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2021 13th International Conference on Advanced Computational Intelligence (ICACI)最新文献

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RNA-binding protein sequence prediction method based on ensemble learning and data over-sampling 基于集成学习和数据过采样的rna结合蛋白序列预测方法
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435903
Xu Wang, Shunfang Wang
{"title":"RNA-binding protein sequence prediction method based on ensemble learning and data over-sampling","authors":"Xu Wang, Shunfang Wang","doi":"10.1109/ICACI52617.2021.9435903","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435903","url":null,"abstract":"RNA binding proteins play an important role in the process of post-transcription, identifying special RNA binding domains and interacting with RNA. Although many calculation methods have been proposed, most of them have the problems of insufficient features and unbalanced samples. This paper proposes a Stacking classification model composed of 4 base classifiers and 1 meta classifier. While enriching features, it extracts as much information as possible from different feature expression methods. We use the dipeptide distribution matrix to supplement the missing dipeptide position information in the amino acid composition. The sliding window method is used to balance the positive and negative samples, and the sequence length distribution is more reasonable. The results show that the Stacking classification model has a certain improvement in the accuracy of RNA-binding protein sequence prediction. At the same time, the position information contained in the dipeptide distribution matrix shows more excellent performance than amino acid composition information.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134509998","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
COVID-19 Patients Detection in Chest X-ray Images via MCFF-Net MCFF-Net在胸部x线图像中检测COVID-19患者
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435874
Wen Wang, Yutao Li, Xin Wang, Ji Li, Peng Zhang
{"title":"COVID-19 Patients Detection in Chest X-ray Images via MCFF-Net","authors":"Wen Wang, Yutao Li, Xin Wang, Ji Li, Peng Zhang","doi":"10.1109/ICACI52617.2021.9435874","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435874","url":null,"abstract":"COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). This paper proposes a deep learning model to assist medical imaging physicians in diagnosing COVID-19 cases. We designed the Parallel Channel Attention Feature Fusion Module (PCAF), and brand new structure of convolutional neural network MCFF-Net was put forward. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 96.79% for 3-class classification. Simultaneously, the precision, recall, specificity and the sensitivity for COVID-19 are both 100%. Compared with the latest state-of-art methods, the experimental results of our proposed method indicate its uniqueness.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132239755","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: 1
ArtGAN: Artwork Restoration using Generative Adversarial Networks ArtGAN:使用生成对抗网络的艺术品修复
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435888
Abhijit Adhikary, Namas Bhandari, Evan Markou, Siddharth Sachan
{"title":"ArtGAN: Artwork Restoration using Generative Adversarial Networks","authors":"Abhijit Adhikary, Namas Bhandari, Evan Markou, Siddharth Sachan","doi":"10.1109/ICACI52617.2021.9435888","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435888","url":null,"abstract":"We propose a method to recover and restore art-work that has been damaged over time due to several factors. Our method produces great results by completely removing damages in most of the images and perfectly estimating the damaged region. We achieved accurate results due to (i) a custom data augmentation technique which depicts realistic damages rather just blobs (ii) novel CResNetBlocks that subsequently upsample and downsample features to restore the image with efficient backpropagation measures, and (iii) the choice of using patch-discriminators to achieve sharpness and colorfulness. Our network architecture is a conditional Generative Adversarial Network where the generator uses a combination of adversarial loss, L1 loss and the discriminator uses binary cross-entropy loss for optimization. While the expressiveness of existing comparison methods is limited, we present our results with several metrics for future comparison and showcase some visuals of recovered artwork. PyTorch implementation is available at: https://github.connamasl91297/artgan.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609878","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: 3
Classification of strawberry diseases and pests by improved AlexNet deep learning networks 基于改进AlexNet深度学习网络的草莓病虫害分类
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435893
Cheng Dong, Zhiwang Zhang, Jun Yue, Li Zhou
{"title":"Classification of strawberry diseases and pests by improved AlexNet deep learning networks","authors":"Cheng Dong, Zhiwang Zhang, Jun Yue, Li Zhou","doi":"10.1109/ICACI52617.2021.9435893","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435893","url":null,"abstract":"To improve the classification accuracy of strawberry diseases and pests, this paper proposed an improved operator-based convolutional neural network (CNN) approach for classification of images of strawberry diseases and pests. Firstly, by using the deep learning framework of Pytorch, we fine-tuned the AlexNet model so that it was used to train the image dataset of strawberry diseases and pests. Next, combining inner product with l2-norm, we proposed a new operator to replace the inner product operator between input values and weights in the fully connected layers of the AlexNet model. Then the proposed operator was applied to classification of strawberry diseases and pests. By experimental verification, the proposed method on the independent test set for the classification accuracy has been considerably increased. Our source code is available at https://gitee.com/dc2019/improved-alexnet.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122329959","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: 5
Semantic-Guided High-Order Region Attention Embedding for Zero-Shot Learning 基于语义引导的高阶区域注意嵌入的零次学习
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435883
Rui Zhang, Xiangyu Xu, Qi Zhu
{"title":"Semantic-Guided High-Order Region Attention Embedding for Zero-Shot Learning","authors":"Rui Zhang, Xiangyu Xu, Qi Zhu","doi":"10.1109/ICACI52617.2021.9435883","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435883","url":null,"abstract":"In zero-shot learning, knowledge transfer problem is the major challenge, which can be achieved by exploring the pattern between visual and semantic space. However, only aligning the global visual features with semantic vectors may ignore some discriminative differences. The local region features are not only implicitly related with semantic vectors, but also contain more discriminative information. Besides, most of the previous methods only consider the first-order statistical features, which may fail to capture the complex relations between categories. In this paper, we propose a semantic-guided high-order region attention embedding model that leverages the second-order information of both global features and local region features via different attention modules in an end-to-end fashion. First, we devise an encoder-decoder part to reconstruct the visual feature maps guided by semantic attention. Then, the original and new feature maps are simultaneously fed into their respective following branches to calculate region attentive and global attentive features. After that, a second-order pooling module is integrated to form higher-order features. The comprehensive experiments on four popular datasets of CUB, AWA2, SUN and aPY show the efficiency of our proposed model for zero-shot learning task and a considerable improvement over the state-of-the-art methods under generalized zero-shot learning setting.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115692959","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
Research on data storage model of household electrical appliances supply chain traceability system based on blockchain 基于区块链的家电供应链溯源系统数据存储模型研究
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435913
Jian Xie, Shiyu Zhu, B. Li
{"title":"Research on data storage model of household electrical appliances supply chain traceability system based on blockchain","authors":"Jian Xie, Shiyu Zhu, B. Li","doi":"10.1109/ICACI52617.2021.9435913","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435913","url":null,"abstract":"In order to solve the problems of large amount of data storage and low query efficiency in home appliance supply chain traceability system, this paper studies a hyperledger fabric based dual storage block chain model of data on-chain and off-chain. When the total number of traceable data reaches 100000, the model is compared with the traditional model, and the results show that the query efficiency of the model is improved by at least 42.36%, which solves the problem of low efficiency of traditional data query, and can ensure the traceability reliability of supply chain data information of home appliance manufacturers.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133498480","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: 3
Investigation and Improvement of Distributed Differential Evolution Algorithm Cloudde 分布式差分进化算法Cloudde的研究与改进
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435867
Liu-Yue Luo, Lin Shi, Zhi-hui Zhan
{"title":"Investigation and Improvement of Distributed Differential Evolution Algorithm Cloudde","authors":"Liu-Yue Luo, Lin Shi, Zhi-hui Zhan","doi":"10.1109/ICACI52617.2021.9435867","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435867","url":null,"abstract":"As a kind of new emerging optimization technology, distributed evolutionary computation (DEC) algorithms have fast developed in recent years. The DEC algorithms, which make use of multiple computers or resources to enhance the optimization capabilities of algorithms, have received widespread attention. Among the DEC algorithms, a cloud-based distributed differential evolution (Cloudde) algorithm has shown excellent performance. The Cloudde has a double-layered heterogeneous distribution structure, which can run different differential evolution (DE) variants with various parameters and/or operators in different populations. Moreover, the Cloudde can adaptively migrate individuals among the populations to make best use of the computational resources among multiple populations. However, since the proposal of the Cloudde, there are still some questions remained to be discussed. The first is how to choose the basic DE algorithms to form various DE variants (i.e., the various populations). The second is how to evaluate the performance of different populations of individuals hence we can rank the populations. The third is how to design an efficient migration strategy to make full use of computing resources among multiple populations. This paper makes investigation on these issues and studies the performance of Cloudde variants with various configurations for these three aspects. The experimental results in this paper are useful for researchers who want to conduct further research on Cloulde and other related DEC algorithms. Moreover, based on the investigation results, an improved Cloudde (I-Cloudde) is proposed and the experimental results show the superiority of I-Cloudde when compared with Cloudde.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122996229","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
Service Quality Loss-aware Privacy Protection Mechanism in Edge-Cloud IoTs 边缘云物联网中的服务质量丢失感知隐私保护机制
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435865
Zice Sun, Yingjie Wang, Xiangrong Tong, Qingxian Pan, Wenyi Liu, Jiqiu Zhang
{"title":"Service Quality Loss-aware Privacy Protection Mechanism in Edge-Cloud IoTs","authors":"Zice Sun, Yingjie Wang, Xiangrong Tong, Qingxian Pan, Wenyi Liu, Jiqiu Zhang","doi":"10.1109/ICACI52617.2021.9435865","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435865","url":null,"abstract":"With the continuous development of edge computing, the application scope of mobile crowdsourcing (MCS) is constantly increasing. The distributed nature of edge computing can transmit data at the edge of processing to meet the needs of low latency. The trustworthiness of the third-party platform will affect the level of privacy protection, because managers of the platform may disclose the information of workers. Anonymous servers also belong to third-party platforms. For unreal third-party platforms, this paper recommends that workers first use the localized differential privacy mechanism to interfere with the real location information, and then upload it to an anonymous server to request services, called the localized differential anonymous privacy protection mechanism (LDNP). The two privacy protection mechanisms further enhance privacy protection, but exacerbate the loss of service quality. Therefore, this paper proposes to give corresponding compensation based on the authenticity of the location information uploaded by workers, so as to encourage more workers to upload real location information. Through comparative experiments on real data, the LDNP algorithm not only protects the location privacy of workers, but also maintains the availability of data. The simulation experiment verifies the effectiveness of the incentive mechanism.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114416619","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: 1
DNA protein binding motif prediction based on fusion of expectation pooling and LSTM 基于期望池和LSTM融合的DNA蛋白结合基序预测
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435861
Zhaofeng Li, Shunfang Wang
{"title":"DNA protein binding motif prediction based on fusion of expectation pooling and LSTM","authors":"Zhaofeng Li, Shunfang Wang","doi":"10.1109/ICACI52617.2021.9435861","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435861","url":null,"abstract":"During the process of DNA being expressed by transcription factors and ultimately generating proteins, motifs are used to label DNA sequences for transcription factors. Thus to predict DNA protein interaction is essentially a task to determine the DNA binding motif. This paper combined CNN and LSTM to the classification and prediction of DNA binding motifs. Experimental results proved that, compared with the classical CNN model, the CNN-LSTM fusion model can achieve a higher prediction accuracy for DNA motifs, for the ACC, ROC and other indicators of the latter are better than the former. Further, expectation pooling method was added to improve the recognition accuracy, which provides a feasible idea for the prediction of DNA binding motifs.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116953044","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
On fuzzy alternate control systems 模糊交替控制系统
2021 13th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2021-05-14 DOI: 10.1109/ICACI52617.2021.9435911
B. Onasanya, Yuming Feng, Wei Zhang, S. Wen, Ning Tang
{"title":"On fuzzy alternate control systems","authors":"B. Onasanya, Yuming Feng, Wei Zhang, S. Wen, Ning Tang","doi":"10.1109/ICACI52617.2021.9435911","DOIUrl":"https://doi.org/10.1109/ICACI52617.2021.9435911","url":null,"abstract":"Some researchers have worked on intermittent control and alternate control. In both cases, the choice of the control was a constant quantity. But, in real life, this situation is hard to come by for the reason that control may vary with time and circumstances owing to machine or human errors or both. Hence is the need to employ a method in which the control matrix is rather uncertain as is obtainable in real life. In this paper, we consider a more general model of both intermittent and alternate control which allows the control matrix to be fuzzy (uncertain). It turns out that both the classical intermittent and classical alternate controls are recovered in this mode.","PeriodicalId":382483,"journal":{"name":"2021 13th International Conference on Advanced Computational Intelligence (ICACI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564394","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: 2
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