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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1776 条记录，以下是61-70 订阅

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Comparison of Multi-Modal Federated learning Framework and SPSS in the Evaluation of Lymph Node Metastasis Probability in Gynecological Malignancies 4

Comparison of Multi-Modal Federated Learning Framework and S...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Hu, Zhijun. Ma, Ling. Yue, Ding Wu, Guangyu Shi, Xiaohua Sirejiding, Shalayiding Liu, Kaijiang. Shanghai Jiao Tong University Renji Hospital School of Medicine Department of Gynecologic Oncology Shanghai China Library Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Jiao Tong University School of Medicine Department of Radiology Renji Hospital Shanghai China

ISBN: (纸本)9798350324303

this study aimed to evaluate the diagnostic efficacy of CT, MRI, and PET/CT in detecting lymph node metastasis (LNM) in patients with gynecologic malignancies. A total of 567 patients diagnosed with cervical carcinoma (CC) and endometrial carcinoma (EC) were included in this study. the results of this study indicated that PET/CT demonstrated higher levels of sensitivity but lower levels of specificity compared to CT and MRI. Additionally, a deep learning-based multimodal federated learning model was implemented, which integrated non-image and MRI data, to improve diagnostic accuracy in the detection of LNM. the model exhibited heightened sensitivity and specificity in comparison with individual imaging modalities. Overall, the findings of this study underscore the potential of employing multimodal federated learning models to accurately detect LNM in patients with gynecological malignancies, which in turn emphasizes the necessity for further research and validation. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction 4

Comparative Analysis of Machine Learning Techniques for Conc...

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4th international conference on data Analytics for Business and Industry, ICDABI 2023

作者： Musleh, Fuad Taha, Ranyah Musleh, Abdel Rahman University of Bahrain Civil Engineering Department Isa Town Bahrain Al Iman School Computer Department Isa Town Bahrain University of Bahrain Electrical Engineering Department Isa Town Bahrain

ISBN: (纸本)9798350369786

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods. © 2023 IEEE.

关键词： Adversarial machine learning

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4th international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2024

4th International Mobile, Intelligent, and Ubiquitous Comput...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2024

ISBN: (纸本)9798350367775

the proceedings contain 69 papers. the topics discussed include: a comprehensive survey on dimension reduction for medical big data using optimization algorithms;software product discovery as a crucial part of software product success;a comparative study of different smart university frameworks;intelligent decision support system for loan evaluation using machine learning;real-time boundary detection for continuous Arabic sign language translation;trade-offs in performance and optimization techniques in Unity 3D to achieve realism;transfer learning based indoor object recognition for visually impaired people;truth revealed: enhancing deception detection using long-term recurrent convolutional networks;and developing a medical question-answering chatbot for Egyptian Arabic: a comparative study.

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

4th International Conference on Pattern Recognition and Arti...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

ISBN: (纸本)9789819787043

the proceedings contain 69 papers. the special focus in this conference is on pattern recognition and Artificial Intelligence. the topics include: Hierarchical Attention Decoder for Solving Math Word Problems;enhancing Output Diversity Improves Conjugate Gradient-Based Adversarial Attacks;open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols;the Hidden Influence of Latent Feature Magnitude When learning with Imbalanced data;AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking;character Identifying Video Language Alignment Network for Weakly-Supervised Video-Subtitle Moment Retrieval;spiral Patch Exemplar-Based Inpainting of 3D Colored Meshes;BEVFormer’s Plugin: Integrating Historical Detection data, BEV-Level Information, Time Information, and Depth Information;adjustable Visual Appearance for Generalizable Novel View Synthesis;flexible Video Matting With Temporally Coherent Trimaps Generation;adversarial Augmentation Training Makes Action recognition Models More Robust to Realistic Video Distribution Shifts;egocentric View Hand Action recognition by Leveraging Hand Surface and Hand Grasp Type;visual Concept Networks: A Graph-Based Approach to Detecting Anomalous data in Deep Neural Networks;contrastive Language-Entity Pre-training for Richer Knowledge Graph Embedding;Fine-Tuning Pre-trained Model with Prototype learning for OOD Detection;prototype-Guided Contrastive Knowledge Graph Representation learning for Diagnosis Prediction;stop Overkilling Simple Tasks with Black-Box Models, Use More Transparent Models Instead;feature-Based Explainable Reinforcement learning in Environments with Multiple Sources of Risk;probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information;enhancing Robustness of Over-Parameterized Models via Feature Reweighting Using Logit-Wise Mixup;language-Aware Non-autoregressive Khmer Textline recognition;ATA: Attentive Text Augment

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Empowering Healthcare with Deep learning: An Application for Early Detection of Skin Cancer 4th

Empowering Healthcare with Deep Learning: An Application for...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Oluwatobi, Komolafe Joseph Asghar, Ikram Diyan, Muhammad Nawaz, Rab Saleem, Sidra Ullah, Rahmat Ahmad, Jawad Univ Teesside Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Agr Faisalabad Faisalabad Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031821523;9783031821530

Skin cancer is a hazardous ailment and a leading contributor to mortality. Early diagnosis of skin cancer can significantly minimize or prevent these fatalities. the diagnostic process can be both lengthy and costly. this study aims to establish a basis for creating an exact and effective convolutional neural network (CNN) model for detecting skin cancer. this model aims to enhance the early and accurate diagnosis of skin cancers, potentially leading to a decrease in the number of associated fatalities. the model utilizes a convolutional neural network with Keras Tensorflow as the backend to categorize seven distinct categories of skin cancer. Subsequently, the results are analyzed to determine the practical applications of the model. the MobileNet optimizer is used for classification. A web application has been developed that offers dermatologists the three most likely diagnostics for a specific blister. It will aid in swiftly recognizing patients with high priorities and accelerating their workflow. the application generates a result within a time frame of 5-10 seconds.

关键词： Skin Cancer CNN machine learning image classification medical data security

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

4th International Conference on Pattern Recognition and Arti...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

ISBN: (纸本)9789819787012

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Speaker Identification Using Deep learning Models for Enhanced Voice Biometrics 4

Speaker Identification Using Deep Learning Models for Enhanc...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Akhilesh Balipa, Mamatha Shetty, Anmol S Nitte Deemed to Be University Nmam Institute of Technology Nitte Department of Mca India

ISBN: (纸本)9798331523923

the goal of this project is to create a sophisticated voice recognition system that accurately recognizes and categorizes spoken data by applying machine learning and audio processing techniques. It uses a deep learning model (model.h5) for high-accuracy audio input categorization and incorporates tools like Librosa for comprehensive audio feature extraction (such as Mel-frequency cepstral coefficients, MFCCs, and spectral contrast). data preprocessing, which includes gathering unprocessed audio data and using methods like feature extraction, noise reduction, and conversion to machine-readable forms, is the first step in the implementation process. through feature extraction, important aspects of the voice are recorded, allowing the system to recognize distinct voice signatures. A Python-based framework (***) that provides both batch and real-time processing options is used to deploy the trained model. the complete method, including model training, validation, and assessment metrics, is documented in detail in a companion Jupyter Notebook. Because predictions are produced in an organized JSON format, integrating them into bigger systems is simple. Speaker verification, tailored user experiences, and biometric authentication are some of the system's primary uses. It may be scaled to accommodate both enterprise and consumer-level service requirements. the study demonstrates how artificial intelligence may revolutionize voice-based identification and presents opportunities for additional enhancements, including support for other languages, environmental adaptability, and integration with IoT devices. © 2025 IEEE.

关键词： Deep learning

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Under Water Safety System 4

Under Water Safety System

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Shali, A. Divakar, B. Sudharshan, T.P. Liyash, R. Sri Sairam Engineering College Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

the increasing frequency of human-shark and human-saltwater crocodile encounters near coastal areas has prompted the need for advanced surveillance techniques to enhance swimmer safety. Conventional thermal cameras are limited in their ability to detect cold-blooded marine species. this study proposes a novel approach to marine species identification utilizing high-resolution cameras and advanced pattern recognition techniques. By harnessing the power of machine learning, deep learning, and computer vision, we aim to develop a robust system capable of accurately identifying and tracking various marine species in real-time. this system has the potential to revolutionize coastal safety measures, providing early warnings and enabling rapid response to potential threats. the proposed methodology holds promise in significantly reducing the risks associated with marine species encounters, ensuring safer swimming experiences for coastal communities. © 2024 IEEE.

关键词： Deep learning

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MoFLeuR: Motion-based Federated learning Gesture recognition 4

MoFLeuR: Motion-based Federated Learning Gesture Recognition

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4th IEEE international conference on Human-machine Systems, ICHMS 2024

作者： Jamal Seyedmohammadi, S. Sheikholeslami, S. Mohammad Abouei, Jamshid Mohammadi, Arash Plataniotis, Konstantinos N. Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada Yazd University Dept. Of Electrical Engineering Yazd Iran

ISBN: (纸本)9798350315790

Most of the recent works on Human Gesture recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: a) the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and b) clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency. © 2024 IEEE.

关键词： Internet of things

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A machine learning Approach for Detection of Heart Diseases Using Wearable Devices 4

A Machine Learning Approach for Detection of Heart Diseases ...

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4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Abutalip, Spandiyar Baikuvekov, Meirzhan Zanggar, Duisen Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan Kazakh National Agrarian Research University Almaty Kazakhstan

ISBN: (纸本)9798350374865

this research paper delves into the integration of wearable devices and deep learning techniques to enhance heart disease detection and monitoring. the study highlights the importance of continuous cardiac health assessment and the transformative potential of wearable technology in the healthcare landscape. Our investigation showcases their efficacy in processing intricate physiological data for early heart disease recognition. Key variables such as age, maximum heart rate, blood pressure, cholesterol levels, and various electrocardiography (ECG) metrics are explored as pivotal factors in predicting and comprehending heart disease risk factors. Furthermore, we emphasize the significance of data normalization methodologies and secure data storage solutions to ensure data reliability and accessibility. Correlation analyses elucidate the intricate interplay among these variables, providing valuable insights into their collective influence on cardiac health. As the realm of machine learning and healthcare technology continues to evolve, this research lays a solid foundation for advancing heart disease detection and management, ultimately aiming to enhance patient outcomes and elevate the quality of healthcare delivery. © 2024 IEEE.

关键词： Blood pressure

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