The COVID-19 epidemic has spread throughout the world and poses a serious threat to human health. Any technical device that provides the accurate and rapid automated diagnosis of COVID-19 can be extremely beneficial to healthcare providers. A new workflow for performing automated diagnosis is proposed in this paper. The proposed methods are built on a well-designed framework, two kinds of CNN architectures including a custom CNN and a pre-trained CNN are utilized to verify the effectiveness of the focal loss function. According to the experimental findings, both CNNs that were enhanced with the focal loss function converged faster and achieved higher accuracy on the test set, outperformed the models that utilized cross-entropy loss that does not consider the class-imbalanced issue in the multi-class image classification with imbalanced Chest X-ray (CXR) image datasets. In addition, image enhancement techniques turned out to be very helpful for enhancing the CXR image signatures to achieve better performance in our work.
{"title":"Convolutional neural networks for imbalanced chest x-ray images classification","authors":"Qi Ouyang","doi":"10.1117/12.2656005","DOIUrl":"https://doi.org/10.1117/12.2656005","url":null,"abstract":"The COVID-19 epidemic has spread throughout the world and poses a serious threat to human health. Any technical device that provides the accurate and rapid automated diagnosis of COVID-19 can be extremely beneficial to healthcare providers. A new workflow for performing automated diagnosis is proposed in this paper. The proposed methods are built on a well-designed framework, two kinds of CNN architectures including a custom CNN and a pre-trained CNN are utilized to verify the effectiveness of the focal loss function. According to the experimental findings, both CNNs that were enhanced with the focal loss function converged faster and achieved higher accuracy on the test set, outperformed the models that utilized cross-entropy loss that does not consider the class-imbalanced issue in the multi-class image classification with imbalanced Chest X-ray (CXR) image datasets. In addition, image enhancement techniques turned out to be very helpful for enhancing the CXR image signatures to achieve better performance in our work.","PeriodicalId":319882,"journal":{"name":"Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115381784","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}
Under high-tech conditions, the communication coverage is wide, the amount of information transmission is large, the structure is complex, the timeliness requirements are high, and the accuracy guarantee is difficult. In view of the new problems faced by the communication network operation and maintenance, relying on the network observability system and massive operation and maintenance integration of data resources, explore the data-driven multi-agent decision-making technology, and improve the whole process communication planning guarantee, event analysis and disposal, and prediction and decision-making ability, obtain and maintain information superiority under the fierce information confrontation.
{"title":"Research on data-driven multi-agent decision-making technology for communication network operation and maintenance","authors":"Wang Lu, Zhang Tao, Le Hengzhi, Zhu Bo","doi":"10.1117/12.2655825","DOIUrl":"https://doi.org/10.1117/12.2655825","url":null,"abstract":"Under high-tech conditions, the communication coverage is wide, the amount of information transmission is large, the structure is complex, the timeliness requirements are high, and the accuracy guarantee is difficult. In view of the new problems faced by the communication network operation and maintenance, relying on the network observability system and massive operation and maintenance integration of data resources, explore the data-driven multi-agent decision-making technology, and improve the whole process communication planning guarantee, event analysis and disposal, and prediction and decision-making ability, obtain and maintain information superiority under the fierce information confrontation.","PeriodicalId":319882,"journal":{"name":"Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176555","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}
Jin Yunpeng, Ou Weiyou, Li Haiyang, Li Kai, Jiang Jieteng, L. Chunmei
The rodent infestation problem is currently one of the important factors in the degradation of grassland in the Sanjiangyuan area. We need to infer the degradation of grassland by the area of grassland being gnawed, and thus provide help for grassland restoration work. To this end we have designed a DeeplabV3+ based mouse infestation scene segmentation method. On the basis of Deeplabv3+, different backbone feature extraction networks are adopted, and attention mechanism is introduced into the backbone to improve the accuracy of feature extraction and solve the problem of sample imbalance in our self-made dataset. For the training and validation of this network, we used a self-developed photographed and produced dataset of the distribution of mouse holes in the grassland pastures of Haibei, Qinghai Province, which contains various features of plateau mouse infestation. The model improvement resulted in a significant reduction in the training time of Deeplabv3+ on this dataset, and a certain degree of improvement in segmentation accuracy.
{"title":"Improving Deeplabv3+ for highland mouse holes segmentation scenarios","authors":"Jin Yunpeng, Ou Weiyou, Li Haiyang, Li Kai, Jiang Jieteng, L. Chunmei","doi":"10.1117/12.2655924","DOIUrl":"https://doi.org/10.1117/12.2655924","url":null,"abstract":"The rodent infestation problem is currently one of the important factors in the degradation of grassland in the Sanjiangyuan area. We need to infer the degradation of grassland by the area of grassland being gnawed, and thus provide help for grassland restoration work. To this end we have designed a DeeplabV3+ based mouse infestation scene segmentation method. On the basis of Deeplabv3+, different backbone feature extraction networks are adopted, and attention mechanism is introduced into the backbone to improve the accuracy of feature extraction and solve the problem of sample imbalance in our self-made dataset. For the training and validation of this network, we used a self-developed photographed and produced dataset of the distribution of mouse holes in the grassland pastures of Haibei, Qinghai Province, which contains various features of plateau mouse infestation. The model improvement resulted in a significant reduction in the training time of Deeplabv3+ on this dataset, and a certain degree of improvement in segmentation accuracy.","PeriodicalId":319882,"journal":{"name":"Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129018888","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}
The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture.
{"title":"Mobile resources big data deduplication and offloading algorithm based on new computing network architecture","authors":"Qi Xiong","doi":"10.1117/12.2655925","DOIUrl":"https://doi.org/10.1117/12.2655925","url":null,"abstract":"The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture.","PeriodicalId":319882,"journal":{"name":"Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115844005","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：