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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21-30 订阅

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Classification of Inflammatory Gene Expression patterns with machine learning Models 4

Classification of Inflammatory Gene Expression Patterns with...

引用

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

作者： Ay, Yeonglong Choo, Yit Hong Bhatti, Asim Lim, Chee Peng Stanford University Department of Neurology and Neurological Science Stanford United States Deakin University Institute for Intelligent Systems Research and Innovation Geelong Australia

ISBN: (纸本)9798350324303

Dementia is a complex neurological disorder characterized by a progressive decline in cognitive functions. Recent studies have suggested a link between neuroinflammation and the development of dementia, particularly in Alzheimer's disease. this study focuses on the analysis of inflammatory gene expression patterns in the parietal cortex (PCx) and temporal cortex (TCx) from a human brain RNA sequence data set, aiming to derive insights into the underlying genetic link associated with dementia, as well as prediction of dementia. Using five machine learning and statistical methods, our study reveals better detection and classification of dementia patients using PCx-related gene patterns, as compared with those from the TCx. Our findings support the hypothesis that the PCx plays a significant role in neuroinflammatory responses associated with dementia. Based on this preliminary study, machine learning models offer a useful approach to recognize inflammatory gene expression patterns and providing a valuable biomarker analysis and prediction for dementia diagnosis. © 2023 IEEE.

关键词： Gene expression

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Research on Multimodal Biometric Computer-Assisted Identification System Based on Deep learning 4

Research on Multimodal Biometric Computer-Assisted Identific...

引用

4th IEEE international conference on data Science and Computer Application, ICDSCA 2024

作者： Xu, Zhihua School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350368239

this paper puts forward a multimodal biometric recognition system founded on deep learning. through integrating various biometric data like fingerprints, faces, and irises, the precision and sturdiness of recognition are observably enhanced. the method of combining CNN and LSTM is used to fully explore the feature interaction relationship of different modal data. In the process of model training, this paper introduces an adaptive weighted loss function to dynamically adjust the influence weights of different modal features, thereby enhancing the stability and anti-interference ability of recognition. this system is tested using a public data set containing many multimodal biometric features and compared with traditional recognition methods. the results show that the system has achieved significant improvements in both recognition accuracy and response speed. Specifically, the system achieved an accuracy rate of 98.7% under the fusion of different modalities, which is about 12.5% higher than that of a single modality. In addition, this system also shows higher robustness when processing noisy data, and the recognition error rate is reduced to 2.3%. ©2024 IEEE.

关键词： Adversarial machine learning

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Enhanced Medicinal Plant Identification: Leveraging machine learning and Image Processing for Accurate Raw Material recognition 4

Enhanced Medicinal Plant Identification: Leveraging Machine ...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Hussain, G. Fayaz Mahnaaz, Sayeeda Sameera Parveen, Sheema Tazeen, Shaik Shazia Sravika, Arigela Ravindra College of Engineering for Women Dept. of Cse Kurnool India Kurnool India

ISBN: (纸本)9798350349900

data on medicinal plants and raw materials encompasses details about diverse botanical resources employed for their healing attributes in both traditional and contemporary medical practices. the difficulties in improving the identification of medicinal plants include fluctuations in image quality caused by elements such as lighting, background clutter, and camera angles, which affect the accuracy of algorithms, as well as the shortage of varied training data, potentially resulting in biases or inaccuracies during recognition. To address the challenges mentioned earlier, utilize customized Extended Convolutional Neural Network (CNN) techniques capable of managing variations in image quality and utilizing diverse training data to enhance the precision of medicinal plant identification. the Extended CNN approach provides better flexibility in handling varied image attributes and heightened precision in medicinal plant recognition, thanks to its specialized architecture and utilization of extensive training datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Human activity detection and classification by AI approaches 4th

Human activity detection and classification by AI approaches

引用

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

作者： thakur, Anish Kaur, Geet Kiran Singh, Ranjit Department of CSE Chandigarh University Mohali Gharaun India

ISBN: (纸本)9781032911571

the real area where it is well researched is Human Activity recognition in the field of Computer Vision. Computer vision is one of the well-explored areas of interest in the proper processing and analysis of visual information with respect to specified applications, e.g., security, health care, human-computer interaction. this will be a huge step forward in effective identification and categorization of human behaviors based on sensor data due to the development of machine/deep learning techniques. Within the present paper outline, there is an attempt to provide a comprehensive review of recent work and advances in the area of human activity detection based on machine/deep learning approaches. this task will review the major challenges in human activity detection, covering dealing with several activities, sensor data unpredictability, and reliable methodologies for classification. We have thus analyzed these models through machine learning-based techniques: traditional, such as Support Vector machines (SVM) and Random Forests, and innovative deep learning architectures like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). It also covers the problems of small data, challenges for model interpretability, and difficulties to use the models for its purposes in real time. Next, it also gives a glance at potential use cases under health, sport data analysis, and intelligent environments. © 2025 the Author(s).

关键词： Active learning

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Micro-Expression recognition Method Based on Transformer with Separable Self-Attention 4

Micro-Expression Recognition Method Based on Transformer wit...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Yang, Peng Zeng, Zhifeng Zhu, Tianyuan Zhuhai Coll Sci & Technol Dept Comp Sci Zhuhai Peoples R China Zhuhai Coll Sci & Technol Dept Aliyun Big Data Zhuhai Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Micro-expressions, which are brief facial movements reflecting an individual's emotions and psychological state, are crucial in fields like psychological counseling and trust evaluation. However, they are challenging to capture, and traditional methods struggle with effective recognition. While neural networks are a primary tool for this task, they are limited by their large number of parameters and slow processing speeds. Transformer architectures have shown promise in micro-expression recognition but face similar challenges. To address these issues, an improved HTNet model, called Lightweight HTNet (LWHTNet), has been proposed. Specifically, by using a Separable Self-Attention mechanism to replace the multi-head attention mechanism in Transformer layers and incorporating depthwise separable convolutions in the block aggregation layers, the model reduces the number of training parameters. In experiments, LWHTNet demonstrated strong performance on composite datasets, achieving an Unweighted F1 Score (UF1) of 0.8498 and an Unweighted Average Recall (UAR) of 0.8307. Additionally, the optimized model showed significant improvements in inference speed and parameter efficiency.

关键词： Separable Self-Attention Transformer Lightweight LWHTNet Micro-expression recognition Depthwise Separable Convolutions

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Navigating the Edge: A Comprehensive Comparison of machine learning Algorithms on Prominent Edge Platforms 4th

Navigating the Edge: A Comprehensive Comparison of Machine L...

引用

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

作者： Sayed, Aya Nabil Al-Hasan, Tamim Mahmud Bensaali, Faycal Nhlabatsi, Armstrong Qatar Univ Dept Elect Engn Coll Engn Doha Qatar Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar

ISBN: (纸本)9783031821493;9783031821509

As the demand for deploying machine learning (ML) and Deep learning (DL) models at the network's edge continues to grow, it becomes imperative to evaluate and compare the performance of various edge platforms. this study introduces an innovative edge-based pipeline for analyzing home energy and environmental data and addressing challenges in managing imbalanced label distributions. the comparison between random under-sampling and over-sampling strategies reveals the superiority of over-sampling, significantly improving accuracy and f1-score. More specifically, over-sampling using the Decision Tree (DT) technique enhanced accuracy and f1-score by 4.97% and 9.24%, respectively, when compared to the original results. the paper expands its contribution by conducting a thorough performance evaluation of various edge platforms, emphasizing critical metrics. the evaluated edge platforms include a Raspberry Pi 4b+, Jetson Nano Developer Kit, Odroid XU4, and Coral Dev Board. the study conducted an exhaustive comparison of the runtime of these prominent edge platforms when running ML/DL algorithms for tabular and image datasets during the testing phase. the Jetson Nano, particularly with its built-in GPU, demonstrates superior performance in ML/DL tasks, showcasing accelerated processing times for Convolutional Neural Network (CNN)-based algorithms. these findings emphasize the efficacy of the Jetson Nano in handling computationally intensive tasks for edge-based applications.

关键词： Edge Computing machine learning Deep learning Energy Efficiency

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Research on Intelligent Sensitive data recognition and Annotation Technology Based on Long Text 4

Research on Intelligent Sensitive Data Recognition and Annot...

引用

4th international conference on Electronics, Circuits and Information Engineering, ECIE 2024

作者： Fu, Hua Meng, Liang Wei, Sisi Nong, Chunkai Tan, Yu Guangxi Power Grid Co. Ltd. Nanning Power Supply Bureau Nanning China Information Communication Branch of Guangxi Power Grid Company Nanning China

ISBN: (纸本)9798350388312

With the rapid development of information technology, the widespread dissemination of long text data in cyberspace has brought challenges to personal privacy and information security. this study aims to develop an intelligent sensitive data recognition and annotation technology for long text content. By integrating natural language processing (NLP) technology with machine learning algorithms, we propose an efficient recognition framework that can accurately extract and annotate sensitive information from long texts. the research results indicate that the framework performs well in processing long text data, effectively improving the accuracy of sensitive data recognition, and providing solid technical support for privacy protection and data security. © 2024 IEEE.

关键词： data assimilation

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Self-attention Siamese Network for Unsupervised Few-Shot learning Tasks 4th

Self-attention Siamese Network for Unsupervised Few-Shot Lea...

引用

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

作者： Bhattarai, Binit Luitel, Pratik Kartheek, Mukku Nisanth Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783031821523;9783031821530

In the real world, it is hard to extract large data with the characteristics that are labeled. this becomes a problem when trying to implement artificial intelligence to real-life problems. In this work, we propose a self-attention siamese network guided by the principles of unsupervised learning and few-shot learning which can learn robust representation without requiring the large number of labeled data. Our approach introduces a self-attention module to capture rich contextual information and a projection head that embeds features into a discriminative space. We have performed a comprehensive experiment evaluation to show the effectiveness of our approach compared to other supervised training methods and other few-shot learning techniques. Our results highlight the potential of adding self-attention to the siamese network for enabling unsupervised few-shot learning tasks, paving the way for more efficient machine learning systems capable of learning from limited unlabeled data.

关键词： Self Attention Unsupervised learning Few-shot learning Siamese Network SASN

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A comprehensive review on hate speech recognition utilizing natural language processing and machine learning 4th

A comprehensive review on hate speech recognition utilizing ...

引用

4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Shilpashree, S. Ashoka, D.V. Department of CSE-JSSATEB Visvesveraya Technological University Belagavi India Department of ISE JSS Academy of Technical Education Visvesveraya Technological University Belagavi Bengaluru India

ISBN: (纸本)9781032426853

Many social media sites offer a low-cost mode of communication that makes it possible for anybody to immediately connect with countless individuals. Because of this, anybody can produce anything on these networks, and everyone who is intrigued by the imaterial can acquire it, which represents a revolutionary transformation in our community. On the other hand, it has resulted in the emergence of a number of problems, one of which is the propagation and distribution of materials that contain hateful rhetoric. the recognition and surveillance of inciting hatred are becoming an increasingly difficult societal problemas a whole, as well as for individuals, strategists, and scholars. In spite of attempts to leverage automated processes for identification, the capabilities of these methods remain a long way from being adequate, which consistently demands further study on the subject. this study presents a comprehensive assessment of the previous research that has been done in this area, with a particular interest in natural language processing (NLP) algorithms. Our goal is to gain an understanding of the prevalence of offensive speech in social media platforms found online, the most popular manifestations of hatred, as well as the impact obscurity has on hateful texts. © 2024 the Author(s).

关键词： machine learning

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Automated Breast Cancer Detection: A Review 4th

Automated Breast Cancer Detection: A Review

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

作者： AlKhater, Rozah Al-maadeed, Somaya Qatar Univ Doha Qatar

ISBN: (纸本)9783031821554;9783031821561

Over the past decade, medical imaging research has grown significantly in image processing and computer vision, particularly in detecting, classifying, and segmenting breast cancer. Advancements in pattern recognition and Deep learning (DL) methodologies have significantly impacted this field. In light of the swift advancements in deep learning technology and the escalating seriousness of breast cancer, it is imperative to synthesize previous achievements and pinpoint forthcoming obstacles that require attention. thus, this paper extensively reviews the classification of histopathological images for breast cancer detection using machine learning and deep learning techniques. the article emphasizes the publicly accessible datasets of histopathological images for classifying breast cancer. this article examines and outlines recent research on histopathology imaging for breast cancer screening and explores potential future developments. Upon reviewing the literature, it was discovered that only a limited number of studies utilize image processing and machine learning approaches. Most research in the past five years has utilized deep learning techniques.

关键词： Deep learning AI machine learning MRI Neural networks

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