检索结果-内蒙古大学图书馆

Machine Learning Techniques for the Management of Diseases: A Paper Review 1st

Machine Learning Techniques for the Management of Diseases: ...

1st EAI International Conference on Safe, Secure, Ethical, Responsible Technologies and Emerging Applications, SAFER-TEA 2023

作者： Tim, Ngolah Kenneth Kamla, Vivient Fute, Elie T. Computer Engineering Department University of Buea Buea Cameroon Mathematics and Computer Science Department ENSAI-University of Ngaoundéré Ngaoundere Cameroon

ISBN: (纸本)9783031563959

The advancement in Artificial Intelligence has led to the improvement in human lives. Machine learning algorithms in particular and Artificial Intelligence in general have become very useful in today’s activities. One of the sectors that has benefited from the new technology is the health sector. Machine learning techniques have been useful in the diagnosis and prediction of rare diseases. Many health sectors are using the techniques for the diagnosis and prediction of diseases thereby improving on the health situations of the patients in record time. Artificial Intelligence-based methods help in reducing the doctor to patients’ ratio gap by providing machine learning alternatives in the prediction of diseases. In this paper, we give an overview of different machine learning techniques and their relevance in the diagnosis of particular diseases. Machine learning algorithms such as Support Vector Machine (SVM), Naïve Bayes, K Nearest Neighbor (KNN),Decision Tree (DT),Random Forest (RF), Artificial neural network (ANN), Convolution Neural Network (CNN), Logistic Regression and Linear Regression used for the diagnosis of diseases have been reviewed. A collection of most non-communicable diseases diagnosed using machine learning has been examined. A comparative analysis of the accuracy performance to diagnose diseases with different machine learning algorithms has also been presented. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Zero Trust Consumer IoT With Robust Federated Learning Over Main-Side Blockchain

引用

IEEE Transactions on Consumer Electronics 2024年第1期71卷 1180-1189页

作者： Zhu, Hai Xue, Xingsi Xu, Mengmeng Kim, Byung-Gyu Henan University of Engineering School of Computer Zhengzhou451191 China Fujian Uni-versity of Technology School of Computer Science and Mathematics Fuzhou350118 China Sookmyung Women's University Department of AI Engineering Seoul Korea Republic of

The rapid proliferation of smart consumer devices has given rise to the consumer Internet of Things (CIoT), enabling immense data collection and valuable insights for enhancing con-sumer experiences. However, the distributed nature of CIoT sys-tems and the sensitivity of consumer data pose significant chal-lenges in ensuring security, privacy, and zero trust. This paper proposes a novel framework integrating robust federated learning with a main-side blockchain architecture and zero trust principles to enable secure and privacy-preserving data sharing and collabo-rative learning in CIoT environments. The proposed system model consists of CIoT devices as side nodes, edge servers as main nodes, and a cloud server for global aggregation. A lightweight privacy-preserving aggregation protocol is designed based on secret shar-ing to protect raw data during local model updates. To enhance the robustness against Byzantine attacks and data heterogeneity, a resampling-based robust aggregation method is developed, which evaluates the cosine similarity of local updates against a ref-erence gradient securely selected via the main-side blockchain. Ex-periments demonstrate that the proposed framework performs well regarding model accuracy, convergence speed, and resiliency compared with state-of-the-art methods. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, C.H. Hari Jayan, Sarada Amrita School of Computing Department of Computer Science Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331538538

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering. © 2025 IEEE.

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

引用

computer Systems science & engineering 2022年第10期43卷 141-157页

作者： A.Arivazhagi S.Raja Kumar Department of Computer Science and Engineering University College of EngineeringAriyalurTamilnaduIndia Department of Mathematics University College of EngineeringAriyalurTamilnaduIndia

Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a *** efficiency of IIDS highly relies on the algorithm *** enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these ***,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the *** also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised ***,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the *** training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification ***,the model efficiency is learned *** multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game *** outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so *** proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion ***,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

关键词： Network security sensor network intrusion detection learning framework linear support vector machine the detection mechanism

来源：评论

学校读者我要写书评

暂无评论

Submerged rigid vegetation effects on flow hydrodynamics within the pool morphology

River

引用

River 2024年第3期3卷 260-271页

作者： Kourosh Nosrati Hossein Afzalimehr Hamidreza Raeisifar Mohammad Nazari-Sharabian Moses Karakouzian Faculty of Civil Engineering Iran University of Science and TechnologyTehranIran Department of Mathematics Engineeringand Computer ScienceWest Virginia State UniversityInstituteWest VirginiaUSA Department of Civil and Environmental Engineering University of Nevada Las VegasLas VegasNevadaUSA

This research is centered on a comprehensive investigation into the impact of turbulence on the movement and dispersion of materials within a three-dimensional(3D)bedform,speciﬁcally when there is a continuous presence of rigid vegetation submerged in theﬂ*** achieve our research objectives,we conducted extensive velocity measurements within a channel featuring this submerged *** measurements were carried out using an Acoustic Doppler Velocimeter(ADV).Additionally,our study delved into the intricate structures and turbulent characteristics of theﬂow,considering the coexistence of submerged vegetation and a 3D gravel *** pool featured entrance and exit slopes measuring 3 and 2.5°,*** experimental setup took place in a straightﬂume,measuring 14 m in length,0.9 m in width,and 0.6 m in ***ﬂume was equipped with transparent side walls to facilitate ***,our investigation extended to the spatial variations in velocity and turbulence *** analyzed various parameters including turbulence kinetic energy,integral turbulence lengths,dispersion coefﬁcients,and advective *** results revealed that integral length scales offer key insights into turbulent eddy *** the presence of vegetation and a 3D bedform,turbulent eddies undergo notable changes,ﬂattening in the longitudinal direction and expanding in the transverse and vertical ***,longitudinal advection is notably higher compared toﬂows without vegetation in a uniformﬂow or bare channel,especially for z/H>*** indicates that the presence of vegetation and a 3D bedform leads to an increase in turbulent kinetic energy(k values)that surpasses the reduction in the time-averaged velocity component(“U”)in the U×k term,thereby enhancing longitudinal advection.

关键词： 3D pool advection dispersion integral turbulent length submerged rigid vegetation

来源：评论

学校读者我要写书评

暂无评论

Skin Disease Detection Using Neural Networks 2

Skin Disease Detection Using Neural Networks

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Gupta, Meenu Kumar, Rakesh Nandan Pradhan, Anil Kumar Obaid, Ahmed J. Chandigarh University Department of Computer Science & Engineering Punjab Mohali India University of Kufa Faculty of Computer Science and Mathematics Dept of Computer Science Najaf Iraq College of Engineering Uruk University Baghdad Iraq

ISBN: (纸本)9798350370188

Skin diseases like acne, psoriasis, eczema, and dermatitis affect millions worldwide. Skin cancer and melanoma are diseases that happen due to exposure to UV radiation. The early detection of skin diseases is crucial for effective treatment and prevention of further complications. This work comprehensively reviews various image-based techniques for skin disease detection, including traditional computer vision methods such as Convolutional Neural Network (CNN), Random Forest (RF), Naïve Bayes (NB), k-nearest Neighbour (k-NN), and Support Vector Machine (SVM). ISIC and dermofit datasets comprised 26,150 images divided into three categories: training (20,150), testing (3000), and validation (2000), respectively. Receiver Operating Characteristics (ROC) and Mean Squared Error (MSE) are used to calculate the accuracy and precision of models. As a result, it concluded that CNN outperformed with 94.91% accuracy. The proposed system can aid dermatologists in making accurate diagnoses and improve patient care. © 2024 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Discrete-Time Uncertain Event-Triggered H∞ Control for Slow Sampling Markov Jump Systems With Strictly Dissipativity

引用

Mathematical Methods in the Applied sciences 2025年第11期48卷 10896-10908页

作者： Kchaou, Mourad Syed Ali, M. Vigneshwar, B. Sanober, Sumaya Ibrahim, Tarek F. D Alanazi, Faizah Electrical Department College of Engineering University of Hail Kingdom Saudi Arabia Complex Systems and Networked Science Research Laboratory Department of Mathematics Thiruvalluvar University Tamil Nadu Vellore India Department of Computer Science Old Dominion University NorfolkVA United States Department of Mathematics Sciences College King Khalid University Abha Saudi Arabia Department of Mathematics Faculty of Sciences Mansoura University Mansoura Egypt Department of Mathematics College of Science Northern Border University Arar Saudi Arabia

This paper investigates the (Formula presented.) control problem for discrete-time uncertain slow sampling Markov jump systems under the event-triggered scheme. Discrete-time Markov jumps are controlled using an event-triggered scheme. An event-based strategy is addressed to reduce the data communication and improve the control efficiency. By applying a switched Lyapunov functional and linear matrix inequalities (LMIs), we derive sufficient conditions for (Formula presented.) -dissipativity of the resulting system. An event-triggered scheme is introduced into LMIs approaches in order to solve the dissipative control problem. The control strategy is implemented by combining the disturbance output with state feedback control law in order to achieve asymptotically stable closed-loop systems. Convex combination approach is utilized to handle slow sampling input, while parameter uncertainty and external disturbances are both included, and the robustness (Formula presented.) performance is analyzed. The developed methods are illustrated with two examples that illustrate their flexibility and applicability. © 2025 John Wiley & Sons Ltd.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Pythagorean Neutrosophic Planar Graphs with an Application in Decision-Making

引用

computers, Materials & Continua 2023年第6期75卷 4935-4953页

作者： P.Chellamani D.Ajay Mohammed M.Al-Shamiri Rashad Ismail Department of Mathematics Sacred Heart College(Autonomous)Tirupattur635601TamilnaduIndia Department of Mathematics St.Joseph’s College of EngineeringOMRChennai600119TamilnaduIndia Department of Mathematics Faculty of Science and ArtsKing Khalid UniversityMuhayl AssirSaudi Arabia Department of Mathematics and Computer Faculty of ScienceIbb UniversityIbbYemen

Graph theory has a significant impact and is crucial in the structure of many real-life *** simulate uncertainty and ambiguity,many extensions of graph theoretical notions were *** graphs play a vital role in modelling which has the property of non-crossing *** crossing edges benefit,they have some drawbacks,which paved the way for the introduction of planar *** overall purpose of the study is to contribute to the conceptual development of the Pythagorean Neutrosophic *** basic methodology of our research is the incorporation of the analogous concepts of planar graphs in the Pythagorean Neutrosophic *** significant finding of our research is the introduction of Pythagorean Neutrosophic Planar graphs,a conceptual blending of Pythagorean Neutro-sophic and Planar *** idea of Pythagorean Neutrosophic multigraphs and dual graphs are also introduced to deal with the ambiguous *** paper investigates the Pythagorean Neutrosophic planar values,which form the edges of the Pythagorean neutrosophic *** concept of Pythagorean Neutrosophic dual graphs,isomorphism,co-weak and weak isomorphism have also been explored for Pythagorean Neutrosophic planar graphs.A decision-making algorithm was proposed with a numerical illustra-tion by using the Pythagorean Neutrosophic fuzzy graph.

关键词： Pythagorean neutrosophic planar graph planarity value isomorphism dual graphs multigraph

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network (Wireless Personal Communications, (2022), 127, 1, (749-765), 10.1007/s11277-021-08403-5)

引用

Wireless Personal Communications 2025年第1期140卷 781-782页

作者： Setiawan, Roy Ganga, Ramakoteswara Rao Velayutham, Priya Thangavel, Kumaravel Sharma, Dilip Kumar Rajan, Regin Krishnamoorthy, Sujatha Sengan, Sudhakar Department Management Universitas Kristen Petra Surabaya Indonesia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Information Technology Adhiyamaan College of Engineering Tamil Nadu Hosur635109 India Department of Computer Science Wenzhou-Kean University Zhejiang Wenzhou325060 China Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

The Editor-in-Chief has retracted this article because of concerns regarding the substance and overall soundness of this work. An investigation conducted after its publication discovered instances of nonsensical statements, raising questions on both the authorship and validity of the content presented. Sujatha Krishnamoorthy disagrees with this retraction. Dilip Kumar Sharma and Sudhakar Sengan have not stated whether they agree or disagree with this retraction. The remaining authors have not replied to correspondence from the Publisher. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hyperbolic Hopfield Neural Network for Enhanced Solar Power Generation Forecasting 4

Hyperbolic Hopfield Neural Network for Enhanced Solar Power ...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Naveen, K.N. Sukumar, P. Yamini, C. Raghapriya, N.R. Dwivedi, Vijay Kumar Department of Physics Bihar India Dayananda sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Department of Computer Science and Engineering Erode India Psna College of Engineering and Technology Department of Mathematics Dindigul India Erode Sengunthar Engineering College Department of Computer Science and Engineering Erode India Vishwavidyalaya Engineering College Department of Mathematics Surguja India

ISBN: (纸本)9798350352931

Solar power is growing more essential for renewable energy, but measuring its output is difficult due to irradiance, temperature, and weather fluctuations. Planning and controlling energy distribution requires accurate short-term estimates. An innovative Hyperbolic Hopfield Neural Network (HHNN) model for short-term solar power prediction is presented in this paper. The procedure includes preprocessing, feature extraction, and model training. Remove linearly dependent characteristics and filter nighttime data during preprocessing. The HHNN model is trained after feature selection is optimised using PCA and wavelet decomposition. The new HHNN model surpassed the HNN and DNN models in solar power forecasting with an average accuracy of 92.41%.HHNN is a solid method for calculating solar power output. This model has improved solar power management systems by increasing projected accuracy. © 2024 IEEE.

关键词： hyperbolic hopfield neural network (HHNN) principal component analysis (PCA) solar power generation forecasting

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：