In most cases, machine learning involves working with large volumes of data in order to perform optimally. However, in some cases, there is only a limited amount of training data available, resulting in a decrease in ...
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作者：
Ghosh, Sambhramthenmozhi, M.
School of Computing College of Engineering and Technology Department of Networking and Communications SRM Nagar Kattankulathur Tamil Nadu Chengalpattu District 603203 India
Ayurveda has been an integral part of ancient Indian medicine and it still plays an important role in the creation of Homeopathic medicines. In this paper we have introduced a comprehensive system for detecting, gradi...
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Power generation grid connection metering devices are critical for ensuring the accuracy and fairness of energy trading between power generation enterprises and grid operators. However, challenges such as device aging...
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the education industry, as the top priority of social operation, is constantly emerging with education systems or online education platforms based on internet technology. However, most of them are facing problems of r...
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Embodied intelligence emphasizes direct interaction between machines and the physical world, enabling intelligent agents to exhibit intelligent behaviors and autonomous evolution through the interplay of the brain, bo...
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Withthe increasing severity of enterprise information security issues, the identification and evaluation of cross-network data risk has become one of the essential technologies to safeguard enterprise data and networ...
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ISBN:
(纸本)9798331530372;9798331530365
Withthe increasing severity of enterprise information security issues, the identification and evaluation of cross-network data risk has become one of the essential technologies to safeguard enterprise data and network security. However, traditional rule-based or signature-based anomaly detection methods are increasingly unable to cope withthe complex and evolving forms of attacks, necessitating more intelligent and flexible detection mechanisms to counter these constantly changing threats. To address this, a method integrating denoising autoencoder (DAE) and bidirectional long short-term memory network (BiLSTM) is first proposed for monitoring the enterprise cross-network data traffic and identify the occurring abnormal risks. On this basis, we use a fuzzy Bayesian network to effectively measure these known abnormal risks. In the end, experimental results demonstrate that the proposed method outperforms traditional machine learning algorithms and other deep learning models across various evaluation metrics, exhibiting high accuracy and efficiency.
the challenges of cost control in construction and installation projects have perennially been a significant concern for entities in the construction sector. the intricate interplay of various equipment, personnel, an...
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the use of machine learning to generate synthetic data has grown in popularity withthe proliferation of text-to-image models and especially large language models. the core methodology these models use is to learn the...
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ISBN:
(纸本)9798400702402
the use of machine learning to generate synthetic data has grown in popularity withthe proliferation of text-to-image models and especially large language models. the core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this work, we explore the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities. the main problem we work to address is modeling the joint distribution of all the members of the S&P 500, or, in other words, learning a 500-dimensional joint distribution. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization.
Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cogn...
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the article shows the specifics of the use of intelligent information technologies in primary school;their content, the possibilities of their use in teaching younger schoolchildren are determined;an algorithm for usi...
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