and dependence between subjects–on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling. The effects of data leakage on predictive models in neuroimaging studies are not well understood. Here, the authors show that data leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have more minor effects. [ABSTRACT FROM AUTHOR]