2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第2页

ML-SPD: Machine Learning based Sentiment Polarity Detection ML-SPD:基于机器学习的情感极性检测

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194633

Jelena Graovac, M. Radović, Berna Altinel Girgin

{"title":"ML-SPD: Machine Learning based Sentiment Polarity Detection","authors":"Jelena Graovac, M. Radović, Berna Altinel Girgin","doi":"10.1109/INISTA49547.2020.9194633","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194633","url":null,"abstract":"Internet revolution creates very important trends in people's life like news-portals, online-education, home-offices, online shopping, social media, etc. Without any controversy, social media is one of the most important outcomes of the Web. Today, social media is more than a communication channel where people have the opportunity to express their feelings, write their comments on microblogging sites, discussion groups, review sites, etc. These common habits have resulted in two important consequences: 1) Accumulation of very huge data on online platforms, 2) The requirement of automatic systems to classify these accumulated big data by subjective and sentimentally. In many cases, Sentiment Polarity Detection (SPD) in text may be an urgent requirement, rather than identifying the subject of the text. For instance, positively or negatively labeled product reviews may give sufficient summary information to readers about the review. In this study, to solve SPD problem we explore different text representation models in conjunction with state-of-the-art traditional Machine Learning techniques: Support Vector Machines (SVM), Neural Networks (NN), Nave Bayes (NB), and combination of NB and SVM classifier (NBSVM). We perform experiments on three publicly available benchmark movie review datasets in different languages: CornellPD in English, HUMIR in Turkish and SerbSPD-2C in Serbian. Experimental results confirm that the presented techniques achieve improvements over the previously published techniques applied to movie reviews datasets in Turkish and English. Developed software package “ML-SPD” is made publicly available to the research community so it can serve as a good baseline for future research.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457082","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 bi-objective maximal covering location problem: a service network design application 一个双目标最大覆盖定位问题:一个业务网络设计应用

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194660

Lazar Mrkela, Z. Stanimirović

引用次数: 2

Gated Echo State Networks: a preliminary study 门控回波状态网络的初步研究

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194681

Daniele Di Sarli, C. Gallicchio, A. Micheli

{"title":"Gated Echo State Networks: a preliminary study","authors":"Daniele Di Sarli, C. Gallicchio, A. Micheli","doi":"10.1109/INISTA49547.2020.9194681","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194681","url":null,"abstract":"Gating mechanisms are widely used in the context of Recurrent Neural Networks (RNNs) to improve the network's ability to deal with long-term dependencies within the data. The typical approach for training such networks involves the expensive algorithm of gradient descent and backpropagation. On the other hand, Reservoir Computing (RC) approaches like Echo State Networks (ESNs) are extremely efficient in terms of training time and resources thanks to their use of randomly initialized parameters that do not need to be trained. Unfortunately, basic ESNs are also unable to effectively deal with complex long-term dependencies. In this work, we start investigating the problem of equipping ESNs with gating mechanisms. Under rigorous experimental settings, we compare the behaviour of an ESN with randomized gate parameters (initialized with RC techniques) against several other models, among which a leaky ESN and a fully trained gated RNN. We observe that the use of randomized gates by itself can increase the predictive accuracy of a ESN, but this increase is not meaningful when compared with other techniques. Given these results, we propose a research direction for successfully designing ESN models with gating mechanisms.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130549656","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

A New Approach for Predicting an Important User on a Topic on Twitter 一种预测Twitter主题上重要用户的新方法

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194658

H. Phan, Dai Tho Dang, N. Nguyen, D. Hwang

引用次数: 2

How to Segment Turkish Words for Neural Text Classification? 如何分割土耳其语词进行神经文本分类?

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194661

Abdullah Al Nahas, Aysenur Kulunk, Burak Gözütok, S. Kalkan, Hakki Yagiz Erdinc

引用次数: 0

An Urban Intelligence Service for the impact of urbanization on National Park 城市化对国家公园影响的城市情报服务

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194672

Yuan-Chih Yu

{"title":"An Urban Intelligence Service for the impact of urbanization on National Park","authors":"Yuan-Chih Yu","doi":"10.1109/INISTA49547.2020.9194672","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194672","url":null,"abstract":"Yangmingshan National Park occupies half of Taipei's area in Taiwan. However, due to urbanization, many problems have arisen, including transportation, housing, food, shopping, development management, and ecological protection. Among them, the transportation problem is the key issue. To analyze the transportation problem, we construct the graph analytical model to present the logical meaning of the traffic/tourist distribution in time and space and to reflect the traffic functionality of each tourist attraction area. Based on this model, we can deploy it on the cloud as an intelligence analysis service to explore the recreation function of the national park area by analyzing the flows of tourism and traffic with open data. Enabling urban intelligence with big data and AI, sustainable improvement of people's lives, city operation systems, and the environment can be achieved. We hope the introduction of urban intelligence service benefits the development of Yangmingshan National Park, effectively solving the problems of traffic congestion, environmental degradation, and cumbersome planning dilemma.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130525529","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

Document Processing: Methods for Semantic Text Similarity Analysis 文档处理:语义文本相似度分析方法

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194665

A. Qurashi, Violeta Holmes, Anju P. Johnson

引用次数: 22

Speeding up the SUCCESS Approach for Massive Industrial Datasets 加速大规模工业数据集的成功方法

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194656

Krisztián Búza, Aleksandra Revina

引用次数: 4

How well are known protein complexes supported in PPI networks? 已知的蛋白质复合物在PPI网络中的支持程度如何?

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194663

Milana Grbić, Vukasin Crnogorac, M. Predojević, Aleksandar Kartelj, Dragan Matic

引用次数: 1

Fast Object Recognition for Humanoid Robots by Using Deep Learning Models with Small Structure 基于小结构深度学习模型的人形机器人快速目标识别

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194644

Simge Nur Aslan, A. Uçar, C. Güzelı̇ş

引用次数: 6