learning Analytics is a research area that is growing rapidly. It deals with selecting, analyzing, and reporting educational data collected from various learning environments and finding relevant patterns in students&...
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This article studies the steps of email classification, selects the Naive Bayes algorithm for email classification, uses Support Vector machine (SVM) for multi label secondary classification, and designs corresponding...
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ISBN:
(纸本)9798350375084;9798350375077
This article studies the steps of email classification, selects the Naive Bayes algorithm for email classification, uses Support Vector machine (SVM) for multi label secondary classification, and designs corresponding text files and libraries from the stages of data preparation and preprocessing, garbage labeling, etc. The training model is used to output prediction results and determine whether the email is spam. Using the detailed execution process of the confusion matrix, determine the proportion of the test set for this design, and design the implementation process of primary and secondary classification based on the proportion design.
Counterfeit currency is a serious hazard to individuals, financial institutions, and enterprises in the current global economy. Maintaining the integrity of financial transactions and protecting against potential loss...
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The study presents machinelearning regression methods to find the inverse kinematic solution of a 5-DOF manipulator. The work presents a comparative study of twenty-five algorithms predicting the five joint variables...
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Technological advances in mobile computing, wireless communications, and remote sensing have provided the foundation for expanding and improving intelligent transportation systems (ITS), making modern vehicles suscept...
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Technological advances in mobile computing, wireless communications, and remote sensing have provided the foundation for expanding and improving intelligent transportation systems (ITS), making modern vehicles susceptible to cyberattacks due to their evolved functionality and connectivity. In-vehicle networks, such as controller area networks (CAN), are highly vulnerable to attacks due to the lack of security architecture. Considering the temporal and spatial aspects of attacks and the need to develop lightweight models, this study develops a flexible and lightweight anomaly detection model for CAN bus with normal and sensitive duty cycles. To achieve optimal performance and consider spatio-temporal information, the feature space is optimized by extracting new features based on a two-parameter genetic algorithm (2P-GA) and Shannon entropy. Next, a synergistic combination of different supervised machinelearning classifiers based on the ordered weighted averaging (OWA) operators is leveraged to optimize the results and achieve better performance. Also, to show the effectiveness of the proposed method in the present study, a comprehensive and unique comparative analysis with previous works and state-of-the-art models is presented. The results show that the proposed framework achieves the highest performance in terms of accuracy and F1-score and the lowest computational cost compared with previous works.
The proceedings contain 105 papers. The topics discussed include: personal credit risk identification based on combined machinelearning model;compliant robotic assembly based on deep reinforcement learning;recognitio...
ISBN:
(纸本)9781665417365
The proceedings contain 105 papers. The topics discussed include: personal credit risk identification based on combined machinelearning model;compliant robotic assembly based on deep reinforcement learning;recognition and classification of surface defects of aluminum castings based on machine vision;a 3D space violation detection method of substations based on the deep neural network;research on audio playing system design factors modeling based on interpretive structure model;research on audio playing system design factors modeling based on interpretive structure mode;forecasting short-term power grid load based on recurrent neural network;an improved recommendation model based on matrix factorization;and modular attention network based on language model for referring expression.
The safety of our networks and the communications to use on a daily basis depends on them. In order to provide safe networks, cybersecurity researchers are highlighting the importance of new, effective intrusion detec...
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Deep learning is a cutting-edge methodology that has been extensively applied in real-world applications to solve computer vision tasks. Nonetheless, the inherent challenges of deep learning models lie in their black-...
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ISBN:
(纸本)9798331518783;9798331518776
Deep learning is a cutting-edge methodology that has been extensively applied in real-world applications to solve computer vision tasks. Nonetheless, the inherent challenges of deep learning models lie in their black-box nature, rendering them opaque and hard to interpret. Recently, attention-based vision transformers have been introduced to overcome the black-box behaviour of deep learning models. Despite these advances, the decision-making process of the vision transformer is still challenging to interpret. learning classifier systems is a state-of-the-art rule-based evolutionary machinelearning technique that stands out for its ability to provide interpretable decisions. These systems generate niche-based solutions, require less memory, and can be trained using small data sets. We hypothesize integrating attention mechanisms into learning classifier systems, aiming to identify critical components in problem instances, link features to create simple patterns, and model hierarchical relationships in the data. The experimental results for binary-class image classification (cat and dog) tasks demonstrate that the novel system successfully ignores the irrelevant parts and pays attention to the salient features of cats and dogs. Crucially, the proposed system exhibits comparable performance accuracy to that of the state-of-the-art learning classifier systems.
Recommender systems make individualized suggestions for users based on their actions and preferences by utilizing machinelearning (ML) and artificial intelligence (AI).. These systems have evolved significantly, inco...
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The sustainability of machinelearning-Enabled systems (MLS), particularly with regard to energy efficiency, is an important challenge in their development and deployment. Self-adaptation techniques, recognized for th...
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ISBN:
(纸本)9798350366266;9798350366259
The sustainability of machinelearning-Enabled systems (MLS), particularly with regard to energy efficiency, is an important challenge in their development and deployment. Self-adaptation techniques, recognized for their potential in energy savings within software systems, have yet to be extensively explored in machinelearning-Enabled systems (MLS), where runtime uncertainties can significantly impact model performance and energy consumption. This variability, alongside the fluctuating energy demands of ML models during operation, necessitates a dynamic approach. Addressing these challenges, we introduce EcoMLS approach, which leverages the machinelearning Model Balancer concept to enhance the sustainability of MLS through runtime ML model switching. By adapting to monitored runtime conditions, EcoMLS optimally balances energy consumption with model confidence, demonstrating a significant advancement towards sustainable, energy-efficient machinelearning solutions. Through an object detection exemplar, we illustrate the application of EcoMLS, showcasing its ability to reduce energy consumption while maintaining high model accuracy throughout its use. This research underscores the feasibility of enhancing MLS sustainability through intelligent runtime adaptations, contributing a valuable perspective to the ongoing discourse on energy-efficient machinelearning.
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