IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2024-01-06 DOI: 10.1016/j.aiopen.2024.01.001

引用次数: 0

Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection 使用图像、语音和文本的医学诊断中深度学习的多模态奇迹：COVID-19检测的全面回顾

AI Open Pub Date : 2025-01-01 Epub Date: 2025-01-23 DOI: 10.1016/j.aiopen.2025.01.003

Md Shofiqul Islam , Khondokar Fida Hasan , Hasibul Hossain Shajeeb , Humayan Kabir Rana , Md. Saifur Rahman , Md. Munirul Hasan , AKM Azad , Ibrahim Abdullah , Mohammad Ali Moni

{"title":"Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection","authors":"Md Shofiqul Islam , Khondokar Fida Hasan , Hasibul Hossain Shajeeb , Humayan Kabir Rana , Md. Saifur Rahman , Md. Munirul Hasan , AKM Azad , Ibrahim Abdullah , Mohammad Ali Moni","doi":"10.1016/j.aiopen.2025.01.003","DOIUrl":"10.1016/j.aiopen.2025.01.003","url":null,"abstract":"<div><div>This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and analysed 11 deep learning models using COVID-19 image, text, and speech (ie, cough) data. Our analysis revealed that the MobileNet model achieved the highest accuracy of 99.97% for COVID-19 image data and 93.73% for speech data (i.e., cough). However, the BiGRU model demonstrated superior performance in COVID-19 text classification with an accuracy of 99.89%. The broader implications of this research suggest potential benefits for other domains and disciplines that could leverage deep learning techniques for image, text, and speech analysis.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 12-44"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-spatial Semantic Information Aggregation Network for 3D Human Motion Prediction 三维人体运动预测的多空间语义信息聚合网络

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-09-03 DOI: 10.1016/j.aiopen.2025.08.002

Dong He , Jianqi Zhong , Jianhua Ji , Wenming Cao

{"title":"Multi-spatial Semantic Information Aggregation Network for 3D Human Motion Prediction","authors":"Dong He , Jianqi Zhong , Jianhua Ji , Wenming Cao","doi":"10.1016/j.aiopen.2025.08.002","DOIUrl":"10.1016/j.aiopen.2025.08.002","url":null,"abstract":"<div><div>In recent years, GCN-based methods have achieved great success in skeleton-based human motion prediction tasks due to the human body graph structure. However, existing methods leveraged single semantic information to model the whole motion sequence, which cannot fully exploit the motion dependencies. To tackle this issue, we propose a Multi-spatial Semantic Information Aggregation Network(MSIAN) to enrich the semantic information by focusing on the local spatial structure of the human skeleton. MSIAN includes the Graph-based Feature Extraction and Aggregation Block (GFEAB), where the Integration Graph combines local and global attention to extract spatial features, the Gravity-Centered Graph (GCG) captures the state of each joint by treating the central joint of the skeleton as the center of gravity, and the Spatial Position Graph (SPG) fully utilizes the original joint positions to analyze movements. Extensive experiments show that our proposed MSIAN outperforms the current state-of-the-art methods on Human3.6M, 3DPW, and AMASS datasets. Our code is available at <span><span>https://github.com/HDdong-hub/MSIAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 155-166"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computer audition for healthcare: A survey on speech analysis 医疗保健用计算机听力：语音分析的研究进展

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-10-28 DOI: 10.1016/j.aiopen.2025.10.001

Kun Qian , Zhonghao Zhao , Yang Tan , Weijia Zhang , MinKi Cho , Cuiping Zhu , Fuze Tian , Bin Hu , Yoshiharu Yamamoto , Björn W. Schuller

{"title":"Computer audition for healthcare: A survey on speech analysis","authors":"Kun Qian , Zhonghao Zhao , Yang Tan , Weijia Zhang , MinKi Cho , Cuiping Zhu , Fuze Tian , Bin Hu , Yoshiharu Yamamoto , Björn W. Schuller","doi":"10.1016/j.aiopen.2025.10.001","DOIUrl":"10.1016/j.aiopen.2025.10.001","url":null,"abstract":"<div><div>Intelligent speech analysis (ISA) constitutes a significant component within the realm of computer audition (CA) technology. Speech, as a fundamental tool for human communication, not only conveys rich semantic information but also holds significant potential for various healthcare applications. Computational paralinguistics methods can be used to analyse alterations in the acoustic characteristics of speech signals induced by medical conditions, providing valuable insights into shifts in an individual’s health status. More importantly, compared to other physiological monitoring devices, speech acquisition devices are non-invasive and user-friendly, making them accessible for a wide range of individuals. However, despite its promise, ISA in healthcare currently faces a range of notable challenges that hinder its widespread adoption. In this survey, we present an overview of the development and current research in speech analysis technologies within the healthcare domain. First, we summarise the methodologies employed in ISA-based healthcare. Next, we provide an overview of applications in evaluating physical diseases, mental health conditions, and neurological disorders. Additionally, we discuss key limitations and shortcomings in the current state of the field. Finally, we conclude with a summary of the discussed works and offer insights into future research directions aimed at addressing these limitations to advance the practical implementation of ISA in clinical settings. This survey aims to serve as a valuable resource for researchers in speech analysis, biomedicine, and related fields. We hope to inspire greater interest in this promising area within the scientific community and provide guidance for future studies in this evolving field.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 244-275"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum regarding updated Declaration of Competing Interest statement in previously published articles 关于先前发表的文章中更新的竞争利益声明的勘误表

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-12-11 DOI: 10.1016/j.aiopen.2025.12.001

引用次数: 0

From tools to partners: How large language models are transforming urban planning 从工具到合作伙伴：大型语言模型如何改变城市规划

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-11-21 DOI: 10.1016/j.aiopen.2025.11.001

Fangyong Pan , Xinyi Huang , Yuxi Bi , Yunfan Gao , Yu Ye , Haofen Wang

引用次数: 0

ADAP: Adaptive & Dynamic Arc Padding for predicting seam profiles in Multi-Layer-Multi-Pass robotic welding ADAP：用于多层多道次机器人焊接焊缝轮廓预测的自适应动态电弧填充

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-10-21 DOI: 10.1016/j.aiopen.2025.10.003

He Wang , Sen Li , Xiaobo Liu , Chengxiao Dong , Fang Wan

{"title":"ADAP: Adaptive & Dynamic Arc Padding for predicting seam profiles in Multi-Layer-Multi-Pass robotic welding","authors":"He Wang , Sen Li , Xiaobo Liu , Chengxiao Dong , Fang Wan","doi":"10.1016/j.aiopen.2025.10.003","DOIUrl":"10.1016/j.aiopen.2025.10.003","url":null,"abstract":"<div><div>Welding thick metal plates using Multi-Layer-Multi-Pass (MLMP) techniques demands precise control over the weld seam profile as it evolves during the cooling process. In MLMP welding, typically executed with Gas Metal Arc Welding (GMAW) and shielding gas protection, the continuous deposition of weld beads results in dynamic changes to the seam geometry. These challenging traditional robotic welding systems rely on static models. To ensure high-quality joints, real-time adaptation of welding paths requires accurate predictions of weld bead geometry, which in turn guide the estimation of welding positions for adaptive trajectory planning. In this study, we introduce the Adaptive & Dynamic Arc Padding (ADAP) framework. This novel data-driven approach integrates deep learning with an innovative arc-based representation of weld bead profiles. By representing the weld bead geometry through image-derived boundaries and primitive arc parameters (arc center and radius), ADAP establishes a direct link between welding parameters and the evolving weld seam profile. Utilizing datasets generated from Flow-3D simulations of the MLMP process, our framework achieves high-accuracy, real-time predictions: welding positions are estimated within 0.025 s (with an average error of approximately 1.5 mm), and weld seam profiles are predicted in 15 ms, with the arc-based geometric parameters accurately estimated (average errors of 0.73 mm in arc center position and 0.66 mm in radius). This practical approach enhances the efficiency and quality of MLMP robotic welding and contributes to advances in data-driven modeling and intelligent control in manufacturing, paving the way for autonomous welding systems.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 204-219"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLMKG＋: Systematically improving knowledge quality and coverage in KGs using LLMs – A case study in medical domain LLMKG+：使用LLMs系统地提高知识质量和覆盖范围-医学领域的案例研究

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-11-22 DOI: 10.1016/j.aiopen.2025.11.003

Xincan Feng , Hejie Cui , Kazuki Hayashi , Huy Hien Vu , Kenta T. Suzuki , Noriki Nishida , Hidetaka Kamigaito , Yuji Matsumoto , Taro Watanabe , Carl Yang

{"title":"LLMKG＋: Systematically improving knowledge quality and coverage in KGs using LLMs – A case study in medical domain","authors":"Xincan Feng , Hejie Cui , Kazuki Hayashi , Huy Hien Vu , Kenta T. Suzuki , Noriki Nishida , Hidetaka Kamigaito , Yuji Matsumoto , Taro Watanabe , Carl Yang","doi":"10.1016/j.aiopen.2025.11.003","DOIUrl":"10.1016/j.aiopen.2025.11.003","url":null,"abstract":"<div><div>Knowledge graphs (KGs) encode structured information about real-world entities and their relations, supporting core NLP tasks such as question answering and retrieval. Existing LLM-based methods for knowledge extraction and fusion often struggle to balance quality and coverage when adapting to emerging knowledge. We propose LLMKG＋, a framework for KG expansion that integrates the generative strengths of LLMs with relevance verification. LLMKG＋features (1) a two-stage pipeline with retrieval-augmented generation followed by hierarchical expansion filtering, where the latter is the first to jointly assess semantic equivalence to eliminate triple-level redundancy while ensuring factual correctness, and (2) a novel KG Reconstruction Test that recognizes semantically equivalent triples to enable more accurate quality and coverage assessment. Evaluated on PubMed abstracts and the UMLS semantic network using eight state-of-the-art LLMs, LLMKG＋improves KG quality and coverage by 20.47%–73.71% over strong baselines. These results demonstrate that LLMKG＋offers an effective solution for KG expansion in domains requiring high quality, broad coverage, and continual knowledge growth. Code: <span><span>https://github.com/xincanfeng/llmkg</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 299-313"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing AI for science: From the revolution of tools to the tools for revolution 为科学推进人工智能：从工具革命到工具革命

IF 14.8

AI Open Pub Date : 2025-01-01 Epub Date: 2025-12-02 DOI: 10.1016/j.aiopen.2025.11.002

Bowen Zhou , Ning Ding , Lei Bai , Hao Zhou

{"title":"Advancing AI for science: From the revolution of tools to the tools for revolution","authors":"Bowen Zhou , Ning Ding , Lei Bai , Hao Zhou","doi":"10.1016/j.aiopen.2025.11.002","DOIUrl":"10.1016/j.aiopen.2025.11.002","url":null,"abstract":"<div><div>Scientific research is not a linear pipeline but a dynamic system built upon the ever-shifting interactions among three elements — <em>research objects, tools, and researchers</em>. And sustained progress depends on how quickly insights circulate within this network, not on optimizing a single node in isolation. With the impending arrival of more general artificial intelligence, we stand at a critical point in how AI might change scientific research in a systemic manner. Recent “AI for Science” achievements – from protein-structure prediction to accelerated climate simulations – have proven the value of task-level AI-driven solutions. Yet, potential still remains unrealized when these advances are siloed in disciplinary “archipelagos”. This paper argues that the real prize is systemic: AI that simultaneously expands the research objects’ data landscape (AI for Data), rewires computational research tools (AI for Computation), and co-creates hypotheses with researchers (AI for Innovation). When these three pushes converge, AI stops being merely a revolution of tools but becomes the tool of revolution — a catalyst that raises the frequency, breadth, and depth of discovery across disciplines. By enhancing the full research triad rather than isolated nodes, AI can raise the overall tempo and scope of discovery in a measured, discipline-agnostic way.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 323-328"},"PeriodicalIF":14.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal RoPE extension via Bayesian Optimization for training-free length generalization 基于贝叶斯优化的无训练长度泛化的最优RoPE扩展

AI Open Pub Date : 2025-01-01 Epub Date: 2025-01-07 DOI: 10.1016/j.aiopen.2025.01.002

Xinrong Zhang , Shengding Hu , Weilin Zhao , Huadong Wang , Xu Han , Chaoqun He , Guoyang Zeng , Zhiyuan Liu , Maosong Sun

{"title":"Optimal RoPE extension via Bayesian Optimization for training-free length generalization","authors":"Xinrong Zhang , Shengding Hu , Weilin Zhao , Huadong Wang , Xu Han , Chaoqun He , Guoyang Zeng , Zhiyuan Liu , Maosong Sun","doi":"10.1016/j.aiopen.2025.01.002","DOIUrl":"10.1016/j.aiopen.2025.01.002","url":null,"abstract":"<div><div>Transformers are designed to process input of variable length without resource constraints. However, their performance significantly deteriorates when the input surpasses a threshold slightly larger than the pre-training context window. This limitation on the effective context window confines the application of Transformer-based large language models (LLMs) that have been the subject of great anticipation. Consequently, the generalization of pre-trained LLMs to handle varying input lengths becomes a pivotal and formidable challenge. Previous research has endeavored to address this challenge by modifying the Rotary Position Embedding (RoPE), the primary factor responsible for disparities in handling different input lengths. These efforts have provided valuable insights, while they often lack a deep understanding of the root causes of performance degradation and rely heavily on manual parameter tuning. In response to these issues, we conduct a comprehensive analysis and identify two primary causes behind the performance drop: global distribution mismatch and local resolution degradation. In light of these challenges, we introduce an Optimal RoPE (ORoPE) extension using Bayesian Optimization (BO), which alleviates the need for additional model training. Our experiments demonstrate the efficacy of our approach, outperforming baselines by up to 21.9%, 32.1%, and 41.2% at evaluation lengths of 8K, 16K, and 32K, respectively. We will release all code and data when this paper is published.</div></div>","PeriodicalId":100068,"journal":{"name":"AI Open","volume":"6 ","pages":"Pages 1-11"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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