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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是1-10 订阅

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cmlds 2024 - 2024 international conference on computing, machine learning and data science, conference Proceedings

CMLDS 2024 - 2024 International Conference on Computing, Mac...

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2024 international conference on computing, machine learning and data science, cmlds 2024

ISBN: (纸本)9798400716393

The proceedings contain 60 papers. The topics discussed include: privacy-preservation robust federated learning with blockchain-based hierarchical framework;spatio-temporal hypergraph convolutional network based network traffic prediction;hash function based on quantum walks with two-step memory;adversarial analysis and methods for math word problems;object tracking based on adaptive multi-template fusing;analysis of spatial-temporal variability and heterogeneity of soil moisture;can deep learning large language models be used to unravel knowledge graph creation?;binary and multi-label machine learning models for discrete-time survival analysis: a case study to predict complications and mortality in Thai diabetic patients;power factor anomaly detection using data stream summaries;and consensus filter for distributed sensor networks with unknown colored noise.

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031845246

The proceedings contain 21 papers. The special focus in this conference is on Longitudinal Disease Tracking and Modeling with Medical Images and data. The topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: Application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke Through Retinal Graphs and Multimodal Self-supervised learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel dataset and Deep-learning Experiments;multimodality Frequency Feature Customized learning for Pediatric Ventricu

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Multi Dimensional Deep Encoding for Categorical Feature Space 24

Multi Dimensional Deep Encoding for Categorical Feature Spac...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Alamuri, Madhavi Surampudi, Bapiraju Negi, Atul School of Computer and Information Sciences University of Hyderabad Telangana State Hyderabad India Cognitive Science Lab International Institute of Information Technology Telangana State Hyderabad India

ISBN: (纸本)9798400717482

Categorical data classification and clustering are essential to many fields, including pattern recognition, data mining, knowledge discovery, and machine learning. It is crucial to understand how to provide categorical input to machine learning algorithms because the majority of them are developed for numerical inputs. Any machine learning algorithm's performance depends not only on the model and hyper parameters but also on the way the data is prepared for and fed into the model. With high cardinality features and categorical predictor variables, machine learning approaches that handle categorical data frequently encounter challenges. In this scenario, we investigate categorical encoding techniques, develop a taxonomy of encoding techniques, and use the machine learning algorithms KNN, MLP, SVM, and ELM and make a comparative study. We suggest a method of hybrid encoding using CNN that gives better performance results. In this paper, we propose the machine learning strategy employing multi-dimensional encodings with CNN, which outperforms other machine learning approaches on majority datasets. It is clear that CNN's performance in learning categorical features has been much enhanced by the use of multi-dimensional encoded input vectors. In our suggested method for supervised and unsupervised encoding, we have used four distinct encodings. These results encourage us to further study the outcomes of different hybrid combinations of encodings for categorical feature classification. © 2024 Copyright held by the owner/author(s).

关键词： Categorical data

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Revolutionizing healthcare: Unleashing the power of machine learning in remote diagnosis and health management 4th

Revolutionizing healthcare: Unleashing the power of machine ...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Puneet, Parth Shukla, Prakhar Parashar, Gaurav KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (纸本)9781032911571

The medical industry has increasingly used artificial intelligence (AI) as technology progresses. The primary advantage of machine learning comes in its capacity to derive significant insights from the immense volumes of health data gathered daily. The progress in the Internet of Things (IoT), machine learning, big data, and high-performance computing has created exceptional possibilities to enhance public health by maximizing scarce human resources. This work presents a diabetes prediction model that categorizes diabetes based on common parameters such as blood sugar, body mass index (BMI), age, insulin, and other relevant factors, as well as external factors that contribute to the development of diabetes. Our analysis utilizes the PIMA India and Sylhet datasets. machine learning techniques, such as the Support Vector machine (SVM), Naive Bayes classifier (NB), and decision tree, are used in the study. © 2025 the Author(s).

关键词： Electronic health record

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Enhancing Sample Efficiency in Online Reinforcement learning via Policy-Guided Diffusion Models 24

Enhancing Sample Efficiency in Online Reinforcement Learning...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Dong, Yixuan Zhang, Jun Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400717482

Reinforcement learning (RL) as a key machine learning method, has achieved notable success across various complex tasks. However, online RL often suffers from low sample efficiency, which limits its applicability in real-world scenarios. To tackle this challenge, Synthetic Experience Replay[21] employs diffusion models to generate additional samples to augment the replay buffer. Nevertheless, it confronts the obstacle of a significant discrepancy between the distributions of these synthetically generated samples and the current policy, which introduces bias and instability into the reinforcement learning process. Inspired by the application of policy-guided diffusion in offline reinforcement learning, we propose integrating policy-guided diffusion models into the framework of online RL to reduce the discrepancy between the generated data distribution and the policy distribution, improving replay effectiveness, sample efficiency, and applicability in the real world. We show that our method performs better than Synthetic Experience Replay in various RL tasks and promotes the application of RL in more practical scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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data, Information and computing science - Proceedings of the 2nd international conference, CDICS 2024

Data, Information and Computing Science - Proceedings of the...

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2nd international conference on data, Information and computing science, CDICS 2024

ISBN: (纸本)9781643685762

The proceedings contain 10 papers. The topics discussed include: comparative analysis of deep learning methods for automated diagnosis of pulmonary diseases from chest X-ray images: a study based on ICD-10;deep residual learning for fruits of ceremonial plants recognition;PiVisionSort: integrating image processing and machine learning for material recognition on conveyor belts;intelligent detection of potholes using SSD algorithm and auto-alert notification system for user;the use of motion and gaze features to detect speaking intention in VR-mediated communication environments;optimized retinal vessel segmentation using IS-Net and high-resolution dataset;a framework for adopting machine learning in the clinical domain;and a study on a domain BERT-based named entity recognition method for faulty text.

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A Preliminary Study of the Stressed and Drowsy Driving Prediction Models using Semi-Supervised learning 24

A Preliminary Study of the Stressed and Drowsy Driving Predi...

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8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Chui, Kwok Tai Liu, Jiaqi Hung, Kevin Wu, Ho Chun Zhao, Mingbo School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798400718304

Predicting the occurrence of stressed and drowsy driving provides sufficient time to alert drivers and prevent undesired driving behaviours that result in road traffic accidents. Typically, the predictive machine learning models for stress and drowsy driving are formulated using supervised learning with labelled data. Labelled data are available only in training datasets, where labelling new data becomes infeasible and costly. With the ever-growing quantity of new and unlabelled data, once the model is deployed, unlabelled data quickly dominate the whole data population, and bias toward unlabelled data may increase the error rate of the prediction model. A preliminary study was thus conducted to train the predictive model using semi-supervised learning. We analyze and compare the performance of four algorithms, namely extreme gradient boosting (XGBoost), autoregression (AR), autoregressive integrated moving average (ARIMA), and exponential smoothing (ES). Various portions of unlabeled data were introduced, and more window sizes (in advance intervals) were chosen for the analysis. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

4th International Conference on Computational Methods in Sci...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

ISBN: (纸本)9781032911571

The proceedings contain 167 papers. The special focus in this conference is on Computational Methods in science and Technology. The topics include: The proposed framework for hybrid multilevel optimized machine learning classification in apple leaf disease detection;estimation of sediment load in Mahanadi River, India using artificial neural networks;prediction of water discharge in Mahanadi River basin, India using artificial neural networks;online misinformation detection using deep learning on social media: A survey and analysis;deep insight of medical images segmentation and classification by deep learning;human intrusion detection by optimize dragon deep belief network;human activity recognition using residual deep network with extreme learning;enhancing lung cancer diagnosis: A comprehensive analysis of machine learning and deep learning approaches;advanced perspectives on big data security: Navigating challenges in cloud computing;improved segmentation and classification of breast cancer using mammogram images with residual network based deep learning;analysis of early detection and prediction of diabetic retinopathy by deep learning;mustard and wheat mildew disease classification using deep learning;machine and deep learning approaches for heart disease risk assessment;uncovering agile software frameworks;calculate the asphalt pavement’s binder activity level using machine learning techniques;empowering healthcare with Swin Transformer V2: Advancing pneumonia diagnosis through deep learning;object detection in videos using deep learning methods: A survey;analysis of machine learning and deep learning techniques for credit card fraud detection in class imbalanced datasets;enhancing data security through an integrated approach of multiple key cryptography and steganography in the wavelet domain.

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Adversarial Attack Optimization and Evaluation for machine learning-Based Dark Web Traffic Analysis 33rd

Adversarial Attack Optimization and Evaluation for Machine L...

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33rd international conference on Software and data Engineering

作者： Harrison, Nyzaireyus Broome, Heather Shrestha, Yaju Robles, Alexander Gautam, Aayush Rahimi, Nick Univ Southern Mississippi Hattiesburg MS 39402 USA

ISBN: (纸本)9783031752001;9783031752018

machine learning (ML) is quickly becoming one of the most transformative technologies in the field of computing. Applications of MLare wide-spread and growing exponentially, revolutionizing the future of major industries such as finance, healthcare, automotives, and more. This has made it more necessary than ever to recognize the instability created by adversarial attacks-the deliberate manipulation of data to mislead ML models. This instability must be addressed through researching the effects of adversarial attacks and how they can be better recognized. Our research explored the use of adversarial attacks in dark web network traffic analysis by first improving our understanding of how adversarial attacks could be optimized. We manipulated a dataset of dark web traffic data through the analysis of confusion matrices and Euclidean distances, aiming to cause maximum confusion for each of our models. We then trained and tested each model in a variety of scenarios to further our understanding of weaknesses in both the traffic data and the machine learning techniques employed.

关键词： Dark web machine learning Adversarial attacks Network traffic analysis

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

4th International Conference on Computational Methods in Sci...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

ISBN: (纸本)9781032911571

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