MRMRP: Multi-source Review-Based Model for Rating Prediction

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.

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

“…By harnessing the power of LLMs, network intrusion detection systems can benefit from enhanced contextual understanding of network traffic data and associated logs [44]. LLMs possess the ability to analyze and interpret vast amounts of textual information, such as financial information [45][46], segmentation [47][48][49] and classification [50][51][52], machinery [56], and vehicle localization [57][58] enabling them to identify and predict subtle patterns and anomalies indicative of malicious activities within network communications.…”

Section: Prospects Of Large Language Models (Llm)mentioning

confidence: 99%

Advanced Network Intrusion Detection with TabTransformer

Wang,

Qiao,

Xiong

et al. 2024

In today's digital era, the security of networked systems is of utmost importance amidst the increasing prevalence of cyber threats and sophisticated intrusion techniques. This paper addresses the critical need for robust network intrusion detection systems (NIDS) in today's digital landscape, amidst escalating cyber threats. Leveraging a dataset derived from a simulated military network environment, we explore various intrusion scenarios encountered in cyber warfare. Reviewing existing literature reveals a spectrum of methodologies, including anomaly-based and deep learning approaches. To enhance current methodologies, we propose a binary classification framework using TabTransformer, a transformer-based architecture, for network intrusion detection. We present detailed methodology, encompassing data preprocessing, model architecture, and evaluation metrics, with empirical results demonstrating the efficacy of our approach in mitigating cyber threats and enhancing network security.

“…Furthermore, Wang et al [26] introduce EMRM, an enhanced multi-source review-based model for rating prediction. Wang et al [27] further contribute to multi-source review-based models for rating prediction. Liu et al [28] unveil patterns in a study on semi-supervised classification of strip surface defects, contributing to the field of defect detection.…”

Section: Prospects Of Large Language Models (Llm)mentioning

confidence: 99%

Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification

Xie,

Li,

Yin

et al. 2024

The escalating volume and intricacy of legal documents necessitate advanced techniques for automated text classification in the legal domain. Our proposed approach leverages Convolutional Neural Networks (Conv1D), a neural network architecture adept at capturing hierarchical features in sequential data. The incorporation of max-pooling facilitates the extraction of salient features, while softmax activation enables the model to handle the multi-class nature of legal citation categorization. By addressing the limitations identified in previous studies, our model aims to advance the state-of-the-art in legal citation text classification, offering a robust and efficient solution for automated categorization in the legal domain. Our research contributes to the ongoing evolution of NLP applications in the legal field, promising enhanced accuracy and adaptability in the automated analysis of legal texts.