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A Hybrid Deep Learning and natural language processing Model for Plant Ubiquitination Sites Prediction 3rd

A Hybrid Deep Learning and Natural Language Processing Model...

引用

3rd International conference on Advances in Information and Communication Technology, ICTA 2024

作者： Tran, Thi-Xuan Nguyen, Thi-Tuyen Le, Nguyen Quoc Khanh Nguyen, Van-Nui University of Economics and Business Administration Thai Nguyen Viet Nam University of Information and Communication Technology Thai Nguyen Viet Nam Professional Master Program in Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan

ISBN: (纸本)9783031809422

Protein ubiquitination is a critical post-translational modification involved in numerous biological processes, playing a vital role in regulating both physiological and pathological mechanisms. Despite existing tools for predicting ubiquitination sites, variations in ubiquitination patterns among species remain inadequately understood. This study presents LBiPlantUbi, a novel hybrid deep learning model that incorporates Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) networks combined with natural language processing (NLP) techniques for predicting ubiquitination sites in plants. Our model uniquely learns features directly from raw protein sequences using word embedding and integrates feature extraction and prediction within a single architecture. LBiPlantUbi achieved superior performance with an accuracy of 84%, sensitivity of 88.9%, F1-score of 0.844, MCC of 0.675, and AUC of 0.902 on independent testing sets, outperforming existing methods. This advanced model opens new avenues for studying ubiquitination in plants and other species. The implementation is publicly accessible on GitHub at https://***/nuinvtnu/LBiPlantUbi. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Text Summarization for Light Weight Devices

Text Summarization for Light Weight Devices

引用

2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Akshatha, K. Praveen, M. Taleki, Shravan Rakshith Yadav, K V Nataraj Revathi, T. Alliance University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

In this paper, we propose a text summarization method for low-compute devices with limited hardware capabilities like smartphones, tablets and IOT devices. Traditional methods for text summarization face significant challenges in the settings where these devices are used, given their limited computational power and memory. We present optimization algorithms that target reducing computation and memory effort whilst retaining quality in the summarization. Our solution focuses on lightweight, efficient methods to runtime-compress the summaries generated by a powerful but heavy model -in order to allow them to be computed with high mobility and little energy consumption by small devices- for example through techniques of model compression, quantization or similarity detection in datasets- thus achieving tractable abstraction of useful information from digital content [2], enhancing their accessibility experience while taking advantage of relevant mobile behaviors. Our approach enables efficient summarization with high performance and minimal resource costs, as shown by the corresponding experimental results. © 2025 IEEE.

关键词： Edge Computing Lightweight Devices Model Compression natural language processing Quantization Text Summarization

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Anchored Monotonic Alignment and Representation Substitution for Rare Spontaneous Behaviors in Spontaneous Speech Synthesis

Anchored Monotonic Alignment and Representation Substitution...

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

作者： Wu, Ning-Qian Hu, Ya-Jun Chen, Liping Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China iFLYTEK Research iFLYTEK Co. Ltd. China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China

ISBN: (纸本)9798350368741

Spontaneous behaviors in speech pose significant challenges for speech synthesis. Existing research has not adequately addressed these behaviors, with most studies relying on specially recorded datasets. In contrast, real-world data more accurately reflects the natural, spontaneous speaking styles in everyday life and encompasses a wider range of spontaneous behaviors. However, such data is often of lower quality, and the distribution of spontaneous behaviors is highly imbalanced. In this study, we explore spontaneous speech synthesis using real-world data within the VITS2 framework. To overcome these challenges, we introduce two techniques: anchored monotonic alignment and spontaneous hidden representation substitution. Experimental results demonstrate that these methods enhance model alignment and improve the naturalness of the generated speech. Our proposed approach successfully addresses the challenge of synthesizing rare spontaneous behaviors and offers users flexible control over the synthesized speech. © 2025 IEEE.

关键词： real-world data speech synthesis spontaneous behaviors spontaneous speech synthesis VITS

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Assessing Large language Models Effectiveness in Outdated Method Renaming 22nd

Assessing Large Language Models Effectiveness in Outdated Me...

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22nd International conference on Service Oriented Computing

作者： Ben Mrad, Ali Thiombiano, Abdoul Majid O. Mkaouer, Mohamed Wiem Hnich, Brahim Qassim Univ Coll Comp Dept Comp Sci Buraydah Saudi Arabia Univ Sfax CES Lab ENIS Sfax 3038 Tunisia Univ Monastir FSM Monastir 5000 Tunisia Univ Michigan Flint Flint MI USA

ISBN: (纸本)9789819608041;9789819608058

Identifying effective methods for automatic method renaming after code modifications is crucial for maintaining developer productivity and enhancing the performance of source code analysis tools. In this study, we focus on benchmarking the effectiveness of the ChatGPT large language model (LLM) in predicting new method names after code modifications. Leveraging a dataset of method code snippets along with their original and modified names, we conducted experiments on 116 samples to assess the prediction accuracy of ChatGPT. Using Jaccard similarity as the metric, we varied the similarity threshold to evaluate the classification performance of predicted names. However, the Jaccard similarity does not retain the magnitude or direction of the vectors, reflecting the strength and polarity of the similarity. In addition, it ignores the order and context of the words, which results in missing potential syntactic or semantic variations. To solve this problem, we propose another validation process which not only detects whether or not an LLM captured semantic changes of a method, but also its structural changes in order to be able to generate a suitable name for this given method. Our results indicate that ChatGPT achieves a high success rate in predicting method names, obtaining 98% (Resp. 94%) when the threshold is set to 0.5 for the Cosine (resp. Jaccard) similarity. For a threshold of 1 (maximum similarity), ChatGPT maintains a notable performance with 49% (Resp. 74%) Cosine (resp. Jaccard) similarity. This demonstrates the potential of ChatGPT for automating method renaming tasks in software development workflows.

关键词： API Renaming Benchmarking Large language Models Machine Learning natural language processing

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Enhancing One-Shot Pruned Pre-trained language Models through Sparse-Dense-Sparse Mechanism 31

Enhancing One-Shot Pruned Pre-trained Language Models throug...

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31st International conference on Computational Linguistics, COLING 2025

作者： Li, Guanchen Zhao, Xiandong Liu, Lian Li, Zeping Xu, Yixing Li, Dong Tian, Lu He, Jie Sirasao, Ashish Barsoum, Emad Advanced Micro Devices Inc. United States University of Science and Technology Beijing China

ISBN: (纸本)9798891761964

Pre-trained language models (PLMs) are engineered to be robust in contextual understanding and exhibit outstanding performance in various natural language processing tasks. However, their considerable size incurs significant computational and storage costs. Modern pruning strategies employ retraining-free one-shot techniques to compress PLMs;however, these approaches often lead to an indispensable reduction in performance. In this paper, we propose SDS, a Sparse-Dense-Sparse pruning framework to enhance the performance of the pruned PLMs from a weight distribution optimization perspective. We outline the pruning process in three steps. Initially, we prune less critical connections in the model using conventional one-shot pruning methods. Next, we reconstruct a dense model featuring a pruning-friendly weight distribution by reactivating pruned connections with sparse regularization. Finally, we perform a second pruning round, yielding a superior pruned model compared to the initial pruning. Experiments demonstrate that SDS outperforms the state-of-the-art pruning techniques SparseGPT and Wanda under an identical sparsity configuration. For instance, SDS reduces perplexity by 5.16 on Raw-Wikitext2 and improves average accuracy by 3.86% across multiple zero-shot benchmarks for LLaMA-38B compared to Wanda with 2:4 sparsity. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Interpretable Arabic Hate Speech Detection using Large language Model 2

Interpretable Arabic Hate Speech Detection using Large Langu...

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2nd International conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alwateer, Majed Gad, Ibrahim Elmarhomy, Mahmoud Elmarhomy, Ghada Hashim, Hasan Almaliki, Malik Atlam, El-Sayed College of Computer Science and Engineering Taibah University Department of Computer Science Saudi Arabia Tanta University Faculty of Science Computer Science Department Tanta31527 Egypt College of Applied Taibah University Computer Science and Information Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia

ISBN: (纸本)9798331506995

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content. © 2025 IEEE.

关键词： Arabic Hate Speech Detection Large language Mode XAI

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Identifying key indicators words of suicidal ideation through ensemble voting classifier learning methods 4th

Identifying key indicators words of suicidal ideation throug...

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4th International conference on Computational methods in Science and Technology, ICCMST 2024

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad National Institute of Technology Patna Bihar Patna India

ISBN: (纸本)9781032911571

Suicide prevention is a pressing global concern, with a significant number of annual deaths related to suicide and numerous attempts reported each year. The rise of social media as a platform for emotional expression underscores the need for early detection of suicidal thoughts. This study introduces an innovative approach utilizing natural language processing (NLP) techniques to analyze behavioral patterns and linguistic cues in social media interactions. The primary objective is to identify key indicator words in social media post i.e. Reddit posts, that may suggest the presence of suicidal ideation, through the application of advanced NLP techniques and variety of machine learning models, including Decision Tree, Random Forest, Bernoulli Naïve Bayes, Gradient Boosting, XGBoost, and an Ensemble voting classifier method that combines Random Forest, Gradient Boosting, and XGBoost. The models demonstrate impressive accuracy in the detection of posts related to suicidal ideation, providing a potential means for early intervention. On the Reddit dataset, these models achieve accuracy scores of 81%, 88%, 84%, 89%, 89%, and 92%. This research aims to identify keywords associated directly or inversely linked to suicidal ideation. The study offers a potent keywords indicator for detecting signs of suicidal thoughts in social media posts, potentially facilitating timely interventions and, ultimately, saving lives to support suicide prevention efforts. © 2025 the Author(s).

关键词： Decision trees

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Machine learning-based emotional identification of social media text 4th

Machine learning-based emotional identification of social me...

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4th International conference on Computational methods in Science and Technology, ICCMST 2024

作者： Chug, Aayam Sudeep, Sirisetti Kumar, Polineni Arun Sharma, Akhil Maganti, Deekshitha Uchoi, Enjula Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9781032911571

The method of sentiment analysis is employed to ascertain people’s thoughts, feelings, and perceptions of a wide range of topics, including people, objects, organizations, services, problems and goods. A subclass of sentiment analysis is emotion detection which forecasts the distinctive feeling as opposed to just expressing neutral, negative, or positive feelings. Many scholars have previously studied speaking and facial movements for the purpose of recognizing emotions in the past. Sentiment indicators like pitch, facial expression, tone tension and so on are absent in text, making the process of identifying emotions in text time-consuming. Using NLP techniques, many ways were previously proposed to extract emotions from text- the ML approach and the methods of using keywords and lexicons. However, keywords as well as lexicon approaches have inherent limitations since they emphasize semantic connections. In this post, we have introduced a hybrid (DL + ML) model to detect emotions in text. Bi-GRU and Convolutional Neural Network approaches were applied. Support Vector Machines are one type of machine learning technique. The three distinct dataset types employed to evaluate the efficiency of the proposed approach which attains an accuracy of 93.33%—are phrases, messages, and dialogues. © 2025 the Author(s).

关键词： Convolutional Neural Network Deep Learning natural language processing Support Vector Machine

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Fake News Detection Using Large language Models 4th

Fake News Detection Using Large Language Models

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4th International conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Ramya, G. R. Yasaswani, S. Veda Harshitha, P. Bapathi, Archana Thanuja, G. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9783031837920;9783031837937

The spread of fake news, particularly across social media and digital news platforms, poses a growing challenge due to its influence on public opinion and social stability. This study introduces a novel framework for fake news detection, leveraging Large language Models (LLMs), specifically GPT-4, with multimodal data fusion and an external fact-checking module. Unlike conventional approaches that rely mainly on text, the proposed framework addresses the multimodal nature of misinformation by integrating textual and visual features captured through Convolutional Neural Networks (CNNs) for images to detect misleading content more effectively. Additionally, the framework incorporates real-time fact-checking by cross-referencing statements with trusted sources, such as Wikipedia and ***, reducing false positives and model hallucinations. This approach beats current methods, offering a more thorough solution to the challenging problem of disinformation detection, according to extensive testing on benchmark datasets. Future work will explore multilingual and real-time applications to enhance its adaptability and impact.

关键词： Artificial Intelligence Deep Learning natural language processing Multimodal Fusion GPT-4 Large language Models

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Automated Framework for Constructing Knowledge Graphs Oriented for Standard Analysis Using Large language Models 24

Automated Framework for Constructing Knowledge Graphs Orient...

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2nd International conference on Electronics, Computers and Communication Technology, CECCT 2024

作者： Zhu, Sitong Xia, Baobing Duan, Fangwei Zhao, Zhenyang Zhao, Zhenxia Xiao, Teliang Liu, Zhicheng Liu, Xia State Grid Liaoning Electric Power Co. LTD. Electric power Research Institute Liaoning Shenyang China Beijing State Grid Communication Accenture Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9798400710193

In an era characterized by rapid technological growth and digital transformation, the necessity for efficient and structured knowledge representation has grown more crucial. Standards serve as fundamental cornerstones that offer guidelines, specifications, and frameworks to ensure the quality and interoperability of products, services, as well as systems. Nonetheless, the complexity and extensive nature of standard documents present significant challenges in extraction, alignment, and organization. Traditional manual processing methods frequently prove labor-intensive and susceptible to errors, impeding the capturing of intricate relationships and hierarchies within these standards. Knowledge Graphs (KGs) provide a robust methodology for organizing information, facilitating improved functionalities for advanced search, reasoning, and analytics. Despite their potential, structuring KGs from standard documents continues to be challenging because of unstructured text, domain-specific terminology, and the intricacies in accurate information extraction. Recent advancements in natural language processing (NLP), particularly the emergence of Large language Models (LLMs) like GPT-3, have opened new avenues for automating the extraction and structuring of information from unstructured content. These models exhibit exceptional proficiency in comprehending and producing human-like text, positioning them as feasible solutions for addressing the complexities associated with standard documents. This paper presents an automated framework named StandardKG Builder, which utilizes LLMs to construct knowledge graphs tailored for standard analysis from multiple perspectives for complex information extraction. Our evaluation on a comprehensive dataset of standard documents highlights the framework’s effectiveness and scalability. By merging sophisticated knowledge representation with advanced NLP techniques, our work significantly enhances the accessibility and analysis of standard docu

关键词： Knowledge graph

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