检索结果-内蒙古大学图书馆

SSVEPPoolformer: An Improved Poolformer Model With the Adaptive Denoising Algorithm for SSVEP-EEG Signal Classification

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Chunquan Liao, Zhiyuan Cheng, Yuxin Wang, Zitao Wu, Junyun Liu, Ruijun Liu, Peter X. Nanchang University School of Information Engineering Nanchang330031 China Nanchang University School of Mathematics and Computer Science Nanchang330031 China Beihang University School of Software Beijing100191 China Carleton University School of Department of Systems and Computer Engineering OttawaK1S 5B6 Canada

Most EEG classification algorithms based on steady-state visual evoked potentials (SSVEP-EEG) require filtering for denoising. However, manually set thresholds may inadvertently remove useful information, leading to a loss of significant signal features. Additionally, most deep learning-based SSVEP-EEG classification models have limited global feature extraction capabilities, and the self-attention mechanism in Transformers increases computational costs. To address these challenges, this paper proposes a novel SSVEP-EEG classification algorithm, SSVEPPoolformer. SSVEPPoolformer integrates an adaptive denoising algorithm with an improved Poolformer algorithm, enhancing both denoising performance and classification accuracy. The adaptive denoising algorithm dynamically adjusts the threshold using a compensation and adaptive adjustment mechanism, effectively filtering noise while retaining critical signal features. The improved Poolformer algorithm replaces the self-attention mechanism with an average pooling operation, reducing computational costs while maintaining performance. It also uses adaptive average pooling to integrate cross-channel feature information and extract global fine-grained features, improving global feature extraction. The SSVEPPoolformer model's efficacy was validated on two public datasets. Experimental results demonstrate that compared with other state-of-the-art methods, SSVEPPoolformer has higher classification accuracy and Information Transfer Rate (ITR) in both intra-class and inter-class recognition scenarios, and has lower computational cost. © 1975-2011 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

An Efficient Community-based Approach for the Influence Maximization Problem in Social Networks 10

An Efficient Community-based Approach for the Influence Maxi...

引用

10th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2021

作者： Samir, Ahmed M. Rady, Sherine Gharib, Tarek F. Kafrelsheikh University Faculty of Computer and Information Sciences Software Engineering Department Kafrelsheikh Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9781665440769

The problem of detecting top influential users in social networks is known as the influence maximization problem. Top social network influencers play a significant role in different fields like virtual marketing, politics, and public health. There are various techniques and methods implemented to identify the top influential users for maximizing the influence within social networks. There exist, however, a real challenge to effectively reach those top influential users with both high accuracy and less computational time. This paper proposes an efficient approach for the problem of influence maximization based on communities' identification. A proposed approach LCS includes three stages: firstly, the application of the Louvain community detection algorithm to detect the different communities of the social network graph. Secondly, executing the CRANK algorithm to rank and sort the generated communities-based CRANK main four metrics. Finally, the selection of the top seed nodes from the sorted communities using degree centrality. Experimental results conducted on three social network datasets, under an Independent Cascade model for verification, show the effectiveness of the proposed approach compared to the centrality-based heuristics. Additionally, comparable results are achieved against greedy algorithms with far less time complexity. © 2021 IEEE.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Cyberbullying Detection Using Bag-of-Words, TF-IDF, Parallel CNNs and BiLSTM Neural Networks 23

Cyberbullying Detection Using Bag-of-Words, TF-IDF, Parallel...

引用

23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Sghaier, Feriel Mejri, Mohamed Bahroun, Sahbi Ghayoula, Ridha Manai, Elyes Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuébecQCG1V 0A6 Canada Ecole Nationale d'Ingénieurs de Carthage Université de Carthage Tunis Tunisia Institut Supérieur d'Informatique Université de Tunis El-Manar Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

Cyberbullying, marked by its persistent and intentional aggression online, yields severe repercussions for its victims, extending beyond immediate distress to long-lasting effects such as heightened anxiety, depression, and social withdrawal. Individuals subjected to Cyberbullying often grapple with diminished self-esteem, compromised academic performance, and strained interpersonal relations. Given the escalating prevalence of this digital menace, there is a pressing need for advanced methodologies to address it effectively. This paper introduces an approach to Cyberbullying detection, integrating techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses, along with the parallel processing capabilities of Convolutional Neural Networks (CNNs) and the contextual comprehension provided by Bidirectional Long Short-Term Memory (BiLSTM) networks. Through an experimentation on the latest Ejaz-Choudhury-Razi Cyberbullying dataset, our framework exhibits satisfactory performance in identifying instances of online hostility. These results underscore the potential of our approach to significantly contribute to ongoing efforts aimed at combating Cyberbullying in digital environments. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

FedAccess: Federated Learning-Based Built Surface Recognition for Wheelchair Routing

FedAccess: Federated Learning-Based Built Surface Recognitio...

引用

IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Rochishnu Banerjee Ethan Han Longze Li Haoxiang Yu Md Osman Gani Vaskar Raychoudhury Roger O. Smith Department of Computer Science & Software Engineering Miami University Oxford OH USA Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA Department of Information Systems University of Maryland Baltimore County Baltimore MD USA Department of Rehabilitation Sciences & Technology University of Wisconsin Milwaukee WI USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Wheelchair and mobility aid users often face challenges in navigating the built environment due to uneven sidewalks, temporary barriers, steep inclines, and narrow lanes. To assist these users, accessible routing systems have been introduced that generate wheelchair-accessible paths to facilitate navigation in unfamiliar environments. In general, accessible routing systems rely on surface and path characteristics like surface type, incline, width, etc., and crowd-sourced information about barriers to provide the optimal route. Emerging routing systems even provide personalized routing to users that are catered to the user's specific needs and requirements. However, these types of systems collect crowd-sourced personal/identifiable information which introduces privacy and data heterogeneity concerns that are not addressed by them or elsewhere in the concerned domain. To address these two issues specifically, we propose the novel FedAccess system for accessible routing that utilizes the federated learning paradigm for surface recognition using vibration data. The surface-induced vibrations are captured through smartphone-embedded motion sensors (accelerometers and gyroscopes) from 23 manual wheelchair users during their regular navigation. We have covered 10 distinct surfaces from the USA. As a result, the distribution of the data is naturally non-IID. Empirical evaluation shows that the FedAccess system can protect user data and identity while dealing with non-IID data and still recognize heterogeneous surfaces with higher accuracy than the state-of-the-art.

关键词： Vibrations Navigation Federated learning Wheelchairs Face recognition Manuals Routing

来源：评论

学校读者我要写书评

暂无评论

Detection of Android Malware using Machine Learning

Detection of Android Malware using Machine Learning

引用

2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Fiky, Ahmed Hashem El Elshenawy, Ayman Madkour, Mohamed Ashraf Al-Azhar University Systems and Computer Engineering Faculty of Engineering Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

ISBN: (纸本)9781665412438

Nowadays, smartphones became an integral part of human life due to the great necessity for their daily activities. Most smartphone users are downloading and installing mobile apps without worrying about security. Therefore, smartphones are a winning goal for malware, mainly with Android devices. So, it was necessary to provide an intelligent model for detecting malware applications before installing them on android smartphones. In this paper, an intelligent model using machine learning algorithms is proposed for detecting the malware applications in smartphones based on the static malware analysis technique. The contents of an android application features are analyzed, such as permissions, intents, system commands, and API-calls that were extracted from the application manifest file and source code. The proposed model is applied on two separate datasets DREBIN and MALGENOME. The experimental results are compared with some other similar researches, and their results were analyzed. The results indicate that the performance of the proposed model outperforms other similar models for some selected classifiers and can reliably detect both malware and benign Android applications with high accuracy. © 2021 IEEE.

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Knowledge Transfer from HBCUs to PWIs -Addressing the Racial Inequities faced byBlack Undergraduate Students

Knowledge Transfer from HBCUs to PWIs -Addressing the Racial...

引用

Black Issues in Computing Education (BICE)

作者： Dale-Marie Wilson Marlon Mejias Felesia Stukes Gloria Washington Celeste Malone Department of Software and Information Systems University of North Carolina at Charlotte United States Department of Computer Science and Engineering Johnson C. Smith University United States Department of Electrical Engineering and Computer Science Howard University United States Department of Human Development & Psychoeducational Studies Howard University United States

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

The number and diversity of researchers in the U.S. must increase significantly to support the demand for global leadership in research and computing. For more equitable representation in computing, there must be equitable access to graduate degrees and careers both academic and industrial in computing. However, there are barriers to the recruitment, retention, and persistence of racial/ethnic minoritized individuals in computer science due to racial inequities. With respect to Black students, these barriers are unique to their intersectional identities. HBCUs offer students a unique and rewarding experience including a familial community of support and understanding among faculty, students, and alumni. From 2015-2019, nearly 25% of Black STEM doctorate recipients earned a bachelor's degree from an HBCU and HBCUs produced 33% of the nation's Black computer scientists [1]. Thus, there are successful enculturation practices within STEM education at HBCUs [2] that lead to their students going onto terminal degrees. Through our funded NSF grants #2140867 & #210246, we proposed a knowledge transfer on Black student mentorship, research enculturation and social computing from two HBCUs (Howard University and Johnson C. Smith), to a PWI (University of North Carolina at Charlotte). Our goal is to democratize knowledge and the transfer of information by identifying and disseminating hidden curriculum that creates racial inequities that impede Black students' progress into graduate school. In this workshop we will disseminate our findings for developing Black undergraduate students' technical research ability but also holistically addressing the psychological and social challenges that they may face.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach Using Survey and Ism Analysis for Security Coding Risks and Best Practices for Gsd Enterprises

SSRN

引用

SSRN 2023年

作者： Khan, Rafiq Ahmad Akbar, Muhammad Azeem Alzahrani, Abdulrahman Habibullah, Safa Alwageed, Hathal Salamah Alzahrani, Musaad School of Software Northwestern Polytechnical University Shaanxi Xian710129 China Software Engineering Research Group Department of Computer Science & IT University of Malakand Pakistan Software engineering LUT University Finland Information Systems and Technology Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Department of Computer Science Al-Baha University Al-Baha65799 Saudi Arabia

Context: Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a ***: This study aims to enhance security in GSD projects by developing a hybrid approach using an empirical survey and Interpretive Structural Model (ISM).Methods: Initially, we conduct a systematic literature review and questionnaire survey with 50 GSD security experts. Moreover, 13 experts were invited to analyze the interrelationships among the practices using ***: Initially, we identified 13 major security-coding risks and 82 practices to mitigate these risks. The ISM analysis revealed that out of the identified security-coding practices, "never submit security measures to illegitimate authority", "avoiding buffer overflow and format string vulnerabilities", "control the brute force attack", and "identify a middleman attack" were considered fully dependent. While "avoid revealing information to achieve a secure design" is entirely independent within the GSD security coding ***: The study aids GSD professionals in assessing readiness in establishing contractual trust, understanding the current process pros and cons, and addressing urgent issues in secure software development processes. © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Novel Algorithm For Adjacent Vertex-Distinguishing Edge Coloring Of Large-Scale Random Graphs

引用

Journal of engineering Science and Technology Review 2021年第3期14卷 69-75页

作者： Huanping, Zhao Peijin, Zhu Jingwen, Li Huojie, Shi School of Computer and Software Nanyang Institute of Technology Nanyang 473004 China School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China Department of Biological Systems Engineering Washington State University PullmanMI98101 United States

Graph coloring is one of the areas in graph theory with high research importance. Adjacent vertex-distinguishing edge coloring is a type of multi-conditional coloring in graph coloring, but existing associated studies lack analysis on constraint conditions. In this study, a novel algorithm was designed to increase the adjacent vertex-distinguishing edge coloring efficiency of large-scale random graphs. Sub-graphs were produced in this work by using a nondestructive segmentation algorithm to reduce the scale of random graphs, and random pre-coloring was performed on the edges of each sub-graph. Iteration was performed step by step in accordance with regulations by searching the conflict set of inaccurate edge coloring until the color met the requirements of the ultimate objective function. Next, the sub-graphs that were colored successfully were combined to realize adjacent vertex-distinguishing edge coloring of large-scale random graphs. Afterward, the accuracy of the adjacent vertex-distinguishing edge chromatic number was proven through theoretical analysis and experimental comparison. Several experiments were also performed on random graphs with less than 4000 vertexes and an edge density smaller than 0.1. Results show that when the number of vertexes is greater than 2000 and the edge density exceeds 0.07, the run time generally rings from 0.9 s to 1.5 s, whereas the run time for other random graphs is between 0.6 and 1.2 s. The algorithm can solve the adjacent vertex-distinguishing edge chromatic number of random graphs effectively, and the time complexity of the algorithm do not exceed O(n3). The proposed algorithm provides evidence for solving the shortest path of large-scale random graphs. © 2021 School of Science, IHU. All rights reserved

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Enhancing Content-Based Image Retrieval for Lung Cancer Diagnosis: Leveraging Texture Analysis and Ensemble Models 7

Enhancing Content-Based Image Retrieval for Lung Cancer Diag...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Jayabalaji, K.A. Laxmaiah, Bagam Thatipudi, Jayendra Gopal Chandraprabha, K. Badhe, Pravin Balaram, Allam Sri Krishna Arts And Science College Department of Software Systems Tamilnadu Coimbatore India Cmr Technical Campus Department of Cse Telangana Kandlakoya India University of North Texas Department of Computer Science DentonTX76203 United States Bannari Amman Institute of Technology Department of Information Technology Tamilnadu Sathyamangalam India Sinhgad College of Pharmacy Swalife Biotech Department of Pharmacology Pune India Mlr Institute of Technology Department of Computer Science And Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350371406

In image retrieval systems, many search engines rely on text-based methods, extracting information from descriptions and tags to retrieve relevant images. However, this conventional approach often overlooks the optical or semantic information present in photographs, leading to errors in the retrieved results. To address this issue, it is essential to consider content-based information such as intensities, edges, and pixel values when designing image retrieval systems, especially in domains like medicine. This study focuses on developing a Content-Based Image Retrieval (CBIR) system for the domain of medicine, with a particular emphasis on lung cancer detection. Early identification and treatment of lung cancer are critical for patient outcomes, making suitable computer-Aided Diagnosis (CAD) systems essential for assisting clinicians. The proposed ensemble model incorporates various feature extraction methods, including image processing techniques and wavelet transform, to harness the valuable information from images. These features are then fed into a deep learning model to assess their effectiveness in image retrieval tasks. By combining machine learning and image processing techniques, this work presents a comprehensive CBIR system capable of efficiently retrieving relevant medical images. Such advancements in image retrieval technology hold significant promise for aiding medical professionals in diagnosing and treating diseases like lung cancer promptly. © 2023 IEEE.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

African Vultures Based Feature Selection with Multi-modal Deep Learning for Automatic Seizure Prediction

African Vultures Based Feature Selection with Multi-modal De...

引用

Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Mounika Nallur Sandhya M Zabiha Khan Mohan B R C P Nayana S A Rajashekhar Software Engineer Masters in Computer Information Systems Murray State University USA Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The superiority of life for people with epilepsy can be greatly improved with the assistance of accurate seizure prediction and early warning. An automatic prediction model is required to procedure the EEG signals and account for the leads optimization problematic, as opposed to the majority of hand-designed prediction approaches. In this research, we put forth a fully automated model for seizure prediction using Channel and Spatial attention (CASA). The first step in the feature extraction practice is to pre-process the raw EEG signals. Large amounts of computation can be saved by adding more features to the system, but finding the right ones can be tricky. The African vulture optimization algorithm's (AVOA) strong capacity to break out of local optima is what makes this procedure possible. CASA saved the raw EEG data's temporal and geographical details. Automatic optimization of EEG full-lead data was completed with channel attention (CA), leading to an increase in the accuracy of predictions. The aforementioned adaptive learning of feature parameters was accomplished via spatial attention (SA). When all else fails, a fully associated layer is used to make the seizure forecast. The suggested algorithm is tested on the Freiburg EEG database, and the results reveal that the AVOA-based system performs admirably when it comes to predicting seizures.

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：