2022 14th International Conference on Machine Learning and Computing (ICMLC)最新文献

Arabic Medical Community Question Answering Using ON-LSTM and CNN 使用ON-LSTM和CNN的阿拉伯医学社区问题回答

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529913

Husamelddin A. M. Balla, Marisa Llorens Salvador, Sarah Jane Delany

引用次数: 0

Variational Autoencoder Based Network Embedding Algorithm For Protein Function Prediction 基于变分自编码器的蛋白质功能预测网络嵌入算法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529922

Guansong Cao, Yuan Zhu, Ming Yi

{"title":"Variational Autoencoder Based Network Embedding Algorithm For Protein Function Prediction","authors":"Guansong Cao, Yuan Zhu, Ming Yi","doi":"10.1145/3529836.3529922","DOIUrl":"https://doi.org/10.1145/3529836.3529922","url":null,"abstract":"The development of high-throughput technology has produced a large number of protein-protein interaction datasets, which provide an effective way to infer the functional annotation of proteins. However, how to make proper use of these datasets to extract effective low-dimensional feature representation of proteins for functional prediction is a challenge. Most existing network integration methods for protein function prediction have some limitations to capture complex and highly non-linear network structure information due to their design architecture. Therefore, we propose a novel multi-network embedding method deepVAE based on deep variational autoencoder (VAE), which uses the variational autoencoder to extract low-dimensional features of proteins from multiple various interactive network datasets and then trains a SVM classifier to predict protein function. Particularly, we denoise the original networks before network embedding, thus the new proposed method is called deepVAE-NE. The experiments are conducted on the yeast and human protein-protein interaction datasets and the experimental performance shows that our methods perform better than the other four compared advanced approaches, which greatly improves the accuracy of functional prediction.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129718433","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

Multi-level Feature Fusion Method for Long Text Classification 长文本分类的多层次特征融合方法

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529938

R. Lin, Lianglun Cheng, Jianfeng Deng, Tao Wang

{"title":"Multi-level Feature Fusion Method for Long Text Classification","authors":"R. Lin, Lianglun Cheng, Jianfeng Deng, Tao Wang","doi":"10.1145/3529836.3529938","DOIUrl":"https://doi.org/10.1145/3529836.3529938","url":null,"abstract":"News classification task is essentially long text classification in the field of NLP (Natural Language Processing). Long text contains a lot of hidden or topic-independent information. Moreover, BERT (Bidirectional Encoder Representations from Transformer) can only process the text with a character sequence length of 512 at most, which may lose the key information and reduce the classification effectiveness. To solve above problems, the paper puts forward a model of mutli-level feature fusion based on BERT, which is suitable for the BERT through the hierarchical decomposition of long text. Then CNN (Convolutional Neural Networks) and stacked BiLSTM (Bidirectional Long Short-term Memory) based on attention mechanism are used to capture local and contextual features of text respectively. Finally, various features are spliced for classification task. The experimental results show that the model achieves 97.4% accuracy and 97.2% F1 score on THUCNews, 1.2% accuracy and 1.6% F1 score higher than that of BERT-CNN, 1.8% accuracy and 1.4% F1 score higher than that of BERT-BiLSTM, indicating that our model can significantly improve the effectiveness of news classification.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124968606","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

Micro-expression Recognition Based on Attention-enhanced LSTM Neural Networks 基于注意增强LSTM神经网络的微表情识别

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529898

Shiqi Xu, Fen Xu

引用次数: 0

Single stock trading with deep reinforcement learning: A comparative study 单股交易与深度强化学习:比较研究

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529857

Jun Ge, Yuanqi Qin, Yaling Li, yanjia Huang, Hao Hu

引用次数: 5

A transformer-based deep learning model for evaluation of accessibility of image descriptions 基于变换的图像描述可访问性评价深度学习模型

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529856

R. Shrestha

{"title":"A transformer-based deep learning model for evaluation of accessibility of image descriptions","authors":"R. Shrestha","doi":"10.1145/3529836.3529856","DOIUrl":"https://doi.org/10.1145/3529836.3529856","url":null,"abstract":"Images have become an integral part of digital and online media and they are used for creative expression and dissemination of knowledge. To address image accessibility challenges to the visually impaired community, adequate textual image descriptions or captions are provided, which can be read through screen readers. These descriptions could be either human-authored or software-generated. It is found that most of the image descriptions provided tend to be generic, inadequate, and often unreliable making them inaccessible. There are tools, methods, and metrics used to evaluate the quality of the generated text, but almost all of them are word-similarity-based and generic. There are standard guidelines such as NCAM image accessibility guidelines to help write accessible image descriptions. However, web content developers and authors do not seem to use them much, possibly due to the lack of knowledge, undermining the importance of accessibility coupled with complexity and difficulty understanding the guidelines. To our knowledge, none of the quality evaluation techniques take into account accessibility aspects. To address this, a deep learning model based on the transformer, a most recent and most effective architecture used in natural language processing, which measures compliance of the given image description to ten NCAM guidelines, is proposed. The experimental results confirm the effectiveness of the proposed model. This work could contribute to the growing research towards accessible images not only on the web but also on all digital devices.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834272","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

Apple Leaf Disease Recognition Based on Attention Mechanics and Multi-Scale Feature Fusion 基于注意力学和多尺度特征融合的苹果叶片病害识别

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529850

Hankun Chai, Zhiqiang Guo, J. Yang

{"title":"Apple Leaf Disease Recognition Based on Attention Mechanics and Multi-Scale Feature Fusion","authors":"Hankun Chai, Zhiqiang Guo, J. Yang","doi":"10.1145/3529836.3529850","DOIUrl":"https://doi.org/10.1145/3529836.3529850","url":null,"abstract":"Early diagnosis and accurate identification of apple diseases play a major role in reducing growing costs and curbing economic losses. The diagnosis and identification of apple diseases are more difficult in the natural farming environment. A large amount of background noise in complex natural environments makes apple disease features relatively inconspicuous and makes the features of different diseases less distinguishable. A single-scale feature extraction network will be more difficult to extract effective information. In order to solve this problem, this paper proposes an apple leaf classification network based on attention mechanism and multi-scale feature fusion. First, the residual unit of ResNet50 is improved by replacing the second convolution in the residual unit with a pyramidal convolution modified by using dilated convolution to obtain multi-scale fused features. Then a channel attention module is added to the residual bypass to enhance the weighting of the disease features and improve the classification accuracy. The experiments in this paper first validate the role of the attention mechanism and pyramidal convolution separately and find that both improve the model performance. Then the combination of attention mechanism and pyramidal convolution is validated, and the optimized model has stronger noise immunity and the classification accuracy on the validation set is 94.96%. The results show that the optimized model has a better classification effect and higher robustness for apple leaf disease pictures in the natural environment.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"336 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133223292","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

Multi-step Point-of-Interest-level Crowd Flow Prediction Based on Meta Learning 基于元学习的多步兴趣点人群流量预测

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529930

Yuting Feng, Xinning Zhu, Xiaosheng Tang, Zheng Hu

{"title":"Multi-step Point-of-Interest-level Crowd Flow Prediction Based on Meta Learning","authors":"Yuting Feng, Xinning Zhu, Xiaosheng Tang, Zheng Hu","doi":"10.1145/3529836.3529930","DOIUrl":"https://doi.org/10.1145/3529836.3529930","url":null,"abstract":"Point-of-Interest-level(POI) crowd flow prediction is an important task for businesses and consumers. Based on POI-level crowd flow prediction, businesses could make more reasonable business arrangements, and consumers could make more suitable travel plans. However, POI-level crowd flow prediction is a challenging task for two aspects: 1) Compared with region-level crowd flow, the area of POI is smaller and the fluctuation of POI-level crowd flow is greater; 2) There are diverse temporal correlations of different POIs and varies over time. To tackle the above challenges, following the antoencoder architecture, we propose a multi-step POI-level crowd flow prediction model(Ms-PLCFP) to predict the crowd flow at all POIs at once. A meta learner is used to obtain meta knowledge from POI category, POI popularity, etc. Then meta-RNN+ is applied to model the relations between temporal correlations and meta knowledge so as to capture diverse temporal correlations. Furthermore, a multi-scale temporal attention mechanism which contains multiple different scales of temporal attention is employed to smooth input crowd flow at lower level and capture global dependencies of input crowd flow at higher level. We evaluated Ms-PLCFP on two real-world datasets and Ms-PLCFP achieved significant improvements over the baselines, which shows the effectiveness of our model.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123215052","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

Remote sensing based yield estimation of wheat using support vector machine (SVM) in semi-arid environment 半干旱环境下基于支持向量机的小麦遥感产量估算

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529842

Hafiza Hamrah Kanwal, I. Ahmad, Muhammad Saad Aziz

{"title":"Remote sensing based yield estimation of wheat using support vector machine (SVM) in semi-arid environment","authors":"Hafiza Hamrah Kanwal, I. Ahmad, Muhammad Saad Aziz","doi":"10.1145/3529836.3529842","DOIUrl":"https://doi.org/10.1145/3529836.3529842","url":null,"abstract":"The increasing demand for food and necessary decision-making on management and security of food crops require prior knowledge of the upcoming yield. The accurate prediction of wheat yield is a hard process that requires information such as location and climatic conditions. In this paper, the accurate prediction of wheat yield is facilitated by integrating both the current and past data of the soil, and climate, along with the spatial features obtained from satellite images. Initially, the normalization of data is carried out to balance the values of different ranges. Then the measurement of current readings of soil characteristics such as soil moisture, air temperature, humidity, and precipitations along with the climatic conditions is performed. These measurements along with the previous historical measurements were considered in order to perform an effective prediction of wheat yield. The multi-kernel-based Support Vector Machine (SVM) is implemented for this purpose. The effectiveness of the proposed approach is validated in terms of performance metrics such as accuracy, precision, recall, and F score. The proposed approach outperforms the existing approaches in predicting the wheat yield with increased accuracy.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"62 288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129529372","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

3D Sign language recognition based on multi-path hybrid residual neural network 基于多路径混合残差神经网络的三维手语识别

2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529943

Xiaoyu Shi, Xiaoli Jiao, Cangzhen Meng, Zhiyun Bian

{"title":"3D Sign language recognition based on multi-path hybrid residual neural network","authors":"Xiaoyu Shi, Xiaoli Jiao, Cangzhen Meng, Zhiyun Bian","doi":"10.1145/3529836.3529943","DOIUrl":"https://doi.org/10.1145/3529836.3529943","url":null,"abstract":"Abstract: Sign language is an important communicating method for deaf-mute people. In recent years, the hybrid model between the Bi-directional Long-Short Term Memory (BiLSTM) and 3D convolutional network model makes full use of the feature extraction ability of convolutional neural networks and the advantages of time series classification of the recurrent neural network model to achieve more accurate recognition. However, high precision, scalability and robustness are still important challenges in future sign language recognition research. The main research direction and responding research methods aim to improve the accuracy and speed of 3D poses and continuous sentences sign language recognition based on hybrid models with the upgrading of computer hardware equipment and network. The paper improves a novel residual neural network and then engages it to extract features and build models with BiLSTM. The proposed hybrid model combines the improved neural network and Bi-directional Long-Short Term Memory (BiLSTM). In order to validate the proposed algorithm, we introduce the Chalearn dataset and Sports-1M dataset captured with depth, color and stereo-IR sensors. On the two challenging datasets, our multi-path hybrid residual neural network achieves an accuracy of 78.9% and 82.7%, outperforms other state-of-the-art algorithms, and is close to human accuracy of 88.4%.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128491431","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