Frontiers in Artificial Intelligence最新文献

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Exploring the role of generative AI in international students' sociocultural adaptation: a cognitive-affective model. 生成性人工智能在留学生社会文化适应中的作用:一个认知-情感模型。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-30 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1615113
Huajun Ma, Qingnan You, Zhiyuan Jin, Xinglin Liu, Zimeng Chen
{"title":"Exploring the role of generative AI in international students' sociocultural adaptation: a cognitive-affective model.","authors":"Huajun Ma, Qingnan You, Zhiyuan Jin, Xinglin Liu, Zimeng Chen","doi":"10.3389/frai.2025.1615113","DOIUrl":"10.3389/frai.2025.1615113","url":null,"abstract":"<p><p>Against the backdrop of increasing global educational exchanges, the sociocultural adaptation of international students has attracted significant attention. The rise of Generative Artificial Intelligence has brought new perspectives to research in this field, yet existing studies have insufficiently explored the mechanisms through which GenAI influences the sociocultural adaptation of international students. Drawing on the cognitive-affective personality system theory and conservation of resources theory, this study employed a three-stage time-lagged questionnaire survey to collect 329 valid responses from international students at three universities in North, South, and East China. The research aims to investigate how GenAI use impacts students' sociocultural adaptation, while examining the mediating roles of positive reappraisal and perceived empathy, as well as the moderating effect of AI anthropomorphism. The findings reveal that GenAI use is significantly positively associated with international students' sociocultural adaptation. Positive reappraisal and users' subjective perceived empathy mediate the relationship between GenAI use and sociocultural adaptation. Additionally, the degree of AI anthropomorphism positively moderates the relationships between GenAI use and both positive reappraisal and perceived empathy, enhancing the indirect effects of these mediating variables on the relationship between GenAI use and sociocultural adaptation. This study enriches the technological premises of cross-cultural adaptation for international students and provides GenAI-based intervention strategies for their educational management.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1615113"},"PeriodicalIF":3.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12256517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638297","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
A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images. 基于nnu - net的FCD II型病变3D FLAIR MRI图像自动分割。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1601815
Shubham Joshi, Millie Pant, Arnav Malhotra, Kusum Deep, Vaclav Snasel
{"title":"A nnU-Net-based automatic segmentation of FCD type II lesions in 3D FLAIR MRI images.","authors":"Shubham Joshi, Millie Pant, Arnav Malhotra, Kusum Deep, Vaclav Snasel","doi":"10.3389/frai.2025.1601815","DOIUrl":"10.3389/frai.2025.1601815","url":null,"abstract":"<p><p>Focal cortical dysplasia (FCD) type II is a common cause of epilepsy and is challenging to detect due to its similarities with other brain conditions. Finding these lesions accurately is essential for successful surgery and seizure control. Manual detection is slow and challenging because the MRI features are subtle. Deep learning, especially convolutional neural networks, has shown great potential in automating image classification and segmentation by learning and extracting features. The nnU-Net framework is known for its ability to adapt its settings, including preprocessing, network design, training, and post-processing, to any new medical imaging task. This study employs an automated slice selection approach that ranks axial FLAIR slices by their peak voxel intensity and retains the five highest-ranked slices per scan, thereby focusing the network on lesion-rich slices and uses nnU-Net to automate the segmentation of FCD type II lesions on 3D FLAIR MRI images. The study was conducted on 85 FCD type II subjects and results are evaluated through 5-fold cross-validation. Using nnU-Net's flexible and robust design, this study aims to improve the accuracy and speed of lesion detection, helping with better presurgical evaluations and outcomes for epilepsy patients.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1601815"},"PeriodicalIF":3.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12247529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627309","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
Data stream-pairwise bottleneck transformer for engagement estimation from video conversation. 基于数据流的视频会话engagement估计瓶颈转换器。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1516295
Keita Suzuki, Nobukatsu Hojo, Kazutoshi Shinoda, Saki Mizuno, Ryo Masumura
{"title":"Data stream-pairwise bottleneck transformer for engagement estimation from video conversation.","authors":"Keita Suzuki, Nobukatsu Hojo, Kazutoshi Shinoda, Saki Mizuno, Ryo Masumura","doi":"10.3389/frai.2025.1516295","DOIUrl":"10.3389/frai.2025.1516295","url":null,"abstract":"<p><p>This study aims to assess participant engagement in multiparty conversations using video and audio data. For this task, the interaction among numerous data streams, such as video and audio from multiple participants, should be modeled effectively, considering the redundancy of video and audio across frames. To efficiently model participant interactions while accounting for such redundancy, a previous study proposed inputting participant feature sequences into global token-based transformers, which constrain attention across feature sequences to pass through only a small set of internal units, allowing the model to focus on key information. However, this approach still faces the challenge of redundancy in participant-feature estimation based on standard cross-attention transformers, which can connect all frames across different modalities. To address this, we propose a joint model for interactions among all data streams using global token-based transformers, without distinguishing between cross-modal and cross-participant interactions. Experiments on the RoomReader corpus confirm that the proposed model outperforms previous models, achieving accuracy ranging from 0.720 to 0.763, weighted F1 scores from 0.733 to 0.771, and macro F1 scores from 0.236 to 0.277.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1516295"},"PeriodicalIF":3.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627310","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
AI-powered creative stimulus: the ascent of virtual virtuoso entrepreneurship. 人工智能驱动的创造性刺激:虚拟大师创业的兴起。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1605855
Ajimon George, Maria Susan Mathew
{"title":"AI-powered creative stimulus: the ascent of virtual virtuoso entrepreneurship.","authors":"Ajimon George, Maria Susan Mathew","doi":"10.3389/frai.2025.1605855","DOIUrl":"10.3389/frai.2025.1605855","url":null,"abstract":"","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1605855"},"PeriodicalIF":3.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12241160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609796","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
AI generations: from AI 1.0 to AI 4.0. AI世代:从AI 1.0到AI 4.0。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1585629
Jiahao Wu, Hengxu You, Jing Du
{"title":"AI generations: from AI 1.0 to AI 4.0.","authors":"Jiahao Wu, Hengxu You, Jing Du","doi":"10.3389/frai.2025.1585629","DOIUrl":"10.3389/frai.2025.1585629","url":null,"abstract":"<p><p>This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI's transformative potential benefits society.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1585629"},"PeriodicalIF":3.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12241030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609795","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
Construction of medical scientific data repositories in China: analysis of survey and recommendations. 中国医学科学数据库建设:调查分析与建议。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-26 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1544200
Jia Song, Chunqiu Li, Wirapong Chansanam
{"title":"Construction of medical scientific data repositories in China: analysis of survey and recommendations.","authors":"Jia Song, Chunqiu Li, Wirapong Chansanam","doi":"10.3389/frai.2025.1544200","DOIUrl":"10.3389/frai.2025.1544200","url":null,"abstract":"<p><strong>Background: </strong>In the context of global open science trends, medical open-access repositories (OARs) promote transparency in research and facilitate the sharing of scientific data. The increase in scientific output necessitates a robust infrastructure to enhance OARs in China.</p><p><strong>Objectives: </strong>This study aimed to evaluate medical open-access repositories (OARs) in China that are indexed in re3data.org and OpenDOAR.org. The study analyzed data classification, descriptions, retrieval, and the utilization of selected repositories.</p><p><strong>Methods: </strong>This study ascertained the current status of the Chinese medical OARs by visiting their respective websites and attempted to identify the disciplinary orientation of each OAR. A content analysis approach was utilized to achieve this study's objective. Twelve Chinese medical open-access repositories were selected from re3data.org and OpenDOAR.org to examine how their information is organized. The data were collected manually from May 1 to 30, 2023, and analyzed using various quantitative techniques to understand the current status of medical scientific repositories in China.</p><p><strong>Results: </strong>Based on the results, this study proposed the following recommendations: (1) implement multi-dimensional data classification, (2) use persistent data identifiers, (3) formalize the description metadata, (4) enhance advanced retrieval and result set filtering functions, and (5) optimize the preview and interaction features of data repositories.</p><p><strong>Conclusion: </strong>The scope of this study is restricted to the medical open-access repositories in China as listed on re3data.org and OpenDOAR.org. Therefore, the results of this study are only generalizable within China. The primary focus of research output in China is on medical open-access repositories. This study is essential for assessing China's current status in research data management within the medical field and its distribution infrastructure in global open science trends.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1544200"},"PeriodicalIF":3.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609797","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
Deep learning-based feature selection for detection of autism spectrum disorder. 基于深度学习的特征选择用于自闭症谱系障碍检测。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-25 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1594372
Ibrahim Nafisah, Nermine Mahmoud, Ahmed A Ewees, Mohamed G Khattap, Abdelghani Dahou, Safar M Alghamdi, Ibrahim A Fares, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz
{"title":"Deep learning-based feature selection for detection of autism spectrum disorder.","authors":"Ibrahim Nafisah, Nermine Mahmoud, Ahmed A Ewees, Mohamed G Khattap, Abdelghani Dahou, Safar M Alghamdi, Ibrahim A Fares, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz","doi":"10.3389/frai.2025.1594372","DOIUrl":"10.3389/frai.2025.1594372","url":null,"abstract":"<p><strong>Introduction: </strong>Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in communication, social interactions, and repetitive behaviors. The heterogeneity of symptoms across individuals complicates diagnosis. Neuroimaging techniques, particularly resting-state functional MRI (rs-fMRI), have shown potential for identifying neural signatures of ASD, though challenges such as high dimensionality, noise, and small sample sizes hinder their clinical application.</p><p><strong>Methods: </strong>This study proposes a novel approach for ASD detection utilizing deep learning and advanced feature selection techniques. A hybrid model combining Stacked Sparse Denoising Autoencoder (SSDAE) and Multi-Layer Perceptron (MLP) is employed to extract relevant features from rs-fMRI data in the ABIDE I dataset, which was preprocessed using the CPAC pipeline. Feature selection is enhanced through an optimized Hiking Optimization Algorithm (HOA) that integrates DynamicOpposites Learning (DOL) and Double Attractors to improve convergence toward the optimal subset of features.</p><p><strong>Results: </strong>The proposed model is evaluated using multiple ASD datasets. The performance metrics include an average accuracy of 0.735, sensitivity of 0.765, and specificity of 0.752, surpassing the results of existing state-of-the-art methods.</p><p><strong>Discussion: </strong>The findings demonstrate the effectiveness of the hybrid deep learning approach for ASD detection. The enhanced feature selection process, coupled with the hybrid model, addresses limitations in current neuroimaging analyses and offers a promising direction for more accurate and clinically applicable ASD detection models.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1594372"},"PeriodicalIF":3.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601760","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
Journaling with large language models: a novel UX paradigm for AI-driven personal health management. 使用大型语言模型的日志:用于人工智能驱动的个人健康管理的新型用户体验范例。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-24 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1567580
Birger Moëll, Fredrik Sand Aronsson
{"title":"Journaling with large language models: a novel UX paradigm for AI-driven personal health management.","authors":"Birger Moëll, Fredrik Sand Aronsson","doi":"10.3389/frai.2025.1567580","DOIUrl":"10.3389/frai.2025.1567580","url":null,"abstract":"<p><strong>Introduction: </strong>The integration of large language models (LLMs) into personal health management presents transformative potential, but faces critical challenges in user experience (UX) design, ethical implementation, and clinical integration.</p><p><strong>Method: </strong>This paper introduces a novel AI-driven journaling application, a functional prototype available open source, designed to encourage patient engagement through a natural language interface. This approach, termed \"AI-assisted health journaling,\" enables users to document health experiences in their own words while receiving real-time, context-aware feedback from an LLM. The prototype combines a personal health record with an LLM assistant, allowing for reflective self-monitoring and aiming to combine patient-generated data with clinical insights. Key innovations include a three-panel interface for seamless journaling, AI dialogue, and longitudinal tracking, alongside specialized modes for interacting with simulated healthcare expert personas.</p><p><strong>Result: </strong>Preliminary insights from persona-based evaluations highlight the system's capacity to enhance health literacy through explainable AI responses while maintaining strict data localization and privacy controls. We propose five design principles for patient-centric AI health tools: (1) decoupling core functionality from LLM dependencies, (2) layered transparency in AI outputs, (3) adaptive consent for data sharing, (4) clinician-facing data summarization, and (5) compliance-first architecture.</p><p><strong>Discussion: </strong>By transforming unstructured patient narratives into structured insights through natural language processing, this approach demonstrates how journaling interfaces could serve as a critical middleware layer in healthcare ecosystems-empowering patients as active partners in care while preserving clinical oversight. Future research directions emphasize the need for rigorous trials evaluating impacts on care continuity, patient-provider communication, and long-term health outcomes across diverse populations.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1567580"},"PeriodicalIF":3.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12234568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592466","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
Irrationality in humans and creativity in AI. 人类的非理性和人工智能的创造力。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-20 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1579704
Olha Sobetska
{"title":"Irrationality in humans and creativity in AI.","authors":"Olha Sobetska","doi":"10.3389/frai.2025.1579704","DOIUrl":"10.3389/frai.2025.1579704","url":null,"abstract":"<p><p>This manuscript explores how human irrationality in decision-making can contribute to artificial intelligence (AI) development, particularly in the domain of creativity. While irrational behavior is typically seen as a cognitive flaw, we argue that certain forms of irrationality, such as those demonstrated by the conjunction fallacy (CF), may represent context-sensitive reasoning that reveals creative problem-solving. Traditional AI research has primarily focused on rational, logic-driven models, overlooking the productive role of non-linear and seemingly illogical human thinking in generating novel insights. Drawing on interdisciplinary insights and recent neuroscientific findings, particularly the interaction of the Default Mode, Executive Control, and Salience Networks, we propose a model that integrates both rational and irrational cognitive dynamics. This framework may inform the design of AI systems that are more adaptive, context-aware, and capable of emulating human-like creativity.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1579704"},"PeriodicalIF":3.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576479","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
Stochastic and deterministic processes in Asymmetric Tsetlin Machine. 非对称Tsetlin机的随机和确定性过程。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2025-06-20 eCollection Date: 2025-01-01 DOI: 10.3389/frai.2025.1377944
Negar Elmisadr, Mohamed-Bachir Belaid, Anis Yazidi
{"title":"Stochastic and deterministic processes in Asymmetric Tsetlin Machine.","authors":"Negar Elmisadr, Mohamed-Bachir Belaid, Anis Yazidi","doi":"10.3389/frai.2025.1377944","DOIUrl":"10.3389/frai.2025.1377944","url":null,"abstract":"<p><p>This paper introduces a new approach to enhance the decision-making capabilities of the Tsetlin Machine (TM) through the Stochastic Point Location (SPL) algorithm and the Asymmetric Steps technique. We incorporate stochasticity and asymmetry into the TM's process, along with a decaying normal distribution function that improves adaptability as it converges toward zero over time. We present two methods: the Asymmetric Probabilistic Tsetlin (APT) Machine, influenced by random events, and the Asymmetric Tsetlin (AT) Machine, which transitions from probabilistic to deterministic states. We evaluate these methods against traditional machine learning algorithms and classical Tsetlin (CT) machines across various benchmark datasets. Both AT and APT demonstrate competitive performance, with the AT model notably excelling, especially in complex datasets.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"8 ","pages":"1377944"},"PeriodicalIF":3.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585058","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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