首页 > 最新文献

Computational Intelligence最新文献

英文 中文
An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time-Distributed 2D CNN-BLSTM Models, and mRMR Feature Selection 基于三维 CNN、时间分布式二维 CNN-BLSTM 模型和 mRMR 特征选择的高效稳健的三维医学图像分类方法
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-23 DOI: 10.1111/coin.70000
Enver Akbacak, Nedim Muzoğlu

The advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time-consuming. In the last decades, artificial intelligence-based computer-aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time-distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.

三维医学影像的出现是诊断各种疾病的转折点,因为相邻切片的体素信息有助于放射科医生更好地理解复杂的解剖关系。然而,不同专业水平的放射科医生对医学影像的解读可能各不相同,而且耗费时间。在过去几十年中,基于人工智能的计算机辅助系统提供了快速、更可靠的诊断见解,在各种临床用途中具有巨大潜力。本文提出了一种重要的基于深度学习的三维医学图像诊断方法。该方法对 MedMNIST3D 进行了分类,MedMNIST3D 由六种三维生物医学数据集组成,分别来自 CT、MRA 和电子显微镜模式。所提出的方法将从三个独立网络、一个 3D CNN 和两个时间分布 ResNet BLSTM 结构中提取的 3D 图像特征合并在一起。通过最小冗余最大相关性(mRMR)特征选择法选出最终的判别特征。然后通过神经网络模型对这些特征进行分类。实验遵循 MedMNIST3D 数据集的官方拆分规则和评估指标。结果表明,所提出的方法在准确率和AUC方面优于同类研究。
{"title":"An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time-Distributed 2D CNN-BLSTM Models, and mRMR Feature Selection","authors":"Enver Akbacak,&nbsp;Nedim Muzoğlu","doi":"10.1111/coin.70000","DOIUrl":"https://doi.org/10.1111/coin.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time-consuming. In the last decades, artificial intelligence-based computer-aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time-distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Comprehensive analysis of feature-algorithm interactions for fall detection across age groups via machine learning 通过机器学习全面分析各年龄组跌倒检测中特征与算法之间的相互作用
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-23 DOI: 10.1111/coin.12697
Erhan Kavuncuoğlu

Fall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3-SVM at 96.18% and F17-LightGBM at 95.79%). The F3-SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time-series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top-performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple-ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real-world fall-detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics.

日常活动中的跌倒检测取决于特征选择和算法选择。本研究利用 Sisfall 数据集深入探讨了它们之间错综复杂的相互作用,在 26 个特征上测试了 10 种机器学习算法,这些特征包括不同的跌倒和年龄组。对单个特征的分析得出了关键的见解。带有自相关特征的 RFC 的表现优于其他分类器,准确率达到 97.94%,灵敏度达到 97.51%(超过了 96.18% 的 F3-SVM 和 95.79% 的 F17-LightGBM)。F3-SVM 在区分日常活动方面表现出了极高的特异性(98.72%)。SVM 采用的时间序列特征在未见数据上达到了 98.60% 的峰值准确率,超过了运动、基本统计和频域特征。特征组合的效果更加突出:融合了最佳特征的 Quintuple 方法与 ETC 的准确率达到了 98.69%,灵敏度达到了 98.28%,特异性达到了 99.08%,由于其适应性强,灵敏度显著提高。这项研究强调了特征和算法之间的重要相互作用,其中五元-ETC 方法最为有效。严格的超参数调整增强了其在实际跌倒检测应用中的性能。此外,该研究还调查了算法的可移植性,即在年轻参与者的数据上训练模型,然后将其应用于老年人--这在机器学习中是一项重大挑战。这凸显了了解医疗保健、老龄化管理和医疗诊断中不同年龄组之间数据转移的重要性。
{"title":"Comprehensive analysis of feature-algorithm interactions for fall detection across age groups via machine learning","authors":"Erhan Kavuncuoğlu","doi":"10.1111/coin.12697","DOIUrl":"https://doi.org/10.1111/coin.12697","url":null,"abstract":"<p>Fall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3-SVM at 96.18% and F17-LightGBM at 95.79%). The F3-SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time-series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top-performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple-ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real-world fall-detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
A Benchmark Proposal for Non-Generative Fair Adversarial Learning Strategies Using a Fairness-Utility Trade-off Metric 使用公平-效用权衡指标的非生成公平对抗学习策略基准提案
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-21 DOI: 10.1111/coin.70003
Luiz Fernando F. P. de Lima, Danielle Rousy D. Ricarte, Clauirton A. Siebra

AI systems for decision-making have become increasingly popular in several areas. However, it is possible to identify biased decisions in many applications, which have become a concern for the computer science, artificial intelligence, and law communities. Therefore, researchers are proposing solutions to mitigate bias and discrimination among decision-makers. Some explored strategies are based on GANs to generate fair data. Others are based on adversarial learning to achieve fairness by encoding fairness constraints through an adversarial model. Moreover, it is usual for each proposal to assess its model with a specific metric, making comparing current approaches a complex task. Therefore, this work proposes a systematical benchmark procedure to assess the fair machine learning models. The proposed procedure comprises a fairness-utility trade-off metric (FU-score$$ FUhbox{-} score $$), the utility and fairness metrics to compose this assessment, the used datasets and preparation, and the statistical test. A previous work presents some of these definitions. The present work enriches the procedure by increasing the applied datasets and statistical guarantees when comparing the models' results. We performed this benchmark evaluation for the non-generative adversarial models, analyzing the literature models from the same metric perspective. This assessment could not indicate a single model which better performs for all datasets. However, we built an understanding of how each model performs on each dataset with statistical confidence.

用于决策的人工智能系统在多个领域越来越受欢迎。然而,在许多应用中都有可能发现有偏见的决策,这已成为计算机科学、人工智能和法律界关注的问题。因此,研究人员正在提出一些解决方案,以减少决策者之间的偏见和歧视。一些已探索的策略基于 GAN 生成公平数据。另一些则基于对抗学习,通过对抗模型对公平性约束进行编码来实现公平性。此外,每个建议通常都会用特定的指标来评估其模型,这使得比较当前的方法成为一项复杂的任务。因此,这项工作提出了一个系统的基准程序,用于评估公平的机器学习模型。建议的程序包括公平性-效用权衡指标(FU-score $$ FUhbox{-} score $$$)、组成该评估的效用和公平性指标、使用的数据集和准备工作以及统计测试。之前的一项工作介绍了其中的一些定义。本研究通过增加应用数据集和比较模型结果时的统计保证,丰富了这一程序。我们对非生成对抗模型进行了基准评估,从相同的度量角度分析了文献模型。这项评估无法指出哪一个模型在所有数据集上都有更好的表现。不过,我们对每种模型在每种数据集上的表现都有了一定的了解,并在统计上有了信心。
{"title":"A Benchmark Proposal for Non-Generative Fair Adversarial Learning Strategies Using a Fairness-Utility Trade-off Metric","authors":"Luiz Fernando F. P. de Lima,&nbsp;Danielle Rousy D. Ricarte,&nbsp;Clauirton A. Siebra","doi":"10.1111/coin.70003","DOIUrl":"https://doi.org/10.1111/coin.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>AI systems for decision-making have become increasingly popular in several areas. However, it is possible to identify biased decisions in many applications, which have become a concern for the computer science, artificial intelligence, and law communities. Therefore, researchers are proposing solutions to mitigate bias and discrimination among decision-makers. Some explored strategies are based on GANs to generate fair data. Others are based on adversarial learning to achieve fairness by encoding fairness constraints through an adversarial model. Moreover, it is usual for each proposal to assess its model with a specific metric, making comparing current approaches a complex task. Therefore, this work proposes a systematical benchmark procedure to assess the fair machine learning models. The proposed procedure comprises a fairness-utility trade-off metric (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>FU-score</mi>\u0000 </mrow>\u0000 <annotation>$$ FUhbox{-} score $$</annotation>\u0000 </semantics></math>), the utility and fairness metrics to compose this assessment, the used datasets and preparation, and the statistical test. A previous work presents some of these definitions. The present work enriches the procedure by increasing the applied datasets and statistical guarantees when comparing the models' results. We performed this benchmark evaluation for the non-generative adversarial models, analyzing the literature models from the same metric perspective. This assessment could not indicate a single model which better performs for all datasets. However, we built an understanding of how each model performs on each dataset with statistical confidence.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Synthetic Image Generation Using Deep Learning: A Systematic Literature Review 使用深度学习生成合成图像:系统性文献综述
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-21 DOI: 10.1111/coin.70002
Aisha Zulfiqar, Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Mohib Ullah, Suraksha Sadhwani

The advent of deep neural networks and improved computational power have brought a revolutionary transformation in the fields of computer vision and image processing. Within the realm of computer vision, there has been a significant interest in the area of synthetic image generation, which is a creative side of AI. Many researchers have introduced innovative methods to identify deep neural network-based architectures involved in image generation via different modes of input, like text, scene graph layouts and so forth to generate synthetic images. Computer-generated images have been found to contribute a lot to the training of different machine and deep-learning models. Nonetheless, we have observed an immediate need for a comprehensive and systematic literature review that encompasses a summary and critical evaluation of current primary studies' approaches toward image generation. To address this, we carried out a systematic literature review on synthetic image generation approaches published from 2018 to February 2023. Moreover, we have conducted a systematic review of various datasets, approaches to image generation, performance metrics for existing methods, and a brief experimental comparison of DCGAN (deep convolutional generative adversarial network) and cGAN (conditional generative adversarial network) in the context of image generation. Additionally, we have identified applications related to image generation models with critical evaluation of the primary studies on the subject matter. Finally, we present some future research directions to further contribute to the field of image generation using deep neural networks.

深度神经网络的出现和计算能力的提高给计算机视觉和图像处理领域带来了革命性的变革。在计算机视觉领域,人们对合成图像生成这一人工智能的创造性领域产生了浓厚的兴趣。许多研究人员引入了创新方法,通过不同的输入模式(如文本、场景图布局等)来识别参与图像生成的基于深度神经网络的架构,从而生成合成图像。人们发现,计算机生成的图像对不同机器和深度学习模型的训练有很大帮助。然而,我们注意到,目前急需一份全面、系统的文献综述,其中包括对当前主要研究的图像生成方法进行总结和批判性评估。为此,我们对 2018 年至 2023 年 2 月期间发表的合成图像生成方法进行了系统的文献综述。此外,我们还对各种数据集、图像生成方法、现有方法的性能指标进行了系统回顾,并在图像生成方面对 DCGAN(深度卷积生成对抗网络)和 cGAN(条件生成对抗网络)进行了简要的实验比较。此外,我们还确定了与图像生成模型相关的应用,并对该主题的主要研究进行了批判性评估。最后,我们提出了一些未来的研究方向,以进一步促进使用深度神经网络生成图像领域的发展。
{"title":"Synthetic Image Generation Using Deep Learning: A Systematic Literature Review","authors":"Aisha Zulfiqar,&nbsp;Sher Muhammad Daudpota,&nbsp;Ali Shariq Imran,&nbsp;Zenun Kastrati,&nbsp;Mohib Ullah,&nbsp;Suraksha Sadhwani","doi":"10.1111/coin.70002","DOIUrl":"https://doi.org/10.1111/coin.70002","url":null,"abstract":"<p>The advent of deep neural networks and improved computational power have brought a revolutionary transformation in the fields of computer vision and image processing. Within the realm of computer vision, there has been a significant interest in the area of synthetic image generation, which is a creative side of AI. Many researchers have introduced innovative methods to identify deep neural network-based architectures involved in image generation via different modes of input, like text, scene graph layouts and so forth to generate synthetic images. Computer-generated images have been found to contribute a lot to the training of different machine and deep-learning models. Nonetheless, we have observed an immediate need for a comprehensive and systematic literature review that encompasses a summary and critical evaluation of current primary studies' approaches toward image generation. To address this, we carried out a systematic literature review on synthetic image generation approaches published from 2018 to February 2023. Moreover, we have conducted a systematic review of various datasets, approaches to image generation, performance metrics for existing methods, and a brief experimental comparison of DCGAN (deep convolutional generative adversarial network) and cGAN (conditional generative adversarial network) in the context of image generation. Additionally, we have identified applications related to image generation models with critical evaluation of the primary studies on the subject matter. Finally, we present some future research directions to further contribute to the field of image generation using deep neural networks.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modified local Granger causality analysis based on Peter-Clark algorithm for multivariate time series prediction on IoT data 基于彼得-克拉克算法的修正局部格兰杰因果关系分析,用于物联网数据的多变量时间序列预测
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-21 DOI: 10.1111/coin.12694
Fei Lv, Shuaizong Si, Xing Xiao, Weijie Ren

Climate data collected through Internet of Things (IoT) devices often contain high-dimensional, nonlinear, and auto-correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter-Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC-MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two-stage causal network learning method on one benchmark dataset and two real-world datasets.

通过物联网(IoT)设备采集到的气候数据往往包含高维、非线性和自相关等特征,一般的因果关系分析方法基于条件独立性检验或格兰杰因果关系等获得变量间的定量因果关系分析结果。然而,时间分布变量之间的动态特性难以捕捉,而时间分布变量之间的动态特性可以获得均值检测方法无法获得的信息。因此,本文提出了一种基于彼得-克拉克(PC)算法和改进的局部格兰杰因果关系(MLGC)分析方法的新因果关系分析方法,称为 PC-MLGC,以揭示变量之间的因果关系,探索时间分布上的动态特性。首先,应用 PC 算法计算每个变量的相关变量。然后,将前一阶段得到的结果输入修正的局部格兰杰因果分析模型,探索变量之间的因果关系。最后,结合定量因果分析结果,可以得到变量间的动态特征曲线,进一步验证变量间因果关系的准确性。通过在一个基准数据集和两个实际数据集上与标准格兰杰因果分析法和两阶段因果网络学习法进行比较,进一步证明了所提方法的有效性。
{"title":"Modified local Granger causality analysis based on Peter-Clark algorithm for multivariate time series prediction on IoT data","authors":"Fei Lv,&nbsp;Shuaizong Si,&nbsp;Xing Xiao,&nbsp;Weijie Ren","doi":"10.1111/coin.12694","DOIUrl":"https://doi.org/10.1111/coin.12694","url":null,"abstract":"<p>Climate data collected through Internet of Things (IoT) devices often contain high-dimensional, nonlinear, and auto-correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter-Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC-MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two-stage causal network learning method on one benchmark dataset and two real-world datasets.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Adaptive Synaptic Adjustment Mechanism to Improve Learning Performances of Spiking Neural Networks 改善尖峰神经网络学习性能的自适应突触调整机制
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-17 DOI: 10.1111/coin.70001
Hyun-Jong Lee, Jae-Han Lim

Spiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike-timing-dependent plasticity (STDP) is an unsupervised learning process that utilizes bio-plausibility based on the relative timing of pre/post-synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps STDP to converge synaptic weights directly based on input data features: Adaptive synaptic template (AST). AST leads synaptic weights to describe synaptic connections according to the data features. It prevents STDP from changing synaptic weights based on abnormal weights that fail to describe the proper synaptic connections. This is because it does not use the current synaptic weights that can disturb proper weight convergence. We integrate AST with an SNN and conduct experiments to compare it with a baseline (the SNN without AST) and benchmarks (previous works to improve STDP). Our experimental results show that the SNN using AST classifies various data sets with 6%–39% higher accuracy than the baseline and benchmarks.

尖峰神经网络(SNN)因其在各种任务中的高效性而备受研究人员的关注。尖峰计时可塑性(STDP)是一种无监督学习过程,它基于神经元突触前/后尖峰的相对计时,利用生物可塑性。与 STDP 相结合,SNN 的性能更佳,能耗更低。然而,由于 STDP 不会随监督而改变突触权重,因此很难确保突触权重始终收敛到能保证准确预测的值。为了解决这一限制,研究人员提出了一些机制,以诱导 STDP 参照当前的突触权重将突触权重收敛到适当的值上。因此,如果当前权重无法描述正确的突触连接,就无法诱导 STDP 正确更新突触权重。为了解决这个问题,我们提出了一种自适应机制,帮助 STDP 直接根据输入数据特征收敛突触权重:自适应突触模板(AST)。AST 根据数据特征来引导突触权重描述突触连接。它可以防止 STDP 根据无法描述正确突触连接的异常权重改变突触权重。这是因为它不会使用会干扰正确权重收敛的当前突触权重。我们将 AST 与 SNN 相结合,并进行了实验,将其与基线(不含 AST 的 SNN)和基准(以前改进 STDP 的工作)进行比较。实验结果表明,使用 AST 的 SNN 对各种数据集进行分类的准确率比基线和基准高 6%-39%。
{"title":"Adaptive Synaptic Adjustment Mechanism to Improve Learning Performances of Spiking Neural Networks","authors":"Hyun-Jong Lee,&nbsp;Jae-Han Lim","doi":"10.1111/coin.70001","DOIUrl":"https://doi.org/10.1111/coin.70001","url":null,"abstract":"<div>\u0000 \u0000 <p>Spiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike-timing-dependent plasticity (STDP) is an unsupervised learning process that utilizes bio-plausibility based on the relative timing of pre/post-synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps STDP to converge synaptic weights directly based on input data features: Adaptive synaptic template (AST). AST leads synaptic weights to describe synaptic connections according to the data features. It prevents STDP from changing synaptic weights based on abnormal weights that fail to describe the proper synaptic connections. This is because it does not use the current synaptic weights that can disturb proper weight convergence. We integrate AST with an SNN and conduct experiments to compare it with a baseline (the SNN without AST) and benchmarks (previous works to improve STDP). Our experimental results show that the SNN using AST classifies various data sets with 6%–39% higher accuracy than the baseline and benchmarks.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review 利用产品评论进行情感分析的混合 HAN-CNN 与方面词提取技术
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-10-07 DOI: 10.1111/coin.12698
P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, T. Kumaravel

In this article, an intensive sentiment analysis approach termed Hierarchical attention-convolutional neural network (HAN-CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN-CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN-CNN achieved a greater performance with maximum accuracy, recall and F1-Score of 91.70%, 90.60% and 91.20%, respectively.

本文利用产品评论提出了一种密集情感分析方法,称为分层注意力-卷积神经网络(HAN-CNN)。首先,对输入的产品评论进行双向变换器编码器表征(BERT)标记化处理,将每个句子的输入数据分割成单词的小比特。然后,进行方面术语提取(ATE),并利用一些特征完成特征提取。最后,情感分析由开发的 HAN-CNN 完成,HAN-CNN 由分层注意力网络(HAN)和卷积神经网络(CNN)组合而成。此外,所提出的 HAN-CNN 取得了更高的性能,最高准确率、召回率和 F1-Score 分别为 91.70%、90.60% 和 91.20%。
{"title":"Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review","authors":"P. C. D. Kalaivaani,&nbsp;K. Sathyarajasekaran,&nbsp;N. Krishnamoorthy,&nbsp;T. Kumaravel","doi":"10.1111/coin.12698","DOIUrl":"https://doi.org/10.1111/coin.12698","url":null,"abstract":"<p>In this article, an intensive sentiment analysis approach termed Hierarchical attention-convolutional neural network (HAN-CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN-CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN-CNN achieved a greater performance with maximum accuracy, recall and F1-Score of 91.70%, 90.60% and 91.20%, respectively.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142404132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
TCSR: Self-attention with time and category for session-based recommendation TCSR:基于会话推荐的时间和类别自我关注
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-25 DOI: 10.1111/coin.12695
Xiaoyan Zhu, Yu Zhang, Jiaxuan Li, Jiayin Wang, Xin Lai

Session-based recommendation that uses sequence of items clicked by anonymous users to make recommendations has drawn the attention of many researchers, and a lot of approaches have been proposed. However, there are still problems that have not been well addressed: (1) Time information is either ignored or exploited with a fixed time span and granularity, which fails to understand the personalized interest transfer pattern of users with different clicking speeds; (2) Category information is either omitted or considered independent of the items, which defies the fact that the relationships between categories and items are helpful for the recommendation. To solve these problems, we propose a new session-based recommendation method, TCSR (self-attention with time and category for session-based recommendation). TCSR uses a non-linear normalized time embedding to perceive user interest transfer patterns on variable granularity and employs a heterogeneous SAN to make full use of both items and categories. Moreover, a cross-recommendation unit is adapted to adjust recommendations on the item and category sides. Extensive experiments on four real datasets show that TCSR significantly outperforms state-of-the-art approaches.

基于会话的推荐(Session-based recommendation)利用匿名用户点击的项目序列来进行推荐,这引起了许多研究人员的关注,并提出了许多方法。然而,目前仍有一些问题没有得到很好的解决:(1)忽略了时间信息,或以固定的时间跨度和粒度利用时间信息,无法理解不同点击速度用户的个性化兴趣转移模式;(2)忽略了类别信息,或认为类别信息独立于项目信息,这违背了类别与项目之间的关系有助于推荐的事实。为了解决这些问题,我们提出了一种新的基于会话的推荐方法--TCSR(基于会话推荐的时间和类别自我关注)。TCSR 使用非线性归一化时间嵌入来感知不同粒度的用户兴趣转移模式,并采用异构 SAN 来充分利用项目和类别。此外,还采用了交叉推荐单元来调整项目和类别方面的推荐。在四个真实数据集上进行的广泛实验表明,TCSR 明显优于最先进的方法。
{"title":"TCSR: Self-attention with time and category for session-based recommendation","authors":"Xiaoyan Zhu,&nbsp;Yu Zhang,&nbsp;Jiaxuan Li,&nbsp;Jiayin Wang,&nbsp;Xin Lai","doi":"10.1111/coin.12695","DOIUrl":"https://doi.org/10.1111/coin.12695","url":null,"abstract":"<p>Session-based recommendation that uses sequence of items clicked by anonymous users to make recommendations has drawn the attention of many researchers, and a lot of approaches have been proposed. However, there are still problems that have not been well addressed: (1) Time information is either ignored or exploited with a fixed time span and granularity, which fails to understand the personalized interest transfer pattern of users with different clicking speeds; (2) Category information is either omitted or considered independent of the items, which defies the fact that the relationships between categories and items are helpful for the recommendation. To solve these problems, we propose a new session-based recommendation method, TCSR (self-attention with time and category for session-based recommendation). TCSR uses a non-linear normalized time embedding to perceive user interest transfer patterns on variable granularity and employs a heterogeneous SAN to make full use of both items and categories. Moreover, a cross-recommendation unit is adapted to adjust recommendations on the item and category sides. Extensive experiments on four real datasets show that TCSR significantly outperforms state-of-the-art approaches.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Multi-scale learning for fine-grained traffic flow-based travel time estimation prediction 基于细粒度交通流的旅行时间估算预测的多尺度学习
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-17 DOI: 10.1111/coin.12693
Zain Ul Abideen, Xiaodong Sun, Chao Sun

In intelligent transportation systems (ITS), achieving accurate travel time estimation (TTE) is paramount, much like route planning. Precisely predicting travel time across different urban areas is vital, and an essential requirement for these privileges is having fine-grained knowledge of the city. In contrast to prior studies that are restricted to coarse-grained data, we broaden the scope of traffic flow forecasting to fine granularity, which provokes explicit challenges: (1) the prevalence of inter-grid transitions within fine-grained data introduces complexity in capturing spatial dependencies among grid cells on a global scale. (2) stemming from dynamic temporal dependencies. To address these challenges, we propose the multi-scaling hybrid model (MSHM) as a novel approach. Initially, a multi-directional convolutional layer is first used to acquire high-level depictions for each cell to retrieve the semantic attributes of the road network from local and global aspects. Next, we incorporate the characteristics of the road network and coarse-grained flow features to regularize the local and global spatial distribution modeling of road-relative traffic flow using an enhanced deep super-resolution (EDSR) technique. Benefiting from the EDSR method, our approach can generate high-quality fine-grained traffic flow maps. Furthermore, to continuously provide accurate TTE over time by leveraging well-designed multi-scale feature modeling, we incorporate a multi-scale feature expression of each road segment, capturing intricate details and important features at different scales to optimize the TTE. We conducted comprehensive trials on two real-world datasets, BJTaxi and NYCTaxi, aiming to achieve superior results compared to baseline methods.

在智能交通系统(ITS)中,实现精确的旅行时间估算(TTE)至关重要,就像路线规划一样。精确预测不同城市区域的旅行时间至关重要,而这些特权的一个基本要求是掌握城市的细粒度知识。与之前局限于粗粒度数据的研究不同,我们将交通流量预测的范围扩大到了细粒度,这就带来了明确的挑战:(1)细粒度数据中普遍存在的网格间转换,为捕捉全球范围内网格单元间的空间依赖关系带来了复杂性。(2) 源自动态时间依赖性。为了应对这些挑战,我们提出了多尺度混合模型(MSHM)作为一种新方法。首先,使用多向卷积层获取每个单元的高层描述,从局部和全局两方面检索路网的语义属性。接下来,我们结合路网特征和粗粒度流量特征,利用增强型深度超分辨率(EDSR)技术对道路相关交通流的局部和全局空间分布建模进行正则化处理。得益于 EDSR 方法,我们的方法可以生成高质量的细粒度交通流地图。此外,为了利用精心设计的多尺度特征建模,持续提供准确的交通流量预测,我们对每个路段进行了多尺度特征表达,捕捉不同尺度上错综复杂的细节和重要特征,以优化交通流量预测。我们在两个真实世界数据集(BJTaxi 和 NYCTaxi)上进行了全面试验,旨在取得优于基线方法的结果。
{"title":"Multi-scale learning for fine-grained traffic flow-based travel time estimation prediction","authors":"Zain Ul Abideen,&nbsp;Xiaodong Sun,&nbsp;Chao Sun","doi":"10.1111/coin.12693","DOIUrl":"https://doi.org/10.1111/coin.12693","url":null,"abstract":"<p>In intelligent transportation systems (ITS), achieving accurate travel time estimation (TTE) is paramount, much like route planning. Precisely predicting travel time across different urban areas is vital, and an essential requirement for these privileges is having fine-grained knowledge of the city. In contrast to prior studies that are restricted to coarse-grained data, we broaden the scope of traffic flow forecasting to fine granularity, which provokes explicit challenges: (1) the prevalence of inter-grid transitions within fine-grained data introduces complexity in capturing spatial dependencies among grid cells on a global scale. (2) stemming from dynamic temporal dependencies. To address these challenges, we propose the multi-scaling hybrid model (MSHM) as a novel approach. Initially, a multi-directional convolutional layer is first used to acquire high-level depictions for each cell to retrieve the semantic attributes of the road network from local and global aspects. Next, we incorporate the characteristics of the road network and coarse-grained flow features to regularize the local and global spatial distribution modeling of road-relative traffic flow using an enhanced deep super-resolution (EDSR) technique. Benefiting from the EDSR method, our approach can generate high-quality fine-grained traffic flow maps. Furthermore, to continuously provide accurate TTE over time by leveraging well-designed multi-scale feature modeling, we incorporate a multi-scale feature expression of each road segment, capturing intricate details and important features at different scales to optimize the TTE. We conducted comprehensive trials on two real-world datasets, BJTaxi and NYCTaxi, aiming to achieve superior results compared to baseline methods.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data 利用浅层神经网络检测智能手机惯性传感器数据中的人类行为偏差
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-09-17 DOI: 10.1111/coin.12699
Sakshi, M. P. S. Bhatia, Pinaki Chakraborty

The integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real-time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.

不同移动边缘计算(MEC)应用的集成大大提升了安全和监控领域的水平,其中人类活动识别(HAR)是一项重要应用。智能手机中的各种传感器为监控应用收集和分析数据提供了便利,使其在人类活动识别(HAR)方面发挥了重要作用。此外,MEC 还可用于自动监控,对限制区域进行智能监控,以识别和应对不受欢迎或可疑的活动。这项研究利用智能手机中的运动传感器开发了一个系统,用于识别人类的异常活动。人们的智能手机被用来监控可疑活动和常规活动。我们收集了被归类为可疑或常规的各种行为的信息。当人做出某个动作时,智能手机会记录一系列感官数据,从基本数据中分析出重要的模式,然后结合来自不同传感器的信息确定人在做什么。为了准备数据,来自不同传感器的信息要与共享的时间轴保持一致。在这项研究中,我们在同步数据上使用了滑动窗口方法,将序列输入 LSTM 和 CNN 模型。这些模型包括 LSTM 和 CNN 的初始层,可自动发现人类活动顺序中的重要模式。我们将 SVM 与浅层神经网络提取的特征相结合,建立了一个可预测可疑活动的混合模型。最后,我们使用一个新的实时数据集对 LSTM、CNN 和新的浅层混合神经网络进行了比较。CNN 和 SVM 混合模型的准确率达到了 94.43%。此外,滑动窗口方法的有效性也得到了证实,准确率提高了 4.28%。
{"title":"Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data","authors":"Sakshi,&nbsp;M. P. S. Bhatia,&nbsp;Pinaki Chakraborty","doi":"10.1111/coin.12699","DOIUrl":"https://doi.org/10.1111/coin.12699","url":null,"abstract":"<p>The integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real-time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
期刊
Computational Intelligence
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1