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intelligent Control of a Permanent Magnet DC Motor 22

Intelligent Control of a Permanent Magnet DC Motor

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22nd international Carpathian Control conference (ICCC)

作者： Bujgoi, Gheorghe Sendrescu, Dorin Univ Craiova Dept Automat & Elect Craiova Romania

ISBN: (纸本)9781728186092

This paper presents the use of artificial intelligence for the control of a permanent magnet DC motor. The system can be found especially in robotic systems that perform repetitive operations. The control law is generated by an intelligent Reinforcement learning algorithm. From the numerous variants of this type of algorithm, the Policy Iteration type algorithm was chosen. The algorithm was experimentally implemented using a data acquisition system and Matlab/Simulink software. The control system was tested for several variants of the load (by changing its inertia moment). The simulation and experimental results show that the intelligent control method based on reinforcement learning has better trajectory tracking and vibrations suppression.

关键词： intelligent control reinforcement learning flexible joint experiment

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An application of Customer Embedding for Clustering 22

An application of Customer Embedding for Clustering

引用

22nd IEEE international conference on data Mining (ICDM)

作者： Bayrak, Ahmet Tugrul Ata Technol Platforms Data Sci & Innovat Istanbul Turkey

ISBN: (纸本)9798350346091

Effective and powerful strategic planning in a competitive business environment brings businesses to the fore. It is important for the growth of the business to move the customer to the center by acting more intelligently in the planning of marketing and sales activities. In order to find customer behavior patterns, the use of clustering models from machine learning algorithms can yield effective results. In this study, traditional customer clustering methods are enriched by using customer representations as features. To be able to achieve that, a natural language processing method, word embedding, is applied to customers. By using the powerful mechanism of word embedding methods, a customer space is created where the customers are represented based on the products they have bought. It is observed that appending customer embeddings for customer clustering have a positive effect and the results seem promising for further studies.

关键词： customer clustering word embedding methods customer embedding feature engineering k-means algorithm rfm analysis

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Human Interaction and Classification Via K-ary Tree Hashing Over Body Pose Attributes Using Sports data 22nd

Human Interaction and Classification Via K-ary Tree Hashing ...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Trivedi, Sandeep Patel, Nikhil Faruqui, Nuruzzaman Tahir, Sheikh Badar ud din IEEE Deloitte Consulting LLP Texas Austin United States University of Dubuque Iowa United States Department of Software Engineering Daffodil International University Dhaka Bangladesh Islamabad Pakistan

ISBN: (纸本)9783031274084

Human interaction has always been a critical aspect of social communication. Human action tracking and human behavior recognition are all indicators that assist in investigating human interaction and classification. Several features are considered to analyze human interaction classification in images and videos, including shape, the position of the human body parts, and their environmental effects. This paper approximated different human body key points to track their occurrence under challenging situations. Such tracking of critical body parts requires numerous features. Therefore, we first estimated human pose using key points and 2D human skeleton features to get full human body features. The extracted features are then served to t-DSNE in order to eliminate the redundant features. Finally, the optimized features are infused into the recognizer engine as a k-ary tree hashing algorithm. The experimental results have shown significant results on two benchmark datasets, including the UCF Sports Action dataset with an accuracy of 88.50% and an 89.45% mean recognition rate on the YouTube Action database. The results revealed that the proposed system had achieved better human body part tracking and classification when compared with other state-of-the-art techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Heart Rate Based Cross-subject Stress Recognition 22

Heart Rate Based Cross-subject Stress Recognition

引用

22nd international conference on Cyberworlds, CW 2023

作者： Tan, Joanne Lim, Wei Lun Li, Ruilin Hsieh, Meng-Hsueh Sourina, Olga Chen, Chun-Hsien Nanyang Technological University School of Mechanical and Aerospace Engineering Singapore Nanyang Technological University Fraunhofer Singapore Singapore

ISBN: (纸本)9798350315653

Being in a state of stress can affect operators' performance and might lead to a decrease in attention and cognition, which could affect vessel operational safety within the Vessel Traffic Management System. By directly measuring operator's biosignals, it is possible to predict the level of his/her stress for corrective action to be taken, such as getting support from colleagues if needed. One of the convenient ways to monitor operators' stress is using mobile heart rate devices. In this paper, we propose a cross-subject stress recognition algorithm utilizing Heart Rate Variability (HRV) features and the Adaboost classifier. The algorithm is subject independent and can be calibrated using previously collected stress data to recognize the 2-level stress states of an unseen subject with 82.4% accuracy. © 2023 IEEE.

关键词： Machine learning

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A Novel Deep learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

引用

2nd international conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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Hyperparameters Optimization of Deep learning Models for Unsupervised Lung Cancer Detection 22

Hyperparameters Optimization of Deep Learning Models for Uns...

引用

22nd international conference on Cyberworlds, CW 2023

作者： Nafti, Najeh Besbes, Olfa Abdallah, Asma Ben Bedoui, Mohammed Hedi Medical Imaging and Technology Lab National Engineering School of Sfax Sfax Tunisia Sousse University Cosim Lab Supcom Isitcom Tunisia Medical Imaging and Technology Lab Isimm Monastir Tunisia Medical Imaging and Technology Lab Faculty of Medicine of Monastir Monastir Tunisia

ISBN: (纸本)9798350315653

Lung cancer remains a significant global cause of mortality, affecting populations worldwide. Deep learning (DL) systems show promise in early detection using clinical data to reduce mortality rates. However, these systems heavily rely on large amounts of annotated anomalous data, and heavily depends on selecting appropriate hyperparameters that define the network's structure and learning *** this study, we propose an optimization scheme based on Tree Parzen Estimator (TPE) and Bayesian optimization (BO) algorithms for hyperparameter optimization in unsupervised lung cancer detection. First, we used a fast residual attention GAN-based model. Then, we employed the Mixup consistency regularization technique to encourage the discriminator to attend the pixel-level details of the input data. Furthermore, a new cost function for the discriminator is defined based on the mixup to enhance the output. This study is evaluated in the context of lung cancer detection. When compared to empirical optimization, both TPE and Bayesian optimization demonstrate significant improvements in the precision of the fast Residual Attention GAN model used in this study. Various metrics, including precision, f1-score, and the area under the curve (AUC) are employed to assess the system's efficiency. © 2023 IEEE.

关键词： Cost functions

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A Continuous Verification Mechanism for Clients in Federated Unlearning to Defend the Right to be Forgotten 22

A Continuous Verification Mechanism for Clients in Federated...

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22nd IEEE international Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chen, Shanshan Liu, Jun Li, Yuying Li, Yamin Yang, Yang Liu, Gaoyang Wang, Chen School of Artificial Intelligence Wuhan China Hubei University School of Computer Science and Information Engineering Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798331509712

In Federated learning (FL), the regulatory need for the "right to be forgotten"requires efficient Federated Unlearning (FU) methods, which enable FL models to unlearn appointed training data. Associating with the emergence FU, verifying the performance of FU plays a critical role in evaluating the consistency FU methods, in case of the unexpected degradation of the FL model. Though well developed, none of the existing verification methods in FU stands for the clients who opt out of the FL process, which is a universal demand in FL. More specifically, after the clients quit the FL cooperation, they can no longer verify whether the FL model unlearns their data after the FL keeps training for several rounds. To this end, we introduce a continuous verification mechanism for FL clients, called Backdoor Attack-based Forgetting Verification (BAFV). Inspired by backdoor attack, BAFV embedded a persistent mark for the client that proposes to leave, with the intention that the client still has the right to verify of FU after leaving the FL cooperation for a relatively long period. Extensive experiments across diverse FU environments and datasets demonstrated that our method maintains the accuracy of model and provides clients with a continuous verification mechanism to defend their rights. Our code of BAFV is publicly available at: https://***/paper-liu/***. © 2024 IEEE.

关键词： Federated learning

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Construction and Application of the Knowledge Graph in Endangered Plants 22

Construction and Application of the Knowledge Graph in Endan...

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22nd IEEE/ACIS international conference on Computer and Information Science, ICIS 2022

作者： Wei, Haochuan Zhang, Qianchi Gao, Weixuan Meng, Xianghao Beijing Forestry University School of Information Science and Technology Beijing China Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Engineering Beijing China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

The current network information of endangered plants is scattered, making acquiring and reusing relevant knowledge difficult. The endangered plants' knowledge graph ePlantKG constructed in this paper can form a visual semantic information network. The data sources in this paper include China Rare and Endangered Plant Information Network, Baidu Encyclopedia, Wikipedia, and Kuaiming Encyclopedia. Firstly, we use a Python crawler to obtain network data and preprocess them. Then we use the obtained structured data combined with previously constructed plant ontology to define and build new ontologies. Next, through the rule-based method, we extract triples from semi-structured and unstructured data, fuse knowledge between heterogeneous data, and store them in the Neo4j database to form ePlantKG. Finally, we design and build a knowledge service platform to illustrate knowledge graphs and intelligent question answering. The intelligent question answering algorithm extracts features from the user's input text with TF-IDF and classifies questions with Naive Bayes. After realizing the similarity matching between entities and relations, we retrieve answers with the returned Cypher statement. The ePlantKG records 1926 species of endangered plants and 37860 species of common plants, and the image filling rate of endangered plants is more than 99 %. The platform implements several functions, e.g., graph display, entity recognition, and intelligent question answering. This paper realizes the information sharing and reuses on endangered plants, providing a method reference for applying knowledge graph in forestry intelligent question answering system and forestry big data analysis. © 2022 IEEE.

关键词： Knowledge graph

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WalkingStreet: Understanding Human Mobility Phenomena Through a Mobile Application 22nd

WalkingStreet: Understanding Human Mobility Phenomena Throug...

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22nd international conference on intelligent data engineering and automated learning

作者： Rosa, Luis Silva, Fibio Analide, Cesar Univ Minho ALGORITMI Ctr Dept Informat Braga Portugal Politecn Porto ESTG CIICESI Felgueiras Portugal

ISBN: (纸本)9783030916077;9783030916084

Understanding human mobility patterns requires access to timely and reliable data for an adequate policy response. This data can come from several sources, such as mobile devices. Additionally, the wide availability of communications networks enables applications (mobile apps) to generate data anytime and anywhere thanks to their general adoption by individuals. Although data is generated from personal devices, if a relevant set of metrics is applied to it, it can become useful for the authorities and the community as a whole. This paper explores new methods for gathering and analyzing location-based data using a mobile application called WalkingStreet. The article also illustrates the great potential of human mobility metrics for moving spatial measures beyond census units, key measures of individual, collective mobility and a mix of the two, investigating a range of important social phenomena, the heterogeneity of activity spaces and the dynamic nature of spatial segregation.

关键词： Geo-location data Human mobility metrics Mobile application Smart cities

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Deep Stable Representation learning on Electronic Health Records 22

Deep Stable Representation Learning on Electronic Health Rec...

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22nd IEEE international conference on data Mining (ICDM)

作者： Luo, Yingtao Liu, Zhaocheng Liu, Qiang Carnegie Mellon Univ Pittsburgh PA 15213 USA Kuaishou Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781665450997

Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypothesis fail to consider the agnostic distribution shifts, diminishing the generalization ability of deep learning models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical correlations between procedures and diagnoses that may change in different environments will be exploited, which can cause sub-optimal performances of deep learning models and spurious correlation between historical EHR and future diagnosis. To address this problem, we propose to use a causal representation learning method called Causal Healthcare Embedding (CHE). CHE aims at eliminating the spurious statistical relationship by removing the dependencies between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of independence between the embedded diagnosis and procedure features. Based on causal view analyses, we perform the sample weighting technique to get rid of such spurious relationship for the stable learning of EHR across different environments. Moreover, our proposed CHE method can be used as a flexible plug-and-play module to enhance existing deep learning models on EHR. Extensive experiments on two public datasets and five state-of-the-art baselines unequivocally show that CHE can improve the prediction accuracy of deep learning models on out-of-distribution data by a large margin. In addition, the interpretability study shows that CHE could successfully leverage causal structures to reflect a more reasonable contribution of historical records for predictions.

关键词： Healthcare informatics causal inference electronic health records out-of-distribution

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