Neural Computing and Applications最新文献

Firearm detection using DETR with multiple self-coordinated neural networks 利用 DETR 和多个自协调神经网络进行枪支探测

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10373-1

Romulo Augusto Aires Soares, Alexandre Cesar Muniz de Oliveira, Paulo Rogerio de Almeida Ribeiro, Areolino de Almeida Neto

引用次数: 0

Potential analysis of radiographic images to determine infestation of rice seeds 利用放射影像分析确定水稻种子虫害的可能性

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10379-9

Ivan David Briceño-Pinzón, Raquel Maria de Oliveira Pires, Geraldo Andrade Carvalho, Flávia Barbosa Silva Botelho, Júlia Lima Baute, Marcela Carlota Nery

{"title":"Potential analysis of radiographic images to determine infestation of rice seeds","authors":"Ivan David Briceño-Pinzón, Raquel Maria de Oliveira Pires, Geraldo Andrade Carvalho, Flávia Barbosa Silva Botelho, Júlia Lima Baute, Marcela Carlota Nery","doi":"10.1007/s00521-024-10379-9","DOIUrl":"https://doi.org/10.1007/s00521-024-10379-9","url":null,"abstract":"The X-ray method, together with image analysis tools, has been used to evaluate the internal structures of seeds and correlate them with the physical, physiological and sanitary quality, providing significant and accurate results. The objective of this study was to analyze radiographic images of rice seeds infested by the rice weevil Sitophilus oryzae (Linnaeus, 1763) (Coleoptera: Curculionidae). Rice seed samples from three different cultivars were infested with S. oryzae for 90 days. Next, seed samples collected at random were analyzed by X-ray testing. The radiographic images were analyzed by ImageJ® software to extract color and shape features. Scanning electron microscopy analyses were also performed. The results showed that X-ray testing was effective in detecting infestation. The gray distribution histograms revealed differences between healthy seeds and those infested by adult insects or empty seeds, confirmed by the significant differences obtained for the area and relative and integrated density variables. The study demonstrated that the analysis of radiographic images can provide quantitative information on insect infestation of rice seeds, which is useful in the evaluation of seed quality and for detecting the presence of pests in rice seeds.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recommendation systems with user and item profiles based on symbolic modal data 基于符号模态数据的具有用户和项目特征的推荐系统

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10411-y

Delmiro D. Sampaio-Neto, Telmo M. Silva Filho, Renata M. C. R. Souza

{"title":"Recommendation systems with user and item profiles based on symbolic modal data","authors":"Delmiro D. Sampaio-Neto, Telmo M. Silva Filho, Renata M. C. R. Souza","doi":"10.1007/s00521-024-10411-y","DOIUrl":"https://doi.org/10.1007/s00521-024-10411-y","url":null,"abstract":"Most recommendation systems are implemented using numerical or categorical data, that is, traditional data. This type of data can be a limiting factor when used to model complex concepts where there is internal variability or internal structure in the data. To overcome these limitations, symbolic data is used, where data can be represented by different types of values, such as intervals, lists, or histograms. This work introduces a single approach to constructing recommendation systems based on content or based on collaborative filtering using modal variables for users and items. In the content-based system, user profiles and item profiles are created from modal representations of their features, and a list of items is matched against a user profile. For collaborative filtering, user profiles are built, and users are grouped to form a neighborhood, products rated by users of this neighborhood are recommended based on the similarity between the neighbor and the user who will receive the recommendation. Experiments are carried out, using a movie domain dataset, to evaluate the effectiveness of the proposed approach. The outcomes suggest our ability to generate ranked lists of superior quality compared to previous methods utilizing symbolic data. Specifically, the lists created through the proposed method exhibit higher normalized discounted cumulative gain and, in qualitative terms, showcase more diverse content.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning 情感诱发 VR 环境中基于脑电图的有效情感指标：来自机器学习的证据

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10240-z

Ivonne Angelica Castiblanco Jimenez, Elena Carlotta Olivetti, Enrico Vezzetti, Sandro Moos, Alessia Celeghin, Federica Marcolin

{"title":"Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning","authors":"Ivonne Angelica Castiblanco Jimenez, Elena Carlotta Olivetti, Enrico Vezzetti, Sandro Moos, Alessia Celeghin, Federica Marcolin","doi":"10.1007/s00521-024-10240-z","DOIUrl":"https://doi.org/10.1007/s00521-024-10240-z","url":null,"abstract":"This study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated defect identification in coherent diffraction imaging with smart continual learning 利用智能持续学习技术自动识别相干衍射成像中的缺陷

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10415-8

Orcun Yildiz, Krishnan Raghavan, Henry Chan, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka

{"title":"Automated defect identification in coherent diffraction imaging with smart continual learning","authors":"Orcun Yildiz, Krishnan Raghavan, Henry Chan, Mathew J. Cherukara, Prasanna Balaprakash, Subramanian Sankaranarayanan, Tom Peterka","doi":"10.1007/s00521-024-10415-8","DOIUrl":"https://doi.org/10.1007/s00521-024-10415-8","url":null,"abstract":"X-ray Bragg coherent diffraction imaging is a powerful technique for 3D materials characterization. However, obtaining X-ray diffraction data is difficult and computationally intensive, motivating the need for automated processing of coherent diffraction images, with the goal of minimizing the number of X-ray datasets needed. We automate a machine learning approach to identify crystalline line defects in samples from the raw coherent diffraction data, in a workflow coupling coherent diffraction data generation with training and inference of deep neural network defect classifiers. In particular, we adopt a continual learning approach, where we generate training data as needed based on the accuracy of the defect classifier instead of generating all training data a priori. Moreover, we develop a novel data generation mechanism to improve the efficiency of defect identification beyond the previously published continual learning approach. We call the improved method smart continual learning. The results show that our approach improves the accuracy of defect classifiers and reduces training data requirements by up to 98% compared with prior approaches.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge detective weights initialization on Darknet-19 model for YOLOv2-based facemask detection 基于 YOLOv2 的面罩检测中 Darknet-19 模型的边缘检测权重初始化

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10427-4

Richard Ningthoujam, Keisham Pritamdas, Loitongbam Surajkumar Singh

引用次数: 0

End-to-end entity extraction from OCRed texts using summarization models 使用摘要模型从 OCR 文本中进行端到端实体提取

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10422-9

Pedro A. Villa-García, Raúl Alonso-Calvo, Miguel García-Remesal

{"title":"End-to-end entity extraction from OCRed texts using summarization models","authors":"Pedro A. Villa-García, Raúl Alonso-Calvo, Miguel García-Remesal","doi":"10.1007/s00521-024-10422-9","DOIUrl":"https://doi.org/10.1007/s00521-024-10422-9","url":null,"abstract":"A novel methodology is introduced for extracting entities from noisy scanned documents by using end-to-end data and reformulating the entity extraction task as a text summarization problem. This approach offers two significant advantages over traditional entity extraction methods while maintaining comparable performance. First, it utilizes preexisting data to construct datasets, thereby eliminating the need for labor-intensive annotation procedures. Second, it employs multitask learning, enabling the training of a model via a single dataset. To evaluate our approach against state-of-the-art methods, we adapted three commonly used datasets, namely, Conference on Natural Language Learning (CoNLL++), few-shot named entity recognition (Few-NERD), and WikiNEuRal domain adaptation (WikiNEuRal + DA), to the format required by our methodology. We subsequently fine-tuned four sequence-to-sequence models: text-to-text transfer transformer (T5), fine-tuned language net T5 (FLAN-T5), bidirectional autoregressive transformer (BART), and pretraining with extracted gap sentences for abstractive summarization sequence-to-sequence models (PEGASUS). The results indicate that, in the absence of optical character recognition (OCR) noise, the BART model performs comparably to state-of-the-art methods. Furthermore, the performance degradation was limited to 3.49–5.23% when 39–62% of the sentences contained OCR noise. This performance is significantly superior to that of previous studies, which reported a 10–20% decrease in the F1 score with texts that had a 20% OCR error rate. Our experimental results demonstrate that a single model trained via our methodology can reliably extract entities from noisy OCRed texts, unlike existing state-of-the-art approaches, which require separate models for correcting OCR errors and extracting entities.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation AD-Net：基于注意力的扩张卷积残差网络与引导解码器，用于稳健的皮损分割

Neural Computing and Applications Pub Date : 2024-09-19 DOI: 10.1007/s00521-024-10362-4

Asim Naveed, Syed S. Naqvi, Tariq M. Khan, Shahzaib Iqbal, M. Yaqoob Wani, Haroon Ahmed Khan

{"title":"AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation","authors":"Asim Naveed, Syed S. Naqvi, Tariq M. Khan, Shahzaib Iqbal, M. Yaqoob Wani, Haroon Ahmed Khan","doi":"10.1007/s00521-024-10362-4","DOIUrl":"https://doi.org/10.1007/s00521-024-10362-4","url":null,"abstract":"In computer-aided diagnosis tools employed for skin cancer treatment and early diagnosis, skin lesion segmentation is important. However, achieving precise segmentation is challenging due to inherent variations in appearance, contrast, texture, and blurry lesion boundaries. This research presents a robust approach utilizing a dilated convolutional residual network, which incorporates an attention-based spatial feature enhancement block (ASFEB) and employs a guided decoder strategy. In each dilated convolutional residual block, dilated convolution is employed to broaden the receptive field with varying dilation rates. To improve the spatial feature information of the encoder, we employed an attention-based spatial feature enhancement block in the skip connections. The ASFEB in our proposed method combines feature maps obtained from average and maximum-pooling operations. These combined features are then weighted using the active outcome of global average pooling and convolution operations. Additionally, we have incorporated a guided decoder strategy, where each decoder block is optimized using an individual loss function to enhance the feature learning process in the proposed AD-Net. The proposed AD-Net presents a significant benefit by necessitating fewer model parameters compared to its peer methods. This reduction in parameters directly impacts the number of labeled data required for training, facilitating faster convergence during the training process. The effectiveness of the proposed AD-Net was evaluated using four public benchmark datasets. We conducted a Wilcoxon signed-rank test to verify the efficiency of the AD-Net. The outcomes suggest that our method surpasses other cutting-edge methods in performance, even without the implementation of data augmentation strategies.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing dataless neural networks for kidney exchange variants 为肾脏交换变体设计无数据神经网络

Neural Computing and Applications Pub Date : 2024-09-18 DOI: 10.1007/s00521-024-10352-6

Sangram K. Jena, K. Subramani, Alvaro Velasquez

{"title":"Designing dataless neural networks for kidney exchange variants","authors":"Sangram K. Jena, K. Subramani, Alvaro Velasquez","doi":"10.1007/s00521-024-10352-6","DOIUrl":"https://doi.org/10.1007/s00521-024-10352-6","url":null,"abstract":"Kidney transplantation is vital for treating end-stage renal disease, impacting roughly one in a thousand Europeans. The search for a suitable deceased donor often leads to prolonged and uncertain wait times, making living donor transplants a viable alternative. However, approximately 40% of living donors are incompatible with their intended recipients. Therefore, many countries have established kidney exchange programs, allowing patients with incompatible donors to participate in “swap” arrangements, exchanging donors with other patients in similar situations. Several variants of the vertex-disjoint cycle cover problem model the above problem, which deals with different aspects of kidney exchange as required. This paper discusses several specific vertex-disjoint cycle cover variants and deals with finding the exact solution. We employ the dataless neural networks framework to establish single differentiable functions for each variant. Recent research highlights the framework’s effectiveness in representing several combinatorial optimization problems. Inspired by these findings, we propose customized dataless neural networks for vertex-disjoint cycle cover variants. We derive a differentiable function for each variant and prove that the function will attain its minimum value if an exact solution is found for the corresponding problem variant. We also provide proof of the correctness of our approach.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models 风速超分辨率和验证：通过扩散模型从ERA5到CERRA

Neural Computing and Applications Pub Date : 2024-09-18 DOI: 10.1007/s00521-024-10139-9

Fabio Merizzi, Andrea Asperti, Stefano Colamonaco

{"title":"Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models","authors":"Fabio Merizzi, Andrea Asperti, Stefano Colamonaco","doi":"10.1007/s00521-024-10139-9","DOIUrl":"https://doi.org/10.1007/s00521-024-10139-9","url":null,"abstract":"The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0