检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms

引用

Evolutionary computation 2025年第1期33卷 1-25页

作者： He, Jun Zhou, Yuren Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom School of Data and Computer Science Sun Yatsen University Guangzhou 510006 China

The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multiobjective optimization problem subject to fitness levels. The tightest metric bounds by fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes with and without shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function. © 2024 Massachusetts Institute of Technology.

关键词： algorithm analysis drift analysis Evolutionary algorithm fitness levels hitting time Markov chain.

来源：评论

学校读者我要写书评

暂无评论

Image Captioning with Audio Reinforcement using RNN and CNN

Image Captioning with Audio Reinforcement using RNN and CNN

引用

2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Patankar, Rohan Sethi, Harshik Sadhukha, Aditi Banjade, Nishant Mathur, Ashwini Jain University Department of Computer Science and Engineering Specialization in Data Science Bengaluru India Jain University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350333602

Image captioning involves generating a descriptive text that encapsulates the visual information contained in an image. This ppt proposes a deep learning model for image captioning that utilizes a Vision Transformer (ViT) and a GPT-2 network. Specifically, the ViT is employed to extract image features, which are then used by the GPT-2 network to generate captions for the images. To evaluate the effectiveness of the model, experiments are conducted on a Stanford dataset comprising images and their corresponding captions, and demonstrate that our model achieves performance comparable to other state-of-the-art image captioning models. Proposed results suggest that our model has the potential to generate accurate and semantically meaningful captions for images, and could be useful for various applications such as improving the accessibility of visual media. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Oral Cancer Detections and Classification Using Region Based Convolutional Neural Network 2

Oral Cancer Detections and Classification Using Region Based...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Kumar, M Prema Ashfaq Ahmed, K. Subash, K. Aeron, Anurag Department of Ece Andhra Pradesh Bhimavaram India Department of Emerging Technologies in Computer Science Andhra Pradesh Kurnool India Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Meerut India

ISBN: (纸本)9798350384277

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Automated Deep Learning Based Knee Osteoarthritis Joint Extraction and Classification 17

Automated Deep Learning Based Knee Osteoarthritis Joint Extr...

引用

17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Abbas, Muhammad Sohail Jamil, Sonain Khurshid, Atif Department of Data Science and Artificial Engineering Islamabad Pakistan Department of Computer Science Gjovik2815 Norway

ISBN: (纸本)9798350381320

Knee osteoarthritis (KOA) is a widespread global condition, impacting over 300 million individuals as per the World Health Organization (WHO). Particularly prevalent among older adults, knee OA is a prominent cause of disability. Its occurrence increases with age, especially after 50, and is more frequent in women, particularly post-menopause. Several studies have been carried out so far for automated grading and classification of knee osteoarthritis (KOA), but none of them built strong foundations enough to make this system automated. This study focuses on machine-controlled knee joint extraction and grading classification with improved accuracy and performance. We used the osteoarthritis initiative (OAI) dataset of X-ray images for our study. Initially, a single-stage detector is used for joint extraction of the knee area as the X-ray images contain entire knees with both joints. Enhanced osteoarthritis feature extraction (OAFE) and osteoarthritis dimensionality reduction (OADR) blocks are used for grading classification. We have significantly improved state-of-The-Art results. We have acquired joint extraction with a mean average precision (map) of 95.3% and grading classification accuracy of 78.93%. Furthermore, the performance due to the dimensionality reduction block has improved by a huge factor. © 2023 IEEE.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Class Breast Cancer Classification 2

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Cla...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kansal, Kanika Kansal, Kajal Department of Computer Science and Engineering Data Science ABES Engineering College Uttar Pradesh Ghaziabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530389

Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survival rates. An automatic diagnosis system based on deep learning models is critical for improving detection, prediction accuracy, and survivability. This study used various DenseNet-based architectures for multi-class classification using the BUSI dataset. The experiments indicate that the fine-tuned DenseNet201 model outperforms with an accuracy of 98.28%. The results demonstrate the promising capability of the fine-tuned DenseNet201 model to have a high potential for further implementation in practice for achieving early diagnosis and highly individualized approaches for better treatment. The further development of extensive and diverse data sets will be essential for developing new diagnostic methods and improving the existing ones. © 2025 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks 9th

Leveraging user-item interactions and hotel characteristics ...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Panarto, Francis Alexander Phanghegar, Tyrone Matthew Gunardi, Vincent Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Travelers usually check information about the destination before they decide to go. However, sometimes the information is too much to handle and causes confusion. A filtering mechanism is needed to help them make their choices. The solution is to implement a recommendation system;some common techniques that are used for this are Collaborative Filtering and content-based filtering, but both techniques have their flaws. Therefore, we propose a solution to combine those two methods through Graph Neural Network architectures to help fix their flaws. The proposed method will be used for recommending hotels in Indonesia using the HotelRec dataset as the base dataset and hotel features acquired from the TripAdvisor API. We use several GNN architectures, such as GCN, LightGCN, GraphSAGE, and GAT, to implement our approach. Then, we compare it to several baseline models, such as SVD and NeuMF, which are common baselines in recommendation systems. The performance of these models is calculated based on three metrics: HitRate@K, NDCG@K, and Recall@K. Based on the result of this experiment, we found that incorporating hotel features into the learning process of the model, accompanied by user-item interactions, boosted the model's performance by at least 20% in all metrics, especially in datasets with a smaller number of interactions. © 2024 The Authors.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

引用

Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Hate Speech Detection Using Various Vectorization and Classification Techniques 2

Comparative Analysis of Hate Speech Detection Using Various ...

引用

2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Tali, Sophia Noguera, Daniel Gargees, Rasha S. Central Michigan University Department of Data Science Actuarial Science and Statistics MI United States Central Michigan University Department of Computer Science MI United States

ISBN: (纸本)9798331529529

Hate speech is a prominent, growing problem within our society, and this problem and its effects have only increased with time. According to the Center for Technology and Society, 52% of people reported being harassed online between 2022 and 2023, an increase from 40% in 2022. The new technological advancements make it easier than ever to spread negative, discriminatory, and pejorative comments to attack others with the click of a button. This is unfortunately an issue that affects many and often leads to violence, which is why it is important to identify and remove this online hate speech in order to minimize the spread. In this study, we explore the classification of online hate speech by comparing different vectorization and classification methods. We developed a set of eight classification techniques for identifying hate from a Twitter dataset containing hate speech and benign text examples. We cleaned and vectorized it in two different ways. Each of these sets is used to train deep learning, SVM, SGD, and random forest classifiers. All these methods achieved accuracy above 93%. The machine learning methods (SVM, SGD, and random forest) all achieved a nearperfect classification accuracy of 98.72%, with slight variations in precision and recall. The deep learning approach also showed improved performance with TF-IDF vectorization. Overall, the choice of vectorization had minimal impact on accuracy, and all models performed consistently well. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing wireless sensing via a target-mounted intelligent reflecting surface

引用

National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

关键词：

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：