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Personalized Mental Health Assistance: Integrating Emotion Prediction with GPT-Based Chatbot

Personalized Mental Health Assistance: Integrating Emotion P...

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

作者： Kifayathullah, Muhammad Sekar, Rajeev Abinaya, R. Vijayaprabakaran, K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

Mental health is a fundamental aspect of overall wellbeing, despite its importance, it remains one of the most neglected issues. By integrating Artificial Intelligence with natural language processing into this domain helps to bridge the gaps and challenges. The proposed methodology provides easier access to mental health support by integrating emotion prediction, using the Bi-LSTM Model, with GPT-based chatbots. This system is designed to analyse user inputs, estimating the likelihood of various emotional states such as Anxiety, Depression, Stress, and Suicidal thoughts. These emotional probabilities are then used to inform and guide the responses generated by the GPT model, ensuring that the interactions are more relevant and supportive. Additionally, our approach focuses on providing personalized mental health care using sentiment analysis and prompt engineering. By understanding and interpreting users' emotional states, the chatbot can dynamically adjust its interactions to better meet the needs of everyone, thus improving the quality of mental health support. © 2025 IEEE.

关键词： Electronic health record

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Diffusion Models are Zero-Shot Generative Text-Vision Retrievers

Diffusion Models are Zero-Shot Generative Text-Vision Retrie...

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

作者： Li, Bao Xie, Zeke Zhang, Xiaomei Zhu, Xiangyu Lei, Zhen MAIS Institute of Automation Chinese Academy of Sciences Beijing China School of Artifical Intelligence University of Chinese Academy of Sciences Beijing China Guangzhou China CAIR HKISI Chinese Academy of Sciences Hong Kong

ISBN: (纸本)9798350368741

Large-scale text-to-image diffusion models have demonstrated impressive capabilities for downstream tasks by leveraging strong vision-language alignment from generative pre-training. Recently, a number of works have explored how to use the power of text-to-image diffusion models for text-image matching tasks. While previous generative text-image matching methods have shown potential in retrieving the most challenging candidates, they still suffer from extremely slow retrieval speeds and lack the ability to handle temporal dimensions, making them impractical for text-video retrieval. In this paper, we propose ZSGenRet, a simple yet effective zero-shot generative text-vision retrieval framework for both text-image and text-video tasks, based on pre-trained text-to-image diffusion models. We further incorporate inversion saliency detection to identify key frames in videos and enhance the semantic representation of the vision encoder. Experimental results demonstrate that ZSGenRet significantly improves text-video retrieval performance and achieves competitive results on text-image retrieval while remarkably improving efficiency. To the best of our knowledge, the proposed ZSGenRet is the first to explore zero-shot text-video retrieval based on diffusion models. © 2025 IEEE.

关键词： diffusion models Text-vision retrieval vision-language models zero-shot learning

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An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis

An Attentive Dual-Encoder Framework Leveraging Multimodal Vi...

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

作者： Wei, Yingchen Qiu, Xihe Tan, Xiaoyu Huang, Jingjing Chu, Wei Xu, Yinghui Qi, Yuan School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Co. Ltd. China ENT Institute Department of Otorhinolaryngology Eye & ENT Hospital Fudan University Shanghai China Artificial Intelligence Innovation and Incubation Institute Fudan University Shanghai China

ISBN: (纸本)9798350368741

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnosis using polysomnography (PSG) is expensive, time-consuming, and uncomfortable. Existing deep learning methods using facial image analysis lack accuracy due to poor facial feature capture and limited sample sizes. To address this, we propose a multimodal dual encoder model that integrates visual and language inputs for automated OSAHS diagnosis. The model balances data using randomOverSampler (ROS), extracts key facial features with attention grids, and converts basic physiological data into meaningful text. Cross attention combines image and text data for better feature extraction, and ordered regression loss ensures stable learning. Our approach improves diagnostic efficiency and accuracy, achieving 91.3% top-1 accuracy in a four class severity classification task, demonstrating state of the art performance. Code is available at https://***/luboyan6/VTA-OSAHS. © 2025 IEEE.

关键词： Attention Mesh Clinical BERT Cross Attention Modality Interaction OSAHS Diagnosis

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knowledge Graphs for Enhancing Large language Models in Entity Disambiguation 23rd

Knowledge Graphs for Enhancing Large Language Models in En...

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23rd international Semantic Web conference, ISWC 2024

作者： Pons, Gerard Bilalli, Besim Queralt, Anna Universitat Politècnica de Catalunya UPC-BarcelonaTech Barcelona Spain

ISBN: (纸本)9783031778438

Recent advances in Large language Models (LLMs) have positioned them as a prominent solution for natural language processing tasks. Notably, they can approach these problems in a zero or few-shot manner, thereby eliminating the need for training or fine-tuning task-specific models. However, LLMs face some challenges, including hallucination and the presence of outdated knowledge or missing information from specific domains in the training data. These problems cannot be easily solved by retraining the models with new data as it is a time-consuming and expensive process. To mitigate these issues, knowledge Graphs (KGs) have been proposed as a structured external source of information to enrich LLMs. With this idea, in this work we use KGs to enhance LLMs for zero-shot Entity Disambiguation (ED). For that purpose, we leverage the hierarchical representation of the entities’ classes in a KG to gradually prune the candidate space as well as the entities’ descriptions to enrich the input prompt with additional factual knowledge. Our evaluation on popular ED datasets shows that the proposed method outperforms non-enhanced and description-only enhanced LLMs, and has a higher degree of adaptability than task-specific models. Furthermore, we conduct an error analysis and discuss the impact of the leveraged KG’s semantic expressivity on the ED performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： knowledge graph

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KAN v.s. MLP for Offline Reinforcement Learning

KAN v.s. MLP for Offline Reinforcement Learning

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

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (纸本)9798350368741

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements. © 2025 IEEE.

关键词： KAN Kolmogorov-Arnold networks MLP multilayer perceptrons offline reinforcement learning

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AgriTalk: Revolutionizing Farming with a Multilingual Chatbot 8

AgriTalk: Revolutionizing Farming with a Multilingual Chatbo...

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Abhishek, M.V. Sreekar, A Jaya Mohith, D.M. Vekkot, Susmitha Bhavana, V. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331520762

Agriculture plays a vital role in the economy of many nations, especially in regions where a significant portion of the population depends on it for their livelihood. Despite its importance, linguistic and technological barriers often hinder access to accurate agricultural information. This article introduces a multilingual chatbot designed to address agricultural queries efficiently. Leveraging natural language processing and deep learning models, this multi-language chatbot accommodates English, Hindi, Malayalam, Kannada, and Telugu. Since the query is matched with relevant responses using cosine similarity on the sentence-transformers architecture, the system ensures that relevant responses are given. With IndicTrans2 (AI4Bharat), a robust neural machine translation model tailored for Indian languages, the facility provides a single-machine foundation on which translations about 22 Indian languages with subsequent additional features-text-to-speech functionality enhances accessibility to varied users. The system design, data preprocessing, implementation, and evaluation metrics are covered in this study, which also shows how it might help farmers and other agricultural stakeholders. © 2025 IEEE.

关键词： Chatbots

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Enhancing Document-Level Relation Extraction through Entity-Pair-Level Interaction Modeling

Enhancing Document-Level Relation Extraction through Entity-...

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

作者： Liu, Wanlong Zeng, Dingyi Zhou, Li Xiao, Yichen Zhang, Malu Chen, Wenyu School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Data Science The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350368741

Document-level relation extraction aims at extracting relational facts between two entities in a document. Existing approaches mainly focus on target entities, utilizing techniques such as graph neural networks to enhance their representations. However, they ignore the rich semantic correlations among entity pairs which provide wider and multifaceted information at a higher level. In this paper, we propose the Relation-based Entity-pair-level Inference (REI) model, which facilitates information interaction at the entity-pair level, enhancing logical reasoning among entities and capturing semantic correlations among entity pairs. Our REI model comprises two modules: Relation-based Information Aggregation (RIA) and Entity-pair-level Information Interaction (EII). The RIA module builds and integrates relation representations to filter out distractions from unrelated entity pairs, while the EII module models entity-pair-level information interaction through multi-head attentions. Extensive experiments on the DocRED, DWIE, CDR, and GDA datasets demonstrate the superiority of the proposed REI model, outperforming previous state-of-the-art approaches. Furthermore, we provide detailed experimental analyses based on the performance gains and illustrate the interpretability. © 2025 IEEE.

关键词： Information Extraction Linguistic Inference natural language processing Relation Extraction

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A model for improving the accuracy of educational content created by generative AI 7

A model for improving the accuracy of educational content cr...

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7th international Workshop on Augmented Reality in Education, AREdu 2024

作者： Talaver, Oleh V. Vakaliuk, Tetiana A. Zhytomyr Polytechnic State University 103 Chudnivsyka Str. Zhytomyr10005 Ukraine Institute for Digitalisation of Education The NAES of Ukraine 9 M. Berlynskoho Str. Kyiv04060 Ukraine Kryvyi Rih State Pedagogical University 54 Universytetskyi Ave. Kryvyi Rih50086 Ukraine Academy of Cognitive and Natural Sciences 54 Universytetskyi Ave. Kryvyi Rih50086 Ukraine

Advancements in artificial intelligence (AI) are reshaping education, enabling personalized and adaptive learning experiences, yet ensuring the reliability of AI-generated content remains a critical challenge. This study addresses this gap by developing a system for text processing and factual claims verification, focusing on extracting factual claims, retrieving evidence from authoritative sources, verifying content, and rewriting it to ensure accuracy while maintaining pedagogical effectiveness. The system is designed to complement manual peer review processes by providing detailed annotations and evidence-based notes related to facts retrieved from different sources. The proposed system employs a multi-layered approach to content verification. At its core, it focuses on three key processes: (1) extracting and refining factual claims from text using sophisticated natural language processing techniques, (2) retrieving and analyzing evidence from multiple authoritative sources, and (3) verifying and rewriting content that will be proposed as a hint to ensure accuracy while maintaining pedagogical effectiveness. It integrates prompt engineering, multi-stage evidence analysis, different source information retrieval, and vector-based embeddings to get and classify evidence as supporting, contradicting, or neutral, using weighted credibility scoring and a proposed decision certainty level for each verification outcome. By employing a multi-layered approach, the system offers a practical and scalable solution for enhancing AI-generated content verification, paving the way for reliable applications in education and organizational knowledge management, and enabling educators and content creators to make informed decisions about content revision. © 2025 Copyright for this paper by its authors.

关键词： natural language processing systems

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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An NLP Based Approach to Automate and Enhance the Systematic Review Within PRISMA Format 8th

An NLP Based Approach to Automate and Enhance the Systematic...

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8th IFIP TC 12 international conference on Computer, Communication and Signal processing, ICCCSP 2024

作者： Thankachan, Jeena A. Srinivasan, Bama Department of Information Science and Technology College of Engineering Anna University Tamilnadu Guindy Chennai600025 India

ISBN: (纸本)9783031736162

Systematic Literature Review (SLR) is an integral part of research;however, to conduct an SLR, the manual review process would be time-consuming due to the high volume of literature involved. This paper presents a method that uses natural language processing (NLP) and Machine Learning (ML) techniques to automate the systematic review process at two key stages: screening and eligibility assessment. The approach utilizes BERT (Bidirectional Encoder Representations from Transformers) embeddings and cosine similarity for automated label assignment and categorization of research articles. The proposed approach aligns to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) in the automation of the domain based categorization of articles, and resolves the challenges posed by the state of art techniques. The proposed work considers three datasets with total 2164 records, collected based on identification criteria of PRISMA, to be classified into three categories. The proposed method based on BERT gives good results on categorization of article with an accuracy of 99% , 98% and 96% for each dataset. © IFIP international Federation for Information processing 2025.

关键词： Embeddings

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