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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

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A Human-Centered Approach to data driven Iterative Course Improvement 17th

A Human-Centered Approach to Data Driven Iterative Course Im...

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17th Annual conference of the international-Association-of-Online-Engineering (IAOE) and the Global-Online-Laboratory-Consortium (GOLC) on Remote Engineering and Virtual Instrumentation (REV) - Cross Reality and data Science in Engineering

作者： Moore, Steven Stamper, John Bier, Norman Blink, Mary Jean Carnegie Mellon Univ HCII Pittsburgh PA 15213 USA TutorGen Inc Wexford PA USA

ISBN: (纸本)9783030525750;9783030525743

In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (dataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student learning. The configurable interface allows users to quickly and accurately identify areas of improvement based on the analysis of learning curves. We present two cases where the interface and accompanying methods have been applied in the domains of geometry and psychology to improve upon existing student models. Both cases present outcomes of better models that more closely model student learning. We reflect on how to iterate upon the educational technology used for the respective courses based on these better models and further opportunities for utilizing the system to other domains, such as computing principles.

关键词： Learning analytics Student model Learning curve data visualization data-driven improvement Educational technology

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Recent advances in the Digital Twin System for Smart Farming: A Five-Year Review

Recent Advances in the Digital Twin System for Smart Farming...

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computing Innovation, Intelligence, Technologies and Education (CIITE), international conference on

作者： Qiyi Xie Yuanjing Zhong MohammadMahdi Ariannejad Setio Timothy Darryl Budi Chen Zeyu Chen Shuyi Department of Electrical and Electronic Engineering Xiamen University Malaysia Sepang Selangor Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

The development of digital technologies has given rise to novel approaches to the problems facing contemporary agriculture. Digital Twin systems (DTS) are one of these technologies that have become an innovative tool in smart farming. DTS facilitates data-driven decision-making, predictive maintenance, and real-time monitoring by emulating actual farming processes virtually. Precision management over agricultural processes is made possible by integrating Internet of Things (IoT) devices, artificial intelligence (AI), and big data analytics into DTS. This reduces environmental effects, optimizes resource consumption, and increases crop yields. To provide an overview of the most recent uses of DTS in smart farms and to emphasize their contributions to productivity, sustainability, and efficiency, this study explores twenty case studies from the previous five years of research to get the benefits, difficulties, and implementation strategies of using DT technology in several agricultural environments. The paper emphasizes how DT systems are revolutionizing smart farming and opening the door to a more resilient and intelligent agrarian future.

关键词： Smart agriculture Technological innovation Reviews Real-time systems Digital twins Internet of Things Resource management Artificial intelligence Sustainable development Predictive maintenance

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data-driven Fault Classification Using Support Vector Machines

Data-Driven Fault Classification Using Support Vector Machin...

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4th international conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Jallepalli, Deepthi Kakhki, Fatemeh Davoudi San Jose State Univ Dept Comp Engn San Jose CA USA San Jose State Univ Dept Technol San Jose CA 95192 USA San Jose State Univ Dept Technol Machine Learning & Safety Analyt Lab San Jose CA 95192 USA

ISBN: (纸本)9783030680176;9783030680169

Detecting faulty condition of rolling-element bearings is significant in improving system reliability and preventing machine failure in industrial operations. In this paper, a machine learning pipeline is developed using filtered data through time domain features to train support vector machines with radial basis function, polynomial and linear kernels for multi-level fault diagnosis and classification. Overall accuracy rate and F-score values were used as figures of merit to evaluate and validate the performance of the machine learning model. SVM classifier showed significantly high overall accuracy rate of 91% to 99% and F-score of 0.81 to 0.99 with time domain statistical features due to the capability of this method in elimination of irrelevant features as well as reducing the dimensionality of the data. In addition, the high accuracy rate of SVMs in class-specific detection of localized bearing faults show the significant potential of data-driven classification modeling for fault detection in industrial and manufacturing operations.

关键词： Bearing fault Support vector machines Time domain statistical features Machine learning

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Design and Implementation of Coroutine Management Environment

Design and Implementation of Coroutine Management Environmen...

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2022 international conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2022

作者： Cai, Bin Qiu, Bingcheng Yao, Jian Fu, Jun Xue, Yunlan Liu, Xiaofei Wu, Ruilong Zhang, Xu Guangdong Guangzhou China Wuhan University Hubei Wuhan China China Hi-Tech Group Co. Ltd. Guangdong Guangzhou China Blue Dot Power Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510663350

advances in network and storage technologies enable data centers to provide high-concurrency and low-latency processing responses. To meet user needs, a variety of low-latency technologies can be used to improve the overall concurrent response capability of the system on the premise of ensuring system stability. User mode thread pool is one of the techniques that can be used. In a multi-core processor system, the scheduling management of the user mode thread pool is much more complicated. At present, the scheduling management of the user mode thread pool mainly focuses on the resource utilization of the user mode thread pool itself and the scheduling management of many user-mode threads, such as dynamically adjusting the number and usage of user mode threads in the user mode thread pool according to the load of the system, and replacing the system thread scheduling with user mode thread scheduling, etc. The existing user mode thread scheduling methods do not distinguish the performance requirements of different data processing tasks, treat all user mode thread tasks in the same way, cannot reflect the performance requirements of different user mode thread tasks, and cannot provide more fine-grained user mode thread execution timing control;the existing user mode thread scheduling methods do not provide more effective scheduling control for the host thread, but instead use the thread scheduling mechanism of the operating system itself to control the execution of all kernel threads which is difficult to isolate the host thread and other kernel threads in the system;The existing user mode thread scheduling methods cannot give full play to the parallel execution advantages of the multi-core processor architecture, and it is difficult to implement unified resource allocation management among multiple user mode thread pools. This paper provides a two-level scheduling management method for user mode thread pools that effectively integrates the operating system scheduling m

关键词： Resource allocation

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Heart Disease Detection from Gene Expression data Using Optimization driven Deep Q-Network 10th

Heart Disease Detection from Gene Expression Data Using Opti...

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10th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2022

作者： Aher, Chetan Nimba Jena, Ajay Kumar School of Computer Engineering Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneshwar India Department of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9789811975233

Heart disease is a clinical illness that is caused by structural and functional tissue dysfunction. According to the arena health company (WHO), heart disease is the leading cause of death worldwide, with a recent study finding that heart disease accounts for 23% of all deaths in the United States. As a result, early and accurate diagnosis of heart-related diseases is crucial in improving the health of patients with heart disease and in reducing their mortality by preventing unsafe deaths through better medical services. In-depth learning algorithms are now widely used in medical science to diagnose various diseases. Using basic models to predict heart disease is very difficult due to insufficient genetic representation and certain problems of inequality. This study employs an effective technique for diagnosing heart illness utilizing a deep Q-network based on the suggested political deer hunting optimization (PDHO) algorithm. The proposed PDHO method, on the other hand, is the result of combining the political optimizer (PO) with the deer hunting optimization (DHO) algorithms. In addition, the proposed PDHO-based deep Q-network for heart disease diagnosis had a maximum accuracy of 0.934, sensitivity of 0.962, and specificity of 0.892. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heart

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AI-Enhanced Predictive Maintenance in intelligent systems for Industries

AI-Enhanced Predictive Maintenance in Intelligent Systems fo...

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advances in computing Research on Science Engineering and Technology (ACROSET), international conference on

作者： Shiv Shankar Sharma Vivek V Ashwini Malviya Department of Mathematics Vivekananda Global University Jaipur India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India Department of uGDX ATLAS SkillTech University Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

A innovative approach to AI-enhanced predictive maintenance in AI-powered industrial systems is presented here. The suggested technique creates an all-encompassing predictive maintenance framework by fusing three powerful algorithms: Random Forest, Long Short-Term Memory (LSTM) networks, and XGBoost. The goal is to improve equipment failure prediction accuracy while simultaneously optimizing maintenance schedules to facilitate productive and economical manufacturing processes. Random Forest is the first step in the process, and it is responsible for feature selection and the first failure predictions. Using an ensemble learning strategy, Random Forest can efficiently process high-dimensional, complicated information. Following this, long short-term memory (LSTM) networks are presented as a means of capturing temporal relationships in the data, making them well-suited for the investigation of failures in equipment over time. Finally, XGBoost improves predictive accuracy by decreasing the number of incorrect predictions. Each algorithm has its own merits, and by combining them, we may create a more robust framework. The suggested technique excels over the status quo because of its flexibility to adapt to new circumstances, high accuracy in failure prediction, low cost, and high sustainability. It enables real-time data-driven optimization of maintenance plans, cutting down on unplanned downtime and wasted resources. The suggested method for AI-enhanced predictive maintenance is a state-of-the-art solution for businesses, as it replaces outdated methods with cutting-edge AI and machine learning strategies. In addition to improving the accuracy and efficiency of intelligent industrial systems, it also helps create a greener and more sustainable industrial landscape.

关键词： Industries Accuracy Prediction algorithms Real-time systems Maintenance Sustainable development Long short term memory Random forests Predictive maintenance Business

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13th international Symposium on Ambient Intelligence, ISAmI 2022

13th International Symposium on Ambient Intelligence, ISAmI ...

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13th international Symposium on Ambient Intelligence, ISAmI 2022

ISBN: (纸本)9783031223556

The proceedings contain 24 papers. The special focus in this conference is on Ambient Intelligence. The topics include: Electronic Plush Toy as a Non-pharmacological Tool in Therapies;federated Learning with Exponentially Weighted Moving Average for Real-Time Emotion Classification;user-Adapted Semantic Description Generation Using Natural Language Models;an Integration of Packet Routing and data Processing in Sensor Networks;Quantum AI: Achievements and Challenges in the Interplay of Quantum computing and Artificial Intelligence;an Architecture for a Coaching System to Support Type 2 Diabetic Patients;machine Learning Based System for the Control and Evaluation of Programming Vulnerabilities;quantum Optimization for IoT Security Detection;ideAir: IoT-Based System for Indoor Air Quality Control;acquisition and Synchronisation of Multi-source Physiological data Using Microservices and Event-driven Architecture;Automated Counting via Multicolumn Network and CytoSMART Exact FL Microscope;using Deep Learning for Implementing Paraphrasing in a Social Robot;epilepsy Seizure Detection Using Low-Cost IoT Devices and a Federated Machine Learning Algorithm;electroencephalographic Signal Processing from Brain-Computer-Interface Following Image-Based Emotion Induction;real-Time Algorithm Recommendation Using Meta-Learning;Percutaneous Electrolysis (EPI®), a Promising Technology in the Treatment of Insertional Patellar Tendinopathy in Soccer Players;ability-Centered Examination of People with Motor Impairments’ Interaction with Television Towards More Accessible Smart Home Entertainment Environments;discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining;Automatic Detection of Oil Spills from SAR Images Using Deep Learning;intelligent Appliances for AAL Environments;time Series Forecasting for Improving Quality of Life and Ecosystem Services in Smart Cities;security of IoT Application Layer: Requirements, Threats, and Solutions.

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Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths

Deep Reinforcement-Learning-Based Adaptive Traffic Signal Co...

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2022 IEEE international conference on systems, Man, and Cybernetics, SMC 2022

作者： Sun, Qi-Wei Han, Shi-Yuan Zhou, Jin Chen, Yue-Hui Yao, Kang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Shandong Provincial Network Security and Infomationization Technology Center Jinan250002 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics. © 2022 IEEE.

关键词： Reinforcement learning

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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Cross-Disciplinary Remote Course for Mixed Group of Students and Professionals 23rd

Cross-Disciplinary Remote Course for Mixed Group of Students...

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23rd international conference on Interactive Collaborative Learning (ICL) / 49th IGIP international conference on Engineering Pedagogy - Educating Engineers for Future Industrial Revolutions

作者： Lahdeaho, Oskari Hilmola, Olli-Pekka LUT Univ Kouvola Unit Prikaatintie 9 Kouvola 45100 Finland Tallinn Univ Technol Estonian Maritime Acad Kopli 101 EE-11712 Tallinn Estonia

ISBN: (纸本)9783030681982;9783030681975

The vast amount of openly available information enables data-driven study of the surrounding world. The aim of this article is to present dissemination of methods to calculating exhaust gas emission and assessing the effect of weather in dispersion of said emissions that are possible to carry out with data from open data repositories. As the measures to prevent spread of COVID-19 virus has led to a temporary cease of contact learning, this course is implemented remotely online. The course offers a controlled environment to test the foreknowledge of the participants regarding environmental challenges tied in transport, and the effectiveness of the remote course in enhancing the awareness in said issues. data will be gathered on the success of the course. It is used to enable continuous improvement of the course in terms of quality of the content and successfulness of the remote setting. In addition, the gathered data is used to determine the effectiveness of remote education and the preparedness of scholars to operate under special conditions. Through collaborative effort of the course organizers and attendees, the remote implementation can be successful. Also, the mixed group of organizers, students and professionals enable problem-oriented approach to the topic that is difficult to limit into a specific discipline. The pandemic and succeeding mitigation of COVID-19 spreading showcases the importance of planning education for such special situations. While the resulting circumstances are not pleasant, they serve as a promoter for rigorous planning of educational events.

关键词： Road traffic emissions Road weather international Cross-disciplinary Education

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