检索结果-内蒙古大学图书馆

Improved Particle Swarm Algorithm Using Multiple Strategies 1

14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Yi, Yunfei Wang, Zhiyong Shi, Yunying School of Big Data and Computer Hechi University Guangxi Yizhou546300 China College of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541001 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

In order to address the issues of premature convergence and low search efficiency in the basic particle swarm algorithm, this paper analyzes the improved particle swarm optimization algorithms proposed by previous researchers. Based on this analysis, a modified algorithm for particle swarm optimization is proposed. The enhanced algorithm adjusts the inertia weight parameter in a nonlinear manner and dynamically changes the self-learning factor using chaos, allowing it to continuously change during iterations and achieve better optimization results. Additionally, to prevent the algorithm from getting trapped in local optima, a mutation operation is introduced into the improved algorithm. Finally, several classical test functions are used to conduct experiments, and the results are compared with the improved particle swarm optimization algorithms described in related literature. The experimental results demonstrate that the newly proposed algorithm achieves a certain degree of improvement in optimization accuracy compared to the basic particle swarm algorithm. This indicates that the modified algorithm effectively avoids premature convergence and possesses high search efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Uncover Secrets Through the Cover: A Deep Learning-Based Side-Channel Attack Against Kyber Implementations With Anti-Tampering Covers

引用

IEEE Transactions on computers 2025年第6期74卷 2159-2167页

作者： Chen, Peng Li, Jinnuo Cheng, Wei Cheng, Chi China University of Geosciences School of Computer Science Wuhan430074 China City University of Macau Faculty of Data Science Macau999078 China Institut Polytechnique de Paris LTCI Télécom Paris Palaiseau91120 France Secure-IC S.A.S. Paris75014 France

The probe can directly contact the microcontrollerin a typical EM side-channel attack (SCA) targeting crypto-graphic implementations. However, in a more practical settingsuch as security level 2 of FIPS 140-3 or ISO/IEC 19790standards, the microcontroller is required to be safeguardedby an opaque anti-tampering cover. This raises an interestingproblem: Can we still launch EM attacks against microcontrollersrunning cryptographic implementations even when equippedwith the cover? This paper proposes an improved deep-learning-based profiled attack against NIST KEM standard Kyber. Ourkey observation is that the distance between the probe and the mi-crocontroller results in attenuation of signal strength. Moreover,the cover restricts the proximity of the probe, thereby limitingthe signal-to-noise ratio. We propose an Adaptive SlimmedPyramid Network (ASPN) model to instantiate a distinguisherin a plaintext-checking oracle-based SCA, which is generic andeasy to implement. The proposed ASPN approach significantlyenhances the feature extraction process by employing a pyramidnetwork structure, while simultaneously avoiding the inclusion ofexcessive parameters. Real-world experiments demonstrate thatour proposed distinguishers achieve an accuracy above 99% withan 18 mm cover and higher than 89% accuracy even with a 24mm cover. © 1968-2012 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

A Deep Phishing Email Detection Model Combing Multi-Features Based on Word Frequency and Contextual Semantics 4

A Deep Phishing Email Detection Model Combing Multi-Features...

引用

4th International Conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Liu, Yuntian Zhu, Erzhou Ma, Zhujuan Anhui University School of Computer Science and Technology Hefei230601 China Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei230088 China

ISBN: (纸本)9798331528973

Due to the ability to automatically extract phishing features without relying on expert knowledge, deep learning methods have been widely applied in the research of phishing email classification and detection. However, the unique structure of emails and the complexity of publishing strategies mean that current deep learning-based phishing email detection studies still require further performance enhancement. To effectively detect phishing emails, this paper proposes TFW-CNBL (Term Frequency and Word2Vec-CNN and BiLSTM), a deep detection model for phishing emails based on the fusion of term frequency and contextual semantic multi-features. The TFW-CNBL model generates a hybrid feature vector that comprehensively understands the characteristics of phishing emails by integrating term frequency and contextual information. The model combines a convolutional neural network (CNN) with an attention mechanism and a bidirectional long short-term memory network (BiLSTM), creating an underlying architecture capable of rapid text classification. The paper utilized a publicly available dataset from Kaggle containing 18,650 samples to train and test the TFW-CNBL model. Experimental results indicate that the TFW-CNBL model can effectively detect phishing emails, achieving an average accuracy, precision, recall, and F1 score of 0.984, 0.986, 0.989, and 0.987, respectively. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

来源：评论

学校读者我要写书评

暂无评论

Power-Measurement-Based Channel Autocorrelation Estimation for IRS-Assisted Wideband Communications

引用

IEEE Transactions on Wireless Communications 2025年第6期24卷 4647-4662页

作者： Sun, He Zhu, Lipeng Mei, Weidong Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications 611731 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS re

关键词： Orthogonal frequency division multiplexing

来源：评论

学校读者我要写书评

暂无评论

Exploring the Role of Generative AI in Constructing Knowledge Graphs for Drug Indications with Medical Context 15

Exploring the Role of Generative AI in Constructing Knowledg...

引用

15th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2024

作者： Alharbi, Reham Ahmed, Umair Dobriy, Daniil Lajewska, Weronika Menotti, Laura Saeedizade, Mohammad Javad Dumontier, Michel Department of Computer Science University of Liverpool Liverpool United Kingdom School of Science and Technology University of Camerino Camerino Italy Institute for Data Process and Knowledge Management Vienna University of Economics and Business Vienna Austria Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Department of Information Engineering University of Padua Padova Italy Department of Computer Science Linköping University Linköping Sweden Institute of Data Science Maastricht University Maastricht Netherlands

The medical context for a drug indication provides crucial information on how the drug can be used in practice. However, the extraction of medical context from drug indications remains poorly explored, as most research concentrates on the recognition of medications and associated diseases. Indeed, most databases cataloging drug indications do not contain their medical context in a machine-readable format. This paper proposes the use of a large language model for constructing DIAMOND-KG, a knowledge graph of drug indications and their medical context. The study 1) examines the change in accuracy and precision in providing additional instruction to the language model, 2) estimates the prevalence of medical context in drug indications, and 3) assesses the quality of DIAMOND-KG against NeuroDKG, a small manually curated knowledge graph. The results reveal that more elaborated prompts improve the quality of extraction of medical context;71% of indications had at least one medical context;63.52% of extracted medical contexts correspond to those identified in NeuroDKG. This paper demonstrates the utility of using large language models for specialized knowledge extraction, with a particular focus on extracting drug indications and their medical context. We provide DIAMOND-KG as a FAIR RDF graph supported with an ontology. Openly accessible, DIAMOND-KG may be useful for downstream tasks such as semantic query answering, recommendation engines, and drug repositioning research. © 2024 Copyright for this paper by its authors.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Budiarto, Arif Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

来源：评论

学校读者我要写书评

暂无评论

HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES 12

HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Zihan Lee, Jason D. Chen, Yuxin Du, Simon S. Department of Electrical and Computer Engineering Princeton University United States Department of Statistics and Data Science University of Pennsylvania United States Paul G. Allen School of Computer Science and Engineering University of Washington United States

A recent line of works showed regret bounds in reinforcement learning (RL) can be (nearly) independent of planning horizon, a.k.a. the horizon-free bounds. However, these regret bounds only apply to settings where a polynomial dependency on the size of transition model is allowed, such as tabular Markov Decision Process (MDP) and linear mixture MDP. We give the first horizon-free bound for the popular linear MDP setting where the size of the transition model can be exponentially large or even uncountable. In contrast to prior works which explicitly estimate the transition model and compute the inhomogeneous value functions at different time steps, we directly estimate the value functions and confidence sets. We obtain the horizon-free bound by: (1) maintaining multiple weighted least square estimators for the value functions;and (2) a structural lemma which shows the maximal total variation of the inhomogeneous value functions is bounded by a polynomial factor of the feature dimension. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Phishing Detection Model Integrating URL Characters and HTML Word Semantic Deep Features 4

Phishing Detection Model Integrating URL Characters and HTML...

引用

4th International Conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Meng, Lihui Ma, Zhujuan Zhu, Erzhou Anhui University School of Computer Science and Technology Hefei230601 China Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei230088 China

ISBN: (纸本)9798331528973

Deep learning methods, known for their powerful feature learning and classification capabilities, are widely used in phishing detection. To improve accuracy, this study proposes DPMLF (Deep Learning Phishing Detection Model with Multi-Level Features), which integrates URL character-level and HTML word-level semantic features. DPMLF utilizes character embeddings and parallel convolutional kernels to precisely extract local URL features. For HTML text, it employs word-level embeddings and stacked convolutional layers with dense connections to capture both local and long-range text information. The fully connected layer then fuses these features into a multi-level, fine-grained feature vector for classification. The results of experiments conducted on two public datasets with different scales show that DPMLF is accurate in phishing attack detection. © 2024 IEEE.

关键词： Phishing

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：