Cognitive Computation最新文献_第10页

Motion Selectivity of the Local Filed Potentials in the Primary Visual Cortex of Rats: A Machine Learning Approach 大鼠初级视觉皮层局部菲尔电位的运动选择性：机器学习方法

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-03-11 DOI: 10.1007/s12559-024-10263-7

Abbas Pourhedayat, Marzie Aghababaeipour Dehkordi, Mohammad Reza Daliri

引用次数: 0

Synchronization of Hypercomplex Neural Networks with Mixed Time-Varying Delays 具有混合时变延迟的超复杂神经网络的同步问题

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-03-11 DOI: 10.1007/s12559-024-10253-9

{"title":"Synchronization of Hypercomplex Neural Networks with Mixed Time-Varying Delays","authors":"","doi":"10.1007/s12559-024-10253-9","DOIUrl":"https://doi.org/10.1007/s12559-024-10253-9","url":null,"abstract":"<h3>Abstract</h3> This article discusses the fixed-time synchronization (FTS) of hypercomplex neural networks (HCNNs) with mixed time-varying delays. Unlike finite-time synchronization (FNTS) based on initial conditions, the settling time of FTS can be adjusted to meet the needs. The state vector, weight matrices, activation functions, and input vectors of HCNNs are all hypercomplex numbers. The techniques used in complex-valued neural networks (CVNNs) and quaternion-valued neural networks (QVNNs) cannot be used directly with HCNNs because they do not work with eight or more dimensions. To begin with, the decomposition method is used to split the HCNNs into ((n+1)) real-valued neural networks (RVNNs) applying distributive law to handle non-commutativity and non-associativity. A nonlinear controller is constructed to synchronize the master-response systems of the HCNNs. Lyapunov-based method is used to prove the stability of an error system. The FTS of mixed time-varying delayed HCNNs is achieved using a suitable lemma, Lipschitz condition, appropriate Lyapunov functional construction, and designing suitable controllers. Two different algebraic criteria for settling time have been achieved by employing two distinct lemmas. It is demonstrated that the settling time derived from Lemma 1 produces a more precise result than that obtained from Lemma 2. Three numerical examples for CVNNs, QVNNs, and octonions-valued neural networks (OVNNs) are provided to demonstrate the efficacy and effectiveness of the proposed theoretical results.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140097932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ArQuAD: An Expert-Annotated Arabic Machine Reading Comprehension Dataset ArQuAD：专家注释的阿拉伯语机器阅读理解数据集

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-03-11 DOI: 10.1007/s12559-024-10248-6

Rasha Obeidat, Marwa Al-Harbi, Mahmoud Al-Ayyoub, Luay Alawneh

{"title":"ArQuAD: An Expert-Annotated Arabic Machine Reading Comprehension Dataset","authors":"Rasha Obeidat, Marwa Al-Harbi, Mahmoud Al-Ayyoub, Luay Alawneh","doi":"10.1007/s12559-024-10248-6","DOIUrl":"https://doi.org/10.1007/s12559-024-10248-6","url":null,"abstract":"Machine Reading Comprehension (MRC) is a task that enables machines to mirror key cognitive processes involving reading, comprehending a text passage, and answering questions about it. There has been significant progress in this task for English in recent years, where recent systems not only surpassed human-level performance but also demonstrated advancements in emulating complex human cognitive processes. However, the development of Arabic MRC has not kept pace due to language challenges and the lack of large-scale, high-quality datasets. Existing datasets are either small, low quality or released as a part of large multilingual corpora. We present the Arabic Question Answering Dataset (ArQuaD), a large MRC dataset for the Arabic language. The dataset comprises 16,020 questions posed by language experts on passages extracted from Arabic Wikipedia articles, where the answer to each question is a text segment from the corresponding reading passage. Besides providing various dataset analyses, we fine-tuned several pre-trained language models to obtain benchmark results. Among the compared methods, AraBERTv0.2-large achieved the best performance with an exact match of 68.95% and an F1-score of 87.15%. However, the significantly higher performance observed in human evaluations (exact match of 86% and F1-score of 95.5%) suggests a significant margin of possible improvement in future research. We release the dataset publicly at https://github.com/RashaMObeidat/ArQuAD to encourage further development of language-aware MRC models for the Arabic language.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140097509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images TB-CXRNet：使用胸部 X 射线图像的结核病和耐药性结核病检测技术

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-02-17 DOI: 10.1007/s12559-024-10259-3

Tawsifur Rahman, A. Khandakar, Ashiqur Rahman, S. Zughaier, Muna Al Maslamani, M. H. Chowdhury, A. Tahir, Md. Sakib Hossain, Muhammad E. H. Chowdhury

引用次数: 0

An Enhanced Decision-Making Framework Driven by Complex Semantics Under Nested Probabilistic Linguistic Environments 嵌套概率语言环境下复杂语义驱动的强化决策框架

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-02-15 DOI: 10.1007/s12559-024-10245-9

Weidong Gan, Zeshui Xu, Xinxin Wang

引用次数: 0

An Enhanced Decision-Making Framework Driven by Complex Semantics Under Nested Probabilistic Linguistic Environments 嵌套概率语言环境下复杂语义驱动的强化决策框架