检索结果-内蒙古大学图书馆

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Real-Time Crop Monitoring system Using Deep Learning Techniques for Increasing Production

Real-Time Crop Monitoring System Using Deep Learning Techniq...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Dutta, Chiranjit Moudgil, Suveg Aluvala, Srinivas Saxena, Virat Raj Devi, K. Singh, Jagendra SRM Institute of Science and Technology Department of Computer Science and Engineering Ghaziabad India Galgotias University School of Computer Science Engineering Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India DAV Autonomous College Department of Commerce Titilagarh India Bennett University School of Computer Science Engineering & Technology Greater Noida India

ISBN: (纸本)9798350394412

This study applies machine learning to determine rice crop production using sensor information from temperature, humidity, and water levels. This project looks forward to providing insights to maximize agricultural practices and allow data-driven decision-making in rice cultivation. This is in line with how well these disparate machine learning models can predict rice crop production when measured against precision, accuracy, recall, and F1 score. To some extent, this is evident, as this study found that the machine learning models can predict yield quite satisfactorily, of which the model Random Forest performs very well in terms of precision and accuracy. Among them were the exactitude of sensor data and the algorithm selection, along with subsequent model assessment, refinement, and the ingression of great datasets inclusive of a broader set of environmental characteristics and crop yields - that were considered to be crucial in improving the precision of yield prediction algorithms. Additionally, the use of machine learning in farming has a number of benefits, such as enabling farmers to make decisions about crop management, irrigation, and optimization of yield. Combining machine learning methodologies with sensor technology can optimize resource allocation, save water, and improve overall agricultural productivity. Further, this study will facilitate the implementation of smart irrigation systems and set the stage for data-driven decision-making in agriculture for better farming practices and food production worldwide. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Massively parallel algorithms for fully dynamic all-pairs shortest paths

引用

Frontiers of computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

来源：评论

学校读者我要写书评

暂无评论

Optimizing Deep Learning for Brain Pathology Detection Using SqueezeNet with CT Imaging

Optimizing Deep Learning for Brain Pathology Detection Using...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Natarajan, Suresh Kumar Ghantasala, G S Pradeep Anthoniraj, S. Vidyullatha, Pellakuri Rajesh Sharma, R. Ananthanagu, U. School of Computer Science and Engineering Ramanagara India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India

ISBN: (纸本)9798350385199

In this research, a explore the realm of medical imaging, specifically focusing on early brain pathology detection through the lens of deep learning. The primary objective is to optimize SqueezeNet, lightweight convolutional neural network architecture, for brain pathology detection using CT imaging. The motivation stems from the critical need for accurate and swift identification of brain abnormalities, with potential implications for patient outcomes. The meticulously preprocess a dataset of CT images of the human brain to enhance the model's ability to discern subtle pathology patterns. SqueezeNet, known for its efficiency, is employed, and optimization techniques during training enhance its performance. These findings suggest SqueezeNet's potential as a valuable tool in the medical field, offering a balance between efficiency and diagnostic accuracy. Throughout the discussion, A critically analyzes the implications of results, drawing comparisons with existing literature and addressing any encountered limitations. In this research contributes to the discourse on leveraging deep learning for brain pathology detection and the experimental results showcase promising outcomes, with a test accuracy of 78%. Precision, recall, and F1-score metrics indicate the model's proficiency in distinguishing between different pathologies: 87% for Hemorrhagic, 70% for Ischemic, and 79% for Normal cases. Confusion matrices and ROC curves provide comprehensive insights into the model's classification capabilities. The optimized SqueezeNet application demonstrates promise in enhancing diagnostic capabilities, with the obtained results providing valuable insights for future advancements in the intersection of deep learning and medical imaging, potentially impacting patient care and outcomes positively. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

引用

IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

来源：评论

学校读者我要写书评

暂无评论

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

引用

computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

来源：评论

学校读者我要写书评

暂无评论

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Class Breast Cancer Classification 2

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Cla...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kansal, Kanika Kansal, Kajal Department of Computer Science and Engineering Data Science ABES Engineering College Uttar Pradesh Ghaziabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530389

Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survival rates. An automatic diagnosis system based on deep learning models is critical for improving detection, prediction accuracy, and survivability. This study used various DenseNet-based architectures for multi-class classification using the BUSI dataset. The experiments indicate that the fine-tuned DenseNet201 model outperforms with an accuracy of 98.28%. The results demonstrate the promising capability of the fine-tuned DenseNet201 model to have a high potential for further implementation in practice for achieving early diagnosis and highly individualized approaches for better treatment. The further development of extensive and diverse data sets will be essential for developing new diagnostic methods and improving the existing ones. © 2025 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Unlocking Security: A Comprehensive Exploration of Biometric Authentication Techniques 14

Unlocking Security: A Comprehensive Exploration of Biometric...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Sriman, Jindam Thapar, Puneet Alyas, Arsalan Ahmed Singh, Urvashi School of Computer Science Engineering Lovely Professional University Phagwara India Wilmington University Information System Technologies United States

ISBN: (纸本)9798350344837

Biometric authentication is a rapidly evolving field in information security, providing a personalized and reliable approach to identity verification. This paper explores various biometric methods, including fingerprints, iris scans, facial recognition, voice authentication, and behavioral biometrics, highlighting their strengths and vulnerabilities. Addressing challenges like spoofing attacks, multimodal integration, and privacy concerns, the paper emphasizes the need for robust security measures. The thorough analysis considers critical factors such as accuracy, user-friendliness, and vulnerability to fraudulent activities, providing a well-rounded assessment of their strengths and weaknesses. Besides, the review delves into the convergence of diverse biometric modalities and the evolving landscape of multimodal authentication systems, underscoring the pressing need for sustained research and advancement to address security challenges and privacy issues. The ultimate objective is to support user acceptance and instill confidence in biometric authentication. By critically appraising established techniques and contemplating potential future developments, this paper aims to provide profound insights for researchers and practitioners in the field of biometric authentication. © 2024 IEEE.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

A Two-Step Modulus-Based Matrix Splitting Iteration Method Without Auxiliary Variables for Solving Vertical Linear Complementarity Problems

引用

Communications on Applied Mathematics and Computation 2024年第4期6卷 2475-2492页

作者： Hua Zheng Xiaoping Lu Seakweng Vong School of Mathematics and Statistics Shaoguan UniversityShaoguan 512005GuangdongChina School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Mathematics University of MacaoMacaoChina

In this paper,a two-step iteration method is established which can be viewed as a generalization of the existing modulus-based methods for vertical linear complementarity problems given by He and Vong(***.134:108344,2022).The convergence analysis of the proposed method is established,which can improve the existing *** examples show that the proposed method is efficient with the two-step technique.

关键词： Vertical linear complementarity problem Modulus-based matrix splitting Two-step metho

来源：评论

学校读者我要写书评

暂无评论

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

引用

computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first *** study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and *** proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN *** GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were *** model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

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：