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Analytics and Evolving Landscape of Machine Learning for Emergency Response [chapter]

Rajendra Akerkar Minsung Hong
2019 Zenodo  
review the application of machine learning techniques to support the decision-making processes for the emergency or crisis management and discuss their challenges.  ...  Based on the literature review, we observe a trend to move from narrow in scope, problem-specific applications of data mining and machine learning to solutions that address a wider spectrum of problems  ...  Acknowledgements The work is funded from the Research Council of Norway (RCN) and the Norwegian Centre for International Cooperation in Education (SiU) grant through INT-PART programme.  ... 
doi:10.5281/zenodo.5106014 fatcat:ui7kmryflbaljkim4u2ync2dqi

2021 Index IEEE Transactions on Fuzzy Systems Vol. 29

2021 IEEE transactions on fuzzy systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., TFUZZ May 2021 1023-1036 Olfactory-Based Navigation via Model-Based Reinforcement Learning and Fuzzy Inference Methods.  ...  Jin, L., +, TFUZZ May 2021 1320-1324 Olfactory-Based Navigation via Model-Based Reinforcement Learning and Fuzzy Inference Methods.  ... 
doi:10.1109/tfuzz.2021.3134727 fatcat:m66dl6wxdbgendhdx4bliy6nky

Data science and AI in FinTech: An overview [article]

Longbing Cao, Qiang Yang, Philip S. Yu
2021 arXiv   pre-print
blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving  ...  The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and  ...  In addition, new developments on deep reinforcement learning, deep Bayesian learning, deep transfer learning and deep federated learning have also intensively transformed the landscape of DSAI-driven finance  ... 
arXiv:2007.12681v2 fatcat:jntzuwaktjg2hmmjypi5lvyht4

ICICT 2020 Table of Contents

2020 2020 International Conference on Inventive Computation Technologies (ICICT)  
, Wen Tao 123 GWO based Task Allocation for Load Balancing in Containerized Cloud 655 Dimple Patel, Manoj Kumar Patra, Bibhudatta Sahoo 124 Indoor Navigation with Deep Reinforcement Learning  ...  Vanitha 98 Object Tracking using Motion Estimation based on Block Matching Algorithm 519 M. 99 100 Data-Driven Prognostics for Run-To-Failure Data Employing Machine Learning Models 528 Saranya  ... 
doi:10.1109/icict48043.2020.9112502 fatcat:dshr6trhmbfoxi6x53grpwxpe4

A Survey of Deep Active Learning [article]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang
2021 arXiv   pre-print
Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features.  ...  Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples.  ...  DRAL (Deep Reinforcement Active Learning) [136] adopts a similar idea and designs a deep reinforcement active learning framework for the person Re-ID task.  ... 
arXiv:2009.00236v2 fatcat:zuk2doushzhlfaufcyhoktxj7e

Sharing to learn and learning to share - Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning : A meta review [article]

Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki
2021 arXiv   pre-print
A global generic learning network, an ensemble of meta learning, transfer learning, and multi-task learning, is also introduced here, along with some open research questions and directions for future research  ...  generalization for new tasks.  ...  To address the are changes in the wireless environment, and for faster adaptation to new unseen data, this work proposes two types of models based on deep transfer learning and meta learning.  ... 
arXiv:2111.12146v1 fatcat:yc4ebap3azfexe75mtg3juhnfe

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives [article]

Yassine Himeur and Khalida Ghanem and Abdullah Alsalemi and Faycal Bensaali and Abbes Amira
2020 arXiv   pre-print
In this regard, this paper an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence.  ...  , such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios.  ...  In this regard, deep reinforcement learning (DRL) is then proposed as a merge of deep learning and reinforcement learning to detect more complex consumption anomalies.  ... 
arXiv:2010.04560v4 fatcat:dpullqvuv5f5lhu6tyqgdbya3q

IEEE Access Special Section Editorial: Big Data Learning and Discovery

Zhong-Ke Gao, An-An Liu, Yanhui Wang, Michael Small, Xiaojun Chang, Jurgen Kurths
2021 IEEE Access  
In [A30], Lin et al. proposed a novel game theory-based model, called Equal Responsibility Rumor Diffusion Game Model (ERRDGM), to simulate the rumor diffusion process.  ...  In [A43] , Mohammadi and Al-Fuqaha investigated the creation of a dynamic ensemble from distributed deep learning models by considering the spatiotemporal patterns embedded in the training data.  ...  His research interests include complex systems, complex networks, chaos and nonlinear dynamics, nonlinear time series analysis, and computational modeling.  ... 
doi:10.1109/access.2021.3127335 fatcat:apph47tuffblnp2dkcc7lezffy

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions [article]

Izzat Alsmadi, Kashif Ahmad, Mahmoud Nazzal, Firoj Alam, Ala Al-Fuqaha, Abdallah Khreishah, Abdulelah Algosaibi
2021 arXiv   pre-print
The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing(NLP) tools to process the unprecedented amount of social media content to make  ...  We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.  ...  Acknowledgment The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 1120.  ... 
arXiv:2110.13980v1 fatcat:e373if4sszed7i4owzwiabmzxu

Fake Media Detection Based on Natural Language Processing and Blockchain Approaches

Zeinab Shahbazi, Yung-Cheol Byun
2021 IEEE Access  
FAKE NEWS DETECTION USING REINFORCEMENT LEARNING Deep reinforcement learning is the incorporation of reinforcement learning and deep learning for decision-making from unstructured data.  ...  The presented model is based on the reinforcement learning (RL) algorithm, which is learning-based and fits this environment because of the decision-making strategy.  ... 
doi:10.1109/access.2021.3112607 fatcat:vwam7vsrjjhzpkgbvdotgypfwy

COVID-19 Modeling: A Review [article]

Longbing Cao, Qing Liu
2021 arXiv   pre-print
and deep machine learning, simulation modeling, social science methods, and hybrid modeling.  ...  The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow  ...  More information about COVID-19 modeling is in  ... 
arXiv:2104.12556v3 fatcat:pj2bketcrveafbjf2m7tx3odxy

Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums

Preslav Nakov, Tsvetomila Mihaylova, Lluís Màrquez, Yashkumar Shiroya, Ivan Koychev
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
The features modeling the profile of the user (in particular trollness) turn out to be most important, but embedding features modeling the answer and the similarity between the question and the answer  ...  The features model the user, the answer, the question, the thread as a whole, and the interaction between them.  ...  (iii) We show that ranking-based SVMs can learn to rank non-credible answers with good performance.  ... 
doi:10.26615/978-954-452-049-6_072 dblp:conf/ranlp/NakovMMSK17 fatcat:2am7ru342jbkzmvclsq4iqhn7y

A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [article]

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
These detection techniques apply popular machine learning and deep learning algorithms. Previous work in this domain covers fake news detection vastly among text circulating online.  ...  Researchers are analyzing online data based on multiple modalities composed of text, image, video, speech, and other contributing factors.  ...  The model is suitable for detecting rumors in the form of text, image, and video.  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4

Social Media based Surveillance Systems for Healthcare using Machine Learning: A Systematic Review

Aakansha Gupta, Rahul Katarya
2020 Journal of Biomedical Informatics  
The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms.  ...  Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by  ...  A large amount of training data is required to train the forecasting models and testing data for valid testing of predictions based on the training of models.  ... 
doi:10.1016/j.jbi.2020.103500 pmid:32622833 pmcid:PMC7331523 fatcat:jfj34tsxkvhlhkwnna6fmgnvni

Social Media Information Sharing for Natural Disaster Response [article]

Zhijie Sasha Dong, Lingyu Meng, Lauren Christenson, Lawrence Fulton
2021 arXiv   pre-print
To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted.  ...  Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models.  ...  It indicates that machine learning models, like linear and logistic models, have higher classification accuracy than complicated machine learning models like ensemble models and deep learning models regarding  ... 
arXiv:2005.07019v4 fatcat:etv7imyeizhytfyi7snbvmy2vm
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