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检索条件"任意字段=International Conference on Computational Intelligence for Modelling, Control and Automation"

共 2199 条记录，以下是41-50 订阅

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation

Efficient Facial Expression Recognition Network Based on Spo...

引用

IEEE international conference on Industrial Technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition computational modeling Noise Neural networks Interference Data models

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Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

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Intelligent control, Computing and Communications (IC3), international conference on

作者： Kurma Mahesh Paladi Aishwarya Sabavath Nithin Shanmugasundaram Hariharan Bandar Vaishnavi Vasantha S V Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance.

关键词： Heart Machine learning algorithms Accuracy computational modeling Medical services Predictive models Prediction algorithms Boosting Classification algorithms Diseases

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international conference on computational Inteligence for modelling control and automation and international conference on Intelligent Agents Web Technologies and international Commerce - Cover

International Conference on Computational Inteligence for Mo...

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international conference on computational intelligence for modelling, control and automation, and international conference on Intelligent Agents, Web Technologies and Internet Commerce

Presents the front cover or splash screen of the proceedings.

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international conference on computational Inteligence for modelling control and automation and international conference on Intelligent Agents Web Technologies and international Commerce - Table of contents

International Conference on Computational Inteligence for Mo...

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international conference on computational intelligence for modelling, control and automation, and international conference on Intelligent Agents, Web Technologies and Internet Commerce

Presents the table of contents of the proceedings.

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A Fully Sparse Detector with Cascade Heads for LiDAR-based 3D Object Detection

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3...

引用

international conference on Intelligent control and Information Processing (ICICIP)

作者： Junnan Chen Gang Zhang Xiaolin Hu Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

LiDAR-based 3D object detection is a critical component of perception systems in autonomous driving. Existing detectors typically rely on dense feature maps for 3D object prediction. However, as the perception range expands, the computational overhead associated with dense feature maps increases quadratically, creating significant challenges for scaling to long-range detection scenarios. To address this issue, fully sparse detectors have recently been proposed, but they often suffer from suboptimal performance or excessively complex architectures. In this paper, we present CHNet, a fully sparse detector designed to overcome these limitations by introducing a cascade head module, which consists of two sequential heads. By generating coarse object predictions, creating new feature points based on centers of the predicted objects, and aggregating features of the entire object to these new center points, CHNet enhances the qualities of object feature representations, achieving higher detection performance. Notably, CHNet effectively addresses the center feature missing problem in LiDAR-based 3D object detection by adding at most a hundred new feature points, offering a potential avenue for future research. On the widely used Waymo Open and Argoverse2 datasets, CHNet achieves impressive results without any additional embellishments.

关键词： Three-dimensional displays Aggregates Detectors Object detection Information processing Computer architecture Feature extraction Autonomous vehicles Intelligent control

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international conference on computational Inteligence for modelling control and automation and international conference on Intelligent Agents Web Technologies and international Commerce - Title

International Conference on Computational Inteligence for Mo...

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international conference on computational intelligence for modelling, control and automation, and international conference on Intelligent Agents, Web Technologies and Internet Commerce

The following topics are dealt with: intelligent agents and ontologies; data mining, knowledge discovery and decision making; intelligent systems; Web technologies and Web services; virtual reality and games; image processing and image understanding techniques; adaptive control and automation; modelling, prediction and control; multi-agent systems and computational intelligence; agent systems, personal assistant agents and profiling; fuzzy systems for industrial automation; control strategies; neural network applications; clustering, classification, data mining and risk analysis; dynamics systems; innovative control systems, hardware design and implementation; robotics and automation; e-business, e-commerce, innovative Web applications; Web databases; diagnosis and medical applications; learning systems; optimization, hybrid systems, genetic algorithms and evolutionary computation control applications; online learning and ERP; knowledge acquisition and classification; nanomechatronics; simulation and control; mobile network applications; information retrieval; Bayesian networks; human computer interaction; cognitive science; mobile agents; knowledge management; intelligent control; e-search and navigation; security.

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Enhancing Industrial automation Flexibility Through Neural Network-Empowered Machine Vision Applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st international conference on Artificial intelligence and its Applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

Vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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International Conference on Computational Inteligence for Mo...

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international conference on computational intelligence for modelling, control and automation, and international conference on Intelligent Agents, Web Technologies and Internet Commerce

Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center. The papers in this book comprise the proceedings of the meeting mentioned on the cover and title page. They reflect the authors' opinions and, in the interests of timely dissemination, are published as presented and without change. Their inclusion in this publication does not necessarily constitute endorsement by the editors or the Institute of Electrical and Electronics Engineers, Inc.

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Topic modelling using Transfer Learning: Issues and Challenges

Topic Modelling using Transfer Learning: Issues and Challeng...

引用

Intelligent control, Computing and Communications (IC3), international conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Preethi Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

A machine learning method, transfer learning, uses information from one job or area to enhance effectiveness in a related but distinct task or area. Transfer learning enables using pre-trained models that were previously learned using a sizable and varied dataset in the original domain instead of creating a model from the beginning for the intended activity. Usually, using a lesser dataset, the pre-trained model's representations and knowledge may be improved upon or applied to the intended goal or area. In natural language processing (NLP) and machine learning, topic modeling is an approach that finds and extracts the fundamental concepts or subjects from a group of documents or textual data. Despite prior experience with the aspects, this unsupervised learning method seeks to uncover the text's latent semantic structure. This paper's primary goal is to investigate the possibilities of transfer learning for text topic modeling. Additionally, to assess the correctness of the pre-trained model's performance. The paper also describes the issues and challenges associated with the text for performing the topic modeling using transfer learning. This paper delves into the methodologies and techniques of topic modeling using transfer learning. It explores how this novel approach transcends the constraints of conventional topic modeling, enabling the extraction of meaningful topics from data-rich source domains to improve topic modeling performance in data-scarce or specialized target domains. By integrating the principles of transfer learning into the topic modeling framework, we unlock the potential to enhance the interpretability, accuracy, and applicability of topic modeling across diverse domains and applications. This paper sheds light on the transformative power of transfer learning in topic modeling.

关键词： Performance evaluation Adaptation models Accuracy computational modeling Transfer learning Semantics Data models Natural language processing Data mining Unsupervised learning

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Opposition-based learning: A new scheme for machine intelligence

Opposition-based learning: A new scheme for machine intellig...

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international conference on computational intelligence for modelling, control and automation/international conference on Intelligent Agents Web Technologies and international Commerce

作者： Tizhoosh, Hamid R. Univ Waterloo Pattern Anal & Machine Intelligence Lab Waterloo ON N2L 3G1 Canada

ISBN: (纸本)0769525040

Opposition-based learning as a new scheme for machine intelligence is introduced. Estimates and counter-estimates, weights and opposite weights, and actions versus counter-actions are the foundation of this new approach. Examples are provided Possibilities for extensions of existing, learning algorithms are discussed. Preliminary results are provided.

关键词： Learning systems

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