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检索条件"任意字段=2nd International Conference on Pattern Recognition and Machine Intelligence"

共 6962 条记录，以下是91-100 订阅

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Application of intelligence System: ANN and ANFIS for Enhanced Slope Stability Analysis 2nd

Application of Intelligence System: ANN and ANFIS for Enhanc...

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2nd international conference on Geotechnical Issues in Energy, Infrastructure and Disaster Management, ICGEID 2024

作者： Kumari, Pratima Sabri, Md Shayan Samui, Pijush Verma, Amit Kumar Department of Civil Engineering National Institute of Technology Patna India Department of Civil and Environmental Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819717569

To comprehensively address the mitigation and prevention of disasters in railroad embankments, landslides, and dam slope failures, a thorough slope stability analysis is essential. Various conventional approaches exist to investigate slope instability. Currently, geotechnical engineers have developed numerous soft computing techniques for conducting slope stability assessments. The current study investigates the machine learning based method including artificial neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) for slope stability assessment. To conduct this investigation, 700 databases were collected, incorporating input variables such as slope height, soil cohesiveness, internal angle of friction, slope angle, unit weight, and peak ground acceleration, with the aim of predicting the Factor of Safety (FoS) of soil slopes. The entire database was partitioned, allocating 70% for training the models and reserving the remaining 30% for model validation. To assess model accuracy, five statistical metrics, namely TIC, R2, RRSE, RAE, and LMI, were employed in both the training and testing phases. The statistical analysis reveals that the Adaptive Neuro-Fuzzy Inference System (ANFIS) model attains higher accuracy with values of (TIC = 0.0010;R2 = 0.9999;RRSE = 0.0105;RAE = 0.0087;LMI = 0.9913) during the training phase and (TIC = 0.0044;R2 = 0.9999;RRSE = 0.0308;RAE = 0.0204;LMI = 0.9796) during the testing phase. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Soil testing

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Enhancing 3D Referential Grounding by Learning Coarse Spatial Relationships 27th

Enhancing 3D Referential Grounding by Learning Coarse Spati...

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27th international conference on pattern recognition, ICPR 2024

作者： Joshi, Soham Tripathi, Aditay Gopalakrishnan, Viswanath Chakraborty, Anirban International Institute of Information Technology Bangalore India Indian Institute of Science Bengaluru India

ISBN: (纸本)9783031781124

Large-scale pre-training is commonly used in 2D referential grounding tasks owing to the easy availability of a large number of image-text pairs with corresponding bounding box annotations. However, for 3D referential grounding, the unavailability of high-quality 3D scene-text pairs with annotations poses a significant challenge. To address this issue, we leverage the large corpus of 3D scenes with bounding box annotations of object instances and design an automated strategy to synthesize scene-text data for pre-training by utilising the coarse spatial relationships between the objects in the scene without any human supervision. The proposed strategy first clusters the 3D bounding boxes and then uses these clusters to create pairwise and triplet relations between the objects in the 3D scene. We achieved improved results consistently across various top-performing methods in 3D referential grounding, when the proposed pre-training strategy is deployed. In addition to pre-training with the samples containing coarse spatial relations, we also encode semantic relationships between the bounding boxes conditioned on the language utterance, using a compatibility measure between the box features and the language utterance. To evaluate the performance of our proposed techniques, we conduct experiments on large-scale publicly available datasets, namely ScanRefer and ReferIt3D (SR3D and NR3D). Our proposed techniques can be seamlessly integrated with any off-the-shelf 3D referential grounding method. Specifically, when integrated with BUTD-DETR, we observed notable improvements of 2.2% and 1% in performance on the SR3D and NR3D datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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EEG-Based Reaction Time Prediction Using Covariance Augmented 2D Convolutional Neural Network 27th

EEG-Based Reaction Time Prediction Using Covariance Augmente...

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27th international conference on pattern recognition, ICPR 2024

作者： Parekkattil, Adarsh V. Varun, Sanjeev Kumar Bollu, Tharun Kumar Reddy Electronics and Communication Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India

ISBN: (纸本)9783031783975

Drowsy driving emerges as a major factor contributing to traffic accidents. Drowsiness is characterized by a feeling of tiredness and a compelling desire to sleep. It is evident through a gradual decrease in Reaction Time (RT) of the driver. Reaction Time (RT) refers to the duration taken for a person or system to respond to a given sudden unexpected stimuli or event. Accurate prediction of Reaction Time (RT) to unexpected events is crucial for enhancing safety and performance. Electroencephalogram (EEG), capturing the brain’s electrical signals, demonstrates the most substantial correlation with drowsiness. Consequently, EEG is broadly recognized as a trustworthy tool for assessing drowsiness, fatigue, and overall performance. While many studies have utilized traditional machine learning and deep learning techniques to detect drowsiness or alertness from EEG data, there has been limited research focused on accurately predicting Reaction Time (RT). In this study, we aim to forecast drivers’ reaction times using EEG data through a novel Covariance 2D CNN-LSTM framework. Here, the objective is to forecast Reaction Time (RT) based on a 5-second EEG trial that precedes it. The superiority of the proposed method was validated through regression metrics, specifically Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), and was compared against current state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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2nd international conference on Business Data Analytics, ICBDA 2023

2nd International Conference on Business Data Analytics, ICB...

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2nd international conference on Business Data Analytics, ICBDA 2023

ISBN: (纸本)9783031807770

The proceedings contain 28 papers. The special focus in this conference is on Business Data Analytics. The topics include: A Novel Approach for Diabetic Prediction Using Attribute Subset Selection, K-Means and Logistic Regression;diabetic Retinopathy Detection Approach Using Convolution Neural Networks;voting Regression Model for the Air Quality Prediction;Quantitative Assessment on Investigation on the Impact of Artificial intelligence on HR Practices and Organizational Efficiency for Industry 4.0;NIR Spectroscopy for Freshness Detection and Classification of Chicken Eggs;Human Action recognition in Infrared Domain: A Study on IITR-IAR Dataset;Software Requirements Prioritization Using Fuzzy Based TOPSIS Methods;predictive Analytics for Inventory Management in Multi-Vendor E-Commerce;Data Analytics Augmented by AI in the Realm of 6G Wireless Communication;big Data-Powered Analytics for Fortifying Virtualized Infrastructure Security in the Cloud;orthogonal Polynomials and Their Engineering Applications;Explainable AI for Drone Data Analytics in Aerial Computing;correlational Analysis of Risk-Taking Propensity in Adolescents;Analytical Insight into Cutting-Edge Image Captioning for Advanced ChatGPT Functionality;an Expert System for Talent Prediction and Enhancement of Non-Talented Cricket Performers;Predicting Early Dropouts in SWAYAM MOOCs Using machine Learning Techniques: A Comparative Analysis;data Analysis of Social Media’s Impact on Promotion of Organic Products in Uttarakhand;enhancing Academic Performance Prediction Through K-Means Clustering and Comparative Evaluation of machine Learning Algorithms: A Case Study on Student Dataset;cognitive Data Underwriting with Automation: A Survey;Multi-Level Thresholding Segmentation of Chest X-ray Images of COVID-19 Patients Using Chaos Game Optimizer and Utilizing Kapur's Entropy;concrete Strength Prediction Using machine Learning.

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iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection 27th

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detec...

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27th international conference on pattern recognition, ICPR 2024

作者： Jiang, Jian-Jian Wu, Xiao-Ming Chen, Zibo Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031781124

For 6-DoF grasp detection, we aim at introducing a new interactive 2D-3D framework which filters out irrelevant information and makes both modalities collaborate effectively to generate robust grasps and accelerate inference speed greatly. This cannot be accomplished by existing works on learning to grasp that merely utilize 3D point clouds or leverage both 2D textures and 3D point clouds. Our framework is called iGrasp, a novel three-step design between 2D textures and 3D point clouds, where the interaction modelling enhances both modalities. Concretely, we propose the 2D-to-3D interaction to leverage objectness masks generated from 2D textures to filter out target-irrelevant information in 3D point clouds. Then, we introduce the 3D-to-2D interaction to leverage structural priors from 3D point cloud features with cross-attention and cylinder grouping to refine 2D texture features. Finally, we combine the refined 2D texture features and 3D point cloud features for generating high-quality 6-DoF grasp poses. Our experiments on the large-scale real-world dataset, namely GraspNet-1Billion, demonstrate that iGrasp surpasses state-of-the-art methods by 4.66/3.53 mAP on RealSense/Kinect and reduces the inference time by 28%. Real-world experiments further verify the effectiveness of iGrasp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Beyond Poverty: Identifying the Overlooked Factors Influencing Education

Beyond Poverty: Identifying the Overlooked Factors Influenci...

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Computational intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Kritika Jain Ayush Kumar Bennett University Greater Noida

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Poverty is the most critical factor hindering quality education for students all over the world and sustaining a vicious cycle of inequity. Poverty affects over 700 million people worldwide and causes a deep impact on students learning. Education, a widely accepted means of breaking poverty, is pivotal to the United Nations Sustainable Development Goals (SDGs): 1-No Poverty and 4-Quality Education. Keeping these goals in mind, this work explores the correlation between poverty and education, focusing on the socioeconomic linkages, challenges of resource scarcity and many more. The paper discovers 16 socioeconomic factors, in addition to financial factors that affect students’ academics. The work also proposes strategies with special emphasis on improved access, inclusion, and learning outcomes, through past literature, and by leveraging machine learning algorithms: Random Forest and XGBoost.

关键词： Couplings machine learning algorithms Correlation Education Focusing Communications technology Socioeconomics Sustainable development Random forests Computational intelligence

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MLPR 2024 - Proceedings of the 2024 2nd international conference on machine Learning and pattern recognition

MLPR 2024 - Proceedings of the 2024 2nd International Confer...

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2nd international conference on machine Learning and pattern recognition, MLPR 2024

ISBN: (纸本)9798400710001

The proceedings contain 9 papers. The topics discussed include: optimizing classification algorithms through dMeans: a novel dimensionality reduction technique;a data-driven approach to discover association between adverse events and recalls of 510(k) medical devices;a potential study on the application of data cartography techniques to tree-based models: a methodological approach;FSGAN-Key: a novel frequency-shifted generative adversarial network using voice features for cryptographic key;GHOS vs. traditional similarity measures: a comprehensive study of data stream classification efficiency;enhancing segmenter with masked patch modeling: development of the mask-segmenter model;reconstruction of computed tomography images based on the filtered back-projection algorithm;and directed fMRI-based functional connectivity estimation using physics-informed neural networks.

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Synergizing Underwater Robotics and AI: A Tethered ROV and YOLOv7 Approach to Giant Clam Detection 2nd

Synergizing Underwater Robotics and AI: A Tethered ROV and Y...

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2nd international conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Badhan, Al Mominul Haque Hanif, Mariam Faudzi, Ahmad Athif Mohd Hafidz, Muhamad Hazwan Abdul Ruslan, Mohammad Izzuddin Bin Adam, Muhammad Amirul Amri Khamis, Nurulaqilla Faculty of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia A2TECH Sdn. Bhd Johor Bahru81310 Malaysia

ISBN: (纸本)9789819639489

Integrating a tethered Remotely Operated Vehicle (ROV) with the YOLOv7 object recognition algorithm offers a novel approach to marine monitoring, particularly for tracking giant clam populations in the reefs of Tioman Island, Malaysia. The ROV, designed for enhanced underwater exploration, is equipped with advanced imaging and sonar technologies, resilient tethered power management and communication systems. The control and navigation system are built on the pixhawk flight controller, supported by ArduPilot firmware and a customized QGroundControl interface, ensuring real-time operations. The primary objective of this research is to develop an algorithm capable of accurately recognizing and counting giant clams through underwater images and videos. The study compares the performance of the YOLOv7 algorithm with the manual counting. The findings indicate that the YOLOv7 algorithm demonstrates average accuracy of the model of 79.65%. The integration of YOLOv7 enables identification and quantification of giant clam populations enhancing data collection for research and conservation efforts providing practical solutions for environmental monitoring and the sustainable management of marine resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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1st international conference on pattern Analysis and machine intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st international conference on pattern Analysis and machine intelligence, ICPAMI 2024

ISBN: (纸本)9789819633487

The proceedings contain 28 papers. The special focus in this conference is on pattern Analysis and machine intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of machine Vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical Image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for Remote Sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for Remote Sensing Image Change Detection;the Method for Three-Dimensional Visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent Image recognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the Image Sensor and CNN-GRU Fusion Neural Network;Stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A Robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature recognition Method Based on Comb Filter;emotional State recognition of English Learners Based on Deep Learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning;quadruped Robot System Based on Proprioceptive Vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the Visual-Inertial Odometry Algorithms.

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Vision-Based Hand Gesture recognition System for Assistive Communication Using Neural Networks and GSM Integration

Vision-Based Hand Gesture Recognition System for Assistive C...

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Computational intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pooja Singhal Sonia Verma Ritu Gupta Rishi Kumar Rajneesh Kumar Arya Department of Computer Science ABES Engineering College Ghaziabad Uttar Pradesh India Department of Computer Science Bhagwan Parshuram of Institute and Technology GGSIPU Delhi India Department of Computer Science and Enggineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This paper explores the application of vision-based hand gesture recognition in addressing communication challenges faced by deaf and dumb individuals within the realm of human-computer interaction (HCI). Highlighting the barriers posed by conventional communication methods and sign language interpretation, the paper introduces the Dumb Aid Phone system as an innovative solution. This system, functioning as an interface, enables seamless interaction for non-speaking individuals with their surroundings. Utilizing image processing and neural networking, the system captures and interprets hand gestures, matching them with a database to generate corresponding speech sounds. Enhanced by a web camera and a GSM module, the Dumb Aid Phone system not only facilitates effective communication but also holds the potential to revolutionize accessibility, contributing to a more inclusive society for the deaf and dumb community.

关键词： Hands GSM Human computer interaction Sign language Image recognition Databases Neural networks Feature extraction Speech processing Computational intelligence

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