检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao-Chiang Shao Yu-Hsien Lin Chia-Wen Lin Inst. Data Science and Information Computing National Chung Hsing University Taiwan Dept. Electrical Engineering National Tsing Hua University Taiwan

In this paper, we proposed a fine-grained attribute pre-labeling method based on the multi-label recovery techniques. Given a fine-grained image dataset with overlooked attributes in its annotation vectors, our method can predict those missing attribute labels by learning the between-label dependency based on the estimated similarity between known attributes and the similarity of extracted deep image features. Furthermore, to prevent the learnable label dependency matrix from converging to a trivial solution, we designed a trace-loss to penalize the self-dependency of attributes. Comprehensive experiments on the CUB-200-2011 dataset show that, given a training set with 40% of attribute labels randomly dropped: i) our approach achieves a pre-labeling performance with an mAP value of 30.7 on a blind testing set, and ii) the missing attributes in the training set can be corrected with an accuracy of 89%. Our method can effectively and robustly perform the fine-grained pre-labeling task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment

Image based and Point Cloud based Methods for 3D View Recons...

引用

IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Arya Agrawal Teena Sharma Nishchal K. Verma Shantaram Vasikarla School of Computer Science and Engineering Vellore Institute of Technology Vellore India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati India Dept. of Electrical Engineering Indian Institute of Technology Kanpur India Dept. of Computer Science California State University Northridge CA

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Risk estimation in a Markov cost process: lower and upper bounds 24

Risk estimation in a Markov cost process: lower and upper bo...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Gugan Thoppe Prashanth L. A. Sanjay P. Bhat Dept. of Computer Science and Automation Indian Institute of Science (IISc) Bengaluru India and Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ε-accuracy, either in expected or high-probability sense, requires at least Ω(1/ε2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs' mean. Even in that case, our lower bound of Ω(1/ε2) improves upon the existing Ω(1/ε) bound (Metelli et al., 2023).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Internet of Animal Health Things (IoAHT) Framework for Clinical Mastitis Detection in Dairy Cows

Internet of Animal Health Things (IoAHT) Framework for Clini...

引用

Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Ankitha K Venugopala P S Harish Kunder Ankitha Shetty Dept. of Artificial Intelligence and Data Science NMAMIT Nitte Karkala India Dept. of Artificial Intelligence and Machine Learning Alvas Institute of Engineering and Technology Moodbidri India

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Personalized Deep Learning Framework for Early Detection of Glioblastoma Using Multi-Modal MRI data

AI-Powered Personalized Deep Learning Framework for Early De...

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sunitha T Veeramalai Sankaradass V.K Manindra Manish Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The prognosis of glioblastoma, which stands as the most malignant variant of a brain tumour, is clearly unfavourable due to the late diagnosis and the severe heterogeneity it is known to possess. Standard diagnostic techniques nowadays that employ MRI data quite often have a low-level performance in terms of diagnosing glioblastoma during its early stages owing to the diverse manner in which the tumours may grow in various patients. In light of this background, we present a new deep learning framework suitable for multi-modal MRI data and aimed at improving glioblastoma prognosis at an early stage. The framework utilizes convolutional neural networks (CNNs) to obtain features from T1, T2, and FLAIR MRI images, and combines this with an AI-guided individualized diagnostic system. This model utilizes relevant clinical data such as age and genetic data in a more individualized manner to meet patient’s needs during diagnosis. One more novel feature of our approach is the ensemble learning that combines CNNs with RNNs to detect minor tumour deposits more effectively over time. The proposed framework delivered an AUC of 0.94 on large glioblastoma MRI databases, thus progressing the early-stage diagnosis of the tumour type which was the aim. This advanced personalized method not only allows for superior diagnosis but also improves on the treatment plans that are available for such patients. Further development will concern clinical verification as well as increasing the capacity of the framework.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Genetics Feature extraction History Prognostics and health management Standards Tumors

来源：评论

学校读者我要写书评

暂无评论

Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder

Improving Time-Series Classification Accuracy Based on Tempo...

引用

IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Dohee Kim Sunghyun Sim Bori Yoon Ling Liu Hyerim Bae Dept. Industrial Engineering Major in Industrial Data Science & Engineering Pusan National University Busan South Korea Dept. Industrial Management & Big Data Engineering Dong-Eui University Busan South Korea College of Computing Georgia Institute of Technology Georgia USA

Time-series data consists of a sequence of observations recorded in chronological order, where the data changes over time. This type of data exhibits various characteristics, such as temporal volatility, trends, and seasonality. Recently, a new layer structure called Correlation Recurrent Units (CRU) has been proposed to capture not only temporal variability but also trends and seasonality in time-series data. In this study, we propose an end-to-end model that utilizes a CRU autoencoder to learn temporal feature representations and address time-series classification problems simultaneously. To validate the performance of our proposed model, we conducted comparative experiments using 30 time-series classification datasets from six different types. The experimental results showed that the proposed model outperformed the baseline models in 20 out of the 30 time-series datasets. This indicates that the proposed approach effectively captures various temporal features in time-series data and improves the performance of time-series classification tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Explaining Agent Behavior in Agent-Based Modeling Using Subgroup Discovery

SSRN

引用

SSRN 2023年

作者： Romero, Elena Carmona del Jesús, Cristóbal José Cordón, Oscar del Jesus, María José Damas, Sergio Chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. In addition to the key performance indicators ABM provides as main outputs of the modeling process, it also generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Explainable Artificial Intelligence (XAI) techniques can improve the transparency and interpretability of these models. Subgroup Discovery (SD) is a useful tool for XAI that can facilitate the analysis of the data with respect to a variable of interest. In this contribution, we propose a methodology for applying SD to ABM for extracting useful knowledge of agents behavior from the model output. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2023, The Authors. All rights reserved.

关键词： Consumer behavior

来源：评论

学校读者我要写书评

暂无评论

Enhancing Wireless Power Transfer Efficiency for Medical Implants: A Performance Evaluation of Human Tissue Encapsulated Antennas

Enhancing Wireless Power Transfer Efficiency for Medical Imp...

引用

Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Akib Jayed Islam Sultanus Salehin S. Pranta N. Das MRA Shawon Rizwan Ahamed Sayem Ul Alam Md Nurul Absar Siddiky Computational Colour and Spectral Imaging (COSI) Norwegian University of Science and Technology Gjøvik Norway Dept. of ECE The University of Alabama Tuscaloosa USA Dept. of EEE Port City International University Chattogram Bangladesh Dept. of Computer Science and Electrical Engineering West Virginia University Morgantown USA Dept. of ECE UNC at Charlotte Charlotte NC USA

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Development in radio frequency (RF)-based wireless power transfer (WPT) is transforming implantable medical devices (IMDs)-including pacemakers, neurostimulators, and drug delivery systems-into cutting-edge technologies. The current research investigates these developments, focusing in particular on the creation and enhancement of implantable rectennas-which transform electromagnetic waves into DC power necessary for implantable medical devices-and their optimization. Our work addresses a variety of design factors, such as efficiency, bio-compatibility, and antenna size and frequency range. Additionally, it presents a human tissue model that incorporates safety precautions such as the specific absorption rate (SAR) to precisely represent antenna behavior within the body. Findings show that our rectennas outperform previous methods, achieving an amazing RF-to-DC conversion efficiency of up to 75.75%. The improvements made in 3D printing and materials greatly improve the efficiency and compactness of rectennas, which might revolutionize power management in biomedical applications.

关键词： Performance evaluation Wireless communication Rectennas Biological system modeling Transmitting antennas Pacemakers Wireless power transfer Specific absorption rate Safety Optimization

来源：评论

学校读者我要写书评

暂无评论

Robust Self-Guided Deep Image Prior

Robust Self-Guided Deep Image Prior

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Evan Bell Shijun Liang Qing Qu Saiprasad Ravishankar Dept. of Mathematics Michigan State University East Lansing MI Dept. of Biomedical Engineering Michigan State University East Lansing MI Dept. of Electrical and Computer Engineering University of Michigan Ann Arbor MI Dept. of Computational Mathematics Science and Engineering Michigan State University East Lansing MI

In this work, we study the deep image prior (DIP) for reconstruction problems in magnetic resonance imaging (MRI). DIP has become a popular approach for image reconstruction, where it recovers the clear image by fitting an overparameterized convolutional neural network (CNN) to the corrupted/undersampled measurements. To improve the performance of DIP, recent work shows that using a reference image as an input often leads to improved reconstruction results compared to vanilla DIP with random input. However, obtaining the reference input image often requires supervision and hence is difficult in practice. In this work, we propose a self-guided reconstruction scheme that uses no training data other than the set of undersampled measurements to simultaneously estimate the network weights and input (reference). We introduce a new regularization that aids the joint estimation by requiring the CNN to act as a powerful denoiser. The proposed self-guided method gives significantly improved image reconstructions for MRI with limited measurements compared to the conventional DIP and the reference-guided method while eliminating the need for any additional data.

关键词： Training Weight measurement Magnetic resonance imaging Supervised learning Training data Convolutional neural networks Task analysis

来源：评论

学校读者我要写书评

暂无评论

CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference 27

CAD-DA: Controllable Anomaly Detection after Domain Adaptati...

引用

27th International Conference on Artificial Intelligence and Statistics, AISTATS 2024

作者： Le Duy, Vo Nguyen Lin, Hsuan-Tien Takeuchi, Ichiro University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Dept. of Computer Science and Information Engineering National Taiwan University Taiwan Center for Data Intelligence National Taiwan University Taiwan Nagoya University Japan RIKEN Japan

We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA—controllable AD under DA. The distinct advantage of the CAD-DA lies in its ability to control the probability of misidentifying anomalies under a prespecified level α (e.g., 0.05). The challenge within this DA setting is the necessity to account for the influence of DA to ensure the validity of the inference results. We overcome the challenge by leveraging the concept of Selective Inference to handle the impact of DA. To our knowledge, this is the first work capable of conducting a valid statistical inference within the context of DA. We evaluate the performance of the CAD-DA method on both synthetic and real-world datasets. Copyright 2024 by the author(s).

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：