Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing最新文献_第2页

Review of the Pathology, Diagnosis and Treatment for Alzheimer's Disease 阿尔茨海默病的病理、诊断和治疗进展

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448767

Xian-Guo Liu

引用次数: 0

The Diagnosis of Parkinson's Disease Based on Gait, Speech Analysis and Machine Learning Techniques 基于步态、语音分析和机器学习技术的帕金森病诊断

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448804

Yuyang Miao, X. Lou, H. Wu

{"title":"The Diagnosis of Parkinson's Disease Based on Gait, Speech Analysis and Machine Learning Techniques","authors":"Yuyang Miao, X. Lou, H. Wu","doi":"10.1145/3448748.3448804","DOIUrl":"https://doi.org/10.1145/3448748.3448804","url":null,"abstract":"Parkinson's disease (PD) is a long-term degenerative disorder of the central nervous system. The common symptoms are tremor, rigidity, slowness of movement, and difficulty with walking at early stages. Currently, PD can't be cured. And there are not really effective methods to diagnose it. However, machine learning is a new way for the diagnosis of PD. It can build a model from PD patients' dataset, which can help classify PD and healthy people. In this review, the applications of machine learning for PD diagnosis by algorithms and data are analyzed. Several machine learning classifiers are briefly introduced, including artificial neural network (ANN), support vector machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (k-NN). Next, the basis of gait analysis is introduced, including gait circle and gait data, and then, each step of the machine learning processing is focused on. Two ways are concentrated to analyze speech signals - support vector machine (SVM) and artificial neural network (ANN). This review presents that machine learning has good performances for the diagnosis of PD. However, it can only be a diagnosis tool to help doctors because of its limited generalization. In the future, people should explore more effective algorithms with better generalization.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123342044","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: 6

Decoding Categories from Human Brain Activity in the Human Visual Cortex Using the Triplet Network 利用三重网络解码人类视觉皮层中人脑活动的分类

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448769

Lulu Hu, Jingwei Li, Chi Zhang, Li Tong

{"title":"Decoding Categories from Human Brain Activity in the Human Visual Cortex Using the Triplet Network","authors":"Lulu Hu, Jingwei Li, Chi Zhang, Li Tong","doi":"10.1145/3448748.3448769","DOIUrl":"https://doi.org/10.1145/3448748.3448769","url":null,"abstract":"Decoding visual stimuli from functional magnetic resonance imaging (fMRI) is of great significance for understanding the neural mechanism of the visual information processing in the human brain. How to extract effective information from massive voxel data in the brain to predict the brain state is a problem worth discussing in fMRI. However, the inherent characteristics of small quantity and high dimensionality in fMRI data limited the performance of brain decoding. As an effective way to acquire visual information, people usually compare with the prior knowledge learned when recognizing objects, and does not need to have a complete understanding of visual information. In this paper, we proposed a new visual classification model to decode the stimulus categories from the visual information of the brain based on the triplet network. The triplet network is a model framework with a comparison mechanism similar to that of human visual recognition objects, contains three-branches weight sharing subnetworks, which are composed of fully connected networks in our model. Our results showed that the decoding accuracy is 57.5±1.86% and 44.17±1.31% for S1 and S2, respectively. S1 was about 6% higher than the best traditional machine learning classifier SVM, while S2 was nearly 3.5% higher than SVM. Our results fully confirmed the validity of comparing the differences between samples in fMRI data with small quantity.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121673231","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

A Frequency-constrained Spectrum Difference Mapping Framework for Decoding Brain Activity from Functional Magnetic Resonance Imaging Data 从功能磁共振成像数据解码脑活动的频率约束频谱差分映射框架

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448792

Qin Yu, Yulong Xiong, Haitong Tang, Shuang He, Kaiyue Liu, Ni-zhuan Wang

{"title":"A Frequency-constrained Spectrum Difference Mapping Framework for Decoding Brain Activity from Functional Magnetic Resonance Imaging Data","authors":"Qin Yu, Yulong Xiong, Haitong Tang, Shuang He, Kaiyue Liu, Ni-zhuan Wang","doi":"10.1145/3448748.3448792","DOIUrl":"https://doi.org/10.1145/3448748.3448792","url":null,"abstract":"Many studies have shown that spontaneous low-frequency oscillation is an intrinsic attribute of human brain activity based on the resting-state blood oxygen level-dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) technology. Amplitude of low-frequency fluctuations (ALFF) is an effective way to capture the low-frequency fluctuation and has a hugely wide range of applications in mental disorders, neurological diseases, occupational neuroplasticity, etc. Such approaches, however, needs further improvement in two problems: low sensitivity to low-frequency signals; noise signal interference. Based on this, this paper proposes a frequency-constrained spectrum difference mapping framework (SDMF). A frequency domain is transformed through fast Fourier transform (FFT) and divided by designating Frequency(low), Frequency(mid), and Frequency(high). Then, spectrum difference value (SDV) is calculated between the two regions as the characterization value of brain activity state. Through our experimental results, we propose that SDMF can achieve the noise reduction effect, and it is the same as the region of the spontaneous active state in the traditional ALFF. In our method, it also showed that SDMF with different metrics has achieved the suppression of the Temporal, Lingual, and enhancement of the Occipital region. All in all, SDMF is a basic framework to analyze the band constraints, and the traditional ALFF can also be included as a special mode.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131193806","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

A Cooperative Co-evolution Algorithm for Fuzzy Production Planning and Scheduling in Prefabricated Building Construction 装配式建筑施工中模糊生产计划与调度的协同进化算法

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448791

Yisong Yuan, S. Ye, Hang Yang, Lin Lin

引用次数: 1

Study on Dispatching Model of Block Economy Based-Data Mining 基于数据挖掘的块经济调度模型研究

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448990

Xiaohan Gao, Qiang Lin

引用次数: 0

Genome analysis and identification of key pathway in visceral adipose tissue from obesity-related diabetes 肥胖相关糖尿病内脏脂肪组织关键通路的基因组分析和鉴定

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448757

Yue Shi, Wentao Han, Huagen Wei, Siwei Zhou, Weizheng Kong, Li-ping Shi, Huiqun Wu

引用次数: 0

Rapid multiplexed detection of cytokines with microfluidic chip 微流控芯片快速多路检测细胞因子

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448754

Q. Lin, Shih-Mo Yang, Wenjun Zhang

引用次数: 0

High electric conducting 1D nanomaterial/polymer composite fibers for wearable biomedical sensing system 用于可穿戴生物医学传感系统的高导电性一维纳米/聚合物复合纤维

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448750

Haiyang Lu, Zhihao Yang, Long Ba

{"title":"High electric conducting 1D nanomaterial/polymer composite fibers for wearable biomedical sensing system","authors":"Haiyang Lu, Zhihao Yang, Long Ba","doi":"10.1145/3448748.3448750","DOIUrl":"https://doi.org/10.1145/3448748.3448750","url":null,"abstract":"As a fundamental material for smart biomedical suit with multicomponent sensors and signal transmission, high electric conducting polymer fibers with excellent strain durability have gained extensive interesting for years. We have developed a robust protocol for the fabrication of high conducting 1D nanomaterial/polymer composite fibers. The silver nanowires (AgNWs) and single wall carbon nanotubes (SWCNTs) were used as electric conducting elements. By using multiple coating procedure through coaxial nozzle, the 1D materials were uniformly enveloped on commercial polyamide 6 (PA6) fiber surface. The conductivity of the fibers and its stability under tensile strain were measured. It was found that AgNWs coated fiber has length resistance about 12 &OHgr;/cm and SWCNTs coated fiber has length resistance about 3600 &OHgr;/cm. Both fibers have excellent flexibility and strain stability, while the strain stability of SWCNT coated fiber is much higher that of AgNW coated fiber. The analysis indicates that the higher strain stability of SWCNTs coated fiber is attributed to the high tensile strength of carbon nanotube and mutual slip of cross covered network. The stable conductivity of both fibers enables them be used as wide varieties human wearable biomedical sensors and signal transmission lines compatible with textile technologies.","PeriodicalId":115821,"journal":{"name":"Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing","volume":"GE-23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126565378","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

Research on Crisis Warning Model of Enterprise Finance Based on Deep Learning 基于深度学习的企业财务危机预警模型研究

Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing Pub Date : 2021-01-22 DOI: 10.1145/3448748.3448775

Yeming Chen, Xinyuan Han

引用次数: 0