检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Frohn, Martin Holtgrefe, Niels van Iersel, Leo Jones, Mark Kelk, Steven Department of Advanced Computing Sciences Maastricht University Maastricht Netherlands Institute of Applied Mathematics Delft University of Technology Delft Netherlands

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees, such as hybridization, introgression, and lateral gene transfer. Studying phylogenetic networks under a statistical model of DNA sequence evolution can aid the inference of phylogenetic networks. Most notably Markov models like the Jukes-Cantor or Kimura-3 model can been employed to infer a phylogenetic network using phylogenetic invariants. In this article we determine all quadratic invariants for sunlet networks under the random walk 4-state Markov model, which includes the aforementioned models. Taking toric fiber products of trees and sunlet networks, we obtain a new class of invariants for level-1 phylogenetic networks under the same model. Furthermore, we apply our results to the identifiability problem of a network parameter. In particular, we prove that our new class of invariants of the studied model is not sufficient to derive identifiability of quarnets (4-leaf networks). Moreover, we provide an efficient method that is faster and more reliable than the state-of-the-art in finding a significant number of invariants for many level-1 phylogenetic networks. Copyright © 2024, The Authors. 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

Web Extension for Phishing URL Identification 3

Web Extension for Phishing URL Identification

引用

3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Kiruthi Whasan, W.R. Khiran Khumhaar, W.R. Reddy, Kalluri Raviteja Dhanalakshmi, R. Kapilan Radhakrishnan, P. Kcg College of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Applied Computing University of Wales Trinity Saint David United Kingdom

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Phishing attacks have been the most consistent and emerging cyber-attack among other social engineering attacks. In spite of several strict mechanisms and policies to identify and avoid phishing websites, new techniques and tools are used by the attackers to exploit the security measures. This paper proposes a system that uses machine learning algorithm to identify phishing URL based on web parameters. Among several machine learning algorithms, Random Forest algorithm is chosen as it provides more accuracy and performance for the identification process. This system is implemented as a web extension in order to increase the performance of the identification process and to decrease the overhead of the system caused when using a standalone software. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

PREFACE: 3^rd International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2022)

引用

AIP Conference Proceedings 2023年第1期2946卷 1-8页

作者： Arockia Doss, Arockia Selvakumar Short, Michael Schilberg, Daniel Design and Automation Research Group School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computing Engineering and Digital Technologies Teesside University United Kingdom Department of Mechatronics and Mechanical Engineering University of Applied Sciences Bochum Germany

Preface: 3rd International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2022), AIP Conference Proceedings, Volume 2946, Issue

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

引用

Computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

来源：评论

学校读者我要写书评

暂无评论

Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction

Development of Interpretable Machine Learning Models for COV...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shi, Wenqi Murakoso, Mio Guo, Xiaoyan Wang, May D. Georgia Institute of Technology Department of Electrical and Computer Engineering Atlanta United States Kyoto University Department of Applied Life Science Kyoto Japan Georgia Institute of Technology College of Computing Atlanta United States Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering Atlanta United States

ISBN: (纸本)9798350337488

With the extensive time and financial requirements incumbent on drug discovery, computational approaches, such as protein-ligand docking predictions, are increasingly crucial for accelerating the process of drug repurposing. However, the proliferation of identified protein targets has exposed a critical knowledge gap in developing robust models that offer both generalizability and interpretability for docking score prediction. Addressing this, our study presents a machine learning-based surrogate model, employing interpretable artificial intelligence techniques for accurate docking score prediction for SARS-CoV-2 protein targets. We demonstrate the model generalization on its expansion to accommodate unseen protein targets by integrating protein target information through feature concatenation. Moreover, we leverage the SHapley Additive exPlanations (SHAP) method to identify the data-driven feature importance of molecular substructures for knowledge-based validation. Our experiments reveal that the combination of data-driven prediction and knowledge-driven validation could provide biomedical insights into the interactions between drugs and SARS-CoV-2 proteins, elucidating their consequent effects on docking scores. © 2023 IEEE.

关键词： COVID-19 drug target explainable artificial intelligence interaction prediction machine learning

来源：评论

学校读者我要写书评

暂无评论

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

引用

Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

来源：评论

学校读者我要写书评

暂无评论

Enhanced chaos-based image compression-encryption algorithm utilizing 2D compressive sensing and genetic algorithm optimization

引用

Multimedia Tools and Applications 2024年 1-38页

作者： Nanfak, Arnaud de Dieu Nkapkop, Jean Mvogo Ngono, Joseph Tabekoueng Njitacke, Zeric Lessouga Etoundi, Christophe Magloire Effa, Joseph Yves Laboratory of Electronic Electrical Engineering Automation and Telecommunication National Higher Polytechnic School of Douala University of Douala PO Box 2701 Douala Cameroon Technology and Applied Sciences Laboratory University Institute of Technology University of Douala P.O. Box 8698 Douala Cameroon Applied Computing Laboratory Faculty of Science University of Douala P.O. Box 2701 Douala Cameroon University of Buea P.O. Box 63 Buea Cameroon Department of Physics Faculty of Science University of Ngaoundere P.O. Box 454 Ngaoundere Cameroon

This paper introduces an innovative image compression and encryption algorithm that leverages the genetic algorithm (GA), compressive sensing (CS), and a newly developed hyperchaotic map. In this algorithm, the GA is employed to optimize the compression parameters, including those related to generating the measurement matrix and performing thresholding operation. The optimization objective is centred around on the Peak-Signal-to-Noise-Ratio (PSNR) of the reconstructed image. The encryption part involves employing the Arnold transform and byte-wise XOR operator for confusion and diffusion, respectively. The generation of the measurement matrix and chaotic sequences for encryption is facilitated by a novel two-dimensional fractional-Sine–Cosine (2DFSC) hyperchaotic map introduced in this research. Simulation outcomes demonstrate dynamic parameter values adjustments through optimization, resulting in improved decompression efficiency across different compression rates and image types. Assessment of security and decompression performance confirms the effectiveness of the proposed algorithm, showing a good compression effect, a good reconstruction efficacity even for low compression ratio, good speed and high security compared to some recent similar systems. This work may therefore be suitable to compress and secure data in wireless body area network applications, including those related to the internet of medical things. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Exploring Robust DDoS Detection: A Machine Learning Analysis with the CICDDoS2019 Dataset

Exploring Robust DDoS Detection: A Machine Learning Analysis...

引用

India Council International Subsections Conference (INDISCON), IEEE

作者： Kamal Saluja Susama Bagchi Vikas Solanki Muhammad Numan Ali Khan Esha Dhamija Sanjoy Kumar Debnath Chitkara University Institite of Engineering and Technology Chitkara University Punjab India College of Computing and Information Sciences University of Technology and Applied sciences Shinas Sultanate of Oman

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

The ever-present threat posed by Distributed Denial of Service attack is highlighted. The significance of building robust cybersecurity measures that are not just inventive but also flexible. The purpose of this study paper is to investigate the effectiveness of an algorithm based on machine learning in detecting distributed denial of service assault to strengthen the resilience of Internet infrastructure. Random forest, decision tree, naïve bayes, and KNN are some of the machine learning techniques that are investigated in this study. The dataset used for this investigation is the CICDDoS 2019 data, Moreover, the research broadened its approach to identify the role of Artificial Intelligence in the Cybersecurity Framework. It does this by providing insight into the relative merits of various algorithms. The study, in its essence, highlights the critical role that adaptive and intelligent defense systems play in decreasing the impact of distributed denial of service assaults (DDoS). This provides a foundation for prepared strategies in the constantly shifting world of cybersecurity.

关键词： Measurement Machine learning algorithms Accuracy Machine learning Nearest neighbor methods Internet Computer crime Random forests Classification tree analysis Resilience

来源：评论

学校读者我要写书评

暂无评论

Does Human Factor Contribute to Mining Accidents? A Systematic Literature Review Approach

Does Human Factor Contribute to Mining Accidents? A Systemat...

引用

Human Engineering Symposium, HUMENS 2021

作者： Ismail, Siti Noraishah Ramli, Azizan Faculty of Industrial Sciences and Technology College of Computing and Applied Sciences Universiti Malaysia Pahang Kuantan Pahang Malaysia Faculty of Chemical and Process Engineering Technology College of Engineering Technology Universiti Malaysia Pahang Kuantan Pahang Malaysia

ISBN: (纸本)9789811641145

Human factor always has been debated as the key factor contributes to industrial accidents such as manufacturing, construction and many more. However, there are lack of studies globally within this research to review and prove the human factor is a primary cause of accidents at mining industry. The objective of this systematic literature review (SLR) study is to investigate the dominant factor contributes to mining accidents by applying a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Three main themes and fifteen subthemes have been developed based on 25 selected articles from Scopus database. The findings showed the organizational factor is the most dominant contributor to mining accidents (53%) followed by human factor (27%) and situational factor (20%). In conclusion, this finding hopefully could facilitate mine owners to improve organizational safety concern and provide a healthy safety culture in reducing mining accidents in future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mining accidents Mining industry Organizational factor Preferred reporting items for systematic reviews and meta-analyses Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Optimizing Phishing URL Detection: A Comparative Analysis of Feature Counts and Training Times 5

Optimizing Phishing URL Detection: A Comparative Analysis of...

引用

5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Alajlouni, Moayyad Tashtoush, Yahya Dossari, Saud Alhajaj Al. Darwish, Omar Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University Department of Electrical Engineering Wadi Addwasir Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic web sites to obtain sensitive information such as passwords and bank statements. One common technique used in these attacks is malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. This paper presents a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the required features and the training time by using fewer features after applying XAI techniques. Our technique only uses statistical features extracted from n-grams and the n-gram itself, without requiring any high-level features. We applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features and 87 features used in the original experiment and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by optimizing feature selection and analyzing the impact on training times. Our findings show the importance of using minimal statistical features to identify malicious URLs effectively and efficiently. © 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：