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AI and Big data for the management of COVID-19

AI and Big Data for the management of COVID-19

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Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET), international Conference on

作者： Dina El Kayaly Sherifa Elhady Nahla Hazem Tawfik Ismail Irene Samy Fahim International Business New Giza University Cairo Egypt Sesc Research Center Nile University Sheikh Zayed Giza Egypt Management of Technology Nile University Sheikh Zayed Giza Egypt Wireless Intelligent Networks Center (WINC) Nile University Giza Egypt

ISBN: (数字)9798350346336

ISBN: (纸本)9798350346343

In March 2020, World Health Organization (WHO) recognized COVID-19 as a pandemic and urged governments to exert maximum efforts to prevent its spreading through political decisions together with public awareness campaigns positively impacting personal behaviors. Moreover, the WHO recommends collecting facts and data from reliable sources to help accurately determine the risks, accordingly governments take reasonable precautions. There are several ways to fight against corona such as accelerating research for the doctors, scientists and organizations working to find a vaccine or a medicine to defeat the COVID-19 virus, cleaning and sterilizing facilities, ensure health and productivity of people while changing their workplace, provide supercomputers to fight the virus. Artificial Intelligence (AI) possesses remarkable potency in its capacity to assist in combating the COVID-19 pandemic. This is achieved through a diverse range of methodologies such as machine learning, Natural Language Processing, and Computer Vision applications. By instructing computers to effectively employ models based on extensive data sets, the objective of pattern recognition, explication, and prognostication is pursued [1]. These techniques will generate knowledge that can be useful in diagnosing, predicting, and treating COVID-19. [2]–[3] AI can also help in detecting patterns that help us to manage COVID 19 socio-economic impacts [4]. Since the outbreak of the pandemic, there has been a scramble to use AI. This article aims to overview the possible applications of AI and Big data in facing COVID 19 pandemic. Four possible applications are identified, namely effective alert helping in monitoring the outbreaks instantaneously; diagnostic cases of COVID 19 and tailor medication; facilitating the implementation of Public Health interventions and resource optimizations; and Building cities with smart healthcare services.

关键词： COVID-19 Pandemics Computer viruses Government Urban areas Big data Supercomputers

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GBCI: Adaptive Frequency Band learning for Gender recognition in Brain-Computer Interfaces 1st

GBCI: Adaptive Frequency Band Learning for Gender Recogniti...

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1st CAAI international Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Pengpai Zhou, Yueying Li, Zhongnian Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783030930455

In recent years, with the rapid development of brain-computer interface (BCI) technology, various applications based on BCI have generated significant interest. A motivating application of BCI is predicting human gender by electroencephalogram (EEG) analysis. However, most of the recent researches only identify the gender of EEG in the resting state. In real-life practice, it is difficult for the subjects to be at a full resting state when it mixes with motor imagery (MI). Therefore, we acquired two kinds of EEG activities data including 9 subjects (5 males and 4 females) with two state (i.e., resting state and MI) based on Chinese sign language. In this paper, to recognize gender form two state, an improved adaptive variational mode decomposition with long short term memory (AVMD-LstM) network is developed to construct a hybrid state learning framework. Besides, the sample entropy (SE) is employed to select the channel of EEG data as the input of AVMD-LstM. The recognition accuracy of gender classification was 89.21 %. Comparing with the task of complete resting state, the model is validated with more robustness. Furthermore, the proposed algorithm has many applications, including biometrics, healthcare, and online entertainment advertising. © 2021, Springer Nature Switzerland AG.

关键词： Variational mode decomposition

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Forklift Truck Activity recognition from CAN data 2nd

Forklift Truck Activity Recognition from CAN Data

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2nd international workshop on IoT streams for data-Driven Predictive Maintenance, IoT streams 2020, and 1st international workshop on IoT, Edge, and Mobile for Embedded machine learning, ITEM 2020, co-located with ECML/PKDD 2020

作者： Chen, Kunru Pashami, Sepideh Nowaczyk, Slawomir Johansson, Emilia sternelöv, Gustav Rögnvaldsson, Thorsteinn Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden Toyota Material Handling Europe Mjölby Sweden

ISBN: (纸本)9783030667696

machine activity recognition is important for accurately estimating machine productivity and machine maintenance needs. In this paper, we present ongoing work on how to recognize activities of forklift trucks from on-board data streaming on the controller area network. We show that such recognition works across different sites. We first demonstrate the baseline classification performance of a Random Forest that uses 14 signals over 20 time steps, for a 280-dimensional input. Next, we show how a deep neural network can learn low-dimensional representations that, with fine-tuning, achieve comparable accuracy. The proposed representation achieves machine activity recognition. Also, it visualizes the forklift operation over time and illustrates the relationships across different activities. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Applying data mining technique to predict trends in air pollution in Mumbai 1

Applying data mining technique to predict trends in air poll...

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1st international Conference on Advances in Computational Science and Engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Muthukumaran, V. Thimmia Raja, J. Vinothkumar, V. Deepa, R. Nivedita, V. Vellore Institute of Technology Vellore India Department of Mathematics School of Applied Science REVA University Bengaluru64 India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India Telangana Hyderabad India Department of Computer Science and Engineering Dr. MGR Educational and Research Institute Chennai India

Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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machine learning Methods for Extraction and Classification for Biometric Authentication 1st

Machine Learning Methods for Extraction and Classification f...

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1st international Conference on data Science, machine learning and Applications, 2019

作者： Gunjan, Vinit Kumar Prasad, Puja S. Pathak, Rashmi Kumar, Amit Department of CSE CMR Institute of Technology Hyderabad India Department of CSE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Siddhant College of Engineering Pune India Department of CSE SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789811514197

In this paper our main focus is to discover different machine learning techniques that are useful biometric System. As biometric authentication system is a combination of both image processing and pattern recognition, in this classification of pattern is a difficult task. machine learning have number of algorithm that makes classification task easy. machine learning is divided as supervised as well as unsupervised learning. In unsupervised learning the machine construct representation of input by getting inputs x1, x2, x3, …, and this is used for decision making, predicting future inputs or we say unsupervised learning finds patterns in the data and mainly solve clustering problem. In supervised learning set of output is already given, only we have to find this set of output from respective input value. In this paper we also discuss the area of machine learning where already work has done for biometric. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Unsupervised learning

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An Ensemble learning-Based Undersampling Technique for Handling Class-Imbalance Problem 1st

An Ensemble Learning-Based Undersampling Technique for Handl...

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1st international Conference on Emerging Trends in Information Technology (ICETIT)

作者： Sarkar, Sobhan Khatedi, Nikhil Pramanik, Anima Maiti, J. IIT Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

ISBN: (纸本)9783030305772;9783030305765

Real-world data commonly have an issue of class-imbalance, which poses a big challenge in pattern recognition and machine learning tasks. To handle this issue, we have proposed an ensemble learning-based undersampling technique using Extreme Gradient Boosting (XGBoost) and Support Vector machine (SVM). The technique has been validated using an accident dataset obtained from a steel plant. The results explore that the proposed technique is capable of resolving the issue of class-imbalance effectively. This method outperforms traditional undersampling technique in terms of performance metrics, i.e., geometric mean (G-mean), recall, and precision.

关键词： Class imbalance Undersampling XGBoost SVM Ensemble

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15th workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2020, 1st international workshop on Dependable Development-Operation Continuum Methods for Dependable Cyber-Physical Systems, DepDevOps 2020, 1st international workshop on Underpinnings for Safe Distributed AI, USDAI 2020, and 3rd international workshop on Artificial Intelligence Safety Engineering, WAISE 2020, held in conjunction with the 39th international Conference on Computer Safety, Reliability and Security, SAFECOMP 2020

15th Workshop on Dependable Smart Embedded and Cyber-Physica...

引用

15th workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2020, 1st international workshop on Dependable Development-Operation Continuum Methods for Dependable Cyber-Physical Systems, DepDevOps 2020, 1st international workshop on Underpinnings for Safe Distributed AI, USDAI 2020, and 3rd international workshop on Artificial Intelligence Safety Engineering, WAISE 2020, held in conjunction with the 39th international Conference on Computer Safety, Reliability and Security, SAFECOMP 2020

ISBN: (纸本)9783030555825

The proceedings contain 31 papers. The special focus in this conference is on Dependable Smart Embedded and Cyber-Physical Systems. The topics include: A Framework for Building Uncertainty Wrappers for AI/ML-Based data-Driven Components;rule-Based Safety Evidence for Neural Networks;safety Concerns and Mitigation Approaches Regarding the Use of Deep learning in Safety-Critical Perception Tasks;positive Trust Balance for Self-driving Car Deployment;Integration of Formal Safety Models on System Level Using the Example of Responsibility Sensitive Safety and CARLA Driving Simulator;a Safety Case pattern for Systems with machine learning Components;structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications;Collecting and Classifying Security and Privacy Design patterns for Connected Vehicles: SECREDAS Approach;an Assurance Case pattern for the Interpretability of machine learning in Safety-Critical Systems;A structured Argument for Assuring Safety of the Intended Functionality (SOTIF);safety and Security Interference Analysis in the Design stage;formalising the Impact of Security Attacks on IoT Safety;assurance Case patterns for Cyber-Physical Systems with Deep Neural Networks;safety-Critical Software Development in C++;an Instruction Filter for Time-Predictable Code Execution on standard Processors;ISO/SAE DIS 21434 Automotive Cybersecurity standard - In a Nutshell;preface;supervisory Control Theory in System Safety Analysis;WiCAR - Simulating Towards the Wireless Car;Automated Right of Way for Emergency Vehicles in C-ITS: An Analysis of Cyber-Security Risks;integrity Checking of Railway Interlocking Firmware;LoRaWAN with HSM as a Security Improvement for Agriculture Applications;multilevel Runtime Security and Safety Monitoring for Cyber Physical Systems Using Model-Based Engineering.

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Deep learning Approaches to Intrusion Detection: A new Performance of ANN and RNN on NSL-KDD 20

Deep Learning Approaches to Intrusion Detection: A new Perfo...

引用

1st international Conference on Intelligent Systems and pattern recognition, ISPR 2020

作者： Chaibi, Nassima Atmani, Baghdad Mokaddem, Mostefa Universitè Oran Oran Algeria

ISBN: (纸本)9781450375023

In recent years, Cyber Security has become a major concern in scientific research. machine learning (ML) and Deep learning (DL) methods detect intrusions and attacks on the network by predicting risk using data training. DL methods have proved to be more accurate than other network intrusion detection systems. In this paper, we propose an architecture implemented as two methodologies: in the first one, we apply Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) with information gain (IG) as a feature selection method and in the second one using an RNN applying information gain (IG), grain ratio (GR) and correlation attribute (CA) as feature selection method. These two methodologies use the NSL-KDD data-set. Their performance is proven by comparing our results with other previous results. Our results show that RNN outperforms ANN in the two methodologies, while ANN outperforms other machine learning classifiers. © 2020 ACM.

关键词： Recurrent neural networks

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CycleFootprint: A Fully Automated Method for Extracting Operation Cycles from Historical Raw data of Multiple Sensors 2nd

CycleFootprint: A Fully Automated Method for Extracting Oper...

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作者： Fanaee-T, Hadi Bouguelia, Mohamed-Rafik Rahat, Mahmoud Blixt, Jonathan Singh, Harpal Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden Alfa Laval Tumba AB Tumba Sweden

ISBN: (纸本)9783030667696

Extracting operation cycles from the historical reading of sensors is an essential step in IoT data analytics. For instance, we can exploit the obtained cycles for learning the normal states to feed into semi-supervised models or dictionaries for efficient real-time anomaly detection on the sensors. However, this is a difficult problem due to this fact that we may have different types of cycles, each of which with varying lengths. Current approaches are highly dependent on manual efforts by the aid of visualization and knowledge of domain experts, which is not feasible on a large scale. We propose a fully automated method called CycleFootprint that can: 1) identify the most relevant signal that has the most obvious recurring patterns among multiple signals;and 2) automatically find the cycles from the selected signal. The main idea behind CycleFootprint is mining footprints in the cycles. We assume that there should be a unique pattern in each cycle that shows up repeatedly in each cycle. By mining those footprints, we can identify cycles. We evaluate our method with existing labeled ground truth data of a real separator in marine application equipped with multiple health monitoring sensors. 86% of cycles extracted by our method match fully or with at least 99% overlap with true cycles, which sounds promising given its unsupervised and fully automated nature. © 2020, Springer Nature Switzerland AG.

关键词： data Analytics

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Understanding Automated Feedback in learning Processes by mining Local patterns 16th

Understanding Automated Feedback in Learning Processes by Mi...

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14th BPI international workshop / 11th BPM workshop / 1st PODS4H international workshop / 1st AI4BPM international workshop / 1st CCBPM international workshop / 3rd PQ international workshop / 2nd BP-meet-IoT international workshop

作者： Deeva, Galina De Weerdt, Jochen Katholieke Univ Leuven Dept Decis Sci & Informat Management Fac Econ & Business Leuven Belgium

ISBN: (纸本)9783030116408;9783030116415

Process mining, and in particular process discovery, provides useful tools for extracting process models from event-based data. Nevertheless, certain types of processes are too complex and unstructured to be able to be represented with a start-to-end process model. For such cases, instead of extracting a model from a complete event log, it is interesting to zoom in on some parts of the data and explore behavioral patterns on a local level. Recently, local process model mining has been introduced, which is a technique in-between sequential pattern mining and process discovery. Other process mining methods can also used for mining local patterns, if combined with certain data preprocessing. In this paper, we explore discovery of local patterns in the data representing learning processes. We exploit real-life event logs from JMermaid, a Smart learning Environment for teaching Information System modeling with built-in feedback functionality. We focus on a specific instance of feedback provided in JMermaid, which is a reminder to simulate the model, and locally explore how students react to this feedback. Additionally, we discuss how to tailor local process model mining to a certain case, in order to avoid the computationally expensive task of discovering all available patterns, by combining it with other techniques for dealing with unstructured data, such as trace clustering and window-based data preprocessing.

关键词： Process discovery Local process models Automated feedback Trace clustering

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