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共 2833 条记录，以下是271-280 订阅

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Generation of data for Training Retinal Image Segmentation Models 10th

Generation of Data for Training Retinal Image Segmentation M...

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

作者： Bhuiya, Srinjoy Chakraborty, Suchandra Sadhukhan, Subhopriyo Mandal, Deba Prasad Bhandari, Dinabandhu Heritage Inst Technol Kolkata 700107 West Bengal India Indian Stat Inst Kolkata 700108 West Bengal India

ISBN: (纸本)9783031451690;9783031451706

Biomedical image segmentation requires pixel-wise labelling which is extremely time-consuming and the availability of additional training data is highly beneficial for training Deep learning models. In addition to using Classical Image augmentation, generative adversarial networks have been used to augment the training data. this work is an investigation of the usefulness of generated medical images for Deep learning segmentation models. An attempt has been made to create a computer-generated retinal image segmentation dataset using various state-of-the-art image generative deep learning models and use that dataset to train a supervised image segmentation model. Our experiments demonstrate that the generated data can be used successfully for medical segmentation tasks and improves the model's performance over using classical augmentations.

关键词： Retinal Image Segmentation Image Generation Image Augmentation Image Translation

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Multimodal machine learning for Sign Language Prediction 6th

Multimodal Machine Learning for Sign Language Prediction

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6th international conference on Nanotechnologies and Biomedical Engineering, ICNBME 2023

作者： Khalafaoui, Yassèr Grozavu, Nistor Matei, Basarab Rogovschi, Nicoleta ETIS ENSEA UMR 8051 CY Cergy Paris University 2 Avenue Adolphe Chauvin Cergy Pontoise Cedex95302 France LIPN UMR CNRS 7030 Sorbonne Paris Nord University 99 Avenue J-B Clément Villetaneuse93430 France LIPADE Paris-Decartes University 45 rue des Saintes Pères Paris75005 France

ISBN: (纸本)9783031427817

Numerous applications, including translation tools, interpreting services, video remote interpreting, human-computer interaction, online hand tracking of human communication in desktop settings, real-time multi-person recognition systems, games, virtual reality settings, robot controls, and natural language communications, benefit from sign language recognition advantages. Multimodal data contains information from different sources such as video, sensors, electrocardiograms (ECGs), while emotions refer to the non-verbal cues that accompany language use, such as facial expressions and body posture. Integrating these additional sources of information helps to better understand the user’s intent, which improves the performance of the sign language recognition model. To build such a model, a set of multimodal data and emotions must be collected. this data set should be differentiated and cover different individual/isolated signs, emotions and body gestures. the model is designed to integrate multimodal data and emotions, which would involve combining different machine and deep learning algorithms adapted to different types of data. In addition, the model will need to be trained to recognize the different emotions that accompany sign language. Once the model is trained, it can be tested on the test dataset to assess its performance and also plan for a test on real data (with signing people). In this paper we propose a study to use the multi-modal machine learning for sign recognition language. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Transfer learning: Kernel-Based Domain Adaptation with Distance-Based Penalization 10th

Transfer Learning: Kernel-Based Domain Adaptation with Dista...

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

作者： Prakash, Jainendra Ghorai, Mrinmoy Sanodiya, Rakesh Indian Inst Informat Technol Sri City India

ISBN: (纸本)9783031451690;9783031451706

this paper introduces a novel approach to address the challenges of transfer learning, which aims to efficiently train a classifier for a new domain using supervised information from similar domains. Traditional transfer learning methods may fail to maintain the discriminative features of the target domain due to the scarcity of labelled data and the use of irrelevant source domain data distribution subspace, resulting in poor metrics. To overcome these challenges, the proposed approach, called KDADP, transforms the data distribution of both the source and target domains into a lower-dimensional subspace while preserving their discriminatory information. the KDADP model maximizes between-class variance and minimizes within-class variance with L1 penalization, enabling the recovery of the most useful characteristics and reducing the model's complexity. Experimental results on three real-world domain adaptation datasets demonstrate that the proposed KDADP model significantly improves classification performance and outperforms state-of-the-art primitive, shallow, and deeper domain adaptation methods.

关键词： classification domain adaptation features space transfer learning supervised learning semi-supervised learning

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Real-Time Innovation in Future Generation Security for data Privacy Intrusion System in machine learning Approach 6

Real-Time Innovation in Future Generation Security for Data ...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Wankhede, Hansaraj Shalikram Venkatesh, R. Pundr, Sumit Madala, Ravikiran Babu, Kamunuri Ganapathi Mallaiah, Badepally G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India University of Cumberlands DurhamNC United States St. Martin's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India CVR College of Engineering Department of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350304480

Programming called an intrusion discovery system watches over a solitary PC or an organization of PCs for pernicious movement that intends to take or edit data or change network conventions. Most of intrusion recognition methods utilized today are unequipped for guarding against the dynamic and perplexing nature of cyber-attacks on PC organizations. Albeit successful versatile procedures, for example, unique machine learning calculations, may prompt more noteworthy discovery rates, diminished deception rates, and suitable calculation and transmission costs. data mining might be utilized to make miniature data streams, classification, and example mining rather frequently. the real-time intrusion identification system and related intrusion counteraction system are introduced in this work utilizing machine learning. To prepare the model, a convolutional brain organization (CNN) is utilized. A 5G data network assessment of the innovation was directed. the improvement of programming characterized networks is thought about by brilliant intrusion location systems (IDS). It utilizes man-made reasoning based demonstrating. Convolutional brain network-based programming parts empower the system to distinguish intrusions that were not recently recognized. Intrusion Prevention System 'IPS'is to stop malicious traffic. Based on the evaluation, this solution provides more buffers than comparable alternatives, facilitating real-time management in 5G settings. © 2023 IEEE.

关键词： machine learning

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Overlapping Granular Clustering: Application in Fuzzy Rule-Based Classification 6th

Overlapping Granular Clustering: Application in Fuzzy Rule-B...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Muda, Muhammad Zaiyad Panoutsos, George Univ Teknol MARA Coll Engn Serdang Selangor Malaysia Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)9783031669644;9783031669651

A clustering technique often aims to create a number of disjoint clusters or granules, in which an element or instance is only permitted to belong to one cluster. However, the majority of real-world data sets have information that overlaps, causing specific data objects or patterns to belong to multiple clusters. For instance, an individual may concurrently be a member of more than one social group, such as a family group and a friend group. therefore, the purpose of this study is to use a parameter called R-value to allow and provide parametric control for cluster overlaps. In this research, it is demonstrated that the inclusion of R-value in the Granular Clustering (GrC) enables GrC to control the amount of overlapping between clusters. datasets from the UCI machine learning Repository are used to illustrate the new GrC algorithm with overlapping measure. Results reveal that the GrC with overlapping measure surpasses the traditional GrC in terms of classification accuracy, highlighting the possible application of the overlapping GrC for creating Fuzzy Logic rule bases.

关键词： Granular Clustering Iterative data Granulation Fuzzy Logic overlapping

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Predictive Modeling of Gold Prices: Integrating Technical Indicators for Enhanced Accuracy 6th

Predictive Modeling of Gold Prices: Integrating Technical In...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Husaini, Noor Aida Gan, Yee Jing Ghazali, Rozaida Hassim, Yana Mazwin Mohmad Yeap, Jie Shen Joseph, Jerome Subash Tunku Abdul Rahman Univ Management & Technol Fac Comp & Informat Technol Jalan Genting Kelang Kuala Lumpur 53300 Malaysia Univ Tun Hussien Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Kagawa Malaysia

ISBN: (纸本)9783031669644;9783031669651

this paper discusses the crucial requirement for reliable gold price prediction, which is necessary for financial market decision-making. We propose a comprehensive approach to develop a robust predictive model capable of predicting both the rise and fall of gold prices. For this, three (3) machine learning (ML) models-Decision Tree Regressor (DTR), Support Vector Regression (SVR), and Random Forest (RF);must be carefully chosen, and model parameters must be adjusted so that predicted values roughly match actual results. Given this, this paper investigates the influence and effectiveness of incorporating technical indicators in predicting fluctuations in gold prices, which might have an impact on the overall performance of the ML models. By achieving a prediction accuracy rate of at least 80%, the model becomes a favorable tool for informed decision-making and provides valuable insights to investors in the gold markets.

关键词： Gold Price Relative Strength Index Bollinger Bands Average Directional Index machine learning

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Explanation-based Graph Neural Networks for Graph Classification 26

Explanation-based Graph Neural Networks for Graph Classifica...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Seo, Sangwoo Jung, Seungjun Kim, Changick Korea Adv Inst Sci & Technol KAIST Sch Elect Engn 291 Daehak Ro Daejeon 34141 South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant influence on the whole graph, providing accurate explanations for predictions of GNN. We noted that explanation components could help improve classification performance as unique representations of each class. therefore, we suggest the GNN performance can be further improved by using explanation components. In this paper, we propose an Explanation-Based Graph Neural Networks (EBGNN) that utilizes contrastive learning at the instance level, by applying explanation components. In EBGNN, the explanation components ensure similarity for instances within the same class, and promote separability for instances in different classes. Finally, we conducted an evaluation on five benchmark datasets (MUTAG, IMDB-BINARY, PROTEINS, NCI1, and DD). Our experiment showed a significant increase in graph classification performance compared to state-of-the-art methods.

关键词： Proteins Computer vision Analytical models machine learning Benchmark testing Graph neural networks data models

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Human Identification Based on Gaits Analysis Using Sensors-Based data Bands 6

Human Identification Based on Gaits Analysis Using Sensors-B...

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6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Zhu, Rui Dong, Liang Zhang, Yangyang Zhaohong, Jia Anhui University China

ISBN: (纸本)9798350399059

We all have such learning experiences that our teachers told us that there were no two identical leaves in the world. Based on this, this paper proposes that since there are no two identical leaves in the world, so as that no two people in the world have the same walking postures and gaits. In this study, a human identification system based on gait analysis via sensors-based data bands was introduced. the data bands were affixed to knee joints and calf for human gaits detection. the human gaits detection method was performed based on multi-sensor data fusion, and calculation method based on Cartesian geometric coordinates. In addition, experimental results show that the human gaits were accurately detected, and the human identities were accurately identified by the proposed methods. Moreover, the classifier is applied to output the final human identification ordinary category of people. © 2023 IEEE.

关键词： pattern recognition

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Federated Optimization with Linear-Time Approximated Hessian Diagonal 10th

Federated Optimization with Linear-Time Approximated Hessian...

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

作者： Sen, Mrinmay Mohan, C. Krishna Qin, A. Kai IIT Hyderabad Sangareddy India SUT Melbourne Melbourne Australia

ISBN: (纸本)9783031451690;9783031451706

Federated learning (FL) or federated optimization is a type of distributed optimization where multiple clients collaboratively train a global model without sharing local data. One of the key challenge in FL is the communication overhead due to slow convergence of the global model. In this paper, we propose a federated learning algorithm to handle this slow convergence by incorporating Hessian diagonal while training client's models. To reduce the computational and memory complexity in local clients, we introduce a linear time Hessian diagonal approximation technique by using only the first row of the Hessian. Our extensive experiments show that our proposed method outperforms state-of-the-art FL algorithms, FedAvg, FedProx, SCAFFOLD and DONE in terms of training loss, test loss and test accuracy.

关键词： Federated learning Slow convergence Hessian diagonal approximation data heterogeneity

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A Comparative Assessment of the Contemporary Models for Automatic Short Answer Grading 6th

A Comparative Assessment of the Contemporary Models for Auto...

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

作者： Haldar, Subhajit Sengupta, Souvik Das, Asit Kumar Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur India Aliah University Kolkata India

ISBN: (纸本)9789819780891

Automated Short Answer Grading (ASAG) is a popular task in education technology that automates the assessment of student-written short answers by leveraging computational techniques. Although ASAG has been investigated for many years, recent advancement in machine learning and deep learning has ignited new research in this field. In the past, researchers have come up with different approaches that can be broadly classified into two categories—handcrafted feature-based traditional approaches and deep-learning-based approaches. In recent times, transformer-based word embedding has revolutionized all the fields of NLP applications, including ASAG. this chapter aims to compare the performance of various methodologies employed in ASAG with particular attention to comparing feature extraction techniques, machine learning and deep learning models employed, the dataset used, and the assessment measures applied. the review of the literature reveals that combining transformer-based text embedding with handcrafted features is more effective than using conventional handcrafted features and deep learning features in both regression and classification tasks for ASAG. this finding is validated through an empirical study performed to evaluate and compare the efficiency of different handcrafted and semantic features on the ASAG task with a common dataset and two baseline models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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