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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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2023 IEEE 3rd international conference on Computer Communication and Artificial intelligence, CCAI 2023

2023 IEEE 3rd International Conference on Computer Communica...

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3rd IEEE international conference on Computer Communication and Artificial intelligence, CCAI 2023

ISBN: (纸本)9798350335262

The proceedings contain 83 papers. The topics discussed include: self-supervised representation learning for time series via temporal contrasting and transformation;early warning and screening of elderly cognitive impairment based on machine learning algorithm;chewing detection using brightness changes in video based on deep learning;multi-agent reinforcement learning investigation based on football games;predicting the trajectory of ai utilizing the Markov model of machine learning;machine learning approach to sentiment recognition from periodic reports;deep learning models for cancer classification from microarray gene expression profiles;neural abstractive summarization: a brief survey;towards accurate crowd counting via smoothed dilated convolutions and transformer;hyperbolic graph convolutional networks for aspect-based sentiment analysis;survey of neuromorphic computing: a data science perspective;poultry disease identification based on light weight deep neural networks;fairness and effectiveness in federated learning on non-independent and identically distributed data;and corpus database management design for Chinese-Portuguese bidirectional parallel corpora.

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Utilizing Swarm intelligence for Optimized Video Data Distribution in Edge Networks 3

Utilizing Swarm Intelligence for Optimized Video Data Distri...

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3rd international conference on Computing and machine intelligence (ICMI)

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy Univ Mt Union Dept Math & Comp Sci Alliance OH 44601 USA Univ Mississippi Dept Elect & Comp Engn University MS 38677 USA Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

ISBN: (纸本)9798350372977;9798350372984

The proliferation of high-definition video streaming services poses significant challenges in data distribution, particularly in edge networks where bandwidth and resources are often limited. This paper explores the application of swarm intelligence algorithms to optimize video data distribution in such networks. We propose a novel framework that employs swarm-based techniques, specifically focusing on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to enhance streaming. By simulating real-world edge network conditions, we implemented and evaluated our framework. Our approach dynamically adjusts the data flow based on network conditions and user demand, ensuring optimal resource utilization and improved streaming quality. Initial results demonstrate a significant enhancement in data throughput and resource allocation efficiency. Specifically, our framework improved video data distribution efficiency by 35% compared to traditional methods. Furthermore, the use of ACO algorithms resulted in a 40% reduction in latency and a 30% increase in overall user satisfaction, as measured by standard Quality of Experience (QoE) metrics. The PSO algorithm showed a notable improvement in balancing load among edge nodes, leading to a 25% increase in network resource optimization.

关键词： video streaming swarm intelligence edge networks particle swarm optimization ant colony optimization network optimization

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EngageDat-vL: A Multimodal Engagement Dataset Comprising of Emotional, Cognitive, and Behavioral Cues in Virtual Learning Environment 10th

EngageDat-vL: A Multimodal Engagement Dataset Comprising of ...

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10th Biennial international conference on pattern recognition and machine intelligence (PReMI)

作者： Akre, Sarthak Palandurkar, Nilesh Iyengar, Akshat Chayande, Gourav Kumar, Praveen Visvesvaraya Natl Inst Technol South Ambazari Rd Nagpur 440010 Maharashtra India

ISBN: (纸本)9783031451690;9783031451706

To assess student engagement in an e-learning environment, this work proposes a novel learning analytics dataset collection that combines emotional, cognitive, and behavioral data. The dataset includes facial expressions recorded by a webcam during e-learning sessions and log data such as play, pause, seek, course views, course material views, lecture views, quiz responses, etc. The procedure for gathering the data and the difficulties encountered are covered in the study. Combining emotional, cognitive, and behavioral cues for engagement detection in e-learning settings is made possible by this dataset, which represents a significant advancement in the field of learning analytics.

关键词： Learning analytics Facial emotion recognition Engagement detection

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CABPT: Predicting Southern California Marine Biotoxins in Shellfish via Deep, Spatiotemporal, Multivariate Prediction 3

CABPT: Predicting Southern California Marine Biotoxins in Sh...

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3rd international conference on Computing and machine intelligence (ICMI)

作者： Ma, Yuqin Harker Sch Dept Sci San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

The threat of marine biotoxin contamination shellfish jeopardizes public health, ocean biodiversity, and aquaculture. This paper establishes the first California Marine Biotoxin Prediction Tool (CABPT), a novel and scalable AI-based framework for predicting the concentrations of domoic acid and saxitoxins in California. Development of the CABPT includes the following tasks: system designing, data sourcing and preprocessing, AI model building, and web server framework deploying. The CABPT integrates six data types, dissolved oxygen, pH, sea surface temperature, salinity, and precipitation, for predictive modeling in two Southern California regions, Santa Cruz and Santa Barbara. Categorical classification prediction was tested on two deep learning architectures (long short-term memory (LSTM), gated recurrent units (GRUs)) and two classical machine learning architectures (random forest (RF), gradient boosting (GB)). The results indicated deep learning models performed better than classical machine learning models. Specifically, LSTM models consistently outperformed all other models, particularly in handling class imbalance, achieving an average accuracy of 99.14% and performing more accurately on minority classes by an average of 50.92% compared to its deep learning counterpart, GRUs. Based on location-specific data from Santa Cruz and Santa Barbara and with time series cross-validation, the tool's 3-4-week forecast horizon aids in the timely and reliable flagging of shellfish species as dangerous or lethal for consumption. The CABPT has direct impact as a powerful architecture for marine safety and is poised to provide authorities and the public with actionable insights for proactive biotoxin management.

关键词： marine biotoxin artificial intelligence multivariate prediction spatiotemporal analysis Southern California

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Proceedings of 2023 3rd international conference on Robotics and Artificial intelligence, JCRAI 2023

Proceedings of 2023 3rd International Conference on Robotics...

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3rd international conference on Robotics and Artificial intelligence, JCRAI 2023

ISBN: (纸本)9798400707704

The proceedings contain 35 papers. The topics discussed include: design of inspection robot system for mining belt conveyor;efficient and robust robotic grasping in cluttered scenes: a point cloud-based approach with heuristic evaluation;a food package recognition and sorting system based on structured light and deep learning;research on machine learning algorithm-based approach for detecting abnormal data from environmental sensors in different dimensions;a novel method for detecting the width of tobacco filaments based on coordinate point fitting;design and implementation of the gait debugging platform for a kid-sized humanoid soccer robot;research on the application of artificial intelligence in comparative analysis of man-machine translation;conceptual design of a soft robot for camera guidance in endoscopic surgical interventions;and a prediction algorithm for user satisfaction rating based on random forest optimized by simulated annealing.

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Language Learning machine Design based on Artificial intelligence 3

Language Learning Machine Design based on Artificial Intelli...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Yang, Chenhe School Of Foreign Languages Guangdong University Of Science And Technology Dongguan China

ISBN: (纸本)9798350318609

In view of the limitations of the traditional D-P algorithm in the design of language learning machine, a design scheme of language learning based on artificial intelligence was proposed. Firstly, the influencing factors is accurately located through the constitutional neuron theory, and the indicators is reasonably divided to reduce interference, and the language learning machine design scheme is constructed by artificial intelligence. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional D-P algorithm in terms of language learning machine design accuracy and processing time of influencing factors, and has obvious advantages. In this paper, this paper proposes an AI-based language learning design scheme to better solve this problem. The scheme accurately locates the influencing factors through the constitutional neuron theory, so as to determine the division of indicators, and uses artificial intelligence to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the use of artificial intelligence-based simulation scheme in the design of language learning can better solve the limitations of traditional D-P algorithms and improve the simulation accuracy and efficiency. © 2024 IEEE.

关键词： Learning algorithms

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Architectural Decorative Image pattern recognition in the Context of Iconography for Efficient Information Mining 3

Architectural Decorative Image Pattern Recognition in the Co...

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Qin, Yu College of Art and Design Guangzhou Panyu Polytechnic Guangzhou City511483 China

ISBN: (纸本)9798350396157

This study addresses the crucial task of architectural decorative image pattern recognition in the context of iconography, with an emphasis on efficient information mining. The proposed research work presents a novel algorithm that combines edge detection, ResNet50, and the triplet attention mechanism for image style analysis. The proposed methodology is organized into comprehensive sections, including a literature review that explores various studies in image pattern recognition. The proposed algorithm focuses on overcoming the challenges faced by existing deep learning methods in capturing aesthetic style features. It introduces a multifaceted self-supervised task and incorporates edge detection using the Canny method, followed by the application of ResNet50 with a triplet attention mechanism. The paper then focuses on the optimization of the network structure for the targeted task of architectural decorative image recognition, introducing a feature embedding encoder (FEE) to effectively handle multi-level structures. In the experimental phase, the proposed model is tested against traditional CNN and FCM models, demonstrating superior performance with recognition accuracy consistently ranging from 97% to 99%. The comparative analysis highlights the effectiveness of the proposed algorithm in achieving high accuracy, positioning it as a promising solution for image recognition tasks. © 2024 IEEE.

关键词： Deep learning

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Transfer Learning and Fine-tuned CNN Architecture for Dog Breed Classification 3

Transfer Learning and Fine-tuned CNN Architecture for Dog Br...

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3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Gunaranjan, K. Vijay, K. Naveen, P. St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India SRM institute of science and technology Department of computational intelligence Chennai India

ISBN: (纸本)9798350372120

This study presents a comprehensive approach to dog breed classification, leveraging transfer learning algorithms and Convolutional Neural Networks (CNNs). The dataset utilized for training and evaluation is sourced from the Dog Breed Identification Dataset, encompassing 120 distinct breeds. A pre-trained CNN model is employed, and through the application of transfer learning, the proposed models achieve an impressive accuracy rate of 99%. The primary objective is to enable users to submit images of dogs, with the model accurately identifying the specific breed from the diverse dataset. The research underscores the efficacy of transfer learning and pretrained models in achieving exceptional accuracy for the nuanced task of dog breed classification, contributing to the advancements in image recognition within the context of diverse canine breeds. © 2024 IEEE.

关键词： Convolutional neural networks

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machine Learning Based pattern recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

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3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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AI-Powered Real-Time Math Equation Editor with Multi-Modal Input for Enhanced Interaction 3

AI-Powered Real-Time Math Equation Editor with Multi-Modal I...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Saravanan, Dhanush Muthuraj, Murugesh Rangasamy, Prasanth Raja, S. Tamizharasan, D. K.S.Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode India K. S. Rangasamy College of Technology Department of Mathematics Tiruchengode India

ISBN: (纸本)9798331509675

This paper proposes a mobile application that translates vocal input into real-time, editable arithmetic equations, thus serving as a flexible input method for mathematical expressions. The application will allow users to speak, change, and export equations as images. It offers such features as real-time recognition of handwriting, creation of documents from stored files, and scanning pictures to digitize handwritten mathematical content. In future versions, it will incorporate step-by-step solutions for equations. The solution thus enhances access and efficiency in working with mathematical information by the user through offline capability, support for more than one language, and seamless integration with other applications. © 2025 IEEE.

关键词： Character recognition

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