Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning

Su, Jing; Nair, Suku; Popokh, Leo

doi:10.1109/comnet55492.2022.9998475

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…Technological research encompasses a wide array of topics, including Particle Filter SLAM for vehicle localization [26], optimal resource allocation in SDN/NFV-enabled networks via deep reinforcement learning [27], and understanding private interactions in underground forums [28]. Additionally, studies delve into unveiling patterns in semi-supervised classification of strip surface defects [29] and applying large language models for forecasting and anomaly detection [30].…”

Section: Discussionmentioning

confidence: 99%

Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Luo,

Wei,

et al. 2024

JTPES

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E-commerce chatbots play a crucial role in customer service but often struggle with understanding complex queries. This study introduces a breakthrough approach leveraging the Falcon-7B model, a state-of-the-art Large Language Model (LLM) with 7 billion parameters. Trained on a vast dataset of 1,500 billion tokens from RefinedWeb and curated corpora, the Falcon-7B model excels in natural language understanding and generation. Notably, its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. By harnessing cutting-edge machine learning techniques, our method aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences.

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Section: Discussionmentioning

confidence: 99%

Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Luo,

Wei,

et al. 2024

JTPES

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“…Studies [29][30] investigate anomaly detection and predictive modeling, respectively, utilizing Large Language Models to analyze multi-omics data and optimize personalized education recommendations. In addition [31], addresses resource allocation in network functions virtualization environments, while [32] explores automatic recognition of static phenomena in retouched images, demonstrating the of LLMs across various domains [33][34]. Delve into enterprise financial risk prediction and particle filter SLAM for vehicle localization, highlighting LLMs' capability in tackling complex challenges.…”

Section: Prospects Of Large Language Models (Llm)mentioning

confidence: 99%

Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries

Yin,

Xu,

Xie

et al. 2024

JTPES

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Lithium-ion (Li-ion) batteries are pivotal in energy storage, powering diverse applications from portable electronics to electric vehicles. Optimizing Li-ion battery performance relies on understanding the crystal system properties of constituent materials, notably cathodes. This paper proposes a novel approach using Deep Neural Networks (DNNs) for multi-class classification of Li-ion silicate cathode crystal systems. Previous research underscores crystal chemistry's importance and the potential of machine learning in Li-ion battery materials. However, existing methodologies face challenges in accurately capturing material complexities. Our DNN-based model aims to address these limitations, offering improved predictive performance for crystal system classification.

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“…This ability is critical for implementing advanced object detection systems like MobileNet SSD within mobile platforms [12]. DL's capability for automatic feature extraction means that models can learn to identify relevant patterns and information from raw data without manual intervention [13], optimizing both the accuracy and efficiency of on-device inference. The advent of TensorFlow Lite has further facilitated the deployment of DL models on mobile devices, ensuring they run smoothly without compromising system resources.…”

Section: Deep Learning For On-device Intelligencementioning

confidence: 99%

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

Dang,

Ma,

et al. 2024

AJST

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Visually impaired individuals face substantial challenges in kitchens, where identifying objects accurately is crucial yet difficult due to the complexity and variability of the environment. Traditional object detection1 methods fall short in these settings, struggling with the assortment of items. This research highlights the need for advanced, kitchen-specific solutions that leverage deep learning to improve detection accuracy and offer real-time, interactive guidance through speech technologies. By focusing on the unique demands of kitchen environments, the proposed system aims to significantly enhance the autonomy and safety of visually impaired users, presenting a notable advancement in assistive technology. The effectiveness of this approach is assessed by its ability to accurately identify kitchen items for visually impaired individuals.

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Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning

Cited by 9 publications

References 27 publications

Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries

Enhancing Kitchen Independence: Deep Learning-Based Object Detection for Visually Impaired Assistance

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