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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]

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]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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. [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]

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]

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]

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]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Tunnel engineering is affected by a variety of factors, which results in large detection errors in tunnel engineering. In order to improve the monitoring effect of tunnel engineering, based on BPNN and MARS machine learning regression algorithm, this research constructs a tunnel engineering monitoring and prediction model. Moreover, the gray residual BP neural network designed in this study uses a series combination, and the residuals obtained from the gray model are used as the input data of the BP neural network, and the output of the combined model is used as the prediction result. By applying the monitoring data of the convergence of the upper surrounding of the tunnel surface section and deformation of the arch subsidence, it is verified that the proposed method based on the combined model of BPNN and MASR can predict and analyze the tunnel deformation monitoring data very well. [ABSTRACT FROM AUTHOR]

The results of the cloud liquid water path (LWP) "land–sea" gradient retrieval from ground-based measurements of the downwelling microwave radiation near the Gulf of Finland coastline in the suburbs of Saint-Petersburg are presented. The measurements were carried out at the Department of Physics, St. Petersburg State University, by an RPG-HATPRO radiometer operating in the angular scanning mode. The inverse problem is solved by linear regression with the use of different statistical models of cloudiness for training the algorithm. Seven-year average values of the gradient of LWP for summer and winter have been obtained. The results demonstrate the presence of a positive "land–sea" gradient of LWP (larger values over the land and smaller values over the sea) in both periods, which qualitatively agrees with available satellite data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Madenat Al-Elem University College / Magallaẗ Kulliyyaẗ Madīnaẗ Al-ʿAlam Al-Ğāmi'aẗ is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, rolling horizon control (RHC) approaches have attracted growing attention due to its feature of reducing forecast errors for real-time/online operation and optimization. However, the performance of the existing RHC approach degrades if the intra-day forecast data is unavailable or missing due to Internet or cloud service provider outages, software/hardware failures, and many other factors. In this paper, we propose a new fitted-RHC approach to overcome this challenge. The proposed fitted-RHC framework is designed with a regression algorithm which utilizes the empirical knowledge to make the real-time decisions whenever the intra-day forecast data is unavailable. The regression algorithm utilizes a statistical relative probability method to calculate the relative probability for each decision vector, and output the proper optimization policy. In addition, we adopt a modified version of exogenous information transition function that is more suitable to conduct the simulations in a real-time environment. Simulation results in microgird show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case even with the missing data, and perform efficiently with the uncertain environment in stochastic case study. In comparison, the proposed fitted-RHC approach outperforms several other optimization techniques. [ABSTRACT FROM AUTHOR]

Hammering rocks of different strengths can make different sounds. Geological engineers often use this method to approximate the strengths of rocks in geology surveys. This method is quick and convenient but subjective. Inspired by this problem, we present a new, non-destructive method for measuring the surface strengths of rocks based on deep neural network (DNN) and spectrogram analysis. All the hammering sounds are transformed into spectrograms firstly, and a clustering algorithm is presented to filter out the outliers of the spectrograms automatically. One of the most advanced image classification DNN, the Inception-ResNet-v2, is then re-trained with the spectrograms. The results show that the training accurate is up to 94.5%. Following this, three regression algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) are adopted to fit the relationship between the outputs of the DNN and the strength values. The tests show that KNN has the highest fitting accuracy, and SVM has the strongest generalization ability. The strengths (represented by rebound values) of almost all the samples can be predicted within an error of [−5, 5]. Overall, the proposed method has great potential in supporting the implementation of efficient rock strength measurement methods in the field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An objective image quality assessment tool is proposed to measure image enhancement quality with emphasis on contrast. In the proposed tool, which is based on maximizing contrast with minimum artefact (MCMA), local and global properties of an image are measured through pixel‐wise and histogram‐wise features, respectively. To this aim, three sub‐measures are introduced, each of which able to detect one contrast‐related quality aspect: (i) low dynamic range of image; (ii) histogram shape preservation during image enhancement process; and (iii) local pixel diversity. These sub‐measures are combined through a subjective test to adapt them to the mean opinion scores (MOSs) of a diverse set of training contrast‐enhanced images. A regression algorithm performs the adaptation by fitting the three sub‐measures to the MOS values and finding an optimal linear combination by maximizing the Pearson correlation. In order to evaluate the performance of the MCMA algorithm, another independent, subsequent, subjective test was performed on a set of images enhanced by various known contrast enhancement algorithms to obtain MOS values and to compare them with the output of the proposed MCMA method. The experimental results show that MCMA has the highest correlation to the MOS when compared to the existing tested contrast measurement tools. [ABSTRACT FROM AUTHOR]

Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia, while predicting real-value outcome from a given array of business inputs. The results show that most of the machine learning predictive values for the desired process output deviated from 0 to 15% of actual employees' decision. Hence, it verifies the appropriateness of the chosen approach, with predictive accuracy that can be meaningful in practice. As a machine learning case study in business context, it contains valuable information that can help companies understand the significance of machine learning for enterprise computing. It also points out some potential pitfalls of machine learning misuse. [ABSTRACT FROM AUTHOR]

This paper presents a complete protection scheme for detecting, classifying and locating the fault in HVDC transmission lines using support vector machines (SVM). SVM has been used for protective relaying application in HVAC transmission line, however very limited works have been reported for HVDC transmission line. In this work, a ±500 kV HVDC transmission system is developed in PSCAD/EMTDC and the measurement signals obtained are analyzed in MATLAB. The rectifier side AC RMS voltage, DC voltage and current on both the poles are continuously monitored, and given as input to the SVM binary classifier in order to detect the presence of fault in the line. Once a fault is detected, the SVM multi‐class classification module predicts the type of fault and the SVM regression algorithm predicts the location of fault. The feature vector used in the classification and location modules is the standard deviation of the signals over half cycle before and after the occurrence of fault. The method proposed is simple as it requires single‐end data and a direct standard deviation of one cycle data gives very accurate results. The detection and classification modules are 100% accurate whereas the fault location module has a mean error of 0.03%. [ABSTRACT FROM AUTHOR]

Xu, A. and Sun, J., 2015. The simulation study of marine propeller load characteristics based on regression algorithm. In this paper, in order to avoid the complex calculation with propeller chart, a new model method for marine propeller was put forward. According to the new model method, propeller thrust and torque are calculated based on regression algorithm and it can realize four quadrants simulation. After the propeller model was completed, basic ship operation such as start, stop and emergency astern are simulated with the help of mathematical model of the ship-propeller. Through analyzing the change curves of the revolution speed of the propeller, the navigation speed of the ship and the torque of the ship under all kinds of working conditions, some operable suggestions and theory evidences are provided for the practical operation of the ship. [ABSTRACT FROM AUTHOR]

The possibility of remotely monitoring the total atmospheric ozone content (TAOC) using data from the multichannel geostationary scanning instrument (MGSI) aboard the Elektro-L no. 1 Russian meteorological satellite is explored. In addition to the MGSI measurements in three channels (8.2-9.2, 9.2-10.2, and 10.2-11.2 μm), data on the vertical temperature distributions in the ozone layer and the temperature and pressure at the underlying terrain level (satellite sensing results or forecast data) are used as additional predictors in the process of TAOC estimation. The TAOC estimates are constructed with the use of a regularized regression algorithm (ridge regression). The learning and check samples are formed using independent TAOC estimates based on the data gathered by the OMI instrument aboard the EOS Aura satellite. Numerical experiments in processing the actual MGSI data gathered over certain periods within the interval from November 2011 to August 2012 reveal the possibility of arranging regular monitoring of the TAOC fields with high spatial and temporal resolutions and an acceptable precision: the absolute value of relative mean deviations and the relative root-mean-square deviations of the estimates based on the MGSI data from the estimates based on the OMI data lie within intervals of 1-2% and 5-7%, respectively, depending on the underlying terrain type. [ABSTRACT FROM AUTHOR]

A cross-sensor calibration technique is developed and applied to improve upon the prelaunch radiance calibration and characterization for the Total Ozone Unit (TOU) onboard the FengYun-3/A satellite. The Level 3 products from the National Aeronautics and Space Administration Ozone Monitoring Instrument (OMI) onboard the Earth Observing System Aura are used as input to a radiative transfer model to predict the TOU radiances and characterize the biases for the measurements over the Pacific Ocean in low- and midlatitudes. The coefficients are derived from a regression algorithm to adjust the TOU radiances. It is shown that, after the measurement bias correction, the biases between the retrieved total column ozone products from the TOU with those from the OMI Total Ozone Mapping Spectrometer (TOMS)-Version 8 products and those from a set of ground-based station measurements are 3 % and 5% , respectively. The variations in the estimated total ozone amounts from the TOU are consistent with those derived from Solar Backscatter Ultraviolet Radiometer instruments and OMI for a period from January 2010 to February 2011. [ABSTRACT FROM AUTHOR]

We consider a fully online regression algorithm associated with a general convex loss function and Gaussian kernels with changing variances. Error analysis is conducted in a setting with samples drawn from a non-identical sequence of probability measures. When a fixed Gaussian is used, it was known that the learning ability of induced algorithms is weak. By allowing varying Gaussians, we show that the achieved learning rates can be of polynomial decays. [ABSTRACT FROM AUTHOR]

It is hard for the existing methods to obtain the expression of the system reliability for most of the practical complex systems with a large number of components and possible states. A new regression algorithm based on the lower and upper bounds is presented in this paper, which can obtain the system reliability analytically without concerning the structure of the complex system. The method has been applied to a real system and the reliability results are compared with those acquired by the classical method and the parametric method. The effectiveness and accuracy of the proposed method have been testified. [ABSTRACT FROM AUTHOR]

The ECOSTRESS multi-channel thermal radiometer on the Space Station has an unprecedented spatial resolution of 70 m and a return time of hours to 5 days. It resolves details of oceanographic features not detectable in imagery from MODIS or VIIRS, and has open-ocean coverage, unlike Landsat. We calibrated two years of ECOSTRESS sea surface temperature observations with L2 data from VIIRS-N20 (2019–2020) worldwide but especially focused on important upwelling systems currently undergoing climate change forcing. Unlike operational SST products from VIIRS-N20, the ECOSTRESS surface temperature algorithm does not use a regression approach to determine temperature, but solves a set of simultaneous equations based on first principles for both surface temperature and emissivity. We compared ECOSTRESS ocean temperatures to well-calibrated clear sky satellite measurements from VIIRS-N20. Data comparisons were constrained to those within 90 min of one another using co-located clear sky VIIRS and ECOSTRESS pixels. ECOSTRESS ocean temperatures have a consistent 1.01 °C negative bias relative to VIIRS-N20, although deviation in brightness temperatures within the 10.49 and 12.01 µm bands were much smaller. As an alternative, we compared the performance of NOAA, NASA, and U.S. Navy operational split-window SST regression algorithms taking into consideration the statistical limitations imposed by intrinsic SST spatial autocorrelation and applying corrections on brightness temperatures. We conclude that standard bias-correction methods using already validated and well-known algorithms can be applied to ECOSTRESS SST data, yielding highly accurate products of ultra-high spatial resolution for studies of biological and physical oceanography in a time when these are needed to properly evaluate regional and even local impacts of climate change. [ABSTRACT FROM AUTHOR]

Realistic dynamics models are important for haptic display for virtual reality systems. Such dynamic models are desirably obtained via experimental identification. However, traditional dynamics identification methods normally require large sized training data sets, which maybe difficult to meet in many practical applications. To obtain the dynamics models, we present, in this paper, an identification method using support vector machines regression algorithm which is more effective than traditional methods for sparse training data. This method can be used as a generic learning machine or as a special learning technique that can make full use of the available knowledge about the dynamics structure. The experimental results show the application of our method for identifying friction models for haptic display. [ABSTRACT FROM PUBLISHER]

Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved. [ABSTRACT FROM AUTHOR]