检索结果-内蒙古大学图书馆

An efficient approach for denoising EOG artifact through optimal wavelet selection

International Journal of Information technology (Singapore) 2024年第1期16卷 279-292页

作者： Prakash, Vinod Kumar, Dharmender Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar 125001 India

Electroencephalography (EEG) is a non-intrusive method used to capture electrical potential generated by brain neurons, which is crucial for diagnosing neurological disorders like epilepsy, sleep disorders, brain tumours, and dementia. However, EEG signals can sometimes be contaminated with undesired signals, known as artifacts. These artifacts are generated by retinal dipole movement and eyelid motion, resulting in spikes with significant amplitudes and distortions in EEG recordings. Researchers have explored wavelet-based techniques to remove EOG artifacts from EEG signals, with Haar, Symlet, and Daubechies being common wavelets. However, selecting the most suitable wavelet remains a challenge. The proposed study found that db7 is the optimal wavelet for removing EOG artifacts, with the lowest RMSE value of 54.97 and a calculation time of 0.5798 s. The proposed wavelet-independent component analysis approach outperforms the existing independent component analysis, specifically Fast-ICA, with a promising computation time (Δ C_t) of 6.27 s. The proposed approach aims to enhance the quality of EOG signals by effectively reducing noise through the selection of an appropriate mother wavelet function. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Discrete wavelet transform EOG artifact Independent component analysis Optimal mother wavelet Wavelet family

来源：评论

学校读者我要写书评

暂无评论

Undefinability of Approximation of 2-To-2 Games 33

Undefinability of Approximation of 2-To-2 Games

引用

33rd EACSL Annual Conference on computer science Logic, CSL 2025

作者： Dawar, Anuj Molnár, Bálint Department of Computer Science and Technology University of Cambridge United Kingdom

ISBN: (纸本)9783959773621

Recent work by Atserias and Dawar [6] and Tucker-Foltz [26] has established undefinability results in fixed-point logic with counting (FPC) corresponding to many classical complexity results from the hardness of approximation. In this line of work, NP-hardness results are turned into unconditional FPC undefinability results. We extend this work by showing the FPC undefinability of any constant factor approximation of weighted 2-to-2 games, based on the NP-hardness results of Khot, Minzer and Safra. Our result shows that the completely satisfiable 2-to-2 games are not FPC-separable from those that are not ϵ-satisfiable, for arbitrarily small ϵ. The perfect completeness of our inseparability is an improvement on the complexity result, as the NP-hardness of such a separation is still only conjectured. This perfect completeness enables us to show the FPC undefinability of other problems whose NP-hardness is conjectured. In particular, we are able to show that no FPC formula can separate the 3-colourable graphs from those that are not t-colourable, for any constant t. © Anuj Dawar and Bálint Molnár.

关键词： Rockwell hardness

来源：评论

学校读者我要写书评

暂无评论

Learning a Distributed Hierarchical Locomotion Controller for Embodied Cooperation 8

Learning a Distributed Hierarchical Locomotion Controller fo...

引用

8th Conference on Robot Learning, CoRL 2024

作者： Hong, Chuye Huang, Kangyao Liu, Huaping Department of Computer Science and Technology Tsinghua University China

In this work, we propose a distributed hierarchical locomotion control strategy for whole-body cooperation and demonstrate the potential for scaling to large numbers of agents. Our method utilizes a hierarchical structure to break down complex tasks into smaller, manageable sub-tasks. By incorporating spatiotemporal continuity features, we establish the sequential logic necessary for causal inference and cooperative behaviour in sequential tasks, thereby facilitating efficient and coordinated control strategies. Through training within this framework, we demonstrate enhanced adaptability and cooperation, leading to superior performance in task completion compared to the original methods. Moreover, we construct a set of environments as the benchmark for embodied cooperation. A visual depiction of highlights of the learned cooperative behaviours can be found on this website. © 2024 Proceedings of Machine Learning Research.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers 38

Leveraging Contrastive Learning for Enhanced Node Representa...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Jinsong Liu, Hanpeng Hopcroft, John E. He, Kun School of Computer Science and Technology Huazhong University of Science and Technology China Institute of Artificial Intelligence Huazhong University of Science and Technology China Hopcroft Center on Computing Science Huazhong University of Science and Technology China Department of Computer Science Cornell University United States

While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations. To address this limitation, we propose a novel graph Transformer called GCFormer. Unlike previous approaches, GCFormer develops a hybrid token generator to create two types of token sequences, positive and negative, to capture diverse graph information. And a tailored Transformer-based backbone is adopted to learn meaningful node representations from these generated token sequences. Additionally, GCFormer introduces contrastive learning to extract valuable information from both positive and negative token sequences, enhancing the quality of learned node representations. Extensive experimental results across various datasets, including homophily and heterophily graphs, demonstrate the superiority of GCFormer in node classification, when compared to representative graph neural networks (GNNs) and graph Transformers. © 2024 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

Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

引用

Journal of Bionic Engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

来源：评论

学校读者我要写书评

暂无评论

Splitfed-based Patient Severity Prediction and Utility Maximization in Industrial Healthcare 4.0 24

Splitfed-based Patient Severity Prediction and Utility Maxim...

引用

25th International Conference on Distributed Computing and Networking, ICDCN 2024

作者： Singh, Himanshu Moirangthem, Biken Pratap, Ajay Kumari, Shilpi Kumar, Abhishek K. Das, Sajal Indian Institute of Technology Banaras Hindu University Department of Computer Science Varanasi India Missouri University of Science and Technology Department of Computer Science RollaMO United States

ISBN: (纸本)9798400716737

The healthcare industry has transitioned from traditional healthcare 1.0 to AI-powered healthcare 4.0. However, overall cost for patient treatment remains high and challenging to manage due to the absence of a centralized cost evaluation mechanism before hospital visits. Therefore, in this paper, we devise a cloud-based mechanism to calculate hospitals' star rating based on questionnaire with the application of Z-score and K∗clustering algorithm. To evaluate disease severity at cloud, splitfed technique is utilized in coordination with Wireless Body Area Network (WBAN). Finally, the cloud calculates provisional treatment costs and finds a preferable hospital with a low payable treatment cost and satisfactorily high rating for the patient via utility maximization in a cloud-based environment. Moreover, the effectiveness of the proposed polynomial algorithmic model is shown theoretically, experimentally, and comparing with other state-of-the-art methods on real-world data. © 2024 ACM.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Road Surface Analysis through Machine Learning Techniques

引用

IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Collaborative CNN-LSTM Framework with Attention Mechanism for Aspect-Based Sentiment Analysis

A Collaborative CNN-LSTM Framework with Attention Mechanism ...

引用

2024 International Conference on Frontiers of Information technology, FIT 2024

作者： Rehman, Israr Ur Ali, Zulfiqar Jan, Zahoor Department of Computer Science Islamia College University Peshawar Pakistan Department of Computer Science National University of Technology Islamabad Pakistan

ISBN: (纸本)9798331510503

Aspect-based sentiment analysis (ABSA) seeks to extract fine-grained sentiment information by locating sentiments connected to particular elements in a text. Traditional sentiment analysis techniques often fail to address the intricacies of aspect-based sentiments due to their inability to capture complex contextual dependencies. The suggested approach makes use of long short-term memory networks (LSTMs) to describe sequential dependencies and convolutional neural networks (CNNs) for effective feature extraction. Simultaneously, the attention mechanism improves interpretability and performance by focusing on pertinent portions of the input text. The CNN component extracts local features from word embeddings, which the LSTM then processes to capture long-term dependencies. By giving important words a larger weight, the attention mechanism further refines the result, ensuring that the most informative parts of the text are emphasized during sentiment and aspect prediction. Extensive experiments, including Automobile, Movie, and hotel reviews, use benchmark datasets to demonstrate the efficacy of our model. The collaborative framework significantly outperforms baseline models, improving F1 scores, recall, accuracy, and precision. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits 38

Direct Preference-Based Evolutionary Multi-Objective Optimiz...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Tian Wang, Shengbo Li, Ke School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science University of Exeter United Kingdom

The ultimate goal of multi-objective optimization (MO) is to assist human decision-makers (DMs) in identifying solutions of interest (SOI) that optimally reconcile multiple objectives according to their preferences. Preference-based evolutionary MO (PBEMO) has emerged as a promising framework that progressively approximates SOI by involving human in the optimization-cum-decision-making process. Yet, current PBEMO approaches are prone to be inefficient and misaligned with the DM's true aspirations, especially when inadvertently exploiting mis-calibrated reward models. This is further exacerbated when considering the stochastic nature of human feedback. This paper proposes a novel framework that navigates MO to SOI by directly leveraging human feedback without being restricted by a predefined reward model nor cumbersome model selection. Specifically, we developed a clustering-based stochastic dueling bandits algorithm that strategically scales well to high-dimensional dueling bandits. The learned preferences are then transformed into a unified probabilistic format that can be readily adapted to prevalent EMO algorithms. This also leads to a principled termination criterion that strategically manages human cognitive loads and computational budget. Experiments on 48 benchmark test problems, including the RNA inverse design and protein structure prediction, fully demonstrate the effectiveness of our proposed approach. © 2024 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

An Authentication Protocol for Federated Learning With Blockchain in Consumer Electronic Assisted Autonomous Driving Environments

引用

IEEE Transactions on Consumer Electronics 2024年第1期71卷 2231-2242页

作者： Chen, Chien-Ming Hao, Yiru Kumari, Saru Amoon, Mohammed Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Shandong University of Science and Technology Qingdao China Chaudhary Charan Singh University Department of Mathematics India King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia

Consumer electronics play a crucial role in the automotive sector by providing the essential hardware infrastructure necessary for developing autonomous vehicles. These vehicles significantly enhance driving comfort and accessibility, particularly benefiting specific populations such as the elderly and individuals with disabilities. Researchers utilize federated learning, employing homomorphic encryption and differential privacy techniques;however, these methods can impede efficiency. Traditional centralized federated learning presents a risk of single points of failure. To address this issue, we propose an authentication protocol for federated learning, integrated with blockchain technology, within consumer electronics-supported autonomous driving environments. This protocol enhances vehicle authentication efficiency while prioritizing data security and user privacy. Formal analysis verifies the integrity of the protocol. Experimental results demonstrate that the communication overhead of our protocol is reduced by 40.3% compared to existing similar authentication protocols. © 1975-2011 IEEE.

关键词： Protocols Blockchains Authentication Computational modeling Autonomous vehicles Federated learning Security Data models Consumer electronics Privacy

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：