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

共 7122 条记录，以下是171-180 订阅

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Real-Time Elderly Fall Detection as Ambient intelligence in Old Age Homes 3

Real-Time Elderly Fall Detection as Ambient Intelligence in ...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Shabadi, Laxmi Chaitra, M. Monik, L. Pavan, S.G. Deekshith, R.K. Narayan, Laxmi Sjb Institute of Technology Department of Computer Science and Engineering Bangalore India Bnm Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly because falls from this age group often result in fatalities and serious injuries. Fall detection devices that can quickly recognize falls and alert emergency services have become more and more popular as a result. The primary goal of the project is to increase elderly home safety by implementing an ambient intelligence-based automated emergency recognition system. We present a unique method for fall posture detection utilizing an intelligence surveillance camera and a class of efficient models called MobileNets for mobile and embedded vision applications. A real-time embedded solution persuaded from 'edge AI' is implemented using the state-of-the-art Object detection: single shot detector (SSD) Mobile Net V2 and Internet of things embedded GPU platform NVIDIA's Jetson Nano. © 2025 IEEE.

关键词： Ambient intelligence

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Smart Parking System using YOLO, ANPR and MQTT with Artificial intelligence Features 3

Smart Parking System using YOLO, ANPR and MQTT with Artifici...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yadav, Krrish Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798331527549

Smart parking systems leverage advanced technologies to optimize parking space utilization and enhance user experience. This research paper explores the design, implementation, and evaluation of a smart parking system aimed at improving accessibility and security in urban parking facilities. It utilizes real-time sensor data to provide accurate parking availability information and empowers users to book slots through a secure mobile web app. The app integrates user-friendly navigation to guide drivers to their booked spots. OTP generation bolsters security, preventing unauthorized parking occupancy. One of the most significant and difficult problems in image-based sequence recognition is scene text recognition, which is the focus of this study's investigation. A brand-new neural network architecture is put forth that unifies transcription, sequence modelling, and feature extraction into a single framework. It performs admirably in both lexicon-free and lexicon-based scene text recognition tasks and is not restricted to any predefined lexicon. It produces a much more manageable and effective model, making it more suitable for real-world application scenarios. © 2025 IEEE.

关键词： App development Neural network OTP generation Smart parking text recognition

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Apple image recognition model based on optimized sYOLOv5 network with small object detection 24

Apple image recognition model based on optimized sYOLOv5 net...

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3rd international conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Wei, Cunli Tan, Hao Zhang, Jie Feng, Mengdan Liu, Zirou Wang, Jianwei Zhang, Pan Tianjin Univ Technol Sch Comp Sci & Engn Tianjin 300382 Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Anhui Jianzhu Univ Sch Management & Econ Hefei 230000 Peoples R China Southwest Jiaotong Univ Sch Comp Sci & Artificial Intelligence Chengdu 611756 Peoples R China Sichuan Univ Jinjiang Coll Meishan 620860 Peoples R China

ISBN: (纸本)9798400716959

In this research, we employ a deep learning approach to extract features from an apple image dataset, enabling precise counting and localization of apples. We address challenges like low resolution and small targets by optimizing the network and implementing small target optimization. The focus in another task is on building a mathematical model using sYOLOv5 to assess apple ripeness based on color and texture features. Additionally, we compute the two-dimensional area and estimate mass by combining instance segmentation and traditional image processing techniques. Lastly, we train a fruit recognition model using sYOLOv5 on a harvested fruit dataset.

关键词： YOLOv5 network architecture small target detection optimization CBAM module instance segmentation Gurukin's theorem

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Research on User's Facial Expression Analysis when Watching TV 3

Research on User's Facial Expression Analysis when Watching ...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Wang, HongXia Xu, Fei Yan, XiangYu Li, Hui School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9781510667563

Facial expression is a basic way to express human emotions, and it is the primary medium for individuals to communicate with others. The display of facial expressions can better understand the other person's feelings and emotions. With the continuous development of artificial intelligence technology, the demand for human-computer interaction has also increased. The development of artificial intelligence plays an important role in face recognition and micro-expression changes when users watch *** of the small range of motion and fast change of microexpression, it is difficult to analyze it manually. Therefore, it is very necessary to develop a reliable recognition *** this paper, the face recognition model facenet model is used to learn the image features of the face, Yolov5 and Yolox neural network are used as the basis to compare the models trained, and the performance of the selected model is tested to select the best effect. © 2023 SPIE.

关键词： Face recognition

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Optimized HMI Strategies for Collaborative Driving Interfaces in L3+Autonomous Vehicles 3

Optimized HMI Strategies for Collaborative Driving Interface...

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3rd international conference on Robotics, Artificial intelligence and Intelligent Control (RAIIC)

作者： He, Banben Zhang, Yongliang Li, Lin Wang, Xiaocui Jin, Jingqiang Lan, Tian Dongfeng Motor Grp Res & Dev Inst Wuhan Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Amidst the pivotal transition of autonomous vehicles from Level 2 to Level 3, a fundamental paradigm shift occurs, moving from a shared human-machine control framework to a complete transfer of driving responsibilities. In this evolving context, the automated system assumes a primary role in executing driving tasks, while the driver transitions to a supervisory role, overseeing system behavior and intervening only when deemed necessary. This transition necessitates addressing complex challenges encompassing safety, liability, workload distribution, alongside fostering human-machine trust and enhancing system interpretability. To address these multifaceted challenges, we introduce a comprehensive human-machine shared driving interaction design strategy, referred to as the TSE design method. This strategy encapsulates the entire driving journey, commencing with pre-journey training and preparation, encompassing safe driving practices during the journey, and culminating in post-journey performance evaluation. Our primary emphasis lies in the intricate analysis of human-machine safety interaction strategies during the actual driving phase. Based on the current system status and road conditions, the automated system dynamically apportions driving tasks to the driver, considering factors such as the driver's workload, engagement level, and the complexity of take-over tasks. Furthermore, we delve into the intersectional impact of various factors, including the driver's mental model, attention allocation, situational awareness, human-machine trust, system transparency, and interpretive interfaces, on the overall performance of the human-machine shared driving system. To demonstrate the practical application of our design principles, we present concrete design exemplars derived from driver training sessions and take-over interaction design. These instances illustrate the effectiveness of our TSE design method in enhancing the safety, trust, and interpretability of the human-

关键词： L3+ autonomous driving HMI interaction design

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Karate First Kata Performance Analysis and Evaluation with Computer Vision and machine Learning 3

Karate First Kata Performance Analysis and Evaluation with C...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Elkess, George Elmoushy, Seif Atia, Ayman Faculty of Computer Science Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350306231

Karate kata is a martial art that involves performing a sequence of movements with accuracy and speed. In this paper, we present a system capable of automatically recognizing and evaluating karate performances using computer vision and machine learning. Our system comprises four main steps: pose estimation, movement detection, feature extraction, and classification. We utilize the ensembling of two models and 14 different binary classification models in the classification step. We have successfully developed a method that compensates for camera shake in movement detection within our karate performance recognition system. Subsequently, we compute features such as skew, skewness, kurtosis, and covariance. Lastly, a machine learning model is employed to classify the first karate kata video. We curate a dataset of karate kata videos and achieve an impressive accuracy of 91.99% in our classification task. © 2023 IEEE.

关键词： Computer vision

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Proceedings - 3rd international conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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Research and Application of an Improved TD3 Algorithm in Mobile Robot Environment Perception and Autonomous Navigation 3

Research and Application of an Improved TD3 Algorithm in Mob...

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3rd international conference on Robotics, Artificial intelligence and Intelligent Control (RAIIC)

作者： Fu, Bo Yao, Xulin Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Mobile robots demonstrate broad application potential in industrial, medical, and agricultural fields, with their core autonomous behavior capabilities relying on environmental perception technology. Environmental perception involves multiple aspects such as map construction and target recognition, requiring high real-time performance and accuracy. However, traditional methods are limited by predefined rules and models, making it difficult to adapt to complex and dynamic environments. This paper proposes an improved TD3 algorithm based on the PReLU activation function. The algorithm combines policy gradient and deep learning, optimizing algorithm training through the PReLU activation function to enhance convergence speed and model robustness. By integrating GCNV2 with the improved TD3 algorithm, the approach leverages the feature extraction capabilities of graph-structured data and the decision-making abilities of reinforcement learning, enhancing mobile robot performance in environmental perception and decision-making. This combined method exhibits significant advantages in handling complex graph-structured data and continuous control tasks. In a simulated environment, the dynamic scenario created in this study validates the effectiveness of the improved algorithm, achieving a high task completion rate for exploration. The algorithm was also deployed on a real vehicle, further verifying its robustness.

关键词： TD3 Reinforcement Learning Mobile Robots GCNV2 environmental perception

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3rd international conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

3rd International Conference on Machine Learning and Big Dat...

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3rd international conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

The proceedings contain 150 papers. The topics discussed include: analysis of communication strategy of Guzheng art based on artificial intelligence;research on the health detection and seismic performance evaluation of high-rise buildings;application of ecological energy saving design in building reconstruction project;computer network communication security encryption system based on ant colony optimization algorithm;an analysis of data mining techniques in software engineering database design;intelligent analysis algorithm for hidden danger identification of intelligent network monitoring system from the perspective of big data;human behavior recognition of video surveillance system based on neural network;application of AI intelligent learning system in multimedia demonstration;and application of artificial intelligence technology in power system stability assessment.

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ML_recognition: A Robust Model for Handwritten Digit recognition Using machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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