检索结果-内蒙古大学图书馆

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Shirley, C.P. Joel, J. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India

ISBN: (纸本)9798331527549

Timely identification of cervical cancer is essential for effective therapy and enhanced patient outcomes. Conventional imaging methods, although fundamental, frequently inadequately represent the Detailed characteristics of cancer pathology because they depend on isolated data sources. MedFuseNet tackles these difficulties by presenting an advanced architecture that integrates multi-modal data to improve diagnostic accuracy and dependability significantly. This advanced model employs high-resolution medical imaging and incorporates patient-specific clinical data, establishing a comprehensive analytical foundation for diagnosis. Our dataset includes 4,049 annotated cervical cell images across a spectrum from normal to malignant, each enriched with detailed clinical parameters. MedFuseNet utilizes a hybrid architecture that integrates CNNs for image data and RNNs for sequential clinical data, facilitating a thorough analysis of various data sources. The methodological integration enables MedFuseNet to surpass conventional single-source models, with an accuracy of 98.5%, alongside significant enhancements in precision (97.9%) and recall (98.1%). These substantial diagnostic improvements highlight the promise of multi-modal data fusion in medical imaging, paving the way for creating more sophisticated, AI-driven diagnostic instruments that may revolutionize early cancer diagnosis and treatment approaches. © 2025 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

A New Model Based on Graph Neural Network Feedback for Crystal Property Prediction 5

A New Model Based on Graph Neural Network Feedback for Cryst...

引用

5th International Conference on Big data and artificial intelligence and Software engineering, ICBASE 2024

作者： Zhu, Jian Ren, Yongshen Zang, Huaijuan Zhan, Shu Hefei University of Technology School of Computer and Information Engineering Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Kunming University of Science and Technology Faculty of Metallurgical and Energy Engineering Kunming China Kunming University of Science and Technology National Engineering Research Center of Vacuum Metallurgy Kunming China

ISBN: (纸本)9798331506612

The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy. However, accurately simulating the complex relationships between atoms and molecules in crystal structures remains challenging. The prediction performance of crystal graph networks also depends on the effective extraction of crystal features, typically requiring a close correlation between the properties to be predicted and the extracted features. In this study, a model retraining method is utilized, where the loss function in the retraining phase is constructed using feedback from a pre-trained crystal graph network model to adjust model parameters and enhance the accuracy of predicting crystal properties. This method enhances the predictive performance of multiple crystal graph neural networks and accelerates the development of high-performance crystals without the need to add various features or incorporate additional neural network layers. The research findings demonstrate the significant effectiveness of the proposed method in enhancing the prediction performance of four models: CGCNN, OGCNN, GATGNN, and ALIGNN. Optimization metrics support this claim. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning

引用

BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac339-bbac339页

作者： Zhuo, Linlin Song, Bosheng Liu, Yuansheng Li, Zejun Fu, Xiangzheng College of Data Science and Artificial Intelligence Wenzhou University of Technology 325027 Wenzhou China College of Computer Science and Electronic Engineering Hunan University 410082 Changsha China School of Computer and Information Science Hunan Institute of Technology 421000 Hengyang China

Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, diseases, etc. Traditional experimental methods can accomplish this work but are often labor-intensive and expensive. Machine learning and deep learning methods have achieved great success by exploiting sufficient sequence or structure information. Graph Neural Network (GNN)-based methods consider the topology in ncRNA-protein graphs and perform well on tasks like NPI prediction. Based on GNN, some pairwise constraint methods have been developed to apply on homogeneous networks, but not used for NPI prediction on heterogeneous networks. In this paper, we construct a pairwise constrained NPI predictor based on dual Graph Convolutional Network (GCN) called NPI-DGCN. To our knowledge, our method is the first to train a heterogeneous graph-based model using a pairwise learning strategy. Instead of binary classification, we use a rank layer to calculate the score of an ncRNA-protein pair. Moreover, our model is the first to predict NPIs on the ncRNA-protein bipartite graph rather than the homogeneous graph. We transform the original ncRNA-protein bipartite graph into two homogenous graphs on which to explore second-order implicit relationships. At the same time, we model direct interactions between two homogenous graphs to explore explicit relationships. Experimental results on the four standard datasets indicate that our method achieves competitive performance with other state-of-the-art methods. And the model is available at https://***/zhuoninnin1992/NPIPredict

关键词： dual graph convolutional network explicit relationships implicit relationships ncRNA-protein interaction pairwise learning

来源：评论

学校读者我要写书评

暂无评论

Spectroscopy and molecule opacity investigation on excited states of SiS

引用

Chinese Physics B 2024年第5期33卷 381-387页

作者：李瑞吕浩男桑纪群刘晓华梁桂颖吴勇 College of Teacher Education Qiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

The SiS molecule,which plays a significant role in space,has attracted a great deal of attention for many *** to complex interactions among its low-lying electronic states,precise information regarding the molecular structure of SiS is *** obtain accurate information about the structure of its excited states,the high-precision multireference configuration interaction(MRCI)method has been *** method is used to calculate the potential energy curves(PECs)of the 18Λ–S states corresponding to the lowest dissociation limit of *** core–valence correlation effect,Davidson’s correction and the scalar relativistic effect are also included to guarantee the precision of the MRCI *** on the calculated PECs,the spectroscopic constants of quasi-bound and bound electronic states are calculated and they are in accordance with previous experimental *** transition dipole moments(TDMs)and dipole moments(DMs)are determined by the MRCI *** addition,the abrupt variations of the DMs for the 1^(5)Σ^(+)and 2^(5)Σ^(+)states at the avoided crossing point are attributed to the variation of the electronic *** opacity of SiS at a pressure of 100 atms is presented across a series of *** increasing temperature,the expanding population of excited states blurs the band boundaries.

关键词： SiS opacity excited state spectroscopic constant configuration interaction

来源：评论

学校读者我要写书评

暂无评论

Leveraging Computational Vision Techniques for Weed Detection in Crops

Leveraging Computational Vision Techniques for Weed Detectio...

引用

2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Thendral, R. Ranjeeth, A. Ifet College of Engineering Department of Information Technology Gangarampalayam Tamil Nadu 605108 India Ifet College of Engineering Department of Artificial Intelligence and Data Science Gangarampalayam Tamil Nadu 605108 India

ISBN: (纸本)9798350315127

The precise utilization of herbicides can result in a significant decrease in their application levels within agricultural fields. The detection system plays a vital role in smart sprayers, enabling the identification of target weeds and facilitating informed spraying decisions. The development of near-ground image capture and processing techniques for weed detection in vegetable crops is the main topic of this research. The first step in the image processing involves detecting green plants, which helps to minimize irrelevant information and streamline the weed detection process. Subsequently, the study has emphasized vegetation segmentation through the application of morphological filtering and object labeling techniques, enabling weed detection based on a threshold derived from the area of detection. The proposed methods incorporate color and area analysis techniques, effectively distinguishing between crops, weeds, and soil. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Analysis on Anomaly Detection in Cybersecurity dataset using Deep Learning Algorithms

Comprehensive Analysis on Anomaly Detection in Cybersecurity...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Nidhishree, G. Vidyalakshmi, K. Vidyavardhaka College of Engineering Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning) Mysuru India Ssahe University Sri Siddhartha Institute of Technology Department of Computer Science and Engineering (Data Science) Tumakuru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cybersecurity has become very important due the rapid growth of digital technologies. It plays an important role in protecting computer systems and networks from potential attacks. Cybersecurity involves a set of technologies and algorithms that sometimes bring in threats to the security of computer networks, the end system, attacks on data, inappropriate access, changes and harm. Cyber-defense mechanisms involved at the host, application, network and data level require a constantly improved process to secure the system from any feasible attack, targeting data security and privacy. The network anomaly detection is challenging because of the complex network topologies and high dimensional data, leading to unbalanced performance, high false positive rates and difficulty of handling time-series data. Deep Learning (DL) methods appear as an essential element in network Intrusion Detection Systems (NIDS) to recognize malicious behaviors or anomalies. Anomaly detection and network security are extensively utilized for intrusion detection performed by utilizing Cyber security to reduce malicious behavior in the networks. DL based-based detection utilizes many algorithms, which are capable of capturing complex patterns, effective for high-dimensional data. In DL, the anomaly is solved by two ways: flow-based detection that analyzes the network flows and packet-level data to identify anomalies. © 2024 IEEE.

关键词： anomaly detection cyber security deep learning intrusion detection system privacy and security

来源：评论

学校读者我要写书评

暂无评论

Parkinsonian Gait Severity in Older Adults with Dementia Using Natural Gait Video Analysis

Parkinsonian Gait Severity in Older Adults with Dementia Usi...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Islam, Eissa Medhat, Mariam Waleed, Farah Deif, Mohanad A. Alkhazaleh, Shawkat Hafez, Mohamed 6th of October City Department of Artificial intelligence 12566 Egypt Jadara University Faculty of Science and Information Technology Department of Mathematics Jordan FEQS INTI-IU-University Faculty of Engineering Nilai Malaysia

ISBN: (纸本)9798350351408

Parkinson's disease presents challenges in assessing severity, especially in older adults with dementia. This study introduces a novel approach using vision-based systems for gait analysis [1]. 2D human pose tracking in video data from a dementia unit extracted gait features. Correlation between these features and parkinsonism severity, assessed by UPDRS, was observed [6]. Five machine learning algorithms were evaluated, with KNN demonstrating superior performance [9]. Random Forest and Extra Trees also performed well [10], while Logistic Regression faced challenges [11], [12]. XGBoost showed exceptional predictive ability [8]. The study underscores the effectiveness of these algorithms in non-invasively identifying gait abnormalities, particularly highlighting KNN's adeptness with high-dimensional features [9]. This research advances healthcare diagnostics by showcasing machine learning's potential in early detection and ongoing monitoring of neurodegenerative disorders, promising improved medical outcomes. © 2024 IEEE.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Identification of NPK Deficiency in Toor Dal Leaf Using CNN Technique

Identification of NPK Deficiency in Toor Dal Leaf Using CNN ...

引用

Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Syeda Umme Hani Sujata V. Mallapur Artificial Intelligence & Data Science Faculty of Engineering & Technology Sharnbasva University Kalaburgi Karnataka India

The main economic contributor to India is agriculture. The Nitrogen (N), phosphorus (P), and potassium (K), are the crucial parts of nutrients in plant growth. NPK deficiency levels affect plant development and crop productivity. By analyzing the leaf properties, we hope to build a model for an artificial neural network (Convolutional Neural Network Technique) that will be able to identify and categorize nutrient deficiencies in toor dal leaf. The two main characteristics used to determine NPK deficiencies in plants are the color and shape of a leaf. The dynamic capture of toor dal leaves by scanning was employed in this work to investigate how the regulation of leaf attributes changed in response to nutritional stress. In this work, the impact of various activation functions on the artificial neural network is also investigated. The findings demonstrate that the suggested technique was highly accurate in classifying and identifying NPK deficiencies in toor dal leaf.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Federated intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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：