International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems最新文献

Improving generalization in reinforcement learning through forked agents 通过分叉代理改进强化学习的泛化

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2022-12-13 DOI: 10.48550/arXiv.2212.06451

Olivier Moulin, Vincent François-Lavet, M. Hoogendoorn

引用次数: 0

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation 基于层归一化LSTM和分层相关传播的数字孪生上可解释的在线变道预测

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2022-04-04 DOI: 10.48550/arXiv.2204.01292

C. Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid

{"title":"Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation","authors":"C. Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid","doi":"10.48550/arXiv.2204.01292","DOIUrl":"https://doi.org/10.48550/arXiv.2204.01292","url":null,"abstract":"Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes consuming live data from a digital twin on a German highway, live predictions and explanations of lane changes by extending LRP to layer normalized LSTMs, and an interface for communicating and explaining the predictions to a human user. We aim to demonstrate faithful, understandable, and adaptable explanations of lane change prediction to increase the adoption and trustworthiness of AI systems that involve humans. Our research also emphases that explainability and state-of-the-art performance of ML models for manoeuvre anticipation go hand in hand without negatively affecting predictive effectiveness.","PeriodicalId":357450,"journal":{"name":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116794098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

WawPart: Workload-Aware Partitioning of Knowledge Graphs WawPart:知识图的工作负载感知划分

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2022-03-28 DOI: 10.1007/978-3-030-79457-6_33

A. Priyadarshi, K. Kochut

引用次数: 1

An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices 一种用于消费级可穿戴设备的改进的独立于主体的应力检测模型

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2022-03-18 DOI: 10.48550/arXiv.2203.09663

Van-Tu Ninh, Manh-Duy Nguyen, Sinéad Smyth, Minh-Triet Tran, G. Healy, Binh T. Nguyen, C. Gurrin

{"title":"An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices","authors":"Van-Tu Ninh, Manh-Duy Nguyen, Sinéad Smyth, Minh-Triet Tran, G. Healy, Binh T. Nguyen, C. Gurrin","doi":"10.48550/arXiv.2203.09663","DOIUrl":"https://doi.org/10.48550/arXiv.2203.09663","url":null,"abstract":"Stress is a complex issue with wide-ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to training a stress detection model are subject-dependent and subject-independent training methods. Although subject-dependent training methods have proven to be the most accurate approach to build stress detection models, subject-independent models are a more practical and cost-efficient method, as they allow for the deployment of stress level detection and management systems in consumer-grade wearable devices without requiring training data for the end-user. To improve the performance of subject-independent stress detection models, in this paper, we introduce a stress-related bio-signal processing pipeline with a simple neural network architecture using statistical features extracted from multimodal contextual sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (ST) captured from a consumer-grade wearable device. Using our proposed model architecture, we compare the accuracy between stress detection models that use measures from each individual signal source, and one model employing the fusion of multiple sensor sources. Extensive experiments on the publicly available WESAD dataset demonstrate that our proposed model outperforms conventional methods as well as providing 1.63% higher mean accuracy score compared to the state-of-the-art model while maintaining a low standard deviation. Our experiments also show that combining features from multiple sources produce more accurate predictions than using only one sensor source individually.","PeriodicalId":357450,"journal":{"name":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases 柱状时态数据库中周期性频繁模式的高效发现

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2021-07-26 DOI: 10.1007/978-3-030-79457-6_3

R. Penugonda, Likhitha Palla, Uday Kiran Rage, Y. Watanobe, K. Zettsu

引用次数: 1

Semantic Technologies Towards Missing Values Imputation 缺失值输入的语义技术

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2021-07-26 DOI: 10.1007/978-3-030-79457-6_16

Iker Esnaola-Gonzalez, Unai Garciarena, J. Bermúdez

引用次数: 0

Hub and Spoke Logistics Network Design for Urban Region with Clustering-Based Approach 基于聚类的城市区域枢纽辐状物流网络设计

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2021-07-07 DOI: 10.1007/978-3-030-79457-6_51

Quan Duong, Dang-Quan Nguyen, Q. Nguyen

引用次数: 0

Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning 基于模仿和强化学习的网约车服务数据驱动仿真

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2021-04-06 DOI: 10.1007/978-3-030-79457-6_4

H. Jayasinghe, Tarindu Jayatilaka, Ravin Gunawardena, Uthayasanker Thayasivam

引用次数: 0

Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese 越南语开放域社交媒体评论的建设性和毒性语音检测

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2021-03-18 DOI: 10.1007/978-3-030-79457-6_49

Luan Thanh Nguyen, Kiet Van Nguyen, N. Nguyen

引用次数: 14

A New Approach to Determine 2-Optimality Consensus for Collectives 一种确定集体2-最优共识的新方法

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Pub Date : 2020-09-22 DOI: 10.1007/978-3-030-55789-8_49

Dai Tho Dang, Z. Mazur, D. Hwang

引用次数: 0