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The rapid development of industry intelligence has promoted the importance of sorting robots, but the shortcomings of sorting tasks are also gradually obvious. The target detection of the sorting robot in the process of sorting and grasping is affected by the anchor frame setting, and its grasping efficiency and recognition effect are low. Therefore, a detection network based on an anchor frame-free regression algorithm is proposed in this study, and a positioning and grasping method of the sorting robot based on the Deep Belief Network (DBN) algorithm is proposed. The algorithm experiment shows that the prediction accuracy of the proposed anchor-free detection network in the PASCAL VOC dataset reaches 92.91, significantly higher than that of other detection networks. In addition, the sorting robot was researched and designed with high accuracy in occlusion target recognition. When the occlusion area reaches 70%, the accuracy rate is as high as 81.26%, which is far higher than other detection schemes. The above results show that in improving the sorting robot’s target recognition ability, the anchor-free detection network can significantly improve its recognition accuracy. At the same time, introducing the DBN network can improve the robot’s recognition ability for occluded targets, which is of great significance to the development of the sorting robot and the intelligent industry. [ABSTRACT FROM AUTHOR]

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.

Purpose: Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city. Design/methodology/approach: In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores. Findings: This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R2 score of 0.99, in contrast to Support Vector Regression, which yielded an R2 score of 0.74. Originality/value: The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process. [ABSTRACT FROM AUTHOR]

As technology advances, processor speeds are increasing at a rapid pace and digital systems require a significant amount of data bandwidth. As a result, careful consideration of signal integrity is required to ensure reliable and high-speed data processing. Crosstalk has become a vital area of research in signal integrity for electronic packages, mainly because of the high level of integration. Analytic formulas were analyzed in this study to identify the features that can predict crosstalk in multi-conductor transmission lines. Through the analysis, five variables were found and obtained a dataset consisting of 302,500, data points. The study evaluated the performance of various regression models for optimization via automatic machine learning by comparing the machine learning predictions with the analytic solution. Extra tree regression consistently outperformed other algorithms, with coefficients of determination exceeding 0.9 and root mean square logarithmic errors below 0.35. The study also notes that different algorithms produced varied predictions for the two metrics. [ABSTRACT FROM AUTHOR]

Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy. [ABSTRACT FROM AUTHOR]

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using nearinfrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content. [ABSTRACT FROM AUTHOR]

Motor oil pollutants have a non-negligible impact on crop growth and soil matrix, causing phenomena such as crop yield reduction and even crop failure. In order to solve the problem of predicting the concentration of motor oil pollutants in the soil surface layer, the fluorescence induction technique was used to obtain the spectral curves of motor oil, and the method of predicting the concentration of pollutant oils in the soil surface layer using the wavelet kurtosis as a quantitative parameter was proposed, and a comparative analysis was carried out by combining the three different kinds of motor oils on the market with the random forest regression algorithm. The experimental results show that the concentration prediction results of random forest with the selected wavelet kurtosis parameter for the three kinds of motor oils are evaluated using the correlation coefficient RP and the root mean square deviation (RMSD), and the prediction of gear oil, engine oil, and motorcycle oil are improved by 1. 2%, 2. 2%, and 1. 9%, and 14. 9%, 32. 4%, and 16. 8%, respectively. Among the experimentally prepared samples of three kinds of engine oils, from which 30 groups of samples with concentrations of 0. 01 ~ 0. 3 mL/ g each are selected for model prediction validation, the recognition accuracy of which is improved by 6. 67%, 6. 66%, 9. 96%, respectively. It is also verified that the prediction accuracy of the wavelet kurtosis parameter is improved in several regression models with high prediction performance. The research results provide a certain reference for the regression model for the prediction of the concentration of other hydrocarbon pollutants in the soil surface layer, and provides an effective detection means for the sustainable development of agricultural production and soil environment. [ABSTRACT FROM AUTHOR]

In the emergency scenario of abnormal high speed railway operation, dispatchers need to quickly and correctly handle the emergency response to the emergencies, which plays a decisive role in the safe operation of trains. The cognitive decision-making process of high speed railway dispatchers was took as the research object, using the laboratory simulation experiment platform and facing the emergency scenario of abnormal driving, the structure of the cognitive decision-making mechanism model and the data collection method corresponding to the measurement model were designed. Based on the information processing model, combined with the concept of perceptual load theory and situational awareness, adding elements of mental load, a cognitive decision-making mechanism model for high speed railway dispatchers was established. Aiming at the evaluation of mental load, a particle swarm optimization algorithm for joint optimization was designed and adopted to optimize the training process based on multi-source physiological signals, comprehensively using signal processing theory and mathematical methods to extract the data characteristics. Fitting the structural equation model and studying the relationship between mental load and attention and working memory are of more practical value for the later optimization of dispatchers workload and job adaptability. [ABSTRACT FROM AUTHOR]

Abstract The construction of structures using cross-laminated timber (CLT) has grown in popularity as a result of its environmentally friendly and high-strength characteristics. The primary function of angle bracket connections is to resist the force of CLT structures under horizontal forces, which is essential to ensure the seismic resilience and ductility of CLT structures. A regression algorithms-driven method for predicting the mechanical performance of angle bracket connections is introduced in this study. As input parameters, the geometric dimensions of the angle bracket connector, the connection method of the connector with the wall and floor slabs, and the properties of the CLT panel are utilized to predict the yield load, the maximal load, the initial stiffness, and the ductility ratio of the angle bracket connection. Prediction models were developed using the collected data from 110 angle bracket experiments, and each prediction model's performance was discussed in depth. Lastly, the permutation importance and SHapley Additive exPlanations (SHAP) value analysis were used to interpret the prediction models. The results showed that the extreme gradient boosting (XGB) algorithm could accurately predict the maximum and yielding load of the angle bracket connection, with R2 reaching 0.968 and 0.939. Furthermore, in predicting the initial stiffness of the angle bracket, the XGB algorithm performed the best with an average ratio of predicted to actual values of 0.985. The results indicated that this study proposed an accurate and efficient method for angle bracket connection to predicting its mechanical properties and confirmed the trustworthiness and feasibility of the prediction models.

Smart agriculture has come to be seen as a potential method for boosting agricultural output and sustainability. The study primarily focuses on the application of regression algorithms for predictive analysis in smart agriculture with reference to crop yield prediction. This study's main objective is to develop a reliable regression model that estimates crop yields based on a number of agricultural and environmental factors. This is done by accumulating previous agricultural information, such as weather patterns, soil properties, irrigation methods, and cropspecific traits, via smart sensors positioned throughout agricultural areas. In the study, a variety of regression approaches are investigated, including support vector regression (SVR), random forest regression, linear regression, polynomial regression, and more. The dataset is analyzed before using these methods to build prediction models. To validate the model's accuracy and generalizability, the dataset is split into training and testing sets, and cross-validation techniques are employed. Regression algorithms are highly reliable at predicting agricultural yields, according to the results. Random forest regression outperforms all other methods due to its capability to handle non-linearity and capture nuanced relationships between input characteristics and crop yields. However, the findings from other regression algorithms are equally promising, suggesting that they may be useful in specific situations. Additionally, the study evaluates how different input variables impact the model's efficacy. It draws attention to crucial elements including temperature, precipitation, soil moisture, and nutrient levels that have a significant impact on crop yields. Farmers may select the ideal crops, irrigation schedules, and fertilizer applications with the aid of an understanding of these connections. [ABSTRACT FROM AUTHOR]

The construction of structures using cross-laminated timber (CLT) has grown in popularity as a result of its environmentally friendly and high-strength characteristics. The primary function of angle bracket connections is to resist the force of CLT structures under horizontal forces, which is essential to ensure the seismic resilience and ductility of CLT structures. A regression algorithms-driven method for predicting the mechanical performance of angle bracket connections is introduced in this study. As input parameters, the geometric dimensions of the angle bracket connector, the connection method of the connector with the wall and floor slabs, and the properties of the CLT panel are utilized to predict the yield load, the maximal load, the initial stiffness, and the ductility ratio of the angle bracket connection. Prediction models were developed using the collected data from 110 angle bracket experiments, and each prediction model's performance was discussed in depth. Lastly, the permutation importance and SHapley Additive exPlanations (SHAP) value analysis were used to interpret the prediction models. The results showed that the extreme gradient boosting (XGB) algorithm could accurately predict the maximum and yielding load of the angle bracket connection, with R2 reaching 0.968 and 0.939. Furthermore, in predicting the initial stiffness of the angle bracket, the XGB algorithm performed the best with an average ratio of predicted to actual values of 0.985. The results indicated that this study proposed an accurate and efficient method for angle bracket connection to predicting its mechanical properties and confirmed the trustworthiness and feasibility of the prediction models. [ABSTRACT FROM AUTHOR]

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data. [ABSTRACT FROM AUTHOR]

Global warming is a significant challenge, and carbon stocks in terrestrial ecosystems are crucial for reducing the greenhouse effect and increasing sinks. A study was conducted in Zhengzhou City from 2000 to 2020 using Landsat image spectral reflectance to analyze changes in carbon stock. Environmental variables such as surface moisture, salinity, vegetation index, brightness, and soil texture were constructed. Multiple linear regression (MLR), support vector machine regression (SVR), random forest regression (RFR), and long short-term memory (LSTM) models were used to invert the carbon stock. The results showed that the NDCS index, constructed using Landsat's blue band and NIR band, was the best inversion variable for carbon stock, with the clay index (CI) playing a primary role. The LSTM algorithm had the best fitting effect on carbon stock, with an R2 of 0.84 and RMSE of 3.56. The carbon stock in Zhengzhou City decreased by 13.93% between 2000 and 2020, possibly due to the large-scale reduction of arable land. Future land-use planning should focus on protecting arable land, optimizing land-use patterns, and enhancing the ecosystem's carbon sequestration capacity.

Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.

In order to better improve the level and quality of rural family education, this paper adopts text mining technology to interpret the relevant data of rural family education under the background of deep learning. On the basis of deep learning algorithms, web crawler technology is used to obtain rural family education research data. It is found that there are null values and repetitive values in the acquired research data, so it is necessary to further clean the research data. After washing, the combined Bi-LSTM-CRF algorithm is used to process the data for Chinese word segmentation, and at the same time, the stop word list is improved in order to achieve the best word segmentation effect. The LDA thematic model was used to determine the influencing factors of rural family education, and then the linear regression algorithm was used to analyze rural family education empirically. Rural teachers with different education levels were not significantly different in Q1 (1.608), Q2 (0.117), Q3 (2.019), Q4 (1.733), and Q5 (2.042), according to the results. The adjusted R2 is 0.133, indicating a fair fit between the model obtained from the regression analysis and the actual data, and the regression coefficient of family education is 0.411 after eliminating the confounding interference of grade and political appearance. This study purposefully proposes a path for the innovative development of rural family education, which can provide a theoretical reference program for the development and construction of modern rural family education.

The evaluation of product styling design is transformed into a complex nonlinear solution problem by symmetry analysis in this paper. The equilibrium matrix decomposition evaluation system calculates the stored matrix singular values in product styling design and proves that symmetry can be obtained from the stress matrix row vector. The regression algorithm is used to process multiple industrial product design objectives, and each design element in product styling is coded to obtain the fitness evaluation of individual industrial products. It is found that the satisfaction score in product styling design is 97.4, the weight index of logo novelty element is up to 7, and the regression coefficient of plane shape is 0.269, to improve the scientific and feasibility of aesthetic evaluation of industrial product styling design.

Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, comes in second place with a gross ratio of 91%. Furthermore, Bayesian ridge (BR), linear regressor (LR), and stochastic gradient descent (SGD), with mean square error and with accuracy ratios of 84.365%, 84.363%, and 79%. As a result, the performance precision of these regression models yields. The interaction framework was designed to be a straightforward tool for working with this paradigm. This model is a valuable tool for establishing strategies to counter the swiftness of climate change in the area under study. [ABSTRACT FROM AUTHOR]

The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor. [ABSTRACT FROM AUTHOR]

ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field. [ABSTRACT FROM AUTHOR]

In this study, a unique combination of Silver (Ag) and Ga-doped ZnO (GZO) is used as plasmonic materials where both materials can be used for analyte detection. The sensor exhibits two distinct peaks within the same Refractive Index (RI) by which the sensing applications enhance. GZO is responsible for the first of the two peaks, while Ag contributes to the latter one. Along y-polarization, the highest value of double peak shift sensitivity (DPSS) known to date is found: 27,341.5 nm/RIU. The sensor also displays a high wavelength sensitivity (WS) of 27,360 nm/RIU and an amplitude sensitivity (AS) of 875.72 RIU−1. Additionally, the sensor demonstrates a high wavelength resolution of 6.032 × 10–6 and an amplitude resolution of 1.496 × 10–5. The sensor exhibits a linearity of R2 = 0.9973 and a figure of merit (FOM) of 243.4 RIU−1. Again, a ± 10 tolerance limit is tested without showing any significant change in confinement loss and resonant wavelength shift. The sensor is examined for a wide array of RI ranging from 1.27 to 1.41 extending its application to the detection of pharmaceutical products and various chemicals. Furthermore, machine learning regression algorithms have been explored in this work to find out the sensing parameters in RIs that were not numerically investigated. Random Forrest Regressor and K-Neighbors Regressor were found to be showing high accuracies of 90.176% and 95.54%, respectively. Using these two algorithms, the performance of the sensor in detecting various chemicals were predicted. [ABSTRACT FROM AUTHOR]

The earthquake catalog notes that there have been earthquakes with Mw > 7 that hit the Flores area, three of which occurred in the Flores Sea in 1992, 2015, and 2021. Revealed that the seismic activity of Eastern Indonesia is thought to be influenced by the isolated thrust fault segment of the island of Flores and the island of Wetar. The study of the rising fault segment on Flores Island and Wetar Island helps in further understanding the fault behavior, earthquake pattern, and seismic risk in the Flores Sea region. In earthquakes with giant magneto, an aftershock can occur due to the interaction of ground movements. This research analyzes and compares the data from the evaluation of the classification algorithm and the regression algorithm. The initial stages of this research include requesting IRIS DMC Web Service data. The data is then subjected to a cleaning process to obtain the expected feature extraction. The next stage is to perform the clustering process. This stage is carried out to label dependent data by adding new features as data clusters. The following procedure divides the validation value, which consists of training and test data. The estimation results show that the classification algorithm's evaluation value is better than that of the regression algorithm. The evaluation value of several algorithms indicates this, with an accuracy rate between 80% and 100%. [ABSTRACT FROM AUTHOR]

It is known that the functional reproducing kernel Hilbert space (FRKHS) theory lays the functional analysis foundation for learning non-point-evaluation functional data with kernel-regularized learning. In the present paper, we investigate the convergence of regression learning associated with Radon reproducing kernel Hilbert spaces (RRKHSs) and a parameterized loss. We provide a kind of online learning algorithm and establish an upper bound for the learning rate, and it shows that the learning rate may be improved by adjusting the parameter in the loss function. As incidental conclusions, we provide a method of constructing FRKHSs with Fourier analysis, some RRKHSs are defined for the classes of periodic functions defined on $ [-\pi, \;\pi]^s $; the functions defined on the interval $ [-1, 1] $; the functions defined on $ R_+ = [0, +\infty) $ and the functions defined on the unit sphere. [ABSTRACT FROM AUTHOR]

Predicting academic performance provides an effective way for students and faculties to monitor their academic progress. The identification of the most significant features was a key outcome of this research, and the college/university databases from online learning platforms are the main academic data sets used to ascertain performance. However, previous research emphasized the addition of other significant features in the prediction of academic performance. Universities' organizational features include non-academic essential data used in determining student success, but no research has utilized this data to predict student academic performance. Generally, to evaluate binary classification, the relationship between the predicted classifications and the true classifications is analyzed, this approach can lead to the loss of important information from the data. Furthermore, to avoid such loss, this research proposes a regression model by comparing six regression algorithms, and combining academic and non-academic features for prediction student academic performance. Among the various models examined, the gradient-boosted trees regression model demonstrated the lowest error rate. The proposed features have been observed to have a significant impact on academic performance, making them suitable for use in predictions. [ABSTRACT FROM AUTHOR]

High performance computing is now a major area where business and computing technologies need resilient high performance to meet business continuity and real-time needs. However, many top-level business and technology organizations are still in the process of improving high performance and traffic resiliency to ensure the availability of the system at all times. Machine learning is an important advancement of computer technology that helps in decision making by prediction and classification mechanism based on historical data. In this paper, we propose and integrate the concept of high-performance computing with artificial intelligence machine learning techniques in cloud platforms. The networking and computing performance data are used to validate, predict and classify the traffic and performance patterns and ensure system performance and continuous traffic flow resiliency decisions. The proposed integrated design approach has been analyzed on different step actions and decisions based on machine learning regression and classification models, which auto-correct the performance of the system at real run time instances. Our machine learning integrated design simulated results show its traffic resilience performs proactively 38.15% faster with respect to the failure point recovery along with 7.5% business cost savings as compared to today’s existing non-machine learning based design models.

This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models. [ABSTRACT FROM AUTHOR]

As the number of old bridges increases worldwide, economic and maintenance issues are emerging due to the deterioration of these structures. In general, the conventional approach for the safety assessment of existing bridges is based on performing structural analysis and safety verifications, starting from the material properties obtained from experimental tests. In particular, for some old bridges, the design documents are not computerized or stored, so many additional field tests may be required due to the uncertainty of information. In this paper, we proposed a framework that can estimate the load-carrying capacity of old bridges for which the design documents are absent, and field tests are not used in this process. The framework relies on computational design strength and features procedures for calculating calibration factors to reflect the current conditions. With only limited information available with regard to bridges, the key to this study is its use of AI technology. First, the relationship between externally measurable geometric characteristics and the design strength was established based on 124 design documents. In this process, we compared the performance of five regression algorithms: multiple linear regression (MLR), decision tree (DT), boosting tree (BT), support vector machine (SVM), and Gaussian process regression (GPR). It was confirmed that it is possible to predict the design strength using GPR, with an error rate of 0.3%. Second, an ANN model was built to estimate the calibration factor as a condition assessment of 82 in-service bridges. The ANN was determined using optimal parameters with a mean squared error (MSE) of 0.008. Each type of AI used in the proposed framework showed a high predictive performance, implying that it can be used to evaluate the load-carrying capacity of bridges without a design document. [ABSTRACT FROM AUTHOR]

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data.

High performance computing is now a major area where business and computing technologies need resilient high performance to meet business continuity and real-time needs. However, many top-level business and technology organizations are still in the process of improving high performance and traffic resiliency to ensure the availability of the system at all times. Machine learning is an important advancement of computer technology that helps in decision making by prediction and classification mechanism based on historical data. In this paper, we propose and integrate the concept of high-performance computing with artificial intelligence machine learning techniques in cloud platforms. The networking and computing performance data are used to validate, predict and classify the traffic and performance patterns and ensure system performance and continuous traffic flow resiliency decisions. The proposed integrated design approach has been analyzed on different step actions and decisions based on machine learning regression and classification models, which auto-correct the performance of the system at real run time instances. Our machine learning integrated design simulated results show its traffic resilience performs proactively 38.15% faster with respect to the failure point recovery along with 7.5% business cost savings as compared to today's existing non-machine learning based design models. [ABSTRACT FROM AUTHOR]

Fishery is one sector that is important for human life, because most of human needs come from fisheries, one example is the need for catfish. Currently, there is a gap between crop yields and community needs, which is indicated by very high demand while yields are low. Therefore, this study was conducted to predict catfish yields using a linear regression algorithm so that community needs are met. The method used for the training process in this study uses Multiple Linear Regression. Multiple Linear Regression is an analysis conducted on the dependent variable / dependent variable and two or more independent or independent variables. In contrast to simple regression which only has one independent variable and one dependent variable. Predicting the size of the dependent variable using the independent variable data which is already known. The results of this study analyzed the value of Root Mean Squared Error, and Mean Absolute Error. The result of the line equation from the training data for catfish harvesting is yˆ = - 310.6119 + 0.0759x1 + 0.0245x2 + 0.6104x3 [ABSTRACT FROM AUTHOR]

The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.

In recent years, different technologies based on Machine Learning (ML) algorithms have been developed to evaluate road distress quality and help with pavement maintenance, traffic safety, and road rehabilitation. However, the literature lacks a comparative study of various ML algorithms to detect pavement damage. This study aims to fill that gap by comparing various regression algorithms including Linear Regression (LR), Support Vector Regressor (SVR), Random Forest Regressor (RFR), k-nearest neighbors, Gradient Boosting Regressor (GBR), Light Gradient Boosted Machine (LGBM), stacking regressor, and Decision Tree Regressor (DTR). To evaluate the accuracy of the models, different performance metrics, namely coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are used. Experiments are conducted to validate the models’ performance using a dataset sponsored by the Michigan Department of Transportation and executed at Michigan State University for the Michigan road pavement damage. Obtained results indicate that the GBR modeling algorithm performs better with the most accurate asphalt pavement damage model of R2 = 99%, MAE = 0.02, and RMSE = 0.03. Thus, the use of GBR modeling might be an easy, robust, and efficient method for automated pavement damage prediction.

Variable application by wind is an efficient application technology recommended by the Food and Agriculture Organization (FAO) of the United Nations that can effectively improve the deposition effect of liquid medicine in a canopy and reduce droplet drift. In view of the difficulty of modelling wind forces in orchard tree canopies and the lack of a wind control model, the wind loss model for a canopy was studied. First, a three-dimensional wind measurement test platform was built for an orchard tree canopy. The orchard tree was located in three-dimensional space, and the inner leaf areas of the orchard tree canopy and the wind force in different areas were measured. Second, light detection and ranging (LiDAR) point cloud data of the orchard tree canopy were obtained by LiDAR scanning. Finally, classic regression, partial least squares regression (PLSR), and back propagation (BP) neural network algorithms were used to build wind loss models in the canopy. The research showed that the BP neural network algorithm can significantly improve the fitting accuracy of the model. Under different fan speeds of 1,381 r/min, 1,502 r/min, and 1,676 r/min, the coefficient of determination (R²) of the model were 81.78, 72.85, and 69.20%, respectively, which were 19.38, 7.55, and 12.3% higher than those of the PLSR algorithm and 21.48, 22.25, and 24.3% higher than those of multiple regression analysis. The comparison showed that the BP neural network algorithm obtains the highest model accuracy, but because the model is not intuitive, PLSR has the advantages of intuitive and simple models in the three algorithms. In practical applications, the wind loss model based on a BP neural network or PLSR can be selected according to the operational requirements and software and hardware conditions. This study can provide a basis for wind control in precise variable spraying and promote the development of wind control technologies. [ABSTRACT FROM AUTHOR]

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In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price movement. In today's forecasting studies, ensemble learning is a very prevalent and well-liked study methodology. The regression integration computation of large housing datasets can use a lot of computer resources as well as computation time, and ensemble learning uses more resources and calls for more machine support in integrating diverse models. The Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models, combining the best benefits of separate models. The Average Model has a strong applicability in the field of regression prediction and significantly increases computational efficiency. The technique is also easier to replicate and very effective in regression investigations. Before using regression processing techniques, this work creates an average of different regression models using theAM(Average Model) algorithm in a novelway. By evaluating essential models with 90% accuracy, this technique significantly increases the accuracy of house price predictions. The experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms, and the prediction accuracy is greatly improved compared with other algorithms, and has a good experimental effect in house price prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, breed, weight at birth, weight at weaning, and age at weaning. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The StackingRegressor algorithm yielded a prediction quality of the live weight of Duroc, Landrace, and Yorkshire pigs that was higher than that of the state-of-the art algorithms. Live weight is an important indicator of livestock productivity and serves as an informative measure for the health, feeding, breeding, and selection of livestock. In this paper, the live weight of pig was estimated using six morphometric measurements, weight at birth, weight at weaning, and age at weaning. This study utilised a dataset including 340 pigs of the Duroc, Landrace, and Yorkshire breeds. In the present paper, we propose a comparative analysis of various machine learning methods using outlier detection, normalisation, hyperparameter optimisation, and stack generalisation to increase the accuracy of the predictions of the live weight of pigs. The performance of live weight prediction was assessed based on the evaluation criteria: the coefficient of determination, the root-mean-squared error, the mean absolute error, and the mean absolute percentage error. The performance measures in our experiments were also validated through 10-fold cross-validation to provide a robust model for predicting the pig live weight. The StackingRegressor model was found to provide the best results with an MAE of 4.331 and a MAPE of 4.296 on the test dataset. [ABSTRACT FROM AUTHOR]

This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models.

With the development of imaging devices and image processing algorithms, numerous features have come to be used for the estimation of total nitrogen content (TNC) in plants. However, higher-dimensional inputs contain more correlated variables that can detrimentally affect model performance. In this study, a hybrid feature selection approach was developed for TNC estimation in Aquilaria sinensis. A low-cost modified digital camera with external filters was used to capture canopy images. Three feature selection methods, namely, random forest (RF), Pearson correlation coefficient (PCC)-based feature selection, and sequential backward selection (SBS), were combined into two hybrid feature selection algorithms (RF_SBS and PCC_SBS). In addition, three regression algorithms were used in hybrid feature selection process: random forest regression (RFR), support vector regression (SVR), and partial least squares regression (PLSR). The hybrid feature selection process consists of two steps. First, the lowest number of dimensions is sought based on the feature ranking. Then, SBS is used to find the best feature combinations. Compared with the original models, the R2 values of the RF-SBS-based models are improved by 0.094 (RF_SBS_RFR), 0.190 (RF_SBS_SVR), and 0.116 (RF_SBS_PLSR), while the R2 values of the PCC-SBS-based models are improved by 0.055 (PCC_SBS_RFR), 0.092 (PCC_SBS_SVR) and 0.128 (PCC_SBS_PLSR). Finally, the two best TNC estimation models are found to be PCC_SBS_PLSR, with an R2 of 0.863, and RF_SBS_SVR, with an R2 of 0.872. The proposed hybrid feature selection approach not only has great capacity to improve estimation accuracy but also can reduce model complexity by choosing the best feature subset. • A hybrid feature selection algorithm was proposed in this paper. • A modified digital camera was used for total nitrogen content estimation. • Some texture features also can represent the total nitrogen content of leaves. [ABSTRACT FROM AUTHOR]

As the number of old bridges increases worldwide, economic and maintenance issues are emerging due to the deterioration of these structures. In general, the conventional approach for the safety assessment of existing bridges is based on performing structural analysis and safety verifications, starting from the material properties obtained from experimental tests. In particular, for some old bridges, the design documents are not computerized or stored, so many additional field tests may be required due to the uncertainty of information. In this paper, we proposed a framework that can estimate the load-carrying capacity of old bridges for which the design documents are absent, and field tests are not used in this process. The framework relies on computational design strength and features procedures for calculating calibration factors to reflect the current conditions. With only limited information available with regard to bridges, the key to this study is its use of AI technology. First, the relationship between externally measurable geometric characteristics and the design strength was established based on 124 design documents. In this process, we compared the performance of five regression algorithms: multiple linear regression (MLR), decision tree (DT), boosting tree (BT), support vector machine (SVM), and Gaussian process regression (GPR). It was confirmed that it is possible to predict the design strength using GPR, with an error rate of 0.3%. Second, an ANN model was built to estimate the calibration factor as a condition assessment of 82 in-service bridges. The ANN was determined using optimal parameters with a mean squared error (MSE) of 0.008. Each type of AI used in the proposed framework showed a high predictive performance, implying that it can be used to evaluate the load-carrying capacity of bridges without a design document.

Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R 2 ) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes , providing an efficient method for the rapid determination of cyanidin 3-rutinoside content., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xiao, Zhang, Liu, Li, Jiang, Li and Diao.)

The deployment of photovoltaic technology in global electricity generation has reduced the dependence on fossil fuels and the adverse effect of global warming climate changes. However, the variability of PV output due to its reliance on meteorological parameters hinders the total maximization of the technology. In this paper, the interdependence of meteorological parameters on accurate estimation of the specific yield of the PV system is examined using machine learning algorithms. The analysis uses 2 years of meteorological data in a 10-min interval. The meteorological parameters used as input are global solar radiation (GR), wind speed (WS), wind direction (WD), the standard deviation of wind direction (WSD), air temperature (AT), and relative humidity (RH), while the output variable is the PV specific yield (PVSY). This study uses a Python-based “all regressor” machine learning algorithm, to analyze their accuracy for the estimation analysis at different input feature space. The accuracy of the models and algorithms is analyzed using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R2). The best performance in the two feature space was retrieved for AT, and GR with MSE and MAE values of 0.113 and 0.252, respectively. The best performance in the three feature space was retrieved for WSD, AT, and GR with MSE and MAE values of 0.109 and 0.255, respectively. The results show that the most accurate PVSY estimation was for the four input feature space with input variables of WS, AT, GR, and RH. The K-nearest neighbor (KNN) proved to be the most appropriate algorithm for this feature space with an RMSE and MAE score of 0.101 and 0.266, respectively. The study also shows that the most significant meteorological inputs in PV yield estimation are air temperature and global solar radiation. The study also noted that a large feature space adds uncertainties to the training process. Finally, the paper concludes that the sunshine-based parameters are key inputs for high-quality PV yield estimation regardless of the number of input combinations. It is also concluded that, in this study, the most accurate regression algorithms for estimation are the Least Absolute Shrinkage and Selection Operator (LASSO), KNN, Elastic Net (EN), Decision Tree regressor (DT), and Extra Tree regression (ET). [ABSTRACT FROM AUTHOR]