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 campa...
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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 machinelearning, Natural Language Processing, and Computer Vision applications. By instructing computers to effectively employ models based on extensive data sets, the objective of patternrecognition, 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.
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 gend...
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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-...
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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 stat...
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In this paper our main focus is to discover different machinelearning techniques that are useful biometric System. As biometric authentication system is a combination of both image processing and patternrecognition,...
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Real-world data commonly have an issue of class-imbalance, which poses a big challenge in patternrecognition and machinelearning tasks. To handle this issue, we have proposed an ensemble learning-based undersampling...
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ISBN:
(纸本)9783030305772;9783030305765
Real-world data commonly have an issue of class-imbalance, which poses a big challenge in patternrecognition and machinelearning 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.
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 Dat...
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 machinelearning 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 machinelearning 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.
In recent years, Cyber Security has become a major concern in scientific research. machinelearning (ML) and Deep learning (DL) methods detect intrusions and attacks on the network by predicting risk using data traini...
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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-supervis...
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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 b...
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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 patternmining 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.
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