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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是91-100 订阅

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Classification of Currencies as Authentic or Counterfeit: A data-Driven Approach 3rd

Classification of Currencies as Authentic or Counterfeit: A ...

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3rd international conference on Signal and data Processing, ICSDP 2023

作者： Koushik, K. Venkat Sai, G. Chaurasiya, Rahul K. Department of ECE Maulana Azad National Institute of Technology Bhopal India

ISBN: (纸本)9789819795772

The pervasive issue of counterfeit currency poses a significant threat to global economies, leading to substantial financial losses and eroding trust in financial institutions. While traditional methods of counterfeit detection rely on visual inspections and security feature verification, the advent of sophisticated counterfeiting techniques necessitates more advanced solutions. This study explores the application of machine learning (ML) algorithms to the problem of currency authentication, utilizing a dataset comprising images of banknotes. We employed a convolutional neural network (CNN) architecture, tailored for feature extraction from image data, which has demonstrated exceptional performance in pattern recognition tasks. Our proposed model introduces a novel preprocessing algorithm that enhances the distinctive characteristics of genuine and counterfeit notes, improving CNN's classification efficacy. We benchmarked the performance of our model against traditional ML classifiers, such as Support Vector machines (SVM) and Random Forests. The analysis reveals the strengths of our model, including its robustness to variations in image quality and its ability to generalize from limited training data. However, limitations were observed in terms of computational intensity and the need for a substantial dataset to optimize model parameters. The findings underscore the potential of automated, ML-driven techniques in revolutionizing the detection of counterfeit currencies, presenting a scalable and effective solution to a complex challenge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Counterfeit currency notes data-driven approach machine learning

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Predicting Student Attention And Engagement Through Eye-Tracking Using machine learning

Predicting Student Attention And Engagement Through Eye-Trac...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Kirubakaran R Suganthi N Lokeshwaran V Akilan V E Shahidha S Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents a comprehensive approach to predicting student engagement and learning behavior by analyzing eye-tracking data combined with behavioral analysis using camera-based recognition algorithms. The proposed system leverages WebGazer for eye-tracking to monitor gaze patterns and uses computer vision algorithms to recognize behaviors and emotional states. Feelings like engagement, frustration, or perplexity are identified using facial expression analysis. The system aims to provide real-time, actionable insights to students and educators by relating to emotional and concentration states by combining different modalities, thereby improving personalized learning strategies and educational outcomes.

关键词： Emotion recognition Computer vision Gaze tracking machine learning Educational technology Prediction algorithms Real-time systems Telecommunication computing pattern recognition Monitoring

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

3rd International Conference on Machine Intelligence and Sma...

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

ISBN: (纸本)9783031317224

The proceedings contain 59 papers. The special focus in this conference is on machine Intelligence and Smart Systems. The topics include: Chronic Kidney Disease Prediction and Interpretation Using Explainable AI;systematic Review and Analysis of Artificial Intelligence-Based Breast Cancer Classification and Detection;war of Tweets: Sentiment Analysis on Ukraine Russia Conflict;Implementing HRRN for Evaluating Cloud Performance Using Reinforcement learning;using machine learning for Prediction of Obstructions for Indoor Location Systems;privacy Threats and Protection in Artificial Intelligence and machine learning;Combining Linguistic Information with BERT for Span Based End-to-End Aspect Based Sentiment Analysis;a Dimensionality Reduction Model: A Retrospective Approach on Dementia Triggering Parameters and Feature Ranking;effective Identification of Lung Diseases Using Few-Shot learning;comparative Study on Classification Based-data mining Techniques in Early Diabetes Prediction;optimize machine learning Model for Sentiment Analysis of Online Education During Covid-19 Pandemic;review on the Challenges and Future Directions of Deep learning-Based Techniques for Advance Prediction of Cardiac Attack;different Techniques for Detecting Plant Leaf Disease Using machine learning;proposed Framework of Extensive Humanoid Design Cycle and Recent Developments in Bipedal Walk;natural Language Processing for Waste Management Using Public Opinions in Smart Cities;prediction of Diabetes During Pregnancy Through Fog Environment;empirical Wavelet Transform Grounded Poignant Ground Target recognition and Classification by Seismic Signal Processing;A Powered-Up Classification of Disabling Distributed Network Cloud-Based Attacks Using MLPNN-BP and MLPNN-LM;stroke Prediction Framework Based on Missing Value Information and Outlier Detection by Using machine learning Techniques in E-Healthcare;magnetic Resonance Imaging Digitization for Brain Abnormality recognition.

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

3rd International Conference on Machine Intelligence and Sma...

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

ISBN: (纸本)9783031691140

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2022 3rd international conference on pattern recognition and machine learning, PRML 2022

2022 3rd International Conference on Pattern Recognition and...

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3rd international conference on pattern recognition and machine learning, PRML 2022

ISBN: (纸本)9781665499507

The proceedings contain 76 papers. The topics discussed include: ship detection with optical image based on attention and loss improved YOLO;ECG characteristic detection using DenseNet based on attention mechanism and feature pyramid;a method to detect the onsets and ends of paroxysmal atrial fibrillation episodes based on sliding window and coding;dynamic feature extraction using I-vector for video fire detection;bimodal information fusion network for salient object detection based on transformer;person re-identification method based on multi-view and attention mechanism;research on road unevenness recognition method based on off-road vehicle driving characteristic;and vehicle re-identification approach combining multiple attention mechanisms and style transfer.

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Research on Hybrid Remaining Usable Life Prediction Method Based on Battery Aging Model and data-Driven 3rd

Research on Hybrid Remaining Usable Life Prediction Method B...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Zhang, Yu Xu, Zhenyu Li, Dongqi Han, Shunjie Changchun University of Technology Changchun130012 China

ISBN: (纸本)9789819624676

Reliable prediction of the remaining useful life (RUL) of the battery is capable of providing a critical reference decision for users to use and replace batteries. Although there are many methods for battery RUL prediction, these methods suffer from insufficient training, ignoring local regeneration of capacity degradation curves, complex structure and a wide range of optimization seeking parameters, so this paper proposes a hybrid battery RUL prediction method. In the data processing stage, adaptive noise complete ensemble empirical modal decomposition (CEEMDAN) decomposes the battery capacity sequence, then calculates the fuzzy entropy value of each component and reconstructs the components with similar entropy values. In the model training stage, two data and model class methods, particle filtering to update the battery aging model parameters and Gaussian process regression (GPR) to fit the reconstructed components, are used respectively to further learn the long-term and local fluctuation parts of the battery capacity series. In the prediction stage, the prediction results of the two components are combined to obtain the predicted value of the future capacity of the battery and the uncertainty quantification of its RUL. Finally, the proposed hybrid model is validated on a battery dataset at different temperatures and experimental conditions, as a result, the RUL prediction method is shown to possess favorable prediction accuracy, good stability, and reliable uncertainty management capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Ecological Vulnerability Assessment of Helan Mountain in Ningxia Based on SPR Model 3rd

Ecological Vulnerability Assessment of Helan Mountain in Nin...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： He, Yuanrong Chen, Yuhang Song, Yongfei Zhang, Liheng Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Ningxia Survey and Monitor Institute of Land and Resources Yinchuan China

ISBN: (纸本)9789819624676

In order to explore the ecological vulnerability of Helan Mountain in the context of the ecological protection project, this paper, based on the SRP model, selects 15 evaluation indicators to construct an ecological vulnerability evaluation system for the Helan Mountain, and utilizes the fuzzy hierarchical analysis method to reveal the ecological vulnerability of the study area in 2021 from the distribution of spatial characteristics, different land types, and the relationship between key ecological restoration areas and vulnerability in three dimensions. The results showed that (1) the overall spatial distribution of ecological vulnerability in the study area is dominated by slight and mild vulnerability in the southwest and severe vulnerability in the northeast, and the hinterland of the more developed cities is dominated by moderate and severe vulnerability due to the impacts of industrialization and urbanization as well as the population density at the county scale. (2) Ecological vulnerability is significantly affected by the type of land use, with the lowest vulnerability in paddy fields and forested land, and relatively higher in urban land and rocky land. (3) The analysis of 169 key restoration areas in Helan Mountain of Ningxia showed that 140 of them were severely fragile and 21 were extremely fragile, including Rujigou mine area where coal fire spontaneous combustion was serious. The research can provide data support and decision support for ecological protection and coal fire rectification of Helan Mountain in Ningxia Province. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Forest ecology

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Deep learning-Based Speech Enhancement for Robust Speech recognition in Noisy Environments 3

Deep Learning-Based Speech Enhancement for Robust Speech Rec...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Sowmya, C.S. Das, Neeraj Sharma, Divya Mondal, Sanjoy Soni, Ishika Naveen Kumar, V. Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

It is a very important problem since voice-controlled devices and speech-to-text transcription are only two examples of how automatic recognition of spoken language in noisy environments may impact our lives. Still, speech recognition systems may only sometimes work effectively in background noise, which can result in errors and misinterpretations. This problem dealt with the deep learning model-based speech enhancement techniques for robust ASR in adverse environments. These methods employ deep learning to learn and deny the noisy speech signals, rendering it a better-suited input for recognition in a typical ASR system. Deep learning-based speech enhancement is a machine-learning system using a huge, loud, clean speech data dataset to train DNN. So, the network is trained to find a mapping from some noisy speech signals to ideally clean versions of these speech sounds by removing its background noise. This spectral representation of speech will now have reduced noise, which the ASR much more easily recognizes. These methods have significantly improved against traditional speech enhancement techniques in demising, eventually improving recognition performance. They can turn in high noise levels and are resilient against acoustics drifts. This makes them well-suited to real-world applications when noise levels can vary. © 2025 IEEE.

关键词： Speech enhancement

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machine learning Techniques for Pose Based Human Activity recognition

Machine Learning Techniques for Pose Based Human Activity Re...

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Disruptive Technologies (ICDT), international conference on

作者： Ravindra Singh Koranga Arun Kumar Rai Lalit Mohan Shubham Singh Pritika Priya School of Computer Science and Engineering Galgotias University Greater Noida India CSE Birla Institute of Applied Sciences Bhimtal India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Activity recognition is the task of identifying human actions and physical activities. It can have applications in broad areas including medical, sports and fitness, healthcare, behaviour analysis and security. Activity recognition is a widely sought after area of research in both machine learning (ML) and smart devices. The activity recognition plays an important role in the field of machine learning. There are several machine learning techniques which can be applied for pose based activity recognition. These techniques involve training machine learning models for the dataset. The dataset is obtained by using OpenPose framework. This framework provides keypoints from an input image. These keypoints represent the raw data determining the pose of a human. The machine learning model can predict the pose after training on the dataset. models

关键词： Training Pose estimation machine learning Medical services Predictive models Security Object recognition Human activity recognition Smart devices Sports

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PR-GB-NET: Probabilistic Graph Bridging Network with data Resampling for Scene Graph Generation 3rd

PR-GB-NET: Probabilistic Graph Bridging Network with Data Re...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Lu, Hongyu Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

Scene graphs are structured and powerful representations of images that extract semantic information from images to enhance visual understanding and image reasoning. To improve the utilization of predicate prior knowledge in scene graphs, we use a probability mass function (PMF) for the predicate, given a subject-object pair during the initialization of the graph neural network. The PMFs are updated through a gating function during message passing of the graph neural network. To mitigate the effects of long-tailed distributions in datasets, we employ a data resampling strategy. During each training iteration, we try to balance the head, body and tail predicates as much as possible. Experiments show that the proposed methods achieve competitive performance compared to previous methods on dataset we created for home environment, demonstrating their effectiveness and generality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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