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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是21-30 订阅

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Pixel-level Reconstruction Noisy Handwritten Characters Classification based on Deep Progressively learning Model 6

Pixel-level Reconstruction Noisy Handwritten Characters Clas...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Murali, G. Azhagiri, M. Devarajan, Manikanda N. thiruppathi, M. Kumar, Vinod D. Manojkumar, V. Tamil Nadu Salem India SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Malla Reddy Engineering College Telangana Medchal India Vivekanandha College of Engineering for Women Namakkal Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331522667

Handwritten character recognition (HCR) is still regarded as a difficult learning problem in pattern recognition, even after being studied in-depth for a few decades. Research on script independent models is also scarce. this can be linked to several things, especially character structure similarities, variances in handwriting styles, noisy datasets, script diversity, the conventional research's emphasis on manual feature extraction techniques, and the lack of publicly available datasets and code repositories to replicate the findings. However, deep learning offers from top to bottom learning together with has achieved great success in various pattern recognition domains, such as hand gesture recognition (HCR). Deep learning methods, on the other hand, are computationally costly, require a lot of data to train, and are limited to scripts. We have developed a novel generic Deep Progressively learning Model architecture for script independent handwritten character recognition, known as HCR- DPLM, to overcome the limitations. the foundation of HCR-DPLM is a unique transfer learning strategy for HCR that makes use of a pre-trained network's feature extraction layers in part. HCR DPLM offers better performance and better generalizations, faster and computationally efficient training, and the ability to work with small datasets because of Transferring Knowledge and Imagery augmentation. In this paper, we use a pixel level classifier to extract the character pixels and eliminate noise from handwritten character images. © 2025 IEEE.

关键词： Contrastive learning

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Viability of Using data Augmentation with a Small Sample of Demographical Information to Predict Student Specialization in Master of Applied Computing Course 6th

Viability of Using Data Augmentation with a Small Sample of ...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Kiat, Lau Noel Kuan Ashraf, Humaira Khan, Navid Ali Taylor’s University Selangor Subang Jaya Malaysia

ISBN: (纸本)9789819780891

this research studies the viability of small samples of basic student demographical information as training data for machine learning models to perform multiclass classification of the specializations/majors of students enrolled in the Master of Applied Computing course at Taylor’s University. If successful, this approach can be used by educational institutions with sparse student data to recommend university majors/specializations. this research uses SMOTE-NC to augment the training dataset from 25 rows to 100 rows and trained three machine learning models on the augmented dataset—logistic regression, random forest, and support vector machine. the support vector machine classifier performed the best in terms of accuracy at 86%, but all three classifiers showed highly inconsistent performance when predicting different specializations. Further research is required to understand this outcome, but potential causes include weak correlations between the input features and the target feature and small sample size. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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Forecasting Health Indicators through a data-Driven machine learning Analysis of Exercise, Sleep, and Water Intake patterns 6th

Forecasting Health Indicators Through a Data-Driven Machine ...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Yicheng, Cai Ashraf, Humaira Jhanjhi, N.Z. School of Computer Science SCS Taylor’s University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

this chapter uses machine learning algorithms to build health status prediction models by analyzing multidimensional health behavior data such as exercise frequency, type, sleep duration, and water intake. We collected datasets on health indicators and further explored how these lifestyle factors collectively influence health indicator values. the health data collected are used to calculate the target variable "health_value," which is the column that distinguishes whether an individual is healthy or not, based on comparing the mean value of the column, with 1 and 0 indicating healthy or unhealthy. By analyzing the data and machine learning algorithms, we discovered the essential and inseparable relationship between these health indicators and an individual’s health status. We optimized the model to improve the accuracy of the prediction. We also confirmed the generalization ability and performance of the model by giving the optimized model new health data and obtaining health values, as well as whether it is healthy. the results of this chapter highlight the importance of integrating data on lifestyle habits for health prediction to give data support to the development of a scientific basis for personalized health management strategies and health promotion interventions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

6th International Conference on Security and Privacy in New ...

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

ISBN: (纸本)9783031736988

the proceedings contain 23 papers. the special focus in this conference is on Security and Privacy in New Computing Environments. the topics include: Consortium Blockchain Storage Optimization Based on Fountain Codes;an Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level;Research on Face recognition System Based on RLWE Homomorphic Encryption;feedback Feed-Forward Iterative learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths;open-Closed-Loop Iterative learning Control Based on Differential Evolution Algorithm for Nonlinear System;overview of Vehicle Edge Computing and Its Security;conFlow: Contrast Network Flow Improving Class-Imbalanced learning in Network Intrusion Detection;anomaly Detection of Unstable Log data Based on Contrastive learning;An Integration-Enhanced ZNN Approach for Chaotic Combination Synchronization with External Disturbances∗;a Lightweight Anomaly Detection Method for Industrial Processes Based on Event Correlation Behavior;a Novel Polar Code-Based Key Encapsulation Mechanism with Non-permutation Equivalent Public Key;two-Stage Multi-lingual Speech Emotion recognition for Multi-lingual Emotional Speech Synthesis;EncoderMU: machine Unlearning in Contrastive learning;NoCrypto: A Web mining Behavior Detection Method Based on RGB Images;image Copy-Move Forgery Detection in the Social Media Based on a Prior Density Clustering and the Point Density;detection of Speech Spoofing Based on Dense Convolutional Network;speech Emotion recognition Based on Recurrent Neural Networks with Conformer for Emotional Speech Synthesis;Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs;Stable NICE Model-Based Picture Generation for Generative Steganography;computer-Generated Image Forensics Based on Vision Transformer with Forensic Feature Pre-processing Module;VoIP Steganalysis Using Shallow Multiscale Convolution and

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Deep learning for Solar Panel Fault Detection: Integrating GAN and ResNet Models 6

Deep Learning for Solar Panel Fault Detection: Integrating G...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Manimegalai, V. Oviya, B. Kargvel, S. Umesh Vilashini, P.L. Mohanapriya, V. Elakya, A. Department of Electrical and Electronics Engineering Sri Krishna College of Technology India Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology India

ISBN: (纸本)9798331522667

the rapid expansion of solar energy implementation has highlighted critical challenges related to temperature variations and environmental factors impacting photovoltaic (PV) systems. this paper addresses the need for continuous monitoring to ensure the efficiency and reliability of solar panels. PV panels frequently encounter operational issues including surface contamination, irregular shading patterns, and thermal irregularities, which can result in hotspot formation, leading to substantial power losses and accelerated panel deterioration. the machine learning algorithms play a crucial role in detecting defects in solar panels, such as cracks, shading, and hotspots, by analyzing both thermal and visible light images. the implementation of deep learning architectures, specifically ResNet for feature extraction and Generative Adversarial Networks (GANs) for pattern recognition, enables precise real-time fault detection. the demonstrated success of this integrated deep learning approach establishes a new benchmark for PV system monitoring, enhancing both the reliability and longevity of solar installations while minimizing operational downtime. © 2025 IEEE.

关键词： Generative adversarial networks

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Efficient Discriminative Feature Selection with Grouping Relative Comparison 7th

Efficient Discriminative Feature Selection with Grouping Rel...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Xu, Hang Li, Jiaye Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou 310000 Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Discriminative feature selection helps enhance the performance, efficiency, and interpretability of models, rendering it one of the crucial steps in machine learning and pattern recognition. However, when samples from different classes are close to each other in the data, current discriminative feature selection methods often overlook many features with strong predictive power, resulting in suboptimal feature selection outcomes. To address this issue, we propose a novel discriminative feature selection method that utilizes grouping relative comparison from the perspective of relative distance metrics. In this approach, an anchor is initially selected to form a triplet by comparing the similarity gaps between pairs of data. To reduce triplet calculations and integrate overall information, the triplets consist of groups of samples rather than individual ones. Additionally, the determination of loss values shifts from hard judgment to soft judgment to account for data compactness within classes, thereby enhancing feature discrimination. Furthermore, sparse learning is employed in the proposed approach to constrain discriminative features and determine feature weights. Comprehensive experiments are conducted on various benchmark datasets, including face images, biomedical data, and speech letter recognition data, to validate the effectiveness of the proposed method. the source code of the proposed algorithm is available at https://***/xhchangsha/FS.

关键词： Feature selection Grouping relative comparison Sparse learnig

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Vietnamese Text Summarization Using Transformer Approach 6th

Vietnamese Text Summarization Using Transformer Approach

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Hung, Bui thanh Tan, Phan Nguyen Trong Long, Tran Ho Phi Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Text summarization is a crucial aspect of natural language processing, finding widespread applications in various aspects of our lives. Numerous studies have delved into this task, employing diverse approaches ranging from linguistic analysis to machine learning and deep learning methodologies. However, there has been limited exploration of transformer-based models for Vietnamese text summarization. In our research, we leverage transformer models, namely, PhoBERT, BARTpho, and ViT5, to develop and refine effective summarization models. through experimentation on the Vietnews dataset and evaluation using ROUGE metrics, our proposed method demonstrates noteworthy achievements in the realm of Vietnamese text summarization, showcasing its potential for advancing the quality of summarization tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BARTpho BERT Deep learning PhoBERT Text summarization Transformer ViT5

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6th international conference on communication and computational technologies, ICCCT 2024

6th International conference on communication and computatio...

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6th international conference on communication and computational technologies, ICCCT 2024

ISBN: (纸本)9789819774258

the proceedings contain 39 papers. the special focus in this conference is on communication and computational technologies. the topics include: Conceptual Framework for Risk Mitigation and Monitoring in Software Organizations Based on Artificial Immune System;a Multilevel Home Fire Detection and Alert System Using Internet of things (IoT);smart Baby Warmer with Integrated Weight Sensing;a Robust Multi-head Self-attention-Based Framework for Melanoma Detection;Domain Knowledge Based Multi-CNN Approach for Dynamic and Personalized Video Summarization;Efficient Information Retrieval: AWS Textract in Action;text Summarization Techniques for Kannada Language;Parkinson’s Detection From Gait Time Series Classification Using LSTM Tuned by Modified RSA Algorithm;human Action recognition Using Depth Motion Images and Deep learning;maximizing Portfolio Returns in Stock Market Using Deep Reinforcement Techniques;Detecting AI Generated Content: A Study of Methods and Applications;A Systemic Review of machine learning Approaches for Malicious URL Detection;digital Image Forgery Detection Based on Convolutional Neural Networks;Banana Freshness Classification: A Deep learning Approach with VGG16;greenHarvest: data-Driven Crop Yield Prediction and Eco-Friendly Fertilizer Guidance for Sustainable Agriculture;real-Time Deep learning Based Image Compression Techniques: Review;fog-Cloud Enabled Human Falls Prediction System Using a Hybrid Feature Selection Approach;a 4-Input 8-Bit Comparator with Enhanced Binary Subtraction;multivalued Dependency in Neutrosophic database System;traffic Sign recognition Framework Using Zero-Shot learning;machine learning Techniques to Categorize the Sentiment Analysis of Amazon Customer Reviews;alzheimer’s Disease Diagnosis Using machine learning and Deep learning Techniques;sentinel Eyes Violence Detection System;Detection of Alzheimer’s Disease from Brain MRI Images Using Convolutional Neural Network.

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Using Facial Expressions to Predict Process mining Task Performance

Using Facial Expressions to Predict Process Mining Task Perf...

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international Workshops which were held in conjunction with the 6th international conference on Process mining, ICPM 2024

作者： Shalev, Lital Hadar, Irit Dror, Rotem Solomon, Adir Sorokina, Elizaveta Raymond, Michal Weisman Soffer, Pnina University of Haifa Haifa Israel Ben Gurion University of the Negev Beer Sheva Beersheba Israel

ISBN: (纸本)9783031822247

Process mining analysis is a complex task that presents significant challenges to human analysts. To aid along this process, it is essential to identify difficulties as they occur. this study takes an initial step in this direction, by predicting the quality of task performance based on analysts’ facial expressions while they are engaged in a process mining task. data were collected using participants’ webcams and the iMotions™ cloud application while they performed a process mining task. the data were then utilized to train and evaluate several machine learning classifiers, which classified participants based on the grade given to their task outcome. Our results show the high performance of these classifiers in predicting participants’ success based on facial expressions. We further showed that the chosen outcome classifier could accurately classify additional participants, demonstrating its generalizability. Notably, the classifier was able to predict participants’ success within a very short time frame. these findings could pave the way for developing a near-real-time support system to detect when analysts engaged in process mining may benefit from assistance. © the Author(s) 2025.

关键词： machine learning

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

ISBN: (纸本)9783031838781

the proceedings contain 38 papers. the special focus in this conference is on Hybrid Intelligent Systems. the topics include: Integration of Alexa and Social IoT for Generation X: A Study on the Optimization Use of Smart Treadmill to Improve Physical Health;logarithmic Weighted Random Selector Algorithm: A Novel Approach for Biasing Selection Based on Positional Order Without Hyperparameters;design and Implementation of a machine learning Model for Soccer Match Prediction Based on Player Statistics;electric Scooters and Renewable Energy Integration Associated with Tourist Parks: A Dijkstra-Based Model for Smart Mobility Optimization;use of Convolutional Neural Networks for the recognition of Bird Species in Risk Categories in the State of Chihuahua;exploring Deep learning Applications in Neurodegenerative Diseases: A State-of-the-Art Review;Automated Insights: LLMs in Neurodegenerative Disease Research and Comparison;convolutional Neural Network Models for Classifying of Peach (Prunus persica L);Cleaning Binary Distortion on MNIST dataset;rule-Based Expert System with Bayesian theory and Fuzzy Inference for Vocational Guidance: A Tool to Prevent School Dropouts;detection of Basic Motorcycle Faults Using a Fuzzy Bayesian Expert System;intervention Model with data mining Techniques to Work with Dating Violence Victims Using a Social Support Network;enhanced Pest Detection Using Quaternion-Based Image Segmentation in Yellow Sticky Trap Samples for Precision Agriculture;Optimization of a Treatment Plant through the Incorporation of New Waste Separation Components: A TOPSIS-Focused Multi-criteria Analysis Approach;multicriteria Analysis Applied to the Selection of Shopping Centers for a Family-Owned Restaurant Business in Smart Cities: A Case Study in Ciudad Juárez.

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