Cognitive Systems Research最新文献_第7页

Digital twin application in women’s health: Cervical cancer diagnosis with CervixNet 数字孪生应用于妇女健康：利用 CervixNet 诊断宫颈癌

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-07-10 DOI: 10.1016/j.cogsys.2024.101264

Vikas Sharma , Akshi Kumar , Kapil Sharma

{"title":"Digital twin application in women’s health: Cervical cancer diagnosis with CervixNet","authors":"Vikas Sharma , Akshi Kumar , Kapil Sharma","doi":"10.1016/j.cogsys.2024.101264","DOIUrl":"https://doi.org/10.1016/j.cogsys.2024.101264","url":null,"abstract":"<div><p>Digital Twin (DT) will transform digital healthcare and push it far beyond expectations. DT creates a virtual representation of a physical object reflecting its current state using real-time converted data. Nowadays, Women’s health is more frequently impacted by cervical cancer, but early detection and rapid treatment are critical factors in the cure of cervical cancer. This paper proposes and implements an automated cervical cancer detection DT framework in healthcare. This framework is a valuable approach to enhance digital healthcare operations. In this proposed work, the SIPaKMeD dataset was used for multi-cell classification. There were 1013 images (Input size 224 × 224 × 3) in the collection, from which 4103 cells could be extracted. As a result, the CervixNet classifier model is developed using machine learning to detect cervical problems and diagnose cervical disease. Using pre-trained recurrent neural networks (RNNs), CervixNet extracted 1172 features, and after that, 792 features were selected using an independent principal component analysis (PCA) algorithm. The implemented models achieved the highest accuracy for predicting cervical cancer using different algorithms. The collected information has shown that integrating DT with the healthcare industry will enhance healthcare procedures by integrating patients and medical staff in a scalable, intelligent, and comprehensive health ecosystem. Finally, the suggested method produces an impressive 98.91 % classification accuracy in all classes, especially for support vector machines (SVM).</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"87 ","pages":"Article 101264"},"PeriodicalIF":2.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biologically inspired architecture for the identification of ambiguous objects using scene associations 利用场景关联识别模糊物体的生物灵感架构

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-07-01 DOI: 10.1016/j.cogsys.2024.101262

Ivan Axel Dounce, Félix Ramos

引用次数: 0

Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems 认知教育模型：为智能辅导系统建立智力技能模型的方法论

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-06-26 DOI: 10.1016/j.cogsys.2024.101261

Oleg Sychev

{"title":"Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems","authors":"Oleg Sychev","doi":"10.1016/j.cogsys.2024.101261","DOIUrl":"https://doi.org/10.1016/j.cogsys.2024.101261","url":null,"abstract":"<div><p>Automation of teaching people new skills requires modeling of human reasoning because human cognition involves active reasoning over the new subject domain to acquire skills that will later become automatic. The article presents Thought Process Trees — a language for modeling human reasoning that was created to facilitate the development of intelligent tutoring systems, which can perform the same reasoning that is expected of a student and find deficiencies in their line of thinking, providing explanatory messages and allowing them to learn from performance errors. The methodology of building trees which better reflect human learning is discussed, with examples of design choices during the modeling process and their consequences. The characteristics of educational modeling that impact building subject-domain models for intelligent tutoring systems are discussed. The trees were formalized and served as a basis for developing a framework for constructing intelligent tutoring systems. This significantly lowered the time required to build and debug a constraint-based subject-domain model. The framework has already been used to develop five intelligent tutoring systems and their prototypes and is being used to develop more of them.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"87 ","pages":"Article 101261"},"PeriodicalIF":2.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using a trie-based approach for storage and retrieval of goal-oriented plans in an S1/S2 cognitive architecture 在 S1/S2 认知架构中使用基于三元组的方法存储和检索目标导向计划

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-06-18 DOI: 10.1016/j.cogsys.2024.101257

Massimo Cossentino, Giovanni Pilato

{"title":"Using a trie-based approach for storage and retrieval of goal-oriented plans in an S1/S2 cognitive architecture","authors":"Massimo Cossentino, Giovanni Pilato","doi":"10.1016/j.cogsys.2024.101257","DOIUrl":"https://doi.org/10.1016/j.cogsys.2024.101257","url":null,"abstract":"<div><p>In the last years, the System 1/System 2 cognitive architecture, proposed by psychologist Daniel Kahneman, raised the interest of many researchers in the field. <em>System 1</em> is an intuitive, automatic, and fast-thinking system working effortlessly, without conscious effort. <em>System 2</em> is a deliberate, analytical, and slower-thinking system employing conscious effort and attention. This work proposes an innovative approach that exploits techniques typical of information retrieval (the trie data structure) to efficiently encode the solutions’ repository at the border between System 2 and System 1. This repository stores the solutions (successful plans) the agent has already used and can re-enact to achieve the goals. System 2 conceives new plans and delegates System 1 to execute them. If the plan is successful (and so it becomes a solution), System 1 stores that in the repository to quickly retrieve any solution that may help fulfil the goals deliberated by System 2 in the future.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"87 ","pages":"Article 101257"},"PeriodicalIF":2.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389041724000512/pdfft?md5=90857dbbd82c5a305bde8800fccf4b48&pid=1-s2.0-S1389041724000512-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring biological challenges in building a thinking machine 探索制造思维机器的生物挑战

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-06-16 DOI: 10.1016/j.cogsys.2024.101260

Christ Devia , Camilo Jara Do Nascimento , Samuel Madariaga , Pedro.E. Maldonado , Catalina Murúa , Rodrigo C. Vergara

引用次数: 0

Long horizon episodic decision making for cognitively inspired robots 认知启发型机器人的长视距偶发决策制定

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-06-14 DOI: 10.1016/j.cogsys.2024.101259

Shweta Singh , Vedant Ghatnekar , Sudaman Katti

引用次数: 0

Concept cognition for knowledge graphs: Mining multi-granularity decision rule 知识图谱的概念认知：挖掘多粒度决策规则

IF 3.9 3区心理学

Cognitive Systems Research Pub Date : 2024-06-14 DOI: 10.1016/j.cogsys.2024.101258

Jiangli Duan , Guoyin Wang , Xin Hu , Qun Liu , Qin Jiang , Huamin Zhu

{"title":"Concept cognition for knowledge graphs: Mining multi-granularity decision rule","authors":"Jiangli Duan , Guoyin Wang , Xin Hu , Qun Liu , Qin Jiang , Huamin Zhu","doi":"10.1016/j.cogsys.2024.101258","DOIUrl":"10.1016/j.cogsys.2024.101258","url":null,"abstract":"<div><p>As part of cognitive intelligence, concept cognition for knowledge graphs aims to clearly grasp the typical characteristics of the things referred to by the concept, which can provide prior knowledge for machine understanding and thinking. Different from concept learning and formal concept analysis that learn new concepts from data and the general decision rule that comes from an independent decision table, this paper cognizes an existing concept by decision rules that come from multiple granularities. Specifically, 1) concept cognition for knowledge graphs is realized from the perspective of mining multi-granularity decision rule. 2) Decision tables corresponding to four granularities form a multi-granularity decision table group, and then the result from coarser granularity can guide and help obtaining the result from finer granularity. 3) We propose a framework for mining multi-granularity decision rules, which involves going from a multi-granularity decision table group to the frequent maximal attribute patterns to the decision rules to the credible decision rules. Finally, we verified effectiveness of dividing positive and negative data, monotonicity of attribute patterns in a multi-granularity decision table group, and downward monotonicity of credibility, and observed the impact of the parameter <em>min_cov</em> and <em>min_conf</em> on execution times.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"87 ","pages":"Article 101258"},"PeriodicalIF":3.9,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141407118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Musical harmonies and its relationship with emotional processing: An ERP study in young adults 音乐和声及其与情绪处理的关系：一项针对年轻人的ERP研究

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-06-13 DOI: 10.1016/j.cogsys.2024.101256

Rubén Torres Agustín , Pablo González Francisco , Lilia Mestas Hernández , Martha Alejandra Gómez-López , Francisco Abelardo Robles Aguirre

{"title":"Musical harmonies and its relationship with emotional processing: An ERP study in young adults","authors":"Rubén Torres Agustín , Pablo González Francisco , Lilia Mestas Hernández , Martha Alejandra Gómez-López , Francisco Abelardo Robles Aguirre","doi":"10.1016/j.cogsys.2024.101256","DOIUrl":"10.1016/j.cogsys.2024.101256","url":null,"abstract":"<div><p>Music has been used to express and communicate emotional states through its different dimensions such as tone, rhythm, melody, and harmony. Consonant harmonies consistently are rated as pleasant whereas dissonant are considered unpleasant. The aim of this study was to explore the effect of consonant and dissonant musical harmonies used as prime on the emotional classification of images, as indexed by event-related potentials. Thirty volunteers (ages 21–27, 50 % women) were presented with a task consisting of 4 musical intervals in the C major scale, divided into consonant and dissonant harmonies, followed by 180 positive, negative, or neutral images from the International Affective Picture System (IAPS). Participants had to rate the images as pleasant or unpleasant. We found a bias effect on negative images rated as positive when preceded by a consonant musical interval. A N200 component, non-sensible to the valence of the images, was found. On the other hand, a significant difference was found in the amplitude of the P300 component, with a greater amplitude in the consonant-positive images condition compared to the dissonant-positive images. Lastly, a late positivity component around 500–700 ms was found in both negative conditions dissonant and consonant, but with a larger amplitude for the consonant condition when followed by a negative image. These results indicate that additionally to the P300 processing the relevance of the stimulus there are processes like recognition memory involved. As part of the novelty effect this late positive activity may also be related to the emotional content integration of the relevant stimulus.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"87 ","pages":"Article 101256"},"PeriodicalIF":2.1,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141404873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hypotheses-driven framework for human–machine expertise process 人机专业知识流程的假设驱动框架

IF 3.9 3区心理学

Cognitive Systems Research Pub Date : 2024-06-04 DOI: 10.1016/j.cogsys.2024.101255

Serge Sonfack Sounchio, Laurent Geneste, Bernard Kamsu Foguem

{"title":"A hypotheses-driven framework for human–machine expertise process","authors":"Serge Sonfack Sounchio, Laurent Geneste, Bernard Kamsu Foguem","doi":"10.1016/j.cogsys.2024.101255","DOIUrl":"10.1016/j.cogsys.2024.101255","url":null,"abstract":"<div><p>The hypothesis-driven methodology is a cognitive activity used in expertise processes to solve problems with limited knowledge and understanding. Although some organizations have standardized this approach to guide humans in carrying out expertise in enterprises, it lacks appropriate tools to assist experts in carrying out this cognitive activity, tracking understanding, or capturing the reasoning steps and the knowledge produced during the process.</p><p>To acquire, share and reuse experts’ knowledge applied during expertise processes while assisting humans in bringing understanding to complex problems, this study introduces a human–machine collaborative framework that formalizes experts’ knowledge from the hypothesis-driven methodology described in the France standard NF X50-110 of “Quality of expertise activity”. This framework utilizes Hypothesis Theory extended with qualitative doubt and a systematic reasoning process to generate a hypothesis exploratory graph (HEG).</p><p>The proposed approach makes it easier to carry out expertise processes through a human–machine collaboration, offers a means to share and reuse knowledge from expertise, and provides expertise processes evaluation mechanisms. Furthermore, an experiment conducted on a use-case of expertise process verifies the feasibility and effectiveness of the approach.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"87 ","pages":"Article 101255"},"PeriodicalIF":3.9,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1389041724000494/pdfft?md5=9c52e6fa4afbcba466c874d4febe947f&pid=1-s2.0-S1389041724000494-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foundations of Deep Learning. An introduction to the Special Issue 深度学习的基础。特刊简介

IF 2.1 3区心理学

Cognitive Systems Research Pub Date : 2024-05-06 DOI: 10.1016/j.cogsys.2024.101246

引用次数: 0