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Causal Analysis of Railway Accident Reports Based on natural language processing

Causal Analysis of Railway Accident Reports Based on Natural...

引用

international conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Xinyu Zhang, Zhipeng Yue, Guoyong Hu, Hao State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Shanghai200240 China School of Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819639649

With the rapid development of railway transportation, railway safety problems have become more and more prominent. Previous research on railroad accident causation has mainly focused on structured data, and text data has not been fully mined and explored. This paper employed natural language processing (NLP) technology to extract 10 accident causal factors from 128 railroad accident reports. Then, their hierarchical relationships were divided through the interpretative structural modeling method (ISM), then a causal model was established based on Bayesian Networks (BN). A case study was conducted to validate the feasibility and validity of the model. The model proposed in this paper can excavate important causal factors affecting the occurrence of railroad accidents and their severity of railroad accidents. It may be of great significance for preventing accidents and improving emergency response capability, which can provide valid support for railroad safety. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Railroad accidents

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Machine Learning Based Phishing Website Detection System - natural language processing Approach 2

Machine Learning Based Phishing Website Detection System - N...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Naik, S. Vinay Harika, K. Bahadure, Nilesh Bhaskarrao Verma, Kamal Kant Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

natural language processing (NLP) methods can be used to identify phishing websites in addition to static and dynamic features. Phishing sites frequently include certain phrases, misspellings, or misleading text patterns in their material, as well as other visual cues and language that match those of authentic websites. Through the examination of textual components such anchor texts, meta-descriptions, and homepage titles. The proposed model is able to comprehend the linguistic traits that are frequently seen in phishing websites using NLP-based features like sentiment analysis, keyword matching, and phrase frequency to increase model's sensitivity. In addition, we use real-time URL parsing to detect possible dangers as users come across them, guaranteeing prompt notifications and lowering the possibility that phishing attempts would be successful. Our study emphasizes how crucial it is to keep improving and upgrading phishing detection models because attackers usually change their strategies. © 2025 IEEE.

关键词： Phishing

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Integrating Pause Information with Word Embeddings in language Models for Alzheimer's Disease Detection from Spontaneous Speech

Integrating Pause Information with Word Embeddings in Langua...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Pu, Yu Zhang, Wei-Qiang Department of Electronic Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. Early detection of AD is crucial for effective intervention and treatment. In this paper, we propose a novel approach to AD detection from spontaneous speech, which incorporates pause information into language models. Our method involves encoding pause information into embeddings and integrating them into the typical transformer-based language model, enabling it to capture both semantic and temporal features of speech data. We conduct experiments on the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) dataset and its extension, the ADReSSo dataset, comparing our method with existing approaches. Our method achieves an accuracy of 83.1% in the ADReSSo test set. The results demonstrate the effectiveness of our approach in discriminating between AD patients and healthy individuals, highlighting the potential of pauses as a valuable indicator for AD detection. By leveraging speech analysis as a non-invasive and cost-effective tool for AD detection, our research contributes to early diagnosis and improved management of this debilitating disease. © 2025 IEEE.

关键词： Alzheimer's disease language model speech pauses

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Optimizing English Lexical Databases with BERT and Reinforcement Learning 5

Optimizing English Lexical Databases with BERT and Reinforce...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Saranya, V. Murthy, G.R.K. Alapati, Purnachandra Rao Mythili, M. Swarnamughi, K. Bala, B. Kiran Panimalar Engineering college Department of English Chennai India Narasaraopet India Prasad V Potluri Siddhartha Institute of Technology Department of English Andhra Pradesh India Department of English Erode India Sathyabama Institute of Science And Technology Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

The evolution of natural language processing (NLP) has significantly transformed the way this study analyzes and understand language. This study presents a novel approach to generating lexical databases for English language sources using AI-powered methods, specifically leveraging BERT and reinforcement learning. Traditional methods for lexical database creation often rely on manual compilation or semi-automated techniques, which can be time-consuming and may lack comprehensive coverage of linguistic nuances. These existing approaches struggle with scalability and adaptability, particularly when addressing evolving language use and the inclusion of rare or domain-specific terms. The Open English WordNet dataset serves as a foundational resource, providing rich lexical relationships and contextual information necessary for effective database generation. Our proposed method integrates BERT for context-aware embedding generation and reinforcement learning for continuous optimization of lexical extraction and enrichment processes. This innovative combination not only enhances the accuracy of detecting synonyms, antonyms, hypernyms, and partof-speech tags but also ensures that the system learns from user feedback, leading to ongoing refinement. The advantages of this method include improved precision and recall, rapid processing times, and the ability to adapt to new linguistic data dynamically. Ultimately, this study demonstrates that AI-driven approaches can significantly enhance the quality and usability of lexical databases, paving the way for more sophisticated applications in natural language processing and linguistics. © 2025 IEEE.

关键词： natural language processing systems

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Research on Adaptive Learning Path Optimization Algorithm for Public English Based on natural language processing

Research on Adaptive Learning Path Optimization Algorithm fo...

引用

2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Li, You Heilongjiang International University Heilongjiang Harbin China

ISBN: (纸本)9798331504205

In this paper, a learning path optimization algorithm based on natural language processing (NLP) and adaptive learning system is proposed to improve the personalized and intelligent level of public English learning. The algorithm first constructs a semantic understanding model based on word embedding technology (such as Word2Vec), and calculates the semantic similarity between words by mapping them to a high-dimensional vector space, so as to realize the deep parsing of the learning content. Through deep learning methods, especially long short-term memory network (LSTM), the model can effectively capture learners' text input, deeply analyze their language expression and learning state, and accurately identify learners' knowledge points and potential learning disabilities. In terms of learning path optimization, a domain model-based path generation and optimization mechanism is constructed by combining adaptive learning system. By interacting with the learning environment, the system adjusts the decision-making strategy according to the learner's learning progress feedback in real time, and optimizes the learning path in each iteration to ensure the efficiency and accuracy of personalized recommendation. The experimental results show that the proposed algorithm is significantly better than the traditional adaptive learning model in terms of learning effect, path recommendation accuracy and learner engagement, which shows its wide application potential in practical education scenarios. © 2025 IEEE.

关键词： Contrastive Learning

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Improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on natural language processing 3rd

Improving Arabic Fake News Detection Across Context-Aware At...

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3rd international conference on language processing and knowledge Management, LPKM 2024

作者： Khudair, Enas Tariq O., Lazzez T.M., Hamdani A.T., Sadiq A.M., Alimi BP 1173 Sfax3038 Tunisia Avenue 5 August Rue Said Aboubaker Sfax3002 Tunisia Computer Science Department University of Monastir Monastir Tunisia Department of Electrical and Electronic Engineering Science University of Technology-Iraq Baghdad Iraq Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9783031850660

With the enormous growth of social data in recent years, fake news detection has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes various challenges, such as the complicated morphological structure and the limited resources. Therefore, the current state-of-the-art methods for fake news detection remain to be enhanced, which inspired us to explore the application of the emerging deep-learning architecture to Arabic text classification. In this paper, we present an ensemble model that enhances the attention mechanism to detect fake news in Arabic sentences. An attention mechanism unit is incorporated to highlight the critical information from the contextual feature vectors. The context-related vectors generated by the attention mechanism layers are then concatenated and passed into a classifier to predict the final label. The experimental results show that the attention mechanism improves the model’s performance while yielding 99.8% in accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Arabic Fake News Detection Arabic language Fake News Social Network

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Diversity Seeking Techniques for Red-Teaming Large language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Development and Implementation of Advanced Text Autocorrection via Probabilistic Methods Using Levenshtein Distance and Jaccard Similarity 5

Development and Implementation of Advanced Text Autocorrecti...

引用

5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Saunshimath, Nirmala J Upadhyaya, Dipankar Raj Yadav, Abiskar Kumar Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331522100

The paper aims at creating an advanced autocorrection system specifically for the Nepali language. This system uses sophisticated natural language processing (NLP) techniques to automatically rectify typographical errors of Nepali texts. The autocorrection system suggests the most likely corrections for misspelt words using the Levenshtein Distance and Jaccard Similarity algorithms. This paper aims to improve the efficiency and user experience across a variety of text-based applications, such as social media, document editing, and messaging, by decreasing the amount of time needed for text editing and increasing text accuracy. The groundwork for future developments in NLP applications for the Nepali language is laid by this effort. © 2025 IEEE.

关键词： natural language processing systems

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Automated Regional language News Article Classification System for Kannada and Telugu 5

Automated Regional Language News Article Classification Syst...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Gupta, Deepa Thejaa, K. Nair, Aarathi Rajagopalan Katti, Panduranga Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

The increasing amount of digital news content has created a pressing need for automated systems that can efficiently organize and categorize articles. This study focuses on developing an automated classification system for Kannada and Telugu news articles, targeting predefined categories. For Kannada, the categories include Entertainment, Sports, and Lifestyle, while for Telugu, the categories are Sports, Business, and Entertainment. With the help of the techniques available in natural language processing and the available models in machine learning, the system preprocesses the textual data in both languages to extract meaningful features for classification. Several classification algorithms are explored, with a focus on evaluating model performance using standard metrics. The results demonstrate that embeddings like KannadaSBERT and TeluguSBERT significantly improve classification accuracy, showing a clear advantage in understanding and categorizing regional language text. This work addresses a critical gap in the field of NLP for regional Indian languages by providing a structured approach to classifying Kannada and Telugu news articles. It contributes to enhancing the organization and accessibility of information for Kannada - and Telugu-speaking communities, thereby advancing research in low- resource language processing. © 2025 IEEE.

关键词： natural language processing systems

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Key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large language Model Enhanced Key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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