As malware threats become increasingly complex, the development of advanced detection systems is necessary to protect digital assets. SecureML Using machinelearning presents an innovative approach to combat the escal...
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AI-based song recommendation systems have gained significant popularity as streaming services such as Spotify, Amazon Music, Hungama Music, SoundCloud, Jio Saavn, and Apple Music utilize them to suggest songs based on...
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The industrial production process is becoming increasingly complex, and the requirements for quality assurance are becoming higher. Building a fault identification model is the focus of this study. This paper attempts...
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ISBN:
(纸本)9798350375084;9798350375077
The industrial production process is becoming increasingly complex, and the requirements for quality assurance are becoming higher. Building a fault identification model is the focus of this study. This paper attempts to propose a model incorporating mixed sampling and machinelearning techniques, compare to select the optimal model to achieve production line fault alarm. In addition, We use simulated annealing algorithm to find the optimal solution for personnel allocation based on the relationship between factors such as length of years of service, production line, and output, helping factories achieve efficient allocation of workers.
Efficient management of mainstream traffic flow on freeway networks is a critical challenge in urban transportation, with significant implications for congestion mitigation and environmental sustainability. The purpos...
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ISBN:
(纸本)9798350367607;9798350367591
Efficient management of mainstream traffic flow on freeway networks is a critical challenge in urban transportation, with significant implications for congestion mitigation and environmental sustainability. The purpose of this study is to address the problem of predicting traffic volumes and maintaining flow rates below critical densities, thereby preventing the onset of congestion on interconnected freeway systems. Motivated by the need for real-time traffic control strategies, this research employs machinelearning algorithms to forecast traffic volumes, leveraging a comprehensive dataset of traffic patterns on freeways. In our approach, we conducted a comparative analysis of two advanced machinelearning algorithms: Long Short-Term Memory (LSTM) networks, which are adept at modeling time-series data with long-range temporal dependencies, and Random Forest regression, known for its robust performance across diverse datasets. We enriched the traffic data through feature engineering, incorporating temporal variables, vehicular counts, and a calculated measure of proximity to critical density for the targeted freeway. Our findings indicate a markedly disparate performance between the algorithms. The LSTM model showed a moderate ability to capture the variance in traffic flow, with an R-2 score of 0.619. In contrast, the Random Forest model demonstrated exceptional predictive accuracy, achieving an R-2 of 0.998, and substantially outperforming the LSTM model in terms of both Mean Squared Error and Root Mean Squared Error.
Currently, one of the most crucial jobs in information security is malware detection. Since hackers are always coming up with new ways to break into computer networks and systems, it's critical to have a reliable ...
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In precision agriculture, crop recommendation systems play a crucial role in enhancing crop productivity. This research paper proposes a machinelearning-based crop recommendation system that leverages climatic variab...
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Emotion detection from physiological data is a growing field with important uses in affective computing, HCI, and medical imaging. To achieve significant progress in human-computer interaction, ability to recognise em...
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Adverse machinelearning (AML) is a rising area of study that uses system-mastering algorithms to perceive malicious hobbies or to discover malicious adversaries in cybersecurity settings. This research domain combine...
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In an era marked by the exponential growth of cyber threats, the imperative for robust cyber security measures has never been more pressing. With the proliferation of internet usage globally, cyber criminal activities...
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Due to the complexity of process-based processing, it is difficult to accurately locate the quality factors of final products into specific production links and parameters. The prediction of parameters based on the co...
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ISBN:
(纸本)9798350375084;9798350375077
Due to the complexity of process-based processing, it is difficult to accurately locate the quality factors of final products into specific production links and parameters. The prediction of parameters based on the collected data can improve the production efficiency and processing quality. Based on the integration of extreme learningmachine and adaptive neural fuzzy inference system parameters prediction model is established, the results show that compared with the single machinelearning method, the proposed algorithm model has higher prediction accuracy, at last, through actual production parameter test proved that the model has good prediction performance and generalization performance.
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