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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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Load Fault Diagnosis in Induction Motor using Artificial intelligence Algorithm 3

Load Fault Diagnosis in Induction Motor using Artificial Int...

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Rajesh Kumar, B. Gowrisankar, M. Ramana, S. Dhatchina Aakash, P. Aravind, S. M. Kumarasamy College of Engineering Department of Eee Tamilnadu Karur India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Industries require early diagnosis of induction motor faults to avoid complete failure. The use of machine learning and condition monitoring to detect faults has huge promise. machine learning can be used to detect motor faults. To avoid losses, induction motor faults must be rectified promptly. Fault detection using machine learning algorithms is an excellent method for preventive maintenance. If any fault arises, the motor may continue to process and produce failure in windings core etc. Hence, the faults can be prevented by the monitoring the motor output values and cutoff the power before the motor gets damaged. This research develops a machine learning strategy based on algorithms in order to learn the characteristics from vibration signal's frequency distribution. This is mainly to characterize the operational status of induction motors. The operational status includes the parameters such as temperature, voltage and current. It automates and intelligently diagnoses faults by combining feature extraction and categorization. © 2022 IEEE.

关键词： Failure analysis

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Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling 3rd

Hypercomplex Generative Adversarial Networks for Lightweigh...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Sfikas, Giorgos Retsinas, George Gatos, Basilis Nikou, Christophoros Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Surveying and Geoinformatics Engineering University of West Attica Athens Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (纸本)9783031090363

Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Research on the Application of Computer Artificial intelligence Animation Technology in Short Video Production

Research on the Application of Computer Artificial Intellige...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Huiyu Lin Tongxi Lin School of Information Engineering Guangzhou Huashang Vocational College Guangzhou Guangdong China

This paper introduces a computer-based artificial intelligence short video generation system. A stepwise optimization conversion algorithm is presented. This algorithm can realize the fast calculation of large Angle offset and greatly improve the efficiency of short video generation. The complexity of the existing prediction unit is determined by its correlation with time, space and parent neighbor PUs, and the corresponding prediction model is quickly selected according to its complexity, so as to improve the compression efficiency. The system has the functions of content collection to release process, multi-format material synthesis, transcoding processing and so on. The software mainly realizes face recognition, speech recognition, speech synthesis, text recognition, sensitive information recognition, natural language processing and so on. It can better achieve content aggregation, classification and retrieval, thus improving the production effect. The results show that this method has strong functions of learning and intelligent processing. The system can quickly retrieve and organize the text, so as to improve the efficiency of writing, editing and publishing.

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Intelligent Flower recognition System Based on Deep Learning 3

Intelligent Flower Recognition System Based on Deep Learning

引用

3rd international conference on Artificial intelligence and Electromechanical Automation, AIEA 2022

作者： Gu, Fenfei Dept. of Big Data and Artificial Intelligence Anhui Xinhua University Anhui Hefei China

ISBN: (纸本)9781510657281

With the continuous expansion of the application fields of deep learning, human life is becoming more and more wonderful. This paper mainly uses the application of deep learning in the field of image recognition and uses deep learning algorithm to recognition the problem of flowers. Due to the huge number of flowering species, How to automatically recognition flowers through the system gives people more understanding of flowers while appreciating them, which is a topic with practical application value. This paper constructs a deep learning multi-classification model based on ResNet, and conducts training based on PyTorch framework on the data set of more than 3000 pictures of five kinds of flowers. After 50 rounds of iterative training, it finally achieves 91% average accuracy on the test dataset. Finally, a recognition system based on this model is realized by visualization technology, so as to facilitate users to effectively recognize the kind of flowers through this system. © 2022 SPIE.

关键词： Image recognition

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Analyzing Major Indian Protests using Nature Inspired Algorithms 3

Analyzing Major Indian Protests using Nature Inspired Algori...

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3rd international conference for Emerging Technology, INCET 2022

作者： Kumar, Mohit Badola, Mrigank Sweety, N. Vishwakarma, Dinesh Kumar Delhi Technological University Department of Information Technology Delhi India

ISBN: (纸本)9781665494991

The rates at which strikes, riots and protests are increasing these days is alarming and a matter of grave concern. In order to understand the intensity of a protest and eventually take steps in order to minimize massive possible damage, it is critical to analyze the sentiments towards that particular event. Previous works in this field have majorly used traditional machine learning approaches. However, we believe that incorporating nature-inspired algorithms along with BERT for feature selection can greatly improve the accuracy. In this work, we have proposed a novel approach wherein we use an amalgamation of BERT and nature-inspired algorithms to perform sentiment analysis of major protests in India. For this, we combine the feature extraction capabilities of BERT with both swarm intelligence as well as evolutionary algorithms. For comparative study, we have taken a model which only uses BERT along with KNN and compared it with models which use nature inspired algorithms as an additional step to perform feature selection. We have achieved remarkable improvement in accuracy by incorporating nature inspired algorithms with the highest improvement of 17.78% in classifying sentiments by using the Grey-Wolf optimizer. Significant improvements in accuracy were also observed by using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO) and Genetic Algorithm (GA). Snscrape has been used to extract tweets of major protests in India. Our results accurately correspond to the actual intensity of devastation that was observed during those protests. © 2022 IEEE.

关键词： Swarm intelligence

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A Hybrid Model to Predict the Breast Cancer using Stacking and Bagging Model 3

A Hybrid Model to Predict the Breast Cancer using Stacking a...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yuvalatha, S. Nithyapriya, S. Prabhavathy, S. Priyadharshini, R. Savitha, S. Kayathri, S. Bannari Amman Institute of Technology Department of Computer Science and Business Systems Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Christ the King Engineering College Department of Electronics and Communication Engineering Coimbatore India Sri Ranganathar Institute of Engineering Department of Computer Science and Engineering Coimbatore India PSR Engineering College Department of Computer and Science and Engineering Sivakasi India

ISBN: (纸本)9798350317022

Breast cancer is a malignant tumor that develops in the cells of the breast tissue. Breast cancer is one of the major causes of death for women globally. In the examination of medical data, breast cancer prediction is a difficult task. To make decisions and accurately distinguish between benign and malignant tumors, physicians and pathologists need certain automated technologies. In this paper, hybrid ensemble technique (Bagging and Stacking) is used to predict the breast tumors as benign and malignant tumors. In the proposed work, the subset of data is created from the initial Wisconsin (Diagnostic) Data Set by bootstrapping technique. Each bootstrap dataset is used to train the weak learner. The weak learners are K-Nearest Neighbors (KNN) Random Forest (RF), Decision Tree (DT) and Support Vector machine (SVM). The Logistic Regression (LR) is used as the Meta Learner. The Meta Learner uses the predictions of weak learners as its training data. The proposed hybrid ensemble model obtains an accuracy 98.7%, Precision 98.83%, Recall 98.54%, F1 Score 98.68% and 0.012% error. © 2023 IEEE.

关键词： Bagging Bootstrapping Ensemble Malignant Meta Learner Stacking

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3rd international conference on Artificial intelligence in HCI, AI-HCI 2022 Held as Part of the 24th HCI international conference, HCII 2022

3rd International Conference on Artificial Intelligence in H...

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3rd international conference on Artificial intelligence in HCI, AI-HCI 2022 Held as Part of the 24th HCI international conference, HCII 2022

ISBN: (纸本)9783031056420

The proceedings contain 39 papers presendted at a virtual meeting. The special focus in this conference is on Artificial intelligence in HCI. The topics include: Gamifying the Human-in-the-Loop: Toward Increased Motivation for Training AI in Customer Service;Analysis of the Impact of Applying UX Guidelines to Reduce Noise and Focus Attention;adoption and Perception of Artificial intelligence Technologies by Children and Teens in Education;extracting and Re-mapping Narrative Text Structure Elements Between Languages Using Self-supervised and Active Few-Shot Learning;Speech Disorders Classification by CNN in Phonetic E-Learning System;transformer-Based Multilingual G2P Converter for E-Learning System;misinformation in machine Translation: Error Categories and Levels of recognition Difficulty;emotional Communication Between Chatbots and Users: An Empirical Study on Online Customer Service System;give Me a Hand: A Scene-Fit Hand Posture Drawing Aid;trafne: A Training Framework for Non-expert Annotators with Auto Validation and Expert Feedback;customizable Text-to-Image Modeling by Contrastive Learning on Adjustable Word-Visual Pairs;Attention-Based CNN Capturing EEG Recording’s Average Voltage and Local Change;a New Human Factor Study in Developing Practical Vision-Based Applications with the Transformer-Based Deep Learning Model;scene Change Captioning in Real Scenarios;replacing Human Input in Spam Email Detection Using Deep Learning;Object Size Prediction from Hand Movement Using a Single RGB Sensor;a Systematic Review of Artificial intelligence and Mental Health in the Context of Social Media;evaluation of Webcam-Based Eye Tracking for a Job Interview Training Platform: Preliminary Results;developing and Testing a New Reinforcement Learning Toolkit with Unreal Engine;evaluation on Comfortable Arousal in Autonomous Driving Using Physiological Indexes;Measuring and Predicting Human Trust in Recommendations from an AI Teammate;preface;foreword.

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Application of A ∗ to the Generalized Constrained Longest Common Subsequence Problem with Many pattern Strings 3rd

Application of A ∗ to the Generalized Constrained Longes...

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3rd international conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Djukanovic, Marko Matic, Dragan Blum, Christian Kartelj, Aleksandar Faculty of Natural Sciences and Mathematics University of Banjaluka Banjaluka Bosnia and Herzegovina Campus UAB Bellaterra Spain Faculty of Mathematics University of Belgrade Beograd Serbia

ISBN: (纸本)9783031092817

This paper considers the constrained longest common subsequence problem with an arbitrary set of input strings and an arbitrary set of pattern strings as input. The problem has applications, for example, in computational biology, serving as a measure of similarity among different molecules that are characterized by common putative structures. We develop an exact A ∗ search to solve it. Our A ∗ search is compared to the only existing competitor from the literature, an Automaton approach. The results show that A ∗ is very efficient for real-world benchmarks, finding provenly optimal solutions in run times that are an order of magnitude lower than the ones of the competitor. Even some of the large-scale real-world instances were solved to optimality by A ∗ search. © 2022, Springer Nature Switzerland AG.

关键词： Computers

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Application of image processing in cancer imaging 3

Application of image processing in cancer imaging

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3rd international conference on machine Learning, Big Data and Business intelligence (MLBDBI)

作者： Wu, Jingyan Fuzhou Univ Maynooth Int Engn Coll Fuzhou Peoples R China

Cancer is a harmful disease. With the rapid development of AI technology, the use of AI technology to assist cancer diagnosis can improve the accuracy of diagnosis. In this paper, I review the application of AI image ... 详细信息

ISBN: (纸本)9781665417907

关键词： artificial intelligence image process iconography cancer tumor

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AI4LEGAL-KGSUM 2022 - Joint Proceedings of the 3th international Workshop on Artificial intelligence Technologies for Legal Documents and the 1st international Workshop on Knowledge Graph Summarization, co-located with the 21st international Semantic Web conference, ISWC 2022

AI4LEGAL-KGSUM 2022 - Joint Proceedings of the 3th Internati...

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3rd international Workshop on Artificial intelligence Technologies for Legal Documents and the 1st international Workshop on Knowledge Graph Summarization, AI4LEGAL-KGSUM 2022

The proceedings contain 14 papers. The topics discussed include: an anonymization tool for open data publication of legal documents;building and analyzing the Brazilian legal knowledge graph;introduction of artificial intelligence in Belgian court: failures, challenges and opportunities;LawSampo portal and data service for publishing and using legislation and case law as linked open data on the semantic web;finding case law: leveraging machine learning research to enhance public access to UK judgments;evaluation of data augmentation for named entity recognition in the German legal domain;towards building a legal virtual assistant based on knowledge graphs;an Indian court decision annotated corpus and knowledge graph;and summaries of knowledge graph entities: first steps to measure the relevance of symptoms to infer diseases.

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