检索结果-内蒙古大学图书馆

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Schneider, Nora Goshtasbpour, Shirin Perez-Cruz, Fernando Computer Science Department ETH Zurich Zurich Switzerland Swiss Data Science Center Zurich Switzerland

ISBN: (纸本)9781713899921

We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions. © 2023 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graphs Can be Learned with Just Intersection Features 41

Knowledge Graphs Can be Learned with Just Intersection Featu...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Le, Duy Zhong, Shaochen Liu, Zirui Xu, Shuai Chaudhary, Vipin Zhou, Kaixiong Xu, Zhaozhuo Department of Computer and Data Sciences Case Western Reserve University United States Department of Computer Science Rice University United States Department of Electrical and Computer Engineering North Carolina State University United States Department of Computer Science Stevens Institute of Technology United States

Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the k-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features. Copyright 2024 by the author(s)

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Analysis of Autism Spectrum Disorder Detection Using Deep Learning 4

Comprehensive Analysis of Autism Spectrum Disorder Detection...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sujatha, E. Devi, R.Suguna Pavai, D. Saranya, K. Department of Computer Science and Engineering Chennai India Department of Electronics and Communication Engineering Chennai India Department of Artificial Intelligence and Data Science Chennai India Chennai India

ISBN: (纸本)9798331540364

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by a wide range of symptoms. Early and accurate diagnosis is crucial for effective intervention and support. This research reviews the established diagnostic tools, including clinical assessments, behavioral observations, genetic testing, and neuroimaging techniques. Additionally, we explore the potential of emerging technologies like machine learning and AI-based approaches in enhancing ASD diagnosis. By analyzing the strengths and limitations of various detection methods, this research provides a comprehensive overview of the current state of the field. The findings highlight the importance of continued research and innovation in improving ASD diagnosis for the benefit of affected individuals and their families. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Unifying Genetics and Imaging: MRI-Based Classification of MGMT Genetic Subtypes Using Visual Transformers 18

Unifying Genetics and Imaging: MRI-Based Classification of M...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Babar, Ibrar Tahir, Asjid Haider, Arslan Ur Rehman, Hafeez Alazab, Moutaz National University of Computer and Emerging Sciences Department of Computer Science Peshawar Pakistan Oryx Universal College Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350328172

Brain cancer can take many forms, but glioblastoma (GBM) is one of the most aggressive. To treat it effectively, doctors need to know the genetic subtype of a specific part of the tumor called the O-6-methylguanine-DNA-methyltransferase (MGMT) promoter. This information is critical for making treatment decisions and predicting outcomes. However, traditional methods for predicting MGMT status have significant limitations. One of the main reasons for this poor performance is that these methods fail to capture small variations in MRI signatures that are essential for accurately predicting MGMT promoter methylation status. In this work, we propose a two-part approach to predict the MGMT methylation status. First, we use autoencoders to extract features from the MRI images, capturing intricate spatial patterns and representations. Then, we feed these encoded features into a visual transformer architecture to learn long-range dependencies and contextually significant relationships among image regions. This innovative fusion of autoencoders and visual transformers allows us to harness both local and global information present in the MRI data. We utilized a dataset consisting of 1010 3D MRI images from patients with GBM. Half of the patients had MGMT promoter methylation, while the other half did not. The model achieved impressive results, including an overall accuracy score of 0.904, recall score of 0.902, precision score of 0.914, F1 score of 0.906, and specificity score of 0.906. These metrics far surpass the accuracy of conventional imaging techniques. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Content Based Email Spam Filtering and Detection using Hybrid Supervised Learning Approach 7

Content Based Email Spam Filtering and Detection using Hybri...

引用

7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Saquib, Najam U Khalid, Waqar Shaheen, Sahibzadi Annum Asim, Muhammad Ahmad, Naveed Abasyn University Department of Computer Science Islamabad Pakistan Institute of Management Sciences Department of Computer Science Peshawar Pakistan Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Prince Sultan University Eias Data Science and BlockChain Lab College of Computer and Information Science Riyadh11586 Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350372328

In today's world, email is used widely for communication purposes globally. Email spam are unwanted emails that are sent to many recipients receivers. It is usually used for commercial purposes More likely spam enail is also used for malicious attempts to gain access to receiver computer/device. This article presents a novel hybrid architecture for spam emails filtering using mails content-based approach. For filtering, Word2Vec (W2V) word embedding procedure is used for feature selection with bio-inspired 'Particle Swarm Optimization (PSO)' and 'Support Vector Machine (SVM)' combined with 'Convolutional Neural Network (CNN)'. Moreover, our proposed hybrid architecture is simulated in open-source tool and the performance is evaluated using standard confusion matrix. Afterw ards, the results are compared with various previous algorithms/models. Experimental results show that our proposed hybrid scheme enhances detection accuracy of spam emails. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

引用

computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

来源：评论

学校读者我要写书评

暂无评论

Structured Sampling for Robust Euclidean Distance Geometry 59

Structured Sampling for Robust Euclidean Distance Geometry

引用

59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Kundu, Chandra Tasissa, Abiy Cai, HanQin University of Central Florida Department of Statistics and Data Science OrlandoFL32816 United States Tufts University Department of Mathematics MedfordMA02155 United States University of Central Florida Department of Statistics and Data Science Department of Computer Science OrlandoFL32816 United States

ISBN: (纸本)9798331513269

This paper addresses the problem of estimating the positions of points from distance measurements corrupted by sparse outliers. Specifically, we consider a setting with two types of nodes: anchor nodes, for which exact distances to each other are known, and target nodes, for which complete but corrupted distance measurements to the anchors are available. To tackle this problem, we propose a novel algorithm powered by Nystro m method and robust principal component analysis. Our method is computationally efficient as it processes only a localized subset of the distance matrix and does not require distance measurements between target nodes. Empirical evaluations on synthetic datasets, designed to mimic sensor localization, and on molecular experiments, demonstrate that our algorithm achieves accurate recovery with a modest number of anchors, even in the presence of high levels of sparse outliers. © 2025 IEEE.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Unveiling Early Autism Spectrum Disorder Detection: A Comprehensive Study of Machine Learning and Deep Learning Approaches 6

Unveiling Early Autism Spectrum Disorder Detection: A Compre...

引用

6th IEEE Pune Section International Conference, PuneCon 2023

作者： Tarai, Trilochan Parhi, Manoranjan Mishra, Debahuti Shaw, Kailash University Department of Computer Science and Engineering Odisha Bhubaneswar India University Centre of Data Science Department of Computer Science and Engineering Odisha Bhubaneswar India Department of Aiml Pune India

ISBN: (纸本)9798350324204

According to research by the world health organization (WHO), approximately 0.63% of children are diagnosed with autism spectrum disorder (ASD). ASD commonly emerges during childhood and persists through adolescence and adulthood. Early detection of ASD is crucial as it can lead to effective interventions and improved outcomes. Machine learning and deep learning techniques have gained significant recognition for their efficacy in various fields including biomedicine and medicine. These models offer valuable tools for data interpretation in clinical diagnostics and decision-making processes. Consequently, researchers emphasize the utility of both machine learning and deep learning methods for disease screening. This study aims to leverage both machine learning and deep learning techniques to classify ASD cases using a dataset sourced from the UCI repository. The dataset encompasses pediatric cases. The classification task employed k-nearest neighbors' algorithm (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Among these, CNN outperformed SVM and k-NN, achieving a remarkable accuracy of 99.71%. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A Framework for Video Based Sign Language Interpretation Using Machine Learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Home Energy Consumption Optimization Based on User Habit Analysis

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第3期8卷 839-855页

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Lin, Tao Kong, Lingping College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science VSB-Technical University of Ostrava Czech Republic

With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings. In the study of smart electricity consumption in households, the differences in users’ lifestyles and their preferences for the use of various appliances can have a great impact on the results. And many existing methods need to rely on users’ awareness, which does not meet the popular demand. In this paper, we propose a new method for residential load scheduling that takes into account the load characteristics of appliances and electricity consumption habits. By analyzing the household electricity consumption data set and mining the personalized needs and us-age preferences of this user for various appliances, we establish an optimization model for electricity consumption behavior that combines the minimization of electricity expenses and user comfort. Finally, an improved artificial bee colony algorithm is proposed for solving the optimization model and generating a personalized dispatching strategy combined with real-time electricity pricing (RTEP) tariff. The proposed improved artificial swarm algorithm is compared with other classical algorithms, including GA, PSO, ABC, and QABC, and the analysis of cases shows that the model can effectively reduce the electricity consumption cost and ensure the customer satisfaction, and the proposed improved ABC-based algorithm outperforms other algorithms in terms of cost and user comfort. © 2023, Journal of Network Intelligence.

关键词： Customer satisfaction

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：