Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence最新文献_第3页

Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model 基于MS-LSTM模型的多数据融合上市企业营销预测

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446169

Ziyang Pan, Zhishan Huang, Xiaowen Lin, Songxia Li, Huanze Zeng, Daifeng Li

{"title":"Multi-data Fusion Based Marketing Prediction of Listed Enterprise Using MS-LSTM Model","authors":"Ziyang Pan, Zhishan Huang, Xiaowen Lin, Songxia Li, Huanze Zeng, Daifeng Li","doi":"10.1145/3446132.3446169","DOIUrl":"https://doi.org/10.1145/3446132.3446169","url":null,"abstract":"The intelligent analysis and marketing prediction of high-tech enterprises based on artificial intelligence is a hot topic in the field. Most of the existing researches mainly focus on taking the internal structural features of the enterprise as the starting point to study the influencing factors of enterprise marketing trends. Different with previous studies, This research attempts to simulate the analyzing and decision processes of domain experts towards issues of enterprise operations by using artificial intelligent. One main challenge of the research is to simulate domain experts’ behaviors of analyzing multi-data including both structured and unstructured data, especially how to extract knowledge, patterns and import factors from unstructured data to support enterprise decisions. In order to solve the challenge mentioned above, an intelligent analysis framework MS-LSTM based on business management theory is proposed. Firstly, MS-LSTM collects, processes and analyzes multi-data by using an encoder strategy module, which contains more than 10 strategy models such as normalization, one-hot, distribution fitting, time series completion, semantic encoding, Bert, etc., providing high quality input for downstream tasks. Finally, a LSTM based time series processing model is proposed to make marketing prediction based on upstream processed multi-source data. Extensive experiments are conducted to verify the proposed model. Compared with traditional benchmark model, the proposed MS-LSTM could efficiently extract meaningful knowledge and patterns, which could be explained to a certain extent by business management theory, from multi-source data. The model has improved the accuracy of enterprise trend prediction by 19.3 times compared with state-of-art baselines, which further verify the application values of the research.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125524025","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

Early Diagnosis of Alzheimer's Disease Using Hybrid Word Embedding and Linguistic Characteristics 基于混合词嵌入和语言特征的阿尔茨海默病早期诊断

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446197

Yangyang Li

{"title":"Early Diagnosis of Alzheimer's Disease Using Hybrid Word Embedding and Linguistic Characteristics","authors":"Yangyang Li","doi":"10.1145/3446132.3446197","DOIUrl":"https://doi.org/10.1145/3446132.3446197","url":null,"abstract":"Early detection of Alzheimer's Disease (AD) is of great importance to the benefits of AD patients, including lessening symptoms and alleviating the financial burden of health care. As one of the leading signs of AD, changes of language capability can potentially be used for early diagnosis of AD. In this paper, I develop an automatic and accurate diagnostic model by using the linguistic characteristics of the subjects and hybrid word embedding. I detected linguistic features such as pauses, unintelligible words, repetitions, etc. from transcripts of interviews. Then I create a text embedding by combining word vectors from Doc2vec and ELMo. Moreover, by tuning hyperparameters of the machine learning pipeline (e.g., model regularization parameter, learning rate and vector size of Doc2vec, and vector size of ELMo), I achieve 91% classification accuracy and an Area Under the Curve (AUC) of 97% for distinguishing early AD from healthy subjects. Compared with the method which only uses word count, I improved the absolute detection accuracy by 10%, and the absolute AUC by 9%. Moreover, I study the stability of the model by repeating experiment and find out that the model is stable even though my training data is split randomly. My algorithms have high detection accuracy and are stable. This model could be used as a large-scale screening method for AD, as well as a complement to doctors’ detection of AD.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130655538","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 Graph Convolution model considering label co-occurrence LC_GCN 考虑标签共现的图卷积模型LC_GCN

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446155

Chaoshun Chang, Xiaoyong Li, Yali Gao

引用次数: 0

Ensemble Neural Networks with Random Weights for Classification Problems 基于随机权值的集成神经网络分类问题

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446147

Ye Liu, Weipeng Cao, Zhong Ming, Qiang Wang, Jiyong Zhang, Zhiwu Xu

引用次数: 3

Text Categorization by Multi-instance Multi-label and Momentum Stochastic Gradient Descent Strategy 基于多实例多标签和动量随机梯度下降策略的文本分类

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446158

Xiang Bao, Guifeng Liu, Manrong Wang

引用次数: 1

Generating Adversarial Examples Based on Subarea Noise Texture for Efficient Black-Box Attacks 基于子区域噪声纹理生成对抗样本的高效黑盒攻击

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446174

Zhijian Chen, Jing Liu, Hui Chen

引用次数: 0

Research the Method of Joint Segmentation and POS Tagging for Tibetan using BiGRU-CRF 基于BiGRU-CRF的藏文联合分词与词性标注方法研究

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446395

Zhixiang Luo, Jie Zhu, Zhensong Li, Saihu Liu

引用次数: 1

TDOA Parameter Estimation based on VMD-WTD in Satellite Interference Location 卫星干扰定位中基于VMD-WTD的TDOA参数估计

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446156

Shibing Zhu, Haifeng Shuai, Changqing Li, Rui Liu

{"title":"TDOA Parameter Estimation based on VMD-WTD in Satellite Interference Location","authors":"Shibing Zhu, Haifeng Shuai, Changqing Li, Rui Liu","doi":"10.1145/3446132.3446156","DOIUrl":"https://doi.org/10.1145/3446132.3446156","url":null,"abstract":"Satellite interference source location system is one of the important means of satellite communication anti-interference. High-precision satellite interference source positioning technology can accurately lock the location of the interference source and take measures to remove the interference. Time difference of arrival (TDOA) parameter estimation is a key link in the satellite interference source location system, and the accuracy of TDOA parameter estimation directly affects the location accuracy. This paper proposes a new TDOA parameter estimation algorithm that combines variational model decomposition (VMD) and wavelet threshold denoising (WTD). Firstly, the signal is adaptively decomposed into multiple components through the VMD algorithm to ensure the preservation of the original signal during the denoising process, then an improved WTD algorithm is used to remove the influence of noise in the satellite reception signal. Finally, accurate TDOA parameters are obtained. Numerical simulations show that the accuracy of the improved algorithm are better than conventional methods, thereby indirectly improving the accuracy of satellite interference source location.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131375394","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 Jiuqi Game Strategy Based on Chess Shape 基于棋形的九七博弈策略研究

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446175

Qiangwang Shen, Meng Ding, Shuqin Li, Wentao Du, Wenlong Zhao

引用次数: 0

The Design and Development of Virtual Simulation Experiment for Online learning 在线学习虚拟仿真实验系统的设计与开发

Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2020-12-24 DOI: 10.1145/3446132.3446164

Ran Wang, Jinglu Liu, Qijing Yu

{"title":"The Design and Development of Virtual Simulation Experiment for Online learning","authors":"Ran Wang, Jinglu Liu, Qijing Yu","doi":"10.1145/3446132.3446164","DOIUrl":"https://doi.org/10.1145/3446132.3446164","url":null,"abstract":"With the development of artificial intelligence, virtual reality, and multimedia, numerous digital interactive learning resources are produced for the purpose of e-learning in which the virtual experiment plays a vital role. It provides not only an advanced tool but also an open platform with high-quality studying resources for learners to conduct research-oriented learning, self-directed experiment, and innovative practice. This method increases the quality of personnel training and adds extra energy and motivation to the reformation of practice teaching and laboratory construction. In this paper, a complete solution of virtual experiments is proposed which includes both overall design and module design. The overall design of the virtual experiment can be divided into three modules i.e. hardware platform design, software platform design, and unified access portal design, which are all illustrated. In the end, the results of virtual experiments are shown. These steps, which should require the generation of the final output from the styled paper, are mentioned here in this paragraph. Firstly, users have to run \"Reference Numbering\" from the \"Reference Elements\" menu and this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the \"Cross Linking\" menu.","PeriodicalId":125388,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349549","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