检索结果-内蒙古大学图书馆

Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system

引用

APPLIED SOFT COMPUTING 2024年 156卷

作者： Parkavi, R. Karthikeyan, P. Abdullah, A. Sheik Thiagarajar Coll Engn Dept Informat Technol Madurai Tamilnadu India Vellore Inst Technol Sch Comp Sci & Engn SCOPE Chennai Campus Chennai Tamil Nadu India

In the realm of Educational Technology, personalized learning is pivotal, yet predicting students ' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using machine learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students ' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self -Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO ' s superiority. The study identifies the significant impact of ICT on self -progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision -making. Further exploration in real -world educational settings and comparative analyses with alternative optimization techniques are recommended for future research.

关键词： Educational data mining machine learning algorithms Particle Swarm Optimization Meta -heuristic based algorithms Explainable Artificial Intelligence Techniques

来源：评论

学校读者我要写书评

暂无评论

Small-Scale Martian Crater Detection by Deep learning With Enhanced Capture of Features Information and Long-Range Dependencies

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Yu, Zhichao Fong, Simon Millham, Richard Charles Univ Macau Dept Comp Sci & Informat Sci Taipa Macau Peoples R China Chongqing Technol & Business Univ Dept Artificial Intelligence Chongqing 404100 Peoples R China Durban Univ Technol Dept Informat Technol ZA-4058 Durban South Africa Space Sci Ctr ZA-4058 Durban South Africa

Currently, with the rapid advancement of aerospace technology, scientists are increasingly capable of exploring other planets, which has brought greater attention to the challenge of crater detection, particularly the detection of smaller craters. This letter presents a novel single-stage target detector based on YOLOv9, aimed at improving the detection of craters, especially small ones. First, we propose a novel feature extraction module that enhances the detection capabilities of the network. By integrating deformable convolution into the original feature extraction module, we improve the capability of convolutional neural network (CNN) to catch long-range dependencies and spatial relationships, thereby enhancing the detection accuracy for craters of various sizes. Second, we introduce a new pooling structure called averaged spatial pyramid pooling (ASPP). This structure uses a parallel configuration of average and maximum pooling techniques to enrich the overall feature extraction process, thereby improving the detection capability for small craters. To confirm the efficacy of our proposed approach, we performed comprehensive experiments using a large public Mars crater dataset. The results indicate that our approach significantly surpasses most current mainstream one-stage object detection algorithms in both precision and recall.

关键词： Feature extraction YOLO Convolution Accuracy Training Meteorites Data mining Geoscience and remote sensing Mars machine learning algorithms Crater detection deep learning objection detection one-stage object detection YOLOv9

来源：评论

学校读者我要写书评

暂无评论

Reinforcement learning-Based Delay Line Design for Crosstalk Minimization

引用

IEEE ACCESS 2024年 12卷 161557-161568页

作者： Jung, Jaeho Yu, Younggyun Lee, Soobum Chungbuk Natl Univ Sch Mech Engn Cheongju 28644 Chungbuk South Korea Korea Atom Energy Res Inst Daejeon 34057 South Korea Univ Maryland Baltimore Cty Dept Mech Engn Baltimore MD 21250 USA

Reinforcement learning (RL) is one of the artificial intelligence techniques that build an artificial neural network to achieve the most optimum decision. In this study, Deep Q-Network (DQN) RL is applied to the design of delay lines in electrical circuits for signal synchronization. The delay line is usually designed densely in a confined space that results in electrical noise named crosstalk. The challenges of delay line design root from the fact that the line should connect the start and end point using a given length, not to be entangled in a predefined two-dimensional space. The genetic algorithms (GA) or the random exploration method can be used, but their learning efficiency is very low and time-consuming. We propose and implement a novel connected exploration method to significantly expedite the design process. In each state, the direction of the line rendering (left, straight, or right) is considered as an action, and the artificial intelligence agent learns how to design a delay line of the desired length. As a result, we are able to obtain the optimal designs 3,000 times faster than the case of using the GA from our previous study. The proposed method can be applied to various routing design problems such as circuit routing or flow path configuration with seriously reduced design time, and can potentially lead to the discovery of new designs not relied on human intuition.

关键词： Delay lines Crosstalk Circuits Routing Layout Optimization Genetic algorithms Evolutionary computation Circuit synthesis machine learning algorithms Reinforcement learning artificial intelligence delay line crosstalk circuit design

来源：评论

学校读者我要写书评

暂无评论

A learning Probabilistic Boolean Network Model of a Smart Grid with Applications in System Maintenance

引用

ENERGIES 2024年第24期17卷

作者： Torres, Pedro Juan Rivera Chen, Chen Macias-Aguayo, Jaime Gonzalez, Sara Rodriguez Tejedor, Javier Prieto Santiago, Orestes Llanes Garcia, Carlos Gershenson Izquierdo, Samir Kanaan Univ Salamanca Dept Comp Sci & Automatics Patio Escuelas 1 Salamanca 37006 Spain Univ Cambridge St Edmunds Coll Mt Pleasant Cambridge CB3 0BN England Univ Politecn Cataluna Escuela Tecn Super Ingn Ind Barcelona Av Diagonal 647 Barcelona 08028 Spain Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England MIT Ctr Transportat & Logist 1 Amherst StMIT Bldg E40-376 Cambridge MA 02139 USA Inst Super Politecn Jose Antonio Echeverria CUJAE Dept Control & Automatica Havana 19390 Cuba Binghamton Univ Sch Syst Sci & Ind Engn Binghamton NY 13902 USA

Probabilistic Boolean Networks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a Probabilistic Boolean Network architecture with a learning process that significantly improves the prediction of the occurrence of faults and failures in smart-grid systems. This idea was tested in a Probabilistic Boolean Network model of the WSCC nine-bus system that incorporates Intelligent Power Routers on every bus. The model learned the equality and negation functions in the different experiments performed. We take advantage of the complex properties of Probabilistic Boolean Networks to use them as a positive feedback adaptive learning tool and to illustrate that these networks could have a more general use than previously thought. This multi-layered PBN architecture provides a significant improvement in terms of performance for fault detection, within a positive-feedback network structure that is more tolerant of noise than other techniques.

关键词： fault detection and isolation machine learning algorithms probabilistic Boolean networks probabilistic Boolean network modeling smart grids complex network modeling

来源：评论

学校读者我要写书评

暂无评论

Using Methods From Dimensionality Reduction for Active learning With Low Query Budget

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 4317-4330页

作者： Tharwat, Alaa Schenck, Wolfram Hsch Bielefeld Univ Appl Sci & Arts Ctr Appl Data Sci CfADS Bielefeld Germany

Recently, it has been challenging to generate enough labeled data for supervised learning models from a large amount of free unlabeled data due to the high cost of the labeling process. Here, the active learning technique provides a solution by annotating a small but highly informative set of unlabeled data. This ensures high generalizability in space and improves classification performance with test data. The task is more challenging when the query budget is small, the data is imbalanced, multiple classes are present, and no predefined knowledge is available. To address these challenges, we present a novel active learner geometrically based on principal component analysis (PCA) and linear discriminant analysis (LDA). The proposed active learner consists of two phases: The PCA-inspired exploration phase, in which regions with high variances are explored, and the LDA-inspired exploitation phase, in which boundary points between classes are selected. The proposed geometric strategy improves the search capabilities of the active learner, allowing it to explore the space of minority classes even with multiple minority classes and a small query budget. Experiments on synthetic and real binary and multi-class imbalanced data show that the proposed algorithm has significant advantages over multiple known active learners.

关键词： Data models Training data Space exploration Principal component analysis Labeling Adaptation models machine learning algorithms Active learning dimensionality reduction imbalanced data LDA PCA

来源：评论

学校读者我要写书评

暂无评论

Entropy generation and heatline visualization of magnetized thermal convection in an irregular enclosure filled with nanofluid and subject to non-uniform heating: FEM-machine learning hybrid approach

引用

CASE STUDIES IN THERMAL ENGINEERING 2024年 60卷

作者： Shahzad, Hasan Li, Zhiyong Tang, Tingting Xie, Yongyu Lin, Zhuobin Dongguan Univ Technol Fac Energy & Power Engn Sch Chem Engn & Energy Technol Dongguan Peoples R China Univ Sci & Technol Dept Chem Engn & Energy Technol Hefei Peoples R China

This investigation's primary objective is to analyze the fluid dynamics and heat transfer enhancement in an irregular enclosure. Several physical phenomena such as Alumina-water nanofluids, magnetic field, non-uniform heating and amplitude of non-uniform heating are taken into account. A thorough investigation using machine learning algorithms (MLAs) and computational fluid dynamics (CFD) is carried out. Initially, for CFD phase the coupled non-linear dimensionless governing equations of the considered problem are solved numerically by adapting a Finite Element method (FEM) using appropriate boundary conditions. The analysis considers a range of physical parameters, such as the Hartmann number (0 - 20), nanoparticle volume friction (0 - 0.04), Ryleigh number (10 3-10 5 ), offset temperature (0.1 - 1), frequency (1-10) and nonuniform heating amplitude (0.1 - 1). The numerical results are explicated in the form of streamlines, isotherms, heatlines structures. The impact of these factors on entropy generation is also explored. In addition, three popular MLAs, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector machine (SVM) were used to save the computational costs. The finding explore Hartmann number and nanoparticle volume friction has decreasing trend on total entropy production and heat transfer rate. The entropy production decreases up to 42.02 % for Hartman number and for nanoparticles it decreases 12.61 %. However, the non-uniform heating amplitude has opposite effect on irreversibility factor. Also, compared to other MLAs, the ANN model demonstrates more accurate predictions. The objective is to propose novel concept for enhancing the performance of cooling systems and optimizing energy utilization across diverse technical domain.

关键词： machine learning algorithms Nanofluids Irregular cavity Offset temperature Half-sinusoidal heating magnetohydrodynamic (MHD) Finite element method (FEM)

来源：评论

学校读者我要写书评

暂无评论

Optimal Resource Allocation for AGIN 6G: A learning-Based Three-Sided Matching Approach

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1553-1565页

作者： Qin, Peng Wang, Miao Cai, Ziyuan Ding, Rui Zhao, Xiongwen Fu, Yang Wu, Xue North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China China Acad Elect & Informat Technol Beijing 100041 Peoples R China China Satellite Network Grp Co Ltd Beijing 100029 Peoples R China

As ubiquitous interconnection becomes a reality for human beings, addressing the challenge of seamless coverage in the near future 6G network, particularly for remote area connections, has become increasingly urgent. HAPs and UAVs can efficiently extend the network coverage which is unfeasible by ground base stations. However, due to the high dynamic nature of air ground communications, both the available network resource and channel state information (CSI) are time varying which is typically called the dilemma of information uncertainty, often making the resource allocation intractable. To overcome the challenges, first we propose an air ground integrated 6G heterogeneous network (AGIN 6G) leveraging the wide coverage of HAPs and the hot spot communication enhancement of UAVs. Second we formulate the challenge as a three-sided matching problem with the size and cyclic preference (TMSC) among HAPs, UAVs and users. To find a feasible solution, we transform the problem into a R-TMSC issue by applying some reasonable constraints, especially when the CSI is assumed known, we can develop the HOR$<^>{2}$ A-CSI algorithm. Third, to cope with the information uncertainty, we propose the CVA-UCB matching solution augmented with machine learning. Finally, we conduct extensive experiments comparing with benchmark algorithms. Our works can verify that the proposed algorithm is superior to others in terms of the data transmission rate, system revenue and throughput.

关键词： 6G mobile communication Resource management Uncertainty Autonomous aerial vehicles machine learning algorithms Heuristic algorithms Throughput Air ground integrated 6G heterogeneous networks (AGIN 6G) matching learning resource allocation three-sided cyclic matching

来源：评论

学校读者我要写书评

暂无评论

Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and machine learning

引用

IEEE ACCESS 2024年 12卷 174255-174273页

作者： Al-Kahla, Wafaa Taqieddin, Eyad Shatnawi, Ahmed S. Al-Ouran, Rami Jordan Univ Sci & Technol Dept Network Engn & Secur Irbid 22110 Jordan Al Hussein Tech Univ Dept Cybersecur Amman 11831 Jordan Jordan Univ Sci & Technol Dept Software Engn Irbid 22110 Jordan Al Hussein Tech Univ Dept Data Sci & AI Amman 11831 Jordan

The need to detect and classify malware on Android devices has become crucial due to the widespread use of these devices daily. This paper presents a method for detecting and classifying malware that uses static and dynamic features. Specifically, we extract permissions and intent filters from static analysis files and APK API data from dynamic analysis files. Then, we use Simhash to encode the selected parts of the analysis files to create feature vectors. These vectors are then used to train different machine learning algorithms for detecting and classifying malware. We conducted several experiments to evaluate the effectiveness of our approach on a dataset of 38,355 labeled Android apps and tested various hash functions and tokenizing methods in the Simhash algorithm. Our results showed that using Random Forest as a classifier, SHA-512 the hash function and 2-WORD tokenization led to high accuracy with reduced time and memory requirements for training, validating, and testing. This indicates a highly accurate model for detecting and classifying malware into 24 families.

关键词： Malware Feature extraction Operating systems Static analysis Accuracy Filters Heuristic algorithms machine learning algorithms machine learning Codes Android malware family classification Simhash feature extraction static analysis dynamic analysis hybrid analysis

来源：评论

学校读者我要写书评

暂无评论

Experimental Study on a Novel machine learning-Assisted Covariance-Based Spectrum Sensing Algorithm in the Presence of Noise

引用

IEEE SENSORS JOURNAL 2024年第16期24卷 26926-26934页

作者： Sabra, Ali Berbineau, Marion Inst Rech Technol Railenium F-59300 Famars France Univ Gustave Eiffel COSYS LEOST F-59650 Villeneuve Dascq France

Spectrum sensing (SS) plays a pivotal role in cognitive radio (CR) systems, facilitating efficient spectrum utilization. Covariance-based spectrum sensing (CSS) techniques have demonstrated superior performance compared with traditional methods. Recently, the integration of machine learning (ML) models has further improved CSS algorithms. In this article, we introduce a novel ML-assisted CSS algorithm and evaluate its efficacy using real-world datasets, considering practical hardware imperfections. In addition, we examine its performance in the presence of the $\alpha $ -stable distributed noise, in its impulsive and white Gaussian forms. In addition, we conduct comparative experiments with various ML-aided CSS techniques. Our experimental results consistently highlight the superiority of the proposed ML-assisted CSS algorithm over alternative methods, regardless of the ML model employed. Furthermore, our findings underscore the impact of realistic noise conditions, attributed to hardware imperfections and correlated noise, on the performance of CSS algorithms. This insight emphasizes the importance of considering practical factors and different types of noise in the design and evaluation of SS techniques.

关键词： Noise Sensors Eigenvalues and eigenfunctions Covariance matrices machine learning algorithms Signal processing algorithms Radio frequency Cognitive radio (CR) impulsive noise (IN) machine learning (ML) sensor signal processing spectrum sensing (SS)

来源：评论

学校读者我要写书评

暂无评论

Design of a Novel PID Controller Based on machine learning Algorithm for a Micro-Thermoelectric Cooler of the Polymerase Chain Reaction Device

引用

IEEE ACCESS 2024年 12卷 61959-61977页

作者： Arikusu, Yilmaz Seryar Bayhan, Nevra Manisa Celal Bayar Univ Dept Elect Engn TR-45400 Manisa Turkiye Istanbul Univ Cerrahpasa Dept Elect & Elect Engn TR-34320 Istanbul Turkiye

The combined use of Extreme Gradient Boosting (XGBoost) algorithm, one of the machine learning (ML) methods, and a generalization of Hermite-Biehler theorem to obtain a novel PID controller that will ensure robustly stable and optimized operation of a micro thermoelectric cooler (Micro-TEC), which is the main part of a Polymerase chain reaction (PCR) device, is a unique approach of our study compared to previous studies. Therefore, we first established a mathematical model of the micro-TEC by making real-time measurements and then, a new data set was created to find the optimum parameter values of PID controller, and finally, XGBoost Hyperparameters with GridSearchCV was used for the first time to predict PID controller parameters. The XGBoost algorithm achieved 97% training success and 91% test success in estimating the parameters of the PID controller. Moreover, the novel controller developed using the XGBoost algorithm in this study has an impressive speed of 3 seconds. Additionally, our proposed method was compared with various metaheuristic optimization algorithms in terms of error percentage. The error percentages of XGBoost, the equilibrium optimization, the particle swarm optimization and the artificial bee colony optimization algorithms were found to be 0.4%, 1.1%, 3.7% and 11.1%, respectively. It is observed the settling times of micro-TEC with ML-PID controller for all five PCR cycles are 4.86, 44, 83.4, 123 and 162.5 seconds, respectively, and the overshoot values are below 5%. The proposed method gave the smallest settling time, error and overshoot percentages compared to these metaheuristic optimization algorithms.

关键词： Temperature control machine learning algorithms Polymers Stability analysis Prediction algorithms Power system stability Thermoelectric materials XGBoost algorithm GridSearchCV thermoelectric cooler machine learning

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：