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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 13394 条记录，以下是4911-4920 订阅

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FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion

FLAME Diffuser: Wildfire Image Synthesis using Mask Guided D...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Wang, Hao Boroujeni, Sayed Pedram Haeri Chen, Xiwen Bastola, Ashish Li, Huayu Zhu, Wenhui Razi, Abolfazl Clemson University School of Computing ClemsonSC United States The University of Arizona TucsonAZ United States Arizona State University School of Computing and Augmented Intelligence TempeAZ United States

ISBN: (纸本)9798350362480

Wildfires are a significant threat to ecosystems and human infrastructure, leading to widespread destruction and environmental degradation. Recent advancements in deep learning and generative models have enabled new methods for wildfire detection and monitoring. However, the scarcity of annotated wildfire images limits the development of robust models for these tasks. In this work, we present the FLAME Diffuser, a training-free, diffusion-based framework designed to generate realistic wildfire images with paired ground truth. Our framework uses augmented masks, sampled from real wildfire data, and applies Perlin noise to guide the generation of realistic flames. By controlling the placement of these elements within the image, we ensure precise integration while maintaining the original image's style. We evaluate the generated images using normalized Frechet Inception Distance (nFID), CLIP Score, and a custom CLIP Confidence metric, demonstrating the high quality and realism of the synthesized wildfire images. Specifically, the fusion of Perlin noise in this work significantly improved the quality of synthesized images. The proposed method is particularly valuable for enhancing datasets used in downstream tasks such as wildfire detection and *** resources for accessing the code and datasets are provided at the link: https://***/***/project/flame © 2024 IEEE.

关键词： Premixed flames

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Next-Gen Hybrid Deep learning for Accurate Pepper Leaf Mosaic Virus Classification 2

Next-Gen Hybrid Deep Learning for Accurate Pepper Leaf Mosai...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kaur, Arshleen Kukreja, Vinay Gopal, Lisa Verma, Garima Sharma, Rishabh Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350306415

This research presents a new way to sort out the stages of pepper leaf mosaic virus illness using an advanced learning model. It combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for this purpose. This study looks at how using space and time data can improve the way we find out about plant diseases, which is a better method than what we already have. The set of pictures has 10,500 illness images sorted into different health stages. These were handled and used to teach the model about disease by testing it out too. The suggested CNN-LSTM mix model uses the skill of CNN to get space details from leaf pictures and LSTM's ability to look at how these features change over time. This mix is made to make disease stage classification better and faster. The model got a total correctness of 96.5%, much better than single CNN and LSTM models. The model was very good when checked with new data. This shows it can work well in real life too. The results of this study are very important for precision farming and plant disease research. The fused CNN-LSTM model is very accurate and works well in most cases. This makes it great for finding plant diseases early, which can help manage them better to save more crops from getting sick. The study also shows where more research can be done in the future. This includes changing models to fit different environmental situations and sick plants, along with not depending too much on big sets of data. © 2024 IEEE.

关键词： Confusion Matrix Deep learning Model Fusion Pepper Leaf Disease Performance Parameter

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Real-time Asset False Matching Detection via Attentive Siamese Networks 6

Real-time Asset False Matching Detection via Attentive Siame...

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6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Miao, Ziwan Zhang, Li School of Computing Beijing Information Science & Technology University Beijing China School of Management Science and Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798331541750

Accurate and efficient asset matching is crucial for remote management and diversified investment entities. To address the challenges of low efficiency, high labor costs, and false matching, this paper proposes a real-time asset false matching detection method based on an attentive Siamese network. By integrating the Convolutional Block Attention Module (CBAM) into a Siamese neural network framework, the model enhances feature extraction capabilities, focusing on discriminative regions within asset images. Pretrained weights are transferred using transfer learning, and data augmentation techniques are applied to enhance the model's generalization ability. Experiments on various types of asset data demonstrate that the proposed method achieves an accuracy of 92.42% and an F1 score of 92.48%, significantly improving the precision and efficiency of asset similarity calculations. This research not only advances asset identification and matching but also showcases exceptional performance in intelligent asset inventory, providing reliable support for asset management. Future work will focus on improving model robustness and optimizing the network structure for large-scale and complex environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Artificial Neural Networks for Prostate Cancer Detection and Classification 1

引用

8th international conference on data Management, Analytics and Innovation, ICDMAI 2024

作者： Lingappa, Ediga Jareena Begum, D. Rakesh, Mothe Venkatesh, Bura Kishor, Golla Lakshmi Reddy, L. Telangana State Secunderabad Maisammaguda500100 India Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Malla Reddy University Hyderabad India Hyderabad India CSE Department St. Martins Engineering College Secunderabad Dhulapally500100 India

ISBN: (数字)9789819732425

ISBN: (纸本)9789819732418

Histopathological scans identify prostate cancer, which medical professionals say is common in men. Pathologists’ modest differences in prostate cancer grading using the Gleason system persist. This work replicates automated prostate cancer segmentation and classification. The proposed method uses gland-oriented segmentation and classification. Prostate cancer is one of the major killers of men in the USA. Complex masses can cause radiologists to overlook prostate cancer. Recent prostate cancer detection technologies are weak. A robust deep learning ANN and transfer learning are used in this research. Decision Tree, SVM with different kernels, and Bayes are compared. GoogleNet models and machine learning classifiers employ cancer MRI database properties. Morphological, entropy-based, textural, SIFT, and elliptic Fourier descriptors are examples. Performance assessment uses numerous indicators. Specificity, sensitivity, and accuracy transfer learning and ANN (Google Net) yielded the greatest results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance imaging

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Bio-inspired Intrusion Detection System for Internet of Things Networks Security

Bio-inspired Intrusion Detection System for Internet of Thin...

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2024 Cognitive Models and Artificial Intelligence conference, AICCONF 2024

作者： Harbi, Yasmine Merat, Salsabyl Aliouat, Zibouda Harous, Saad Ferhat Abbas University Setif Algeria Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400716928

The contemporary era has witnessed substantial growth in the Internet of Things (IoT), accompanied by an escalation in data volume due to the proliferation of Internet-connected devices. Unfortunately, this surge has attracted the attention of cybercriminals, who are now targeting IoT networks for malicious activities. Consequently, security and privacy concerns have emerged as the primary obstacles impeding the widespread adoption of IoT. While complete prevention of attacks on any system is not feasible, real-time detection of such attacks is crucial for effectively safeguarding IoT systems. This paper introduces a novel Intrusion Detection System (IDS) that employs supervised machine learning and bio-inspired algorithms to identify security anomalies in IoT networks. The IDS scrutinizes all dataset features to detect intrusive data and trains itself to predict potential network intrusions. However, some features may be extraneous or unrelated to the detection process, resulting in computational complexities and increased detection time. To tackle this issue, we implemented a feature selection process using a modified firefly algorithm to eliminate irrelevant features from the dataset and choose the optimal subset of features (reducing from 74 to 39). This optimization enhances the performance of the IDS by decreasing detection time and improving prediction accuracy. The obtained results affirm that the proposed intrusion detection system is capable of identifying real-world intrusions and can serve as an effective security solution for IoT systems. © 2024 ACM.

关键词： Decision trees

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Federated learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram data 24

Federated Learning and Differential Privacy Techniques on Mu...

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8th international conference on Medical and Health Informatics, ICMHI 2024

作者： Vikhyat, Agrawal Sunil, Kalmady Venkataseetharam, Manoj Malipeddi Manisimha, Varma Manthena Weijie, Sun Islam, Saiful Abram, Hindle Padma, Kaul Russell, Greiner Canadian VIGOUR Centre University of Alberta Canada Department of Physics IIT Bombay India Department of Computing Science University of Alberta Canada Canadian VIGOUR Centre Department of Medicine University of Alberta Canada Department of Computer Science and Engineering IIT Kharagpur India

ISBN: (纸本)9798400716874

This research paper explores ways to apply Federated learning (FL) and Differential Privacy (DP) techniques to population-scale Electrocardiogram (ECG) data. The study learns a multi-label ECG classification model using FL and DP based on 1,565,849 ECG tracings from 7 hospitals in Alberta, Canada. The FL approach allowed collaborative model training without sharing raw data between hospitals, while building robust ECG classification models for diagnosing various cardiac conditions. These accurate ECG classification models can facilitate the diagnoses while preserving patient confidentiality using FL and DP techniques. Our results show that the performance achieved using our implementation of the FL approach is comparable to that of the pooled approach, where the model is trained over the aggregating data from all hospitals. Furthermore, our findings suggest that hospitals with limited ECGs for training can benefit from adopting the FL model compared to single-site training. In addition, this study showcases the trade-off between model performance and data privacy by employing DP during model training. Our code is available at https://***/vikhyatt/Hospital-FL-DP. © 2024 ACM.

关键词： Federated learning

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Deep learning for Automated Face Mask Detection: Enhancing Public Health Measures 7

Deep Learning for Automated Face Mask Detection: Enhancing P...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Tanwar, Vishesh Anand, Vatsala Upadhyay, Deepak Singh, Mukesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Uttarakhand Dehradun India

ISBN: (纸本)9798350372816

The COVID-19 pandemic has emphasized the critical importance of face masks in mitigating the transmission of respiratory infections. In this research paper, we explore the role of deep learning in automated face mask detection systems, aiming to enhance public health measures and safety protocols. Through a comprehensive literature survey, we review advancements in AI technology, particularly in the context of detecting mask-wearing individuals. Our methodology involves dataset preparation and training of convolutional neural network (CNN) architectures for binary classification tasks. Results demonstrate the effectiveness of deep learning models in accurately identifying individuals wearing face masks, with deeper architectures showcasing superior performance in terms of loss reduction and accuracy. We conclude that automated face mask detection systems offer invaluable support in enforcing mask-wearing regulations, especially in densely crowded regions and large gatherings. Furthermore, these systems provide valuable data for informing public health policies and resource allocation. This study highlights the transformative potential of deep learning in addressing current and future public health crises, emphasizing the centrality of technology in safeguarding community health and well-being. © 2024 IEEE.

关键词： COVID-19

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Deep Interpretable Component Decoupled Dictionary Neural Network for Image Denoising in Industrial Cyber-Physical System 16

Deep Interpretable Component Decoupled Dictionary Neural Net...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart data, Smartdata and Congress on Cybermatics (Cybermatics)

作者： Deng, Lizhen Pan, Yushan Xu, Guoxia Yan, Taiyu Wang, Zhongyang Zhu, Hu Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing Peoples R China Xian Jiaotong Liverpool Univ Dept Comp Suzhou Peoples R China Xidian Univ Sch Cyber Engn Xian Peoples R China

ISBN: (纸本)9798350309461

Image denoising techniques are pivotal in preprocessing noisy images, greatly enhancing the quality of visual data in applications within the realm of Cyber-Physical Systems (CPS). Take scenarios like autonomous vehicles and surveillance systems, for instance, where denoising plays a pivotal role in significantly improving the accuracy of object detection and recognition. However, the adoption of image denoising tasks in CPS is hindered by the fragility, robustness, and interpretability issues associated with neural networks. To address these challenges, this study introduces an innovative and interpretable approach to image denoising. We propose an image denoising model that combines dictionary learning with a deep neural network. This hybrid approach leverages decoupling and sparse convolution techniques, strategically designed to mitigate model fragility and reinforce model robustness. Furthermore, our model is geared towards untangling and reducing redundancy across different image components. The architecture of the network is crafted as a model-data-driven neural network, facilitating the simultaneous learning of various image components and deploying fusion mechanisms to mitigate perturbations and noise. Finally, we provide a theoretical framework to explain our method and substantiate its effectiveness through rigorous experimentation and validation.

关键词： Dictionary learning Sparse Coding Image Denoising Decomposition Decoupling

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Meta-learning Algorithms for Few-Shot Classification in Imbalanced datasets 4

Meta-Learning Algorithms for Few-Shot Classification in Imba...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Raman Fezaa, Laith H. Alkhayyat, Ahmed Pareek, Bhagwati Lal Kumar, Jatinder Jadhav, Abhilasha School of Mechanical Engineering Rayat Bahra University Mohali India Al-Zahrawi University College Department of Optical Techniques Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq Sangam University Rajasthan Bhilwara India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali140307 India Nims School of Computing Science and Artificial Intelligence Nims University Rajasthan Jaipur India

ISBN: (纸本)9798350387490

This work aims to examine meta-learning on few-shot learning schemes, specifically on the assessment of how well current approaches manage imbalance in the examined datasets. Meta-learning to improve model performance with a small amount of data by learning from related tasks is tested against benchmarks such as MAML, ProtoNet and RelationNet. An important insight of our research is that these methods are still facing important issues, such as scalability or the problem of domain generalization, as well as the major issue of the extreme imbalance of the data distribution. To overcome these problems, we suggest a new solution, which employs elaborate methods and is characterized by increased correspond and F1 coefficient, as well as the degree of balanced accuracy at different degrees of unbalanced data. However, the proposed study also points out the main challenges for future research including new and efficient algorithms for high-dimensional and real-time scenario, better methods for handling massive shift, as well as the possibility to increase the interpretability of models. The research also highlights the possibilities of applying meta-learning in the few-shot classification, but at the same time also highlight the imperfections and the research needs to be continued in order to further improve the existing gaps and to meet the practical requirements. This study adds to the overall knowledge base concerning meta learning approaches and provides an initial frameworkfor work to be conducted in the future in an effort to produce methods that are considerably more portable, generalizable and also more comprehensible in nature across the broader field of study. © 2024 IEEE.

关键词： Contrastive learning

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DiffGSL: A Graph Structure learning Diffusion Model for Dynamic Spatio-Temporal Forecasting

DiffGSL: A Graph Structure Learning Diffusion Model for Dyna...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Jung, Chanyoung Jang, Yun Sejong University Seoul Korea Republic of

ISBN: (纸本)9798350362480

Researchers have been actively engaged in recent studies utilizing the denoising diffusion probabilistic model (DDPM), a generative model that shows stable convergence during training, unlike other generative models, and represents data distributions that approximate real-world data. Due to these advantages, DDPM is widely employed for image, video, and audio generation. Recent DDPM research extends beyond image, video, and audio to various datasets and applications. The majority of real-world scenarios involve spatiotemporal data. Consequently, researchers propose prediction models using spatiotemporal data across domains such as traffic forecasting, weather prediction, and disease tracking. While most spatiotemporal data prediction models focus on traffic data, weather and epidemic data present higher uncertainty due to regional variations, seasonal changes, fluctuations in infection rates, and uncertainties in reported cases. To address these challenges of spatiotemporal data, we propose a DDPM-based model. In contrast to previous DDPM studies, we propose a denoising network combining Graph Structure learning (GSL) to utilize the dynamic nature of spatiotemporal data. We assess performance using three real-world datasets encompassing traffic, weather, and epidemics and conduct an ablation study comparing GSL methods. © 2024 IEEE.

关键词： Prediction models

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