The use of machinelearning technology in the agricultural industry has grown rapidly in recent decades, especially in the 1990s-2000s which became important with the emergence of information and communication technol...
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Student performance vaticination plays a pivotal part in relating and addressing academic challenges early [3], enabling targeted interventions and personalized support. This study aims to improve academic outcomes by...
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Student performance vaticination plays a pivotal part in relating and addressing academic challenges early [3], enabling targeted interventions and personalized support. This study aims to improve academic outcomes by accurately predicting student performance. To achieve this thing, varied machinelearning algorithms were employed to develop dependable models [2] based on student-related attributes such as demographic information, socio-economic background, past academic records, and engagement factors. The models were trained on a portion of the data set and evaluated using appropriate metrics to measure predictive accuracy. The results demonstrate that the machinelearning algorithms were effective in predicting student performance [5], with varying levels of accuracy. This information enables educators to identify and support students who may require additional resources. By employing machinelearning algorithms, educational stakeholders canmake informed decisions and allocate resources effectively to improve student outcomes.
Wireless power transfer (WPT) technologies are currently researched and developed for charging the batteries of electric unmanned air and ground vehicles. This paper presents systems with special polyphase inductive c...
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ISBN:
(纸本)9798350375596;9798350375589
Wireless power transfer (WPT) technologies are currently researched and developed for charging the batteries of electric unmanned air and ground vehicles. This paper presents systems with special polyphase inductive coils, which generate rotating fields and achieve high power density and efficiency. The complex geometry is modeled and studied with 3D electromagnetic finite element analysis (FEA). In order to reduce the substantial computational effort, machinelearning techniques are proposed for surrogate modeling. A deep learning algorithm is introduced to capture the physics-based relationships between geometry and electromagnetic properties in inductive coils for wireless charging. Parametric models are systematically generated and analyzed by 3D FEA to create a data base with hundreds of designs, which are then used as training and testing data for the machinelearning model. A multi-input univariate output for the mutual inductance between the transmitter and receiver for an example two-phase WPT system is established. The outputs of the deep learning model are satisfactorily validated with 3.3% NRMSE and a R-2 value of 0.985.
The dynamic shifts in the chemical composition of coffee with roasting have been successfully tracked using the electronic nose, representing its potential as a tool for profiling the aromatic complexity of coffee. Tr...
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ISBN:
(纸本)9798350381771;9798350381764
The dynamic shifts in the chemical composition of coffee with roasting have been successfully tracked using the electronic nose, representing its potential as a tool for profiling the aromatic complexity of coffee. Traditional methods have confirmed the physical transformation of beans during roasting, a well-known phenomenon. Complementing these findings, the e-nose demonstrates its efficacy by capturing the aromatic changes that occur throughout the roasting process. Furthermore, machinelearning models applied to the e-nose data such as kNN, SVM, decision tree, and ANN, have shown promising results. Among these, the SVM model provides the greatest accurately reflecting the roasting profiles. This innovative, non-invasive approach provides a valuable alternative for the industry, paving the way for future applications in quality control and flavor profiling within the coffee industry.
Catastrophic forgetting emerges when a neural network's parameters undergo continuous updates during the sequential training of multiple tasks. The ongoing adaptation, while enhancing the model's suitability f...
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ISBN:
(纸本)9798350344868;9798350344851
Catastrophic forgetting emerges when a neural network's parameters undergo continuous updates during the sequential training of multiple tasks. The ongoing adaptation, while enhancing the model's suitability for new tasks, inadvertently leads to a degradation in performance on previously learned tasks. This challenge significantly impedes the sequential learning capabilities essential for the advancement of artificial general intelligence. The phenomenon of catastrophic forgetting also occurs in the field of quantum machinelearning, where parametric quantum circuits serve a role analogous to neural networks. In this study, our focus is to explore strategies for mitigating catastrophic forgetting within quantum learning models for quantum data. In this context, we employ a task-based hard attention mechanism, which automatically generates masks for each task to regulate the network's learning process and resist catastrophic forgetting. To our knowledge, the proposed method is the first one in the field of quantum machinelearning to adjust the model structure to prevent catastrophic forgetting. Numerical simulation results demonstrate that our approach preserves the model's performance on previous tasks without compromising its ability to acquire new knowledge. This effective resistance to catastrophic forgetting marks a significant stride in advancing quantum continual learning.
The development of technology has surged people’s curiosity about new technology, thus improving research works. This led to an increase in the number of article submissions. Hence, an already difficult task gets mor...
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Considering the improvements in Brain -Computer interface (BCI) systems and their cost reduction, a new chance for a normal life appeared for people with a wide range of disabilities. With the rise of artificial neura...
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Considering the improvements in Brain -Computer interface (BCI) systems and their cost reduction, a new chance for a normal life appeared for people with a wide range of disabilities. With the rise of artificial neural networks (ANN), traditional machinelearning (ML) techniques heavily decreased in popularity. These methods are usually a good alternative and, also, an easier-to-understand way to get into datascience in plenty of domains. Considering the use of scalograms as training material, the current paper proposes the utilization of classical ML algorithms, such as Random Forest (RF), Support Vector machine (SVM), Linear Discriminant Analysis (LDA) and k -Nearest Neighbors (KNN). The images are obtained by transforming the electroencephalogram (EEG) signals using continuous wavelet transform (CWT) in order to show their strengths in terms of learning. To illustrate the advantages of the proposed approach, a comparison of the obtained results with those obtained from training on EEG signals directly has been performed. Moreover, another comparison has been made between the results obtained with a neural network -based solution applied to both representations of the training data.
This paper presents a comprehensive study on classifying depressed and healthy individuals using the Depresjon dataset, which contains motor activity data collected from wearable devices. We prepared six different dat...
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ISBN:
(纸本)9783031686382;9783031686399
This paper presents a comprehensive study on classifying depressed and healthy individuals using the Depresjon dataset, which contains motor activity data collected from wearable devices. We prepared six different datasets, including raw data, normalised raw data, PCA-transformed data, and statistical features extracted from the raw data. We trained and evaluated six popular machinelearning algorithms and their combinations using a 5-fold cross-validation technique. Our results demonstrate that most models achieved the highest accuracy with the normalised statistical feature dataset. Furthermore, we fine-tuned these algorithms using GridSearchCV and selected the best threshold using the ROC curve. Our findings provide valuable insights into the potential of wearable sensor data for detecting and predicting depressive episodes.
The authors proposed a distributed machinelearning method that generates and combines machinelearning models created by group set of users in previous study. This paper discusses a method for selecting distributed m...
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With the rapid advancement of artificial intelligence, machinelearning has been increasingly applied across various domains. However, the issues of data security and privacy protection in machinelearning have also e...
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