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Enhanced Emotion-Aware Conversational Agent: Analyzing User Behavioral Status for Tailored Reponses in Chatbot Interactions

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IEEE ACCESS 2025年 13卷 19770-19787页

作者： Abinaya, S. Ashwin, K. S. Alphonse, A. Sherly Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

While chatbots are increasingly popular for communication, their effectiveness is limited by their difficulty in understanding users' emotions. To address this, this study proposes a new hybrid chatbot model called "TEBC-Net" (Text Emotion Bert CNN Network), which combines text and video analysis to interpret user emotions and generate more empathetic responses. At the core of TEBC-Net is a multi-modal emotion analysis system. One component uses bidirectional encoder representations from transformers (BERT), a well-regarded model in natural language processing (NLP), achieving an 87.21% accuracy rate in detecting emotional cues from text inputs. The second component captures users' facial expressions through webcam footage. It begins by detecting faces using a pre-trained classifier like Haarcascade. Then, to improve emotion recognition, it preprocesses the image through brightness adjustments and contrast enhancement with Automatic CLAHE and dual gamma correction. This processed image is analyzed by a Convolutional Neural Network (CNN) model trained specifically for emotion recognition, reaching 74.14% accuracy by assigning probabilities to different emotions. By integrating insights from both text and video analysis, TEBC-Net gains a comprehensive understanding of the user's emotional state and intent. This combined data then informs the chatbot's response generation module, enabling it to craft responses that are both empathetic and more directly aligned with the user's emotional needs.

关键词： Chatbots Emotion recognition Encoding bidirectional control Convolutional neural networks Accuracy Faces Classification algorithms transformers Training Chatbot bidirectional encoder representations from transformers natural language processing emotion recognition automatic CLAHE with dual gamma correction convolutional neural network

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Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text

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JMIR MEDICAL INFORMATICS 2025年 13卷 e63020页

作者： Zhuang, Yan Zhang, Junyan Li, Xiuxing Liu, Chao Yu, Yue Dong, Wei He, Kunlun Chinese Peoples Liberat Army Gen Hosp Med Big Data Res Ctr 28 Fuxing Rd Beijing 100853 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Digital Hlth China Technol Co LTD Beijing Peoples R China Peoples Liberat Army Gen Hosp Med Ctr 6 Sr Dept Cardiol Beijing Peoples R China

Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate. We developed a fully automated pipeline based on the Key-bidirectional encoder representations from transformers (BERT) approach and large-scale medical records for continued pretraining, which effectively converts long free text into standard ICD codes. By adjusting parameter settings, such as mixed templates and soft verbalizers, the model can adapt flexibly to different requirements, enabling task-specific prompt learning. Objective: This study aims to propose a prompt learning real-time framework based on pretrained language models that can automatically label long free-text data with ICD-10 codes for cardiovascular diseases without the need for semiautomatic preprocessing. Methods: We integrated 4 components into our framework: a medical pretrained BERT, a keyword filtration BERT in a functional order, a fine-tuning phase, and task-specific prompt learning utilizing mixed templates and soft verbalizers. This framework was validated on a multicenter medical dataset for the automated ICD coding of 13 common cardiovascular diseases (584,969 records). Its performance was compared against robustly optimized BERT pretraining approach, extreme language network, and various BERT-based fine-tuning pipelines. Additionally, we evaluated the framework's performance under different prompt learning and fine-tuning settings. Furthermore, few-shot learning experiments were conducted to assess the feasibil

关键词： BERT bidirectional encoder representations from transformers pretrained language models prompt learning ICD International Classification of Diseases cardiovascular disease few-shot learning multicenter medical data

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AI-Driven Transformer Frameworks for Real-Time Anomaly Detection in Network Systems

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 1121-1130页

作者： Reddy, P. Santosh Chaudhari, Tarunika Godla, Sanjiv Rao Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Kranthi, A. Smitha El-Ebiary, Yousef A. Baker BNM Inst Technol Dept Comp Sci & Engn Bangalore India Govt Engn Coll Comp Engn Dept Dahod India Aditya Coll Engn & Technol Dept Comp Sci & Engn Surampalem Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India Graph Era Hill Univ Dept Math Dehra Dun India Graph Era Deemed Be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522502 AP India UniSZA Univ Fac Informat & Comp Besut Malaysia

the detection of evolving cyber threats proves challenging for traditional anomaly detection because signature- based models do not identify new or zero-day attacks. This research develops an AI Transformer-based system with bidirectional encoder representations from transformers (BERT) technology with Zero-Shot Learning (ZSL) for real-time network system anomaly detection while solving these security challenges. The goal positions the development of an effective alerting system that detects Incident response and proactive defenses cyber threats both known and unknown while needing minimal human input. The methodology uses BERT to transform textual attack descriptions found in CVEs alongside MITRE ATT&CK TTPs into multidimensional embedding features. Visual embeddings generated from textual documents undergo comparison analysis with current network traffic data containing packet flow statistics and connection logs through the cosine similarity method to reveal potential suspicious patterns. The Zero-Shot Learning extension improves the system by enabling threat recognition of new incidents when training data remains unlabeled through its analysis of semantic links between familiar and unfamiliar attack types. Here utilizes three different tools that include Python for programming purposes alongside BERT for embedding analytics and cosine similarity for measuring embedded content similarities. Numerical experiment outcomes validate the proposed framework by achieving a 99.7% accuracy measure with 99.4% precision, 98.8% recall while maintaining a sparse 1.1% false positive rate. The system operates with a detection latency of just 45ms, making it suitable for dynamic cybersecurity environments. The results indicate that the AI- driven Transformer framework outperforms conventional methods, providing a robust, real-time solution for anomaly detection that can adapt to evolving cyber threats without extensive manual intervention.

关键词： -Anomaly detection network security transformer framework bidirectional encoder representations from transformers zero-shot learning

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Leveraging Unlabeled Corpus for Arabic Dialect Identification

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Computers, Materials & Continua 2025年第5期83卷 3471-3491页

作者： Mohammed Abdelmajeed Jiangbin Zheng Ahmed Murtadha Youcef Nafa Mohammed Abaker Muhammad Pervez Akhter School of Computer Science Northwestern Polytechnical UniversityXi'an710072China Department of Computer Science Applied CollegeKing Khalid UniversityMuhayil6311Saudi Arabia Computer Science Department National University of Modern LanguagesFaisalabad38000Pakistan

Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in *** state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given *** the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world *** alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID ***,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal *** key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the ***,we evaluate the proposed solution on benchmark datasets for *** extensive experiments show that it performs signifcantly better,especially,with sparse labeled *** comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID *** code will be available on GitHub upon the paper's acceptance.

关键词： Arabic dialect identification natural language processing bidirectional encoder representations from transformers pre-trained language models gradient reversal layer

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Teenager Substance Use on Reddit: Mixed Methods Computational Analysis of Frames and Emotions

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JOURNAL OF MEDICAL INTERNET RESEARCH 2025年 27卷 e59338页

作者： Zhang, Xinyu Zhu, Jianfeng Kenne, Deric R. Jin, Ruoming Michigan State Univ Coll Commun E Lansing MI USA Kent State Univ Dept Comp Sci 241 Math Sci Bldg Kent OH 44224 USA Kent State Univ Coll Publ Hlth Kent OH USA

Background: Adolescent substance use disorder is a pressing public health issue, with increasing prevalence as individuals age. Social media platformslike Reddit (Reddit Inc) serve as significant venues for teenagers to discuss and navigate substance use. Social media platforms, such as Reddit, serve as increasingly important spaces where teenagers discuss, share, and navigate their experiences with substance use, presenting unique opportunities and challenges for understanding and addressing this issue. Objective: This study aims to explore how teenagers frame substance-use discussions on the r/teenagers subreddit, focusing on their personal interpretations, causal attributions, and the social and psychological contexts that shape these online support groups. By identifying these interpretive frames, we aimed to better understand the complex drivers of adolescent substance use behavior and their potential interventions. Methods: Using natural language processing techniques, we analyzed 32,674 substance use-related posts from 2018 to 2022. A framing approach was used to identify and categorize prevalent themes, supplemented by emotional profiling using the EmoLLaMA-chat-13B model developed by Liu and colleagues. Results: In total, 7 primary frames emerged: normalization, risk awareness, social integration, autonomy and rebellion, coping mechanisms, media influence, and stigmatization. These frames varied in prevalence and were associated with distinct emotional profiles, highlighting the complex interplay between substance use and adolescent experiences. We observed that, for example, the normalization frame was often associated with a mix of sadness and anxiety, while the coping frame exhibited elevated levels of anger, sadness, and anxiety. These distinctive emotional landscapes associated with each frame reveal unique insights into the mental state of adolescents navigating substance use. Conclusions: The findings underscore the multifaceted nature of adolescent

关键词： teenager substance use Reddit emotional analysis bidirectional encoder representations from transformers BERT frame approach

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MDGCN-Lt:Fair Web API Classification with Sparse and Heterogeneous Data Based on Deep GCN

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1294-1314页

作者： Boyuan Yan Yankun Zhang Wenwen Gong Haoyang Wan Wenwei Wang Weiyi Zhong Caixia Bu Department of School of Computer Science Qufu Normal UniversityRizhao 276500China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Department of Univalsoft Joint Stock Co. Ltd.Weifang 261000China

Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that *** develop edge applications,developers search API categories to select APIs that meet specific ***,the accurate classification of APIs becomes critically ***,existing approaches,as evident on platforms like ***,face significant ***,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API ***,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data ***,the long-tailed distribution of API data introduces biases,compromising the fairness of classification *** these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous *** tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API ***-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data *** results affirm our approach's superiority over existing methods.

关键词： bidirectional encoder representations from transformers deep graph convolutional networks logit adjustment web application programming interface classification application programming interface correlation graph

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Legal information extraction and classification using BERT, Bi-LSTM, and CRF models

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Jingwei Xi An Jiao Tong Univ Coll Law 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

This article combined BERT (bidirectional encoder Representation from transformers), Bi-LSTM (bidirectional Long Short-Term Memory), and CRF (Conditional Random Field) models to transform unstructured legal text into structured data through information extraction, improving the effectiveness of legal information extraction. The BERT model can be used for deep semantic embedding of legal texts, generating context-sensitive representations for each word. The Bi-LSTM network can capture long-distance dependencies in the text, extract sequence features, and apply CRF layers to globally optimize sequence labels to ensure accurate annotation of entity boundaries and relationships. In the dataset for extracting legal entity relationships related to prostitution constructed in this article, the accuracy, precision, recall rate, and F1 score of entity classification reached 93.6%, 92.7%, 92.1%, and 92.4%, respectively. All 153 samples in the Engage_in_ prostitution relationship were correctly classified. In order to analyze the stability of legal information extraction and classification, the model proposed by this article was tested on five datasets: CAIL2019, CJRC (Chinese Judicial Reading Comprehension), LexGLUE (Legal General Language Understanding Evaluation), COLIEE (Competition on Legal Information Extraction/Appointment), and ECHR (European Court of Human Rights). The accuracy of the article's model fluctuated only 1.2% on different datasets, while the precision remained stable and the recall fluctuated by 0.7%. This article provided reliable technical support for legal intelligence research by combining BERT, Bi-LSTM, and CRF to accurately extract and classify legal information.

关键词： legal information extraction relation classification bidirectional encoder representations from transformers bidirectional long short-term memory conditional random field prostitution laws

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Preschool language development prediction model based on deep learning: Cross-cultural validity evaluation

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Sun, Yuanyuan Huo, Jiasong ZhongYuan Inst Sci & Technol Fac Educ & Art Zhengzhou Peoples R China Zhengzhou Univ Sch Management 100 Kexue Ave Zhengzhou 450001 Peoples R China

Traditional research on preschool language development often fails to capture the complex nonlinear relationships and high-dimensional characteristics of language growth, leading to low prediction accuracy and poor cross-cultural applicability. This paper introduces a novel BERT (bidirectional encoder representations from transformers)-based model to predict preschool language development and evaluate its cross-cultural effectiveness. Text data from preschool children's language datasets across multiple cultural backgrounds is collected, cleaned, and preprocessed to create suitable training samples. Special attention is given to the unique grammatical structures and cultural expressions in each language to ensure compatibility with the model. The BERT model is used to encode the processed text, leveraging its bidirectional self-attention mechanism to extract contextual information and generate deep feature representations essential for understanding preschool language development. The model combines both grammatical and semantic features for meaningful representations in subsequent predictions. Fine-tuning the pre-trained BERT model using the Adam optimizer enhances prediction accuracy, while cross-validation and hyperparameter tuning further improve its performance. Culturally specific annotations and vocabularies are incorporated to ensure the model's effective prediction of language development across different regions. Experimental results show that the BERT model achieves an MAE (Mean Absolute Error) between 0.20 and 0.25, an MSE (Mean Squared Error) between 0.05 and 0.08, and an average R2 value of 0.84 across English, Chinese, Spanish, and Japanese. These results demonstrate the model's high accuracy and strong cross-cultural stability in predicting preschool language development.

关键词： preschool language language development prediction prediction model deep learning bidirectional encoder representations from transformers

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Using transformer-based models and social media posts for heat stroke detection

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SCIENTIFIC REPORTS 2025年第1期15卷 1-9页

作者： Anno, Sumiko Kimura, Yoshitsugu Sugita, Satoru Sophia Univ Grad Sch Global Environm Studies Tokyo Japan Yanagi Pearls Shima Mie Japan Chubu Univ Chubu Inst Adv Studies Kasugai Aichi Japan

Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time-space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.

关键词： Event-based surveillance bidirectional encoder representations from transformers Language understanding with knowledge-based embeddings Tweets Heat stroke

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Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description- Based Code Selection

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JMIR FORMATIVE RESEARCH 2025年 9卷 e60095页

作者： Puts, Sander Zegers, Catharina M. L. Dekker, Andre Bermejo, Inigo Maastricht Univ GROW Res Inst Oncol & Reprod Med Ctr Dept Radiat Oncol Maastro POB 616 NL-6200 MD Maastricht Netherlands Hasselt Univ Data Sci Inst DSI Hasselt Belgium

Background: The International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support health care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy and lacks the explainability needed for adoption in medical settings. Objective: The potential of large language models for assisting medical coders in the ICD-10 coding was explored through the development of a computer-assisted coding system. This study aimed to augment human coding by initially identifying lead terms and using retrieval-augmented generation (RAG)-based methods for computer-assisted coding enhancement. Methods: The explainability dataset from the CodiEsp challenge (CodiEsp-X) was used, featuring 1000 Spanish clinical cases annotated with ICD-10 codes. A new dataset, CodiEsp-X-lead, was generated using GPT-4 to replace full-textual evidence annotations with lead term annotations. A Robustly Optimized BERT (bidirectional encoder representations from transformers) Pretraining Approach transformer model was fine-tuned for named entity recognition to extract lead terms. GPT-4 was subsequently employed to generate code descriptions from the extracted textual evidence. Using a RAG approach, ICD codes were assigned to the lead terms by querying a vector database of ICD code descriptions with OpenAI's text-embedding-ada-002 model. Results: The fine-tuned Robustly Optimized BERT Pretraining Approach achieved an overall F1-score of 0.80 for ICD lead term extraction on the new CodiEsp-X-lead dataset. GPT-4-generated code descriptions reduced retrieval failures in the RAG approach by approximately 5% for both diagnoses and procedures. However, the overall explainability F1-score for the CodiEsp-X task was limited to 0.305, significantly lower than the state-of-the-art F1-score of 0.633. The diminished performance was partly due to the reliance on code descripti

关键词： International Classification of Diseases ICD-10 computer-assisted-coding GPT-4 coding term extraction code analysis computer assisted coding transformer model artificial intelligence AI automation retrieval-augmented generation RAG large language model LLM bidirectional encoder representations from transformers Robustly Optimized BERT Pretraining Approach RoBERTa named entity recognition NER

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