The utilization of machinelearning has become ubiquitous in addressing contemporary challenges in datascience. Moreover, there has been significant interest in democratizing the decision-making process for selecting...
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The utilization of machinelearning has become ubiquitous in addressing contemporary challenges in datascience. Moreover, there has been significant interest in democratizing the decision-making process for selecting machinelearning algorithms, achieved through the incorporation of meta-features and automated machinelearning techniques for both classification and regression tasks. However, this paradigm has not been readily applied to multistep-ahead time series prediction problems. Unlike regression and classification problems, which utilize independent variables not derived from the target variable, time series models typically rely on past values of the series to forecast future outcomes. The structure of a time series is often characterized by features such as trend, seasonality, cyclicality and irregularity. In our study, we illustrate how time series metrics representing these features, in conjunction with an ensemble-based regression Meta-Learner, were employed to predict the standardized mean square error of candidate time series prediction models. Our experiments utilized datasets covering a broad feature space, facilitating the selection of the most effective model by researchers. A rigorous evaluation was conducted to assess the performance of the Meta-Learner on both synthetic and real time series data.
In order to diagnose lumpy skin disease in cattle herds, machinelearning techniques such as Support Vector machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective...
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Educational dataMining (EDM) is one of the newest topics to emerge in recent years, and it focuses on developing strategies for analyzing various forms of data gathered from the academic circle. EDM fosters collaborat...
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ISBN:
(纸本)9783031686382;9783031686399
Educational dataMining (EDM) is one of the newest topics to emerge in recent years, and it focuses on developing strategies for analyzing various forms of data gathered from the academic circle. EDM fosters collaboration among educators, data scientists, and machinelearning specialists. The interdisciplinary character of EDM creates an atmosphere in which educators and data scientists collaborate to develop and apply efficient approaches for extracting insights from educational data. EDM methods and techniques with machinelearning techniques are utilized to extract meaningful and useful information from large dataset. EDM strives to develop intelligent systems that personalize educational experiences to individual individuals by understanding their unique learning styles and problems. For scientists and researchers, realistic applications of machinelearning in the EDM sectors offer new frontiers and present new problems. This transition to individualized approaches represents a radical shift in educational practices, stressing a student-centered and successful learning environment. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed, to forecast students' performance, which aids the tutor, institution to boost the level of students' performance. Beyond forecasting student achievement, EDM is increasingly focusing on the creation of personalized learning systems and adaptive educational technology. EDM's goal is to construct intelligent systems that personalize educational experiences to individual students by utilizing classification algorithms and data mining tools. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.
Participants in the supply chain may have different information, leading to incomplete or inaccurate information when making decisions. To this end, a process and machinelearning based collaborative scheduling algori...
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ISBN:
(纸本)9798400707032
Participants in the supply chain may have different information, leading to incomplete or inaccurate information when making decisions. To this end, a process and machinelearning based collaborative scheduling algorithm for all materials is proposed. Design a health monitoring process for material supply chain based on R-tree dynamic indexing algorithm. Based on this, artificial neural networks in machinelearning are applied to mine the data of the entire material supply chain. Through data mining, various data in the supply chain can be integrated and analyzed to improve information transparency and accuracy, and reduce information asymmetry. Adopting a dual layer scheduling model to achieve dual layer collaborative scheduling of materials. The experimental results show that the research method effectively improves the accuracy of data mining in the entire material supply chain, and the utilization rate of materials under this method is always higher than 95%.
In this work, machinelearning is applied to develop a LSTM-AutoEncoder for anomaly detection in three-axis CNC machines. This anomaly detection network is then transferred to another three-axis CNC machine for chatte...
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ISBN:
(纸本)9798350355376;9798350355369
In this work, machinelearning is applied to develop a LSTM-AutoEncoder for anomaly detection in three-axis CNC machines. This anomaly detection network is then transferred to another three-axis CNC machine for chatter detection, using significantly less data. This network is then extended to five-axis CNC machines by using the encoder from the three-axis CNC machine to develop an anomaly detection network using transfer and incremental ensemble learning. This approach is compared to a network trained from scratch, with comparable results observed. This approach demonstrates the feasibility of augmenting networks designed for three-axis CNC machines to five-axis CNC machines.
Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolici...
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In current era, the expressive growth of social media platforms like Twitter has made it an invaluable source for understanding public opinion and sentiment. This paper discovers various machinelearning techniques ap...
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The hazelnut possesses a significant economic value and is extensively consumed on a global scale. Physico-mechanical properties such as linear dimensions, deformation, force, stress, and energy play an important role...
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The hazelnut possesses a significant economic value and is extensively consumed on a global scale. Physico-mechanical properties such as linear dimensions, deformation, force, stress, and energy play an important role in the processing of hazelnut and hazelnut kernels, quality assessment, and the development of harvesting and post-harvest technologies. The data used in the data set was determined by applying compression tests and artificial neural networks, support vector regression, and multiple linear regression methods were applied to the data obtained. The aim of the study ws to determine the deformation energy of hazelnuts and hazelnut kernels based on some mechanical properties of hazelnuts using nondestructive machinelearning methods instead of traditional measurement methods with minimum error, minimum labor, and in the shortest time. The average R2 for kernels and hazelnuts was ANN 95.2%, SVR 89.6%, and MLR 86.1%. The average MSE for kernels and hazelnuts was ANN 0.006, SVR 0.012, and MLR 0.072. The machinelearning methods used in the study provided results close to the ideal statistical metrics. According to the analyses of the machinelearning methods, results similar to the optimal statistical metrics were obtained. The most successful and least-error methods were the artificial neural network, support vector regression and multiple linear regression, respectively.
A novel approach to cursor control via real-time eye movement that employs a synergistic combination of OpenCV and machinelearning approaches. The ability to operate a cursor in a natural and intuitive manner using e...
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machinelearning (ML) extends rapidly in many research areas including design of novel processing routines, imaging, and material science. Particularly, ML enables design of new materials with complex structures and s...
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machinelearning (ML) extends rapidly in many research areas including design of novel processing routines, imaging, and material science. Particularly, ML enables design of new materials with complex structures and shorten the development cycle through model prediction from existing database. Calcium carbonate (CaCO3) particles are regarded as promising candidates for drug delivery, biomedical, food, and industrial filler applications due to their good physicochemical properties and biocompatibility. However, a prerequisite for these applications is the production of particles with desired morphology, size, and phase compositions. Here, it is shown that the crystal growth and phase transition induced the transformation of spherical particles into spindlelike, square and needle-like morphologies with increasing temperature, and the increase of concentration increased this transition temperature. Furthermore, it is found that the concentration of the reacting salt solutions shifted the phase transition temperatures to higher values. Subsequently, ML is applied to precisely investigate and predict the polymorph formation of CaCO3 particles based on the experimental data obtained under 85 conditions, which would enable us to track crystallization trends, aiding in the identification of optimal conditions for generating monophase samples, and provide a feasible scheme for learning similar materials.
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