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[Display omitted] Continuous monitoring of wastewater treatment processes is key to mitigate the risk of inadequately treated wastewater on the environment and public health. However, effective control of wastewater treatment processes is challenging because of the numerous relevant variables and their complex physio-chemical-biological interdependence. Most published related studies focused on correlating the effluent concentration of chemical oxygen demand and/or suspended solids using only a limited number of wastewater influent variables. In addition, recent machine learning- (ML) based studies in wastewater treatment systems considered some individual classification algorithms rather than providing a comparison between different algorithm performances. In the current study, different algorithms were developed to categorize a range of wastewater treatment effluent characteristics based on multiple influent variables. To demonstrate their application, 23 ML classification algorithms were deployed on a wastewater treatment reactor-generated dataset and their performances were evaluated considering two different group of metrics related to the removal efficiency and effluent quality. The analysis results showed that, among all considered algorithms, the ensemble bagged trees algorithm had the most superior performance in terms of its overall classification accuracy. An interpretability analysis was further performed on the treatment process variables to detect the correlation between the input and output variables and to assess variable importance. In practice, the developed algorithms can facilitate optimal operation and effective management of wastewater treatment plants. ML algorithms also present efficient tools for rapidly classifying the effluent characteristics in lieu of typical sampling and laboratory analysis processes. [ABSTRACT FROM AUTHOR]

The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel's (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m3/m3) and bias (in m3/m3). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation. [ABSTRACT FROM AUTHOR]

Gait of post-stroke patients have been analyzed and classified in this paper. Data pertaining to gait of both post-stroke patients and healthy people of the same age group were obtained and analyzed. Xsens motion capture system was used for obtaining this data from 40 people belonging to both categories. Advanced modern machine learning techniques such as Logistic Regression, Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were employed for classifying the data obtained. Accurate spatiotemporal parameters values for gait were obtained. Amongst the different machine learning techniques, XGBoost gave the most accurate classification result of about 96%. The walking patterns of the patients who had undergone a stroke attack were analyzed. severity of abnormal gait patterns was a key factor that was taken into consideration and points were given accordingly. The data presented in this paper can be used to develop diagnostic tools for gait rehabilitation of stroke survivors, to evaluate and estimate their way of walking in order to understand their progress. It can be used to understand the different types of walking disorders post-stroke and thereafter select the right kind of treatment that needs to be implemented for proper recovery. [ABSTRACT FROM AUTHOR]

Machine learning models have gained popularity nowadays for their potential to solve real-life issues when trained on pertinent data. In many cases, the real-life data are class imbalanced and hence the corresponding machine learning models trained on the data tend to perform poorly on metrics like precision, recall, AUC, F1, and G-mean score. Since class imbalance issue poses serious challenges to the performance of trained models, a multitude of research works have addressed this issue. Two common data-based sampling techniques have mostly been proposed-undersampling the data of the majority class and oversampling the data of the minority class. In this article, we focus on the former approach. We propose two novel algorithms that employ neural network-based approaches to remove majority samples that are found to reside in the vicinity of the minority samples, thereby undersampling the former to remove (or alleviate) the imbalance issue. We delineate the proposed algorithms and then test the proposed algorithms on some publicly available imbalanced datasets. We then compare the performance of our proposed algorithms to other popular undersampling algorithms. Finally, we conclude that our proposed algorithms outperform most of the existing undersampling approaches on most performance metrics. [ABSTRACT FROM AUTHOR]

A Dandelion algorithm (DA) inspired by the seed dispersal process of dandelions has been proposed as a newly intelligent optimization algorithm. For improving its exploration ability as well as reducing the probability of its falling into a local optimum, this work proposes to add a novel competition mechanism with historical information feedback to current DA. Specifically, the fitness value of each dandelion in the next generation, which is calculated by linear prediction, is compared with the current best dandelion, and the loser is replaced by a new offspring. Current DA generates new offsprings without considering historical information. This work improves its offspring generation process by exploiting historical information with an estimation-of-distribution algorithm. Three historical information models are designed. They are best, worst, and hybrid historical information feedback models. The experimental results show that the proposed algorithms outperform DA and its variants, and the proposed algorithms are superior or competitive to nine participating algorithms benchmarked on 28 functions from CEC2013. Finally, the proposed algorithms demonstrate the effectiveness on four real-world problems, and the results indicate that the proposed algorithms have better performance than its peers. [ABSTRACT FROM AUTHOR]

A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms, and time-of-arrival (TOA)-based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS, and NLOS scenarios. Multiclass support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63%. Threshold-based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5–3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx–Rx distance, presence of another obstruction between the Tx and Rx links, and performance in different environment which shows the suitability of the proposed methodology. [ABSTRACT FROM AUTHOR]

This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of a classical artificial neural network. Each vertex of this complete graph represents a possible synaptic weight set in the $w$ w -dimensional search space, where $w$ w is the number of weights of the neural network. To know the number of iterations required a priori to obtain the solutions is one of the main advantages of the procedure. Another advantage is that the proposed method does not stagnate in local minimums. Thus, it is possible to use the quantum walk search procedure as an alternative to the backpropagation algorithm. The proposed method was employed for a $XOR$ X O R problem to prove the proposed concept. To solve this problem, the proposed method trained a classical artificial neural network with nine weights. However, the procedure can find solutions for any number of dimensions. The results achieved demonstrate the viability of the proposal, contributing to machine learning and quantum computing researches. [ABSTRACT FROM AUTHOR]

• Outperforms existing techniques by identifying both single and non-overlapping delaminations effectively. • Integrates theory, experiments, and algorithms for precise delamination assessment in composites. • Identifies locations and sizes of non-overlapping delaminations in composites. • Potential for future expansion to detect multiple delaminations, enhancing broader applicability. Extensive research has been carried out on the detection of single delamination in fibre-reinforced polymer (FRP) structures using different structural health monitoring (SHM) approaches, including the vibration-based assessment methods. However, there are limited studies that on the detection of multiple delaminations, which could occur in many real-world scenarios. To address this research gap, this paper aims at developing a vibration-based method to assess the two non-overlapping delaminations in FRP beams based on changes in natural frequencies. A theoretical model of vibrating FRP beams with two non-overlapping delaminations was constructed and used for generating the multiple frequencies of the delaminated beams. By analysing the frequency shifts of the FRP beams before and after the occurrence of damage, the delamination characteristics (location and severity) can be inversely predicted using optimisation algorithms. The accuracy and efficiency of the frequency-based inverse algorithm were verified experimentally by modal testing on FRP beam specimens with one and two non-overlapping delaminations. It is interesting to observe that using the proposed vibration-based method, the single delamination in FRP specimens can also be predicted as two non-overlapping delaminations, with a major one matching the original single delamination and a minor one that can be neglected. Thus, the current vibration-based delamination assessment approach is flexible in identifying the prediction of either single or double non-overlapping delaminations in FRP beams, and therefore, it potentially has wider applicability than the existing single delamination detection methods. [ABSTRACT FROM AUTHOR]

In this study, we apply and compare different algorithms from machine learning to describe and predict NEET rates in 31 European countries in the period from 2005 to 2020. With this aim, we considered eleven indicators describing the socio-economic national context and the level of innovation of the economies. Besides improving knowledge about the use of machine learning algorithms for the description of the NEET phenomenon, we discuss the connections between NEETs and other indicators that connect with other relevant sustainable development goals (SDGs), such as education, the reduction of inequalities, and decent work for everyone. The reduction of NEET rates is the only goal directly addressed to young people, The article underscores the need for evidence-based approaches to measure SDG achievement, especially concerning the heterogeneous NEET population. It emphasizes the importance of machine learning algorithms as a modern methodology for understanding and addressing the NEET phenomenon within the framework of SDGs, considering the complex interrelationships of socio-economic factors contributing to social and economic sustainability. • We identify the Best Machine Learning Algorithms for NEET and SDG studies. • We connect NEET paths in 2005–2020 with other relevant SDG goals. • We propose a complex approach, combining 31 countries and various indicators. [ABSTRACT FROM AUTHOR]

[Display omitted] • The high consumption of maize requires primarily the identification of transgenic products. • Elemental Spectroscopy techniques provide sufficient information about the samples. • Both non-supervised and supervised multivariate analyses are effective for discrimination. • Internal and external validation provide reliability for classification tasks. In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. These results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize. [ABSTRACT FROM AUTHOR]

Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms. • There were no convenient methods for early diagnosis of diabetic vascular complications (DVC) • Salivary glycopatterns showed significantly altered in DVC patients compared to type 2 diabetes mellitus patients • The K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort • The integrated classifier exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

Even though the World Health Organization declared the end of the pandemic, COVID-19 is still considered an endemic disease, affecting many people worldwide. As a result, hospitalization rates can continue to strain healthcare systems, particularly in low and middle-income countries. Thus, this article presents two predictive Machine Learning (ML) models to identify COVID-19 patients more likely to be hospitalized or face a higher mortality risk, aiming to assist physicians and healthcare administrators in making informed decisions. Previous research primarily focused on using expensive input data, such as clinical examinations and images, to implement ML models. In contrast, we implemented the models using information on patients' demographics, vaccination history, symptoms, and underlying health conditions. We trained the ML algorithms using data from the OpenDataSus database, focusing on patients undergoing hospital treatment in São Paulo, Brazil. Our study tested 14 different ML algorithms to determine their effectiveness: gradient boosting, light gradient boosting machine, logistic regression, linear discriminant analysis, ridge, AdaBoost, random forest, support vector machine, k-nearest neighbor, extra tree, decision tree, naive Bayes, quadratic discriminant analysis, and dummy. The results showed that the gradient-boosting model outperformed the others, achieving an accuracy rate of 83% and an Area Under the Curve (AUC) of 0.89 for predicting mortality risk. Moreover, the model achieved an accuracy rate of 71% and an AUC of 0.75 for predicting hospitalization risk. Our analysis evidence that including information on the number of vaccine doses can enhance the prediction results for hospitalization and mortality of patients. Based on these significant findings, we have developed a web application to assist healthcare professionals in identifying patients at a higher risk of severe outcomes from COVID-19. • Models for identifying COVID-19 patients at risk of mortality and hospitalization. • Gathered São Paulo hospital data for COVID-19 model training. • Evaluated 14 machine Learning algorithms; gradient boosting proved most effective. • Vaccine doses can improve the risk of mortality and hospitalization. • Web app aids healthcare professionals in identifying high-risk COVID-19 cases. [ABSTRACT FROM AUTHOR]

Soft 2D tactile sensors are becoming increasingly important in robotics and human-machine interaction. In this paper, we propose a new approach to develop a soft tactile sensor using fiber Bragg gratings (FBGs) and machine learning algorithms. The sensor consists of a layer of silicone elastomer with embedded 192 FBGs that can detect deformations caused by point impact. The FBG responses are then processed by machine learning algorithms to measure the position and the force of impacts with the mean absolute errors of 2.1 mm and 0.34 N, respectively. [Display omitted] • 2D flexible tactile sensor with array of 192 FBG sensors was shown. • Experimental calibration of 2D tactile sensors with assistance of machine learning algorithm was presented. • Prediction of pressing force with mean absolute error of 0.34 N was demonstrated. • Prediction of position of the impact with mean absolute error of 2.1 mm was achieved. [ABSTRACT FROM AUTHOR]

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance. • Exploring the changes in LULC, LST, and UTFVI in Nanjing over the past 20 years. • Simulating the LULC, LST, and UTFVI in 2030 and 2040 using ANN-CA and ANN algorithms. • Evaluating LULC and thermal environment changes using machine learning algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • A dual-scale non-invasive technique for crack detection is demonstrated. • Resonance-based device is evaluated for its attenuation and frequency shift. • A comparison between sensors operating in microwave and radiowave regime is evaluated. • The microwave regime device show superior quality factor with ∼250 % improved sensitivity. • Out of multiple machine learning algorithms, k-NN algorithm proved to be most accurate with accuracy of around ∼99 %. Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual-scale resonance-based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro-wave (4.59 GHz) and radio-wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift ∼24 %, while the radio-wave range sensors showed ∼9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed ∼280 % more sensitivity than the radio-wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable surge in the prevalence of cardiovascular diseases (CVD), presenting a significant global public health challenge and a leading cause of mortality worldwide. Among the myriad complications stemming from CVD, heart failure stands out as a critical concern. Addressing heart failure through surgical means poses considerable challenges. The primary objective of this research is to identify pivotal attributes linked to heart failure and employ diverse machine learning methodologies to predict its occurrence, thereby enabling early estimation of mortality rates associated with heart failure. Leveraging a heart failure dataset, we conducted comprehensive model construction using pre-processing techniques such as feature scaling and correlation analysis. The Extreme Gradient Boosting (XGBoost) method was instrumental in evaluating feature relevance, leading to the selection of two distinct datasets: the whole dataset and the XGBoost dataset. In conclusion, we employed thirteen machine learning methods to predict the occurrence of death events within these datasets. Fine-tuning hyperparameters significantly enhanced model performance. Notably, our model demonstrated exceptional performance on this dataset, achieving the highest accuracy of 85.23% with Random Forest on the whole dataset and 86.36% with Flexible Discriminant Analysis on the XGBoost dataset. [Display omitted] • This research addresses the rising global prevalence of cardiovascular diseases, focussing heart failure. • The study aims to predict heart failure occurrence, enabling early estimation of mortalityrates. • Employing model, feature scaling and correlation analysis method to identify pivotal attributes linked to heart failure. [ABSTRACT FROM AUTHOR]

A soft fiber-reinforced bending actuator (SFRBA) is a multi-material system that plays a crucial role in robotics applications due to its high output force and robust bending motion. However, the multi-material composition of SFRBAs causes significant structural nonlinearity. This nonlinear behavior is difficult to capture using traditional analytical models. This study presents a method for modeling the SFRBA using finite element method (FEM) and nonlinear machine learning algorithms (MLAs). First, the key structural parameters of the SFRBA are defined and selected as input variables for the method. An accurate FEM, considering varying actuation pressure, is then employed to generate a simulation training dataset. Subsequently, three nonlinear MLAs, including polynomial regression (PLR), extreme gradient boosting regression (XGBoostR), and normalized multilayer perceptron regression (NMLPR), are developed to model the bending angle of the SFRBA. Moreover, transfer learning is deployed to improve the accuracy and convergence speed of the optimal NMLPR. Experimental measurements are conducted to validate the established MLAs, and the results demonstrate that the refined NMLPR outperforms the other models, yielding an average Root Mean Square Error (RMSE) of 7.935° and Mean Absolute Percentage Error (MAPE) of 6.45%. Furthermore, the refined NMLPR is implemented in a feedback control loop to showcase its real-time ability to convert actuation pressure information into bending angles. The results exhibit excellent control performance, with an average MAPE of less than 6% and a low time delay of 0.0875 s. This work exemplifies a methodology that combines FEM and MLAs for modeling SFRBAs, paving the way for their further development and practical application. [Display omitted] • Multiple compositions of the SFRBAs increase the structural nonlinearity. • FEM-based MLAs are proposed for modeling the bending angle of SFRBA. • The result of optimal MLA yields an average RMSE of 7.935°. • The best MLA is used in a feedback control loop to showcase its real-time ability. [ABSTRACT FROM AUTHOR]

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector. • Novel method using machine learning algorithms for building WLCE prediction. • Promotes WLCE integration into the early stages of building design. • Leverage a comprehensive UK residential dataset for robust model development. [ABSTRACT FROM AUTHOR]

The transformative impact of Artificial Intelligence (AI) technologies, particularly Machine Learning (ML), on the analysis of spectroscopic data in water quality assessment cannot be overstated. We remark the ways in which AI and ML have revolutionized the analysis and prediction of water quality parameters. These technologies efficiently process spectral data from various sources, identify contaminants, and support early detection systems. However, AI tools have limitations, including the need for a large and diverse dataset for optimal performance, and some studies used small datasets, limiting the predictive power of the models. Open databases can aid in expanding AI applications in water quality control and treatment. The potential of AI and spectroscopic techniques reduce costs, promote environmentally sustainable water treatment, and enhance water and environmental quality. Finally, we emphasize the need for legislative changes and collaboration between organizations to harness the synergy between these technologies, and its vital water resources. • Evaluation of machine learning techniques as tools for water quality control. • Establishment of ANN as the most used ML algorithm in water quality control. • Highlighting the need to implement regulations that include AI-based solutions for water quality control and treatment. [ABSTRACT FROM AUTHOR]

• Vibration-based method: changes in structural frequencies utilized to estimate the residual fatigue life. • Comparison of theoretical and machine learning algorithms such as Support Vector Machines (SVM) and ANN. • Check prediction accuracy with different input schemes, including single-mode and multi-mode frequencies. Fiber reinforced polymer (FRP) structures may experience cumulative fatigue damage during their service life which can lead to structural failure. This paper focuses on predicting the residual fatigue life of FRP structures using vibration parameters. The relationship between residual fatigue life and natural frequencies is examined through modal testing and fatigue measurements on FRP beam specimens. Two prediction methods; semi-empirical models and machine learning (ML) algorithms, are utilized. The semi-empirical models are derived from existing "residual stiffness" models based on the relationship between bending stiffness and flexural frequencies. ML algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN), are developed for fatigue life prediction. Experimental validation is performed using measured frequencies during fatigue testing of FRP beams. The ML algorithms can use multimode frequencies unlike single mode of semi-empirical models. The verification results show that the ML algorithms can be used to predict the residual fatigue life with the selection of the appropriate mode of frequency. The results show that ML algorithms outperform single-mode frequency inputs, and the use of higher modes of measured frequencies improves the precision of fatigue life prediction. An inverse algorithm based on SVM exhibits higher prediction accuracy and stability, even with limited training samples. [ABSTRACT FROM AUTHOR]

This paper proposes a novel targeted blend of machine learning (ML) based approaches for controlling wastewater treatment plant (WWTP) operation by predicting distributions of key effluent parameters of a biological nutrient removal (BNR) process. Two years of data were collected from Plajyolu wastewater treatment plant in Kocaeli, Türkiye and the effluent parameters were predicted using six machine learning algorithms to compare their performances. Based on mean absolute percentage error (MAPE) metric only, support vector regression machine (SVRM) with linear kernel method showed a good agreement for COD and BOD 5 , with the MAPE values of about 9% and 0.9%, respectively. Random Forest (RF) and EXtreme Gradient Boosting (XGBoost) regression were found to be the best algorithms for TN and TP effluent parameters, with the MAPE values of about 34% and 27%, respectively. Further, when the results were evaluated together according to all the performance metrics, RF, SVRM (with both linear kernel and RBF kernel), and Hybrid Regression algorithms generally made more successful predictions than Light GBM and XGBoost algorithms for all the parameters. Through this case study we demonstrated selective application of ML algorithms can be used to predict different effluent parameters more effectively. Wider implementation of this approach can potentially reduce the resource demands for active monitoring the environmental performance of WWTPs. • A novel targeted blend of machine learning (ML) based approaches for controlling wastewater treatment plant. • It is used to predict effluent parameters of a biological nutrient removal process. • ML provides opportunity for early intervention to problems in wastewater treatment. • Five different ML algorithms are tested for their performance. • The mean absolute percent errors of models were 9% for COD and 0.9% for BOD5. [ABSTRACT FROM AUTHOR]

Background: Alternative splicing (AS) plays important roles in transcriptome and proteome diversity. Its dysregulation has a close affiliation with oncogenic processes. This study aimed to evaluate AS-based biomarkers by machine learning algorithms for lung squamous cell carcinoma (LUSC) patients. Method: The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database were utilized. After data composition balancing, Boruta feature selection and Spearman correlation analysis were used for differentially expressed AS events. Random forests and a nested fivefold cross-validation were applied for lymph node metastasis (LNM) classifier building. Random survival forest combined with Cox regression model was performed for a prognostic model, based on which a nomogram was developed. Functional enrichment analysis and Spearman correlation analysis were also conducted to explore underlying mechanisms. The expression of some switch-involved AS events along with parent genes was verified by qRT-PCR with 20 pairs of normal and LUSC tissues. Results: We found 16 pairs of splicing events from same parent genes which were strongly related to the splicing switch (intrapair correlation coefficient = − 1). Next, we built a reliable LNM classifier based on 13 AS events as well as a nice prognostic model, in which switched AS events behaved prominently. The qRT-PCR presented consistent results with previous bioinformatics analysis, and some AS events like ITIH5-10715-AT and QKI-78404-AT showed remarkable detection efficiency for LUSC. Conclusion: AS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC. [ABSTRACT FROM AUTHOR]

For the large amount of spatial and spectral information contained in hyperspectral image (HSI), feature description of HSI has attracted widespread concern in recent years. Existing deep learning-based HSI feature description algorithms require a large number of training samples and have poor interpretability. Therefore, it is necessary to develop an efficient HSI features description algorithm with interpretability based on machine learning. Local binary pattern (LBP) is a classical descriptor used to extract the local spatial texture features of images, which has been widely applied to image feature description and matching. However, the existing LBP algorithms for HSI are based on the single-dimensional description, which leads to the limitations on the expression of spatial–spectral information. Therefore, a multidimensional LBP (MDLBP) based on Clifford algebra for HSI is proposed in this article, which is able to extract spatial–spectral feature from multiple dimensions. First, with the theory of the Clifford algebra, a new representation of HSI including spatial and spectral information is built. Second, the geometric relationship between the local geometry of HSI in Clifford algebra space is calculated to realize the local multidimensional description of the local spatial–spectral information. Finally, a novel LBP coding algorithm for HSI is implemented based on the local multidimensional description to calculate the feature descriptor of HSI. The experimental results on HSI classification show that our proposed MDLBP algorithm can achieve higher accuracy than the representative spatial–spectral features and the existing LBP algorithms, especially in the scenery of small-scale training samples. [ABSTRACT FROM AUTHOR]

In this article, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e., a simplex volume maximization in the subspace of the data set. Then, the abundances are estimated using a deep image prior. The main motivation of this work is to boost the abundance estimation and make the unmixing problem robust to noise. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral data set. The proposed method is evaluated on simulated and three real remote sensing data for a range of SNR values (i.e., from 20 to 50 dB). The results show considerable improvements compared to state-of-the-art methods. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/UnDIP. [ABSTRACT FROM AUTHOR]

Detecting radioactive materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, and others. This article presents new results on nuclear material identification and mixing ratio estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep learning (DL)-based machine learning (ML) algorithms were compared. Both simulated and actual experimental data were used in the comparative studies. It was observed that some newly developed ML and DL methods have better performance than a conventional method based on the region of interest (ROI). Extensive experiments also demonstrated that trained models using low signal-to-background ratio (SBR) data can better generalize to other SBR conditions. [ABSTRACT FROM AUTHOR]

Nondestructive experiments are widely employed for determining mechanical properties. In this work, a spherical impact test is virtually performed to determine the plastic properties of a thick aluminum plate. The finite element model is validated experimentally, and several simulations are then performed in accordance with the design of the experiment program. Several algorithms, including support vector machine, Gaussian process regression (GPR), and nonlinear regressions (second- and third-order polynomials) as machine learning techniques, are employed to estimate the material plastic properties. The indentation depth and indentation radius are considered as input variables to predict the tangent modulus (TM) and yield stress (YS). Results reveal that the second-order polynomial and GPR methods realize better performance in terms of determination coefficient and root mean square error criteria in assessing YS. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is a part of Artificial intelligence (AI) that designs and produces systems, which is capable of developing and learning from experiences automatically without making them programmable. ML concentrates on the computer program improvement, which has the ability to access and utilize data for learning from itself. There are different algorithms in ML field, but the most important questions that arise are: Which technique should be utilized on a dataset? and How to investigate ML algorithm? This paper presents the answer for the mentioned questions. Besides, investigation and checking algorithms for a data set will be addressed. In addition, it illustrates choosing the provided test options and metrics assessment. Finally, researchers will be able to conduct this research work on their datasets to select an appropriate model for their datasets. [ABSTRACT FROM AUTHOR]

Climatic parameters influence CO 2 emissions and the complexity of the relationship is not fully captured in biophysical models. Machine learning (ML) is now being applied to environmental problems, and it is, therefore, opportune to investigate ML models in CO 2 predictions from agricultural soils. In this study, six ML models were compared for their predictive performance by comparing field measurements of CO 2 emissions from two fertiliser treatments: inorganic fertiliser (IF) and solid cattle manure supplemented with inorganic fertiliser (SCM) applied to a maize-soy rotation. The study also included a generalised scenario where all the data from IF and SCM were included in one dataset. The ML models include support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), feed-forward neural network (FNN), radial basis function neural network (RBFNN), and extreme learning machine (ELM). The input parameters were soil moisture, soil temperature, soil organic matter, soil total carbon, soil total nitrogen, air temperature, solar radiation and pan evaporation, while the output parameter was field measured CO 2 emissions. The results of this study demonstrated that RF was the best at predicting CO 2 emissions from IF [coefficient of determination (R2) = 0.92 and root mean square error (RMSE) = 2.27], SCM (R2 = 0.94 and RMSE = 2.86) and generalised scenarios (R2 = 0.86 and RMSE = 3.05). We conclude that ML models provide an innovative, robust and time-efficient alternative to biophysical models. [Display omitted] • Machine learning (ML) models are an effective and efficient alternative to mechanistic models for predicting CO 2 emissions from agricultural soils. • Random forest (RF), a classical regression ML model, is a suitable algorithm to predict soil CO 2 emissions regardless of fertiliser scenario. • Feed-forward neural network (FNN) provides acceptable predictive performance for CO 2 emissions, but it does not provide consistent predictive performance in K-Fold cross-validation. • 75% data in the training phase is optimum to provide robust ML performance when predicting CO 2 emissions from agricultural soils under maize-soy rotation. [ABSTRACT FROM AUTHOR]

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies. [ABSTRACT FROM AUTHOR]

In this work, a gradient-based primal-dual method of multipliers is proposed for solving a class of linearly constrained non-convex problems. We show that with random initialization of the primal and dual variables, the algorithm is able to compute second-order stationary points (SOSPs) with probability one. Further, we present applications of the proposed method in popular signal processing and machine learning problems such as decentralized matrix factorization and decentralized training of overparameterized neural networks. One of the key steps in the analysis is to construct a new loss function for these problems such that the required convergence conditions (especially the gradient Lipschitz conditions) can be satisfied without changing the global optimal points. [ABSTRACT FROM AUTHOR]

It is a very well-known fact, that the high complexity of the High Efficiency Video Coding standard (HEVC) is the main hurdle for its wide deployment and use. To tackle this problem, a number of recent research outcomes exploit heuristic algorithms and machine learning, including deep learning, to reduce the coding complexity. However, in most cases, each encoder module, i.e., encoding process, is first accelerated individually, and then different acceleration algorithms are manually combined. Without a holistic strategy, the acceleration potential of multi-module combination is not exploited and the Rate-Distortion (RD) loss is generally not well controlled. To tackle these shortcomings, this paper exploits the acceleration properties of different modules, i.e., the numerical representation of potential time saving and possible RD loss, from which a heuristic model is explored. Then a Heuristic Model Oriented Framework (HMOF) is proposed which adapts the properties of modules to underlying acceleration algorithms. In the framework, two advanced acceleration algorithms, including Border Considered CNN (BC-CNN)-based Coding Unit (CU) partition and Naive Bayes-based Prediction Unit (PU) partition, are proposed for the CU and PU modules, respectively. Further, by leveraging the heuristic model as the guidance to combine the proposed acceleration algorithms, HMOF is globally optimized, where different time saving budgets are wisely allocated to different modules and a theoretically minimal RD loss is achieved. According to the experimental results, through fusing a suitable deep learning technique and a Bayes-Based prediction, the proposed acceleration framework HMOF enable multiple acceleration choices. Here the proposed joint optimization strategy help to make a choice leading to the best cost-performance. Furthermore, within the proposed framework, intra coding time can be precisely controlled with negligible Bjøntegaard delta bit-rate (BDBR) loss. In this context, as a complexity control method, HMOF outperforms the state-of-the-art complexity reduction algorithms under a similar complexity reduction ratio. These results partially demonstrate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

For a better future in machine learning (ML), it is necessary to modify our current concepts to get the fastest ML. Many designers had attempted to find the optimal learning rates in their applications through many algorithms over the past decades, but they have not yet achieved their target of highest speed of back-propagation (BP). This research proposes a novel BP rule called the Instant Learning Ratios-Machine Learning (ILR-ML) or (ILRML). Unlike the traditional BP algorithms, the ILR-ML offers its learning without the concepts of the learning rate(s). The ILR-ML has a new concept called the "Learning Ratio" and indicated by a sign (Δℓ). The ILR-ML performs the full BP algorithm with 100% accuracy per each learning iteration. The ILR-ML is more suitable for the online machine learning. [ABSTRACT FROM AUTHOR]

Intrusion detection systems play a vital role in traffic flow monitoring on Internet of Things networks by providing a secure network traffic environment and blocking unwanted traffic packets. Various intrusion detection systems approaches have been proposed previously based on data mining, fuzzy techniques, genetic, neurogenetic, particle swarm intelligence, rough sets, and conventional machine learning. However, these methods are not energy efficient and do not perform accurately due to the inappropriate feature selection or the use of full features of datasets. In general, datasets contain more than 10 features. Any machine learning–based lightweight intrusion detection systems trained with full features turn into an inefficient and heavyweight intrusion detection systems. This case challenges Internet of Things networks that suffer from power efficiency problems. Therefore, lightweight (energy-efficient), accurate, and high-performance intrusion detection systems are paramount instead of inefficient and heavyweight intrusion detection systems. To address these challenges, a new approach that can help to determine the most effective and optimal feature pairs of datasets which enable the development of lightweight intrusion detection systems was proposed. For this purpose, 10 machine learning algorithms and the recent BoT-IoT (2018) dataset were selected. Twelve best features recommended by the developers of this dataset were used in this study. Sixty-six unique feature pairs were generated from the 12 best features. Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. Similarly, 660 feature-pair-based lightweight intrusion detection systems were developed by training the 10 machine learning algorithms via each feature pair out of the 66 feature pairs. Moreover, the 10 intrusion detection systems trained with 12 best features and the 660 intrusion detection systems trained via 66 feature pairs were compared to each other based on the machine learning algorithmic groups. Then, the feature-pair-based lightweight intrusion detection systems that achieved the accuracy level of the 10 full-feature-based intrusion detection systems were selected. This way, the optimal and efficient feature pairs and the lightweight intrusion detection systems were determined. The most lightweight intrusion detection systems achieved more than 90% detection accuracy. [ABSTRACT FROM AUTHOR]

This study considers the simultaneous optimization problem of the transmit beamforming at the access point and the reflecting beamforming at the intelligent reflecting surface (IRS) in an IRS-assisted multiuser downlink system. This joint optimization problem is nonconvex and challenging due to the intricate coupling of transmit and reflecting beamforming variables and the highly nonconvex constant modulus constraints. All the known solutions apply the alternating optimization framework to decouple the joint optimization problem into two problems. Then, these problems are optimized individually with different optimization methods in an alternating manner. However, such an alternative procedure may cause performance degradation. On the basis of the cross-entropy (CE) framework, which was developed initially for machine learning applications, we propose a machine learning-inspired algorithmic framework for simultaneously optimizing transmit and reflecting beamforming in an IRS-assisted wireless system. Extensive simulation results reveal that the proposed CE-based algorithms significantly perform better than the state-of-the-art test algorithms for various system configurations. [ABSTRACT FROM AUTHOR]

Power transformers represent one of the most abundant and expensive components in the electric power industry. Dissolved gas analysis (DGA) of transformer is the most widely accepted diagnostic tool across the globe to understand insulation incipient failures. Nevertheless, DGA fault gas interpretation is a remarkable challenge for transformer owners and utility engineers. Several computational techniques have been adopted for DGA fault classification along with offline methods. However, limited data availability, high ambiguity in DGA interpretation, suitability, and model accuracy are critical challenges in the DGA fault classification using computational techniques. In this work, highly diverse and large DGA data samples of in-service transformer fleets from five different utilities have been used to develop an efficient fault classification methodology. A total of 4580 DGA samples and IEC TC 10 database are used for training and testing, respectively, for various machine learning algorithms. Discussions on performance indicators and evaluation of several algorithms to verify the most suitable class algorithms are also the focus of this work. Furthermore, a best-performing model is identified based on various performance indicators. The hyperparameters of the best model are further tuned to achieve a most precise fault classification. It is inferred that non-parametric methods and non-linear SVM are best suitable for transformer DGA fault classification. Importantly, the rankings in the present study suggest that transformer DGA fault prediction is better with ensemble learning methods. [ABSTRACT FROM AUTHOR]

Background: Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products. Methods: This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated. Results: (1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm). Conclusion: This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland. [ABSTRACT FROM AUTHOR]

Accurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essential. Thus, this work recommends various supervised machine learning algorithms for predicting water levels with groundwater level correlation. The predicting models have Linear Regression (LR), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Neural Network (NN). This study includes four scenarios; The first scenario (SC1) uses lag 1; second scenario (SC2) uses lag 1 and lag 2; third scenario (SC3) uses lag 1, lag 2, and lag 11 and the fourth scenario (SC4) uses lag 1, lag 2, lag 11 and lag 12. These scenarios have been determined using the autocorrelation function (ACF), and these lags represent the month. The results showed that for SC1, SC2, and SC4, all model performance in GPR gave good results where the highest R equal to 0.71 in SC1, 0.78 in SC2, and 0.73 in SC4 using the Matern 5/2 GPR model. For SC3, the Stepwise LR model gave a better result with an R of 0.79. It can be concluded that Matern 5/2 of Gaussian Processes Regression Models is a reliable model to predict water level as the method gave a high performance in each scenario (except SC3) with a relatively fastest training time. The NN model had the worst performance to the other three models since it has the highest MAE values, RMSE, and lowest value of R in almost all four scenarios of input combinations. These results obtained in this study serves as an excellent benchmark for future water level prediction using the GPR and LR with four scenarios created. [ABSTRACT FROM AUTHOR]

Journal and thrust bearings utilise hydrodynamic lubrication to reduce friction and wear between the shaft and the bearing. The process to determine the lubricant film thickness or the actual applied load is vital to ensure proper and trouble-free operation. However, taking accurate measurements of the oil film thickness or load in bearings of operating engines is very difficult and requires specialised equipment and extensive experience. In the present work, the performance parameters of journal bearings of the same principal dimensions are measured experimentally, aiming at training a Machine Learning (ML) algorithm capable of predicting the loading condition of any similar bearing. To this end, an experimental procedure using the Bently Nevada Rotor Kit 4 is set up, combined with sound and vibration measurements in the vicinity of the journal bearing structure. First, sound and acceleration measurements for different values of bearing load and rotational speed are collected and post-processed utilising 1/3 octave band analysis techniques, for parametrisation of the input datasets of the ML algorithms. Next, several ML algorithms are trained and tested. Comparison of the results produced by each algorithm determines the fittest one for each application. The results of this work demonstrate that, in a laboratory environment, the operational parameters of journal bearings can be efficiently identified utilising non-intrusive sound and vibration measurements. The presented approach may substantially improve bearing condition identification and monitoring, which is an imperative step to prevent journal bearing failures and conduct condition-based maintenance. [ABSTRACT FROM AUTHOR]

Automated machine learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoMLis an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout. In this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions. [ABSTRACT FROM AUTHOR]

This paper applies machine learning to optimize the transmission policies of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks, where time-division multiple access (TDMA) is used to serve multiple primary users and an energy-constrained secondary user is admitted to the primary users’ time slots via NOMA. During each time slot, the secondary user performs the two tasks: data transmission and energy harvesting based on the signals received from the primary users. The goal of the paper is to maximize the secondary user’s long-term throughput, by optimizing its transmit power and the time-sharing coefficient for its two tasks. The long-term throughput maximization problem is challenging due to the need for making decisions that yield long-term gains but might result in short-term losses. For example, when in a given time slot, a primary user with large channel gains transmits, intuition suggests that the secondary user should not carry out data transmission due to the strong interference from the primary user but perform energy harvesting only, which results in zero data rate for this time slot but yields potential long-term benefits. In this paper, a deep reinforcement learning (DRL) approach is applied to emulate this intuition, where the deep deterministic policy gradient (DDPG) algorithm is employed together with convex optimization. Our simulation results demonstrate that the proposed DRL assisted NOMA transmission scheme can yield significant performance gains over two benchmark schemes. [ABSTRACT FROM AUTHOR]

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries. [ABSTRACT FROM AUTHOR]

The purposes of the study were (i) to determine the potential of thermal imaging to assess the difference in the thermal pattern in various body regions of studied population; (ii) to compare the performance of feature extraction, feature fusion, feature ranking and feature dimension reduction (PCA) in classification of obese and normal children using different Machine learning algorithms. About 600 thermograms were obtained from various regions such as abdomen, finger bed, forearm, neck, shank and gluteal region for the studied population. Fifteen statistical textual features were extracted from the six regional thermograms followed by implementing feature fusion with SIFT and SURF algorithm. The PCA method provides the best classification accuracy for SVM (98%) followed by Naïve Bayes and Random Forest (97%). Thus, the regional thermography and computer aided diagnostic tool with machine learning classifier could be used as a basic non‐invasive prognostic tool for the evaluation of obesity in children. [ABSTRACT FROM AUTHOR]

An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimization, considerably accelerating the whole robust design process. First, based on a surrogate model mapping between the design parameters and performance, the WCA is performed using a genetic algorithm to ensure reliability. MITH searching is then carried out using an MLAO-based framework to find the MITH of the given design point. Next, based on the training set obtained using MITH searching, correlations between the design parameters and the MITH are learned. The robust design is carried out using surrogate models for both the performance and the MITH, and these models are updated online following the ML-MLAO scheme. Finally, two examples, including an array synthesis problem and an antenna design problem, are used to verify the proposed ML-MLAO method. The numerical results and computation time are discussed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Objectives: In this paper, machine learning algorithms was used to explore the application value of ultrasound contrast in the early evaluation of neoadjuvant chemotherapy in patients with gastrointestinal malignant liver metastases, and analyzes the effect of sports nursing methods on intestinal function recovery. Methods: Forty-seven patients with gastrointestinal malignancies were divided into 25 patients (combined chemotherapy group) and 22 cases (chemotherapy group) from April 2018 to April 2019. Two groups of patients were treated with CEUS. The effective lesion patients and invalid quantitative parameters were compared between the two groups before and after treatment, and the postoperative routine nursing was implemented. Results: Chemotherapy group effective in 18 cases, accounting for 81.82%; 4 cases, 18.18%. Combination chemotherapy patients 21 cases, accounting for 84.00%; 4 cases, accounting for 16.00%. Conclusion: Based on early is important to assess the efficacy of neoadjuvant chemotherapy in patients with liver metastases peak intensity ultrasound contrast parameters of the machine learning algorithms malignant tumors in the gastrointestinal tract, post-operative care movement helps to restore bowel function. [ABSTRACT FROM AUTHOR]

Summary: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state‐of‐the‐art machine learning algorithms, namely Support Vector Machines (SVM), K‐Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy. [ABSTRACT FROM AUTHOR]

Software testing process is a crucial part in software development. Generally the errors made by developers get fixed at a later stage of the software development process. This increases the impact of the defect. To prevent this, defects need to be predicted during the initial days of the software development, which in turn helps in efficient utilization of the testing resources. Defect prediction process involves classification of software modules into defect prone and non-defect prone. This paper aims to reduce the impact of two major issues faced during defect prediction, i.e., data imbalance and high dimensionality of the defect datasets. In this research work, various software metrics are evaluated using feature selection techniques such as Recursive Feature Elimination (RFE), Correlation-based feature selection, Lasso, Ridge, ElasticNet and Boruta. Logistic Regression, Decision Trees, K-nearest neighbor, Support Vector Machines and Ensemble Learning are some of the algorithms in machine learning that have been used in combination with the feature extraction and feature selection techniques for classifying the modules in software as defect prone and non-defect prone. The proposed model uses combination of Partial Least Square (PLS) Regression and RFE for dimension reduction which is further combined with Synthetic Minority Oversampling Technique due to the imbalanced nature of the used datasets. It has been observed that XGBoost and Stacking Ensemble technique gave best results for all the datasets with defect prediction accuracy more than 0.9 as compared to algorithms used in the research work. [ABSTRACT FROM AUTHOR]