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18th IEEE international conference on e-Business engineering, ICEBE 2022

作者： Zhang, Tianqing Zhang, Jian Peng, Gongzhuang Wang, Hongwei Zhejiang University-University of Illinois at Urbana-Champaign Institute Zhejiang University Haining China University of Science and Technology Beijing National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing Beijing China

ISBN: (纸本)9781665492447

Steel is a highly important product for which there has been an ever increasing demand. A critical challenge for steel production is the development of accurate methods for material mechanical property prediction (MMPP). the increasing availability of data has enabled the use of machine learning (ML) to address this challenge. Recently various ML-based methods have been developed, amongst which, however, no single one can achieve competitive results in all product prediction problems. In this context, automated machine learning (AutoML) has been proposed to automatically solve the model selection problem in a way that is more efficient than traditional machine learning. this method is particularly useful for situations in which a considerable amount of human expert knowledge is required to construct specialized ML models. Although AutoML methods are useful and effective, their applications are still limited and difficult due to the lack of analysis of their strengths and weaknesses. thus, this paper aims to analyze the application of AutoML methods to MMPP processes with datasets collected in the real world by comparing two state-of-the-Art AutoML methods (i.e. Auto-Sklearn and TPOT). Results show that AutoML can be considered as a powerful approach to address the model selection problem in MMPP. In particular, TPOT demonstrates the advantages of computational consumption and competitive accuracy. © 2022 IEEE.

关键词： Machine learning

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Explainable AI-based Federated Deep Reinforcement learning for Trusted Autonomous Driving 18

Explainable AI-based Federated Deep Reinforcement Learning f...

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18th IEEE international Wireless Communications and Mobile Computing conference (IWCMC)

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ Canada Univ Quebec Outaouais Dept Comp Sci & Engn Gatineau PQ Canada

ISBN: (纸本)9781665467490

Recently, the concept of autonomous driving became prevalent in the domain of intelligent transportation due to the promises of increased safety, traffic efficiency, fuel economy and reduced travel time. Numerous studies have been conducted in this area to help newcomer vehicles plan their trajectory and velocity. However, most of these proposals only consider trajectory planning using conjunction with a limited data set (i.e., metropolis areas, highways, and residential areas) or assume fully connected and automated vehicle environment. Moreover, these approaches are not explainable and lack trust regarding the contributions of the participating vehicles. To tackle these problems, we design an Explainable Artificial Intelligence (XAI) Federated Deep Reinforcement learning model to improve the effectiveness and trustworthiness of the trajectory decisions for newcomer Autonomous Vehicles (AVs). When a newcomer AV seeks help for trajectory planning, the edge server launches a federated learning process to train the trajectory and velocity prediction model in a distributed collaborative fashion among participating AVs. One essential challenge in this approach is AVs selection, i.e., how to select the appropriate AVs that should participate in the federated learning process. For this purpose, XAI is first used to compute the contribution of each feature contributed by each vehicle to the overall solution. this helps us compute the trust value for each AV in the model. then, a trust-based deep reinforcement learning model is put forward to make the selection decisions. Experiments using a real-life dataset show that our solution achieves better performance than benchmark solutions (i.e., Deep Q-Network (DQN), and Random Selection (RS)).

关键词： Trajectory Planning Autonomous Vehicles Selection Deep Reinforcement learning Federated learning Edge Computing Trust Explainable Artificial Intelligence

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Generating Wildfire Heat Maps with Twitter and BERT 24th

Generating Wildfire Heat Maps with Twitter and BERT

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Pinto, João Cabral Gonçalo Oliveira, Hugo Cardoso, Alberto Silva, Catarina Department of Informatics Engineering CISUC University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031482311

Recent developments in Natural Language Processing (NLP) have opened up a plethora of opportunities to extract pertinent information in settings where data overload is a critical issue. In this work, we address the highly relevant scenario of fire detection and deployment of firefighting resources. Social media posts commonly contain textual information during a fire event, but their vast volume and the necessity for swift actionable information often precludes their effective utilization. this paper proposes an information extraction pipeline capable of generating a wildfire heat map of Portugal from Twitter posts written in Portuguese. It uses a fine-tuned version of a BERT language model to extract fire reports from large batches of recent fire-related tweets as well as the spaCy NLP library to query the location of each recently reported fire. Wildfire locations are plotted to a colored map indicating the most probable fire locations, which could prove useful in the process of allocating firefighting resources for those regions. the system is easily adaptable to work with any other country or language, provided compatible BERT and spaCy models exist. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Fires

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11th Mining Humanistic data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine learning and Big data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine learning and Big data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

the proceedings contain 35 papers. the special focus in this conference is on Mining Humanistic data. the topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;the Use of Robotics in Critical Use Cases: the 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine learning-Based Resource Management;efficient data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep learning Based Facial Expressions An

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Vision Based automated Pipeline Inspection

Vision Based Automated Pipeline Inspection

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international conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Atul Bhagat Sandeep Reddy Basireddy Centre for Intelligent Cyber Physical Systems Indian Institute of Technology Guwahati India Mechanical Engineering department Indian Institute of Technology Guwahati India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Most nations today have a highly complex and old pipeline network, used for transporting gas, water, telecommunication cables, sewage, etc., that demands regular inspection and repairs. Majority of these pipelines are buried underground for aesthetic purpose, that makes regular maintenance work more costly and difficult. Manual CCTV inspection is a widely adopted inspection method that relies on an operator controlling the forward-looking, CCTV mounted mobile robot and reviewing lengthy CCTV footage. the automation of such a subjective and tiring process is crucial. With recent advances in the area of the neural networks, it is possible to develop automated systems capable of inspection using the CCTV images. the Sewer-ML data set, containing 1.3 million real images real pipeline inspection images, was selected for this study. A smaller subset of the complete data set was used to identify the best-performing deep learning model. Two different approaches, i.e., Hierarchical and Unified Defect Classification approaches were systematically evaluated. Our method using hierarchical defect classification with Mobilenet and Densenet-169 resulted in overall $F2_{CIW}$ score of 63.22% and 57.68% and $F1_{Normal}$ score of 90.2% and 89.4% on validation and test split of the pruned data set.

关键词： Automation Pipelines Transfer learning Neural networks Multi label classification Inspection Maintenance engineering Maintenance Mobile robots Defect detection

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the 18th international conference on intelligent data engineering and automated learning

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智能系统学报 2017年第4期12卷 490-490页

the international conference on intelligent data engineering and automated learning(ideal)is an annual international conference dedicated to emerging and challenging topics in intelligent data analysis,data mining and their associated learning systems and *** core themes include:the Big data challenges,Machine learning,data Mining,Information Retrieval and Management,Bio-and Neuro-Informatics,Bio-Inspired Models(including Neural

关键词：智能系统期刊编辑工作发行工作

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learning pavement surface condition ratings through visual cues using a deep learning classification approach

Learning pavement surface condition ratings through visual c...

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IEEE international conference on intelligent Computer Communication and Processing (ICCP)

作者： Waqar S. Qureshi David Power Joseph McHale Brian Mulry Kieran Feighan Dympna O Sullivan Department of Computer Science Technological University Dublin Ireland Pavement Management Services Dublin Ireland

ISBN: (纸本)9781665464383

Pavement surface condition rating is an essential part of road infrastructure maintenance and asset management, and it is performed manually by the data analyst. the manual rating requires cognitive skills built through training and experience, which is quantitatively challenging and time-consuming. this paper first analyses the complexity of the current manual visual rating system. this paper then investigates the suitability and robustness of a state-of-the-art convolutional neural network (CNN) classifier to automate the pavement surface condition index (PSCI) system used to rate pavement surfaces in Ireland. the dataset contains 3735 images of flexible asphalt pavements from Irish urban and rural environments taken from a video camera mounted in front of a van. the PSCI ratings were applied by experts using a scale of 1-10 to indicate surface conditions. the classification models are evaluated for different input pre-processing variations, image size, learning techniques, and the number of classes. Using 10 PSCI classes, the best classifier achieved a precision of 57% and a recall of 58%. Adjacent combination of classes (e.g., ratings 1 and 2 combined into a single class) to form a 5-class problem produced a classifier with a precision of 70% and recall of 77%. Given the complexity of the problem, classification using CNN holds promise as a first step towards an automated ranking system.

关键词： Training Visualization Roads Manuals Maintenance engineering Robustness Complexity theory

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Efficient Binary Static Code data Flow Analysis Using Unsupervised learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

the ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： data flow analysis

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A data-Driven Approach to Automatic Extraction of Professional Figure Profiles from Resumes 1

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Bondielli, Alessandro Marcelloni, Francesco Univ Florence DINFO Florence Italy Univ Pisa Dept Informat Engn Pisa Italy

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

the process of selecting and interviewing suitable candidates for a job position is time-consuming and labour-intensive. Despite the existence of software applications aimed at helping professional recruiters in the process, only recently with Industry 4.0 there has been a real interest in implementing autonomous and data-driven approaches that can provide insights and practical assistance to recruiters. In this paper, we propose a framework that is aimed at improving the performances of an Applicant Tracking System. More specifically, we exploit advanced Natural Language Processing and Text Mining techniques to automatically profile resources (i.e. candidates for a job) and offers by extracting relevant keywords and building a semantic representation of resumes and job opportunities.

关键词： Semantics

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A Genetic-Based Ensemble learning Applied to Imbalanced data Classification 20th

A Genetic-Based Ensemble Learning Applied to Imbalanced Data...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Klikowski, Jakub Ksieniewicz, Pawel Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland

ISBN: (纸本)9783030336172;9783030336165

Imbalanced data classification is still a focus of intense research, due to its ever-growing presence in the real-life decision tasks. In this article, we focus on a classifier ensemble for imbalanced data classification. the ensemble is formed on the basis of the individual classifiers trained on supervise-selected feature subsets. there are several methods employing this concept to ensure a high diverse ensemble, nevertheless most of them, as Random Subspace or Random Forest, select attributes for a particular classifier randomly. the main drawback of mentioned methods is not giving the ability to supervise and control this task. In following work, we apply a genetic algorithm to the considered problem. Proposition formulates an original learning criterion, taking into consideration not only the overall classification performance but also ensures that trained ensemble is characterised by high diversity. the experimental study confirmed the high efficiency of the proposed algorithm and its superiority to other ensemble forming method based on random feature selection.

关键词： Machine learning Classification Imbalanced data Feature selection Genetic algorithm

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