Neural Computing & Applications最新文献_第5页

DCU-Net: a dual-channel U-shaped network for image splicing forgery detection. DCU-Net:用于图像拼接伪造检测的双通道u型网络。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-021-06329-4

Hongwei Ding, Leiyang Chen, Qi Tao, Zhongwang Fu, Liang Dong, Xiaohui Cui

{"title":"DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.","authors":"Hongwei Ding, Leiyang Chen, Qi Tao, Zhongwang Fu, Liang Dong, Xiaohui Cui","doi":"10.1007/s00521-021-06329-4","DOIUrl":"https://doi.org/10.1007/s00521-021-06329-4","url":null,"abstract":"The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 7","pages":"5015-5031"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06329-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10275922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 17

An intelligent traceability method of water pollution based on dynamic multi-mode optimization. 基于动态多模式优化的水污染智能溯源方法。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07002-0

Qinghua Wu, Bin Wu, Xuesong Yan

{"title":"An intelligent traceability method of water pollution based on dynamic multi-mode optimization.","authors":"Qinghua Wu, Bin Wu, Xuesong Yan","doi":"10.1007/s00521-022-07002-0","DOIUrl":"https://doi.org/10.1007/s00521-022-07002-0","url":null,"abstract":"Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source's characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution's real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes' distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions' number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm's historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm's effectiveness in solving problems-accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 3","pages":"2059-2076"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10537119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 3

Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data. 变压器迁移学习情绪检测模型:在大数据中同步社会认同情绪和自我报告情绪。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08276-8

Sanghyub John Lee, JongYoon Lim, Leo Paas, Ho Seok Ahn

{"title":"Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data.","authors":"Sanghyub John Lee, JongYoon Lim, Leo Paas, Ho Seok Ahn","doi":"10.1007/s00521-023-08276-8","DOIUrl":"https://doi.org/10.1007/s00521-023-08276-8","url":null,"abstract":"Tactics to determine the emotions of authors of texts such as Twitter messages often rely on multiple annotators who label relatively small data sets of text passages. An alternative method gathers large text databases that contain the authors' self-reported emotions, to which artificial intelligence, machine learning, and natural language processing tools can be applied. Both approaches have strength and weaknesses. Emotions evaluated by a few human annotators are susceptible to idiosyncratic biases that reflect the characteristics of the annotators. But models based on large, self-reported emotion data sets may overlook subtle, social emotions that human annotators can recognize. In seeking to establish a means to train emotion detection models so that they can achieve good performance in different contexts, the current study proposes a novel transformer transfer learning approach that parallels human development stages: (1) detect emotions reported by the texts' authors and (2) synchronize the model with social emotions identified in annotator-rated emotion data sets. The analysis, based on a large, novel, self-reported emotion data set (n = 3,654,544) and applied to 10 previously published data sets, shows that the transfer learning emotion model achieves relatively strong performance.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 15","pages":"10945-10956"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9721080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 4

State-of-the-art session key generation on priority-based adaptive neural machine (PANM) in telemedicine. 基于优先级的自适应神经机(PANM)的远程医疗会话密钥生成。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08169-2

Joydeep Dey

{"title":"State-of-the-art session key generation on priority-based adaptive neural machine (PANM) in telemedicine.","authors":"Joydeep Dey","doi":"10.1007/s00521-022-08169-2","DOIUrl":"https://doi.org/10.1007/s00521-022-08169-2","url":null,"abstract":"Telemedicine is one of the safest methods to provide healthcare facilities to the remote patients with the help of digitization. In this paper, state-of-the-art session key has been proposed based on the priority oriented neural machines followed by its validation. State-of-the-art technique can be mentioned as newer scientific method. Soft computing has been extensively used and modified here under the ANN domain. Telemedicine facilitates secure data communication between the patients and the doctors regarding their treatments. The best fitted hidden neuron can contribute only in the formation of the neural output. Minimum correlation was taken into consideration under this study. Hebbian learning rule was applied on both the patient's neural machine and the doctor's neural machine. Lesser iterations were needed in the patient's machine and the doctor's machine for the synchronization. Thus, the key generation time has been shortened here which were 4.011 ms, 4.324 ms, 5.338 ms, 5.691 ms, and 6.105 ms for 56 bits, 128 bits, 256 bits, 512 bits, and 1024 bits of state-of-the-art session keys, respectively. Statistically, different key sizes of the state-of-the-art session keys were tested and accepted. Derived value-based function had yielded successful outcomes too. Partial validations with different mathematical hardness had been imposed here too. Thus, the proposed technique is suitable for the session key generation and authentication in the telemedicine in order to preserve the patients' data privacy. This proposed method has been highly protective against numerous data attacks inside the public networks. Partial transmission of the state-of-the-art session key disables the intruders to decode the same bit patterns of the proposed set of keys.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 13","pages":"9517-9533"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10032630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9752892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Development of a compressed FCN architecture for semantic segmentation using Particle Swarm Optimization. 基于粒子群优化的语义分割压缩FCN架构的开发。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08324-3

Mohit Agarwal, Suneet K Gupta, K K Biswas

引用次数: 2

IoT-based health monitoring system to handle pandemic diseases using estimated computing. 基于物联网的健康监测系统，使用估计计算处理大流行性疾病。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-05-09 DOI: 10.1007/s00521-023-08625-7

Lidia Ogiela, Arcangelo Castiglione, Brij B Gupta, Dharma P Agrawal

引用次数: 1

A flexible framework for anomaly Detection via dimensionality reduction. 通过降维实现异常检测的灵活框架。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-021-05839-5

Alireza Vafaei Sadr, Bruce A Bassett, M Kunz

引用次数: 6

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images. 轻量级ResGRU:基于深度学习的基于多模态胸片图像的SARS-CoV-2 (COVID-19)预测及其严重程度分类

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08200-0

Mughees Ahmad, Usama Ijaz Bajwa, Yasar Mehmood, Muhammad Waqas Anwar

{"title":"Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images.","authors":"Mughees Ahmad, Usama Ijaz Bajwa, Yasar Mehmood, Muhammad Waqas Anwar","doi":"10.1007/s00521-023-08200-0","DOIUrl":"https://doi.org/10.1007/s00521-023-08200-0","url":null,"abstract":"The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 13","pages":"9637-9655"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9330135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 3

Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction. 基于协调PSO-SVM算法的集成滤波器优化听力障碍预测。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08244-2

Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali

{"title":"Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction.","authors":"Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali","doi":"10.1007/s00521-023-08244-2","DOIUrl":"https://doi.org/10.1007/s00521-023-08244-2","url":null,"abstract":"Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 14","pages":"10473-10496"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9372460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. 基于多阈值图像分割的新冠肺炎诊断Harris hawks优化。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08078-4

Mohammad Hashem Ryalat, Osama Dorgham, Sara Tedmori, Zainab Al-Rahamneh, Nijad Al-Najdawi, Seyedali Mirjalili

{"title":"Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.","authors":"Mohammad Hashem Ryalat, Osama Dorgham, Sara Tedmori, Zainab Al-Rahamneh, Nijad Al-Najdawi, Seyedali Mirjalili","doi":"10.1007/s00521-022-08078-4","DOIUrl":"https://doi.org/10.1007/s00521-022-08078-4","url":null,"abstract":"Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 9","pages":"6855-6873"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9376951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 7