检索结果-内蒙古大学图书馆

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Suman Chahar Kawaljit Kaur Kuldeep Singh Kaswan Jagjit Singh Dhatterwal University Institute of Computing Chandigarh University Mohali India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority of a single individual or party. Thus, the introduction of artificial intelligence (AI) in blockchain governing systems complicates such thing as transparency and trust, especially in the case of deep AI systems. But more on this later – this is where Explainable AI or XAI comes into the picture. XAI methods enable understanding of the reasons behind the choices made by AI models and solves the "black box" problem that is predominant in machine learn- ing. When applied to decentralised governance, XAI provides clarifications of AI’s decision making in such areas as voting, resource distribution and the enactment of smart contracts. This way, the stakeholders are confident that the decision-making is fair and impartial and this is made possible by the use of XAI. This paper aims to find out more about the applicability of XAI concept in blockchain –based governance systems in terms of bias and fairness in automated decisions and explanation of the decisions made. The reasons why interpretability plays the crucial role in decentralized systems are explained with the help of the comparison between the classical artificial intelligence and the Xin Intelligent. The study also looks at various use cases such as, Decentralized Autonomous Organizations (DAOs), and voting systems implemented on blockchain platforms and how XAI can enhance the performance of the governance systems. As this study shows, XAI is vital in building trust, improving responsibility, and guaranteeing balanced majorities in decentralized blockchain governance structures.

关键词： Explainable AI Decentralized autonomous organization Computational modeling Smart contracts Decision making Blockchains Resource management Stakeholders Security Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

引用

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

来源：评论

学校读者我要写书评

暂无评论

Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

引用

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

DG-YOLOT: A Lightweight Density Guided YOLO-Transformer for Remote Sensing Object Detection

DG-YOLOT: A Lightweight Density Guided YOLO-Transformer for ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nandini Saini Chiranjoy Chattopadhyay Debasis Das Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

Deep learning-based object detection methods in natural image datasets have demonstrated remarkable accuracy and lower error rates than those of humans. As a result, they have gained significant attention in the field of remote sensing imagery. However, direct transferablity of these methods in remote sensing images face challenges such as scale variations, complex object distributions, and arbitrary orientations. In order to address these challenges, we propose the transformer-based object detector named as DG-YOLOT where we use a guided self-attention mechanism with YOLOv5 to enhance the potentiality of training with minimum computation. Instead of employing uniform size patches like the conventional vision transformer, we leverage density map patches which facilitates the extraction of diverse contextual information related to objects within the image, enhancing the differentiation capability of our model. Through extensive experiments conducted on the DOTAv2.0 dataset, our proposed model has demonstrated superior performance with 57.91% mean Average Precision (mAP) compared to other state-of-the-art object detectors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recommender Systems Based on Tensor Decomposition

引用

Computers, Materials & Continua 2021年第1期66卷 621-630页

作者： Zhoubao Sun Xiaodong Zhang Haoyuan Li Yan Xiao Haifeng Guo School of Information Engineering Nanjing Audit UniversityNanjing211815China School of Computing National University of Singapore117417Singapore Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Recommender system is an effective tool to solve the problems of information *** traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and *** social networks contain abundant social information,such as tags,places and *** show that the social information has a great impact on recommendation *** not only describe the characteristics of items,but also reflect the interests and characteristics of *** the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender *** original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between *** on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

关键词： Recommender system social information tensor decomposition tag

来源：评论

学校读者我要写书评

暂无评论

Type-Representation Enhanced Event Detection via Structure Encoder

Type-Representation Enhanced Event Detection via Structure E...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligent Grazing Strategy of Grassland Via Optimization of Multiple Phases

Intelligent Grazing Strategy of Grassland Via Optimization o...

引用

IEEE International Conference on Big data Analysis (ICBDA)

作者： Limin Liu Jing Wang Runxiu Zhang Guangwen Liu Zhiwei Xu College of Data Science and Application Inner Mongolia University of Technology Hohhot China Chinese Academy of Sciences Institute of Computing Technology Beijing China

In the process of grazing on grassland, the number of grazing needs to consider the income of pastoralists and the affordability of pastures to ensure the healthy development of the ecological environment while maximizing economic benefits. To better help pastoralists graze rationally, this paper proposes an intelligent selection mechanism for grazing on grassland strategies via optimization of multiple phases. From the perspective of mechanism analysis, based on relevant grassland data, the main factors affecting grazing strategies are analyzed, and a multistage optimized intelligent selection model of grazing strategies is established. Finally, an example of the selection of reasonable grazing strategies in the Xilin Gol Grassland under different precipitation levels is given. Experiments have proved that this method can effectively solve the maximum number of sheep grazing, determine appropriate grazing strategies, and provide a basis for sustainable grazing while considering the main factors affecting grazing.

关键词： Economics Analytical models Precipitation Chemistry Biological system modeling Soil moisture Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Prediction of Loan Approval in Banks Using Machine Learning Algorithm

Prediction of Loan Approval in Banks Using Machine Learning ...

引用

Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy P A Manoharan J. Anvar Shathik A. Pandiaraj N. Ramshankar Naveen G Department of Computing Technologies SRM Institute of Science and Technology Chennai Chennai Institute of Technology Chennai AIML and Data science Anjuman Institute of Technology and Management Karnataka India Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Normally, the banking sector gains profit from different kinds of sources, but the main factor in their profitable growth is giving loans to their customers. A bank's profit or loss is determined by the quantity of the loan. A huge number of people are taking loans from banks and not clearing their dues. So there is a tedious process for banks to identify them and collect them. This in turn leads to following up on a complex verification process and issuing loans to their customers. Customer satisfaction is mandatory in every field, but in the banking sector, it is essential to identify genuine applicants and satisfy their needs, as well as to keep bankers free from non-payable customers. The manual method of identifying the right person and providing a loan to them may cause misconceptions. Several studies in this field suggest various kinds of approaches to identifying applicant status. Therefore, the use of machine learning (ML) approaches in the banking sector to resolve difficulties faced by bankers is becoming more popular. This reduces the problems faced by bankers and makes their applicants satisfactory. The time taken for this process is too long. It is essential to select the best ML approach from the different kinds of classifier algorithms available. The attributes chosen for this study include the applicant's education, type of work, applicant income, loan amount, co-applicant income, credit history, and property area. The proposed study reviews ML approaches such as K-nearest Neighboring (KNN), Logistic Regression, Decision Tree and Support vector Mechanism (SVM). Among those, KNN outperforms the other three algorithms, and the efficiency obtained by KNN is 87.50%, Logistic Regression is 83.512%, Decision Tree is 77.09%, and Random forest (RF) is 74.49%.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Banking Nearest neighbor methods Vectors Sustainable development Recommender systems Testing

来源：评论

学校读者我要写书评

暂无评论

Displacement-Sparse Neural Optimal Transport

arXiv

引用

arXiv 2025年

作者： Chen, Peter Xie, Yue Zhang, Qingpeng Department of Mathematics Columbia University New York United States HKU Musketeers Foundation Institute of Data Science Hong Kong HKU Shanghai Intelligent Computing Research Center Shanghai China

Optimal Transport (OT) theory seeks to determine the map T : X → Y that transports a source measure P to a target measure Q, minimizing the cost c(x, T(x)) between x and its image T(x). Building upon the Input Convex Neural Network OT solver (Makkuva et al., 2020;Amos et al., 2017) and incorporating the concept of displacement-sparse maps (Cuturi et al., 2023), we introduce a sparsity penalty into the minimax Wasserstein formulation, promote sparsity in displacement vectors ∆(x):= T(x) − x, and enhance the interpretability of the resulting map. However, increasing sparsity often reduces feasibility, causing T#(P) to deviate more significantly from the target measure. In low-dimensional settings, we propose a heuristic framework to balance the tradeoff between sparsity and feasibility by dynamically adjusting the sparsity intensity parameter during training. For high-dimensional settings, we directly constrain the dimensionality of displacement vectors by enforcing dim(∆(x)) ≤ l, where l d. Among maps satisfying this constraint, we aim to identify the most feasible one. This goal can be effectively achieved by adapting our low-dimensional heuristic framework without resorting to dimensionality reduction. We validate our method on both synthesized sc-RNA and real 4i cell perturbation datasets, demonstrating improvements over existing methods. © 2025, CC BY.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Empirical Study of Query Performance Across GPU DBMSes

A Comprehensive Empirical Study of Query Performance Across ...

引用

作者： Suh, Young-Kyoon An, Junyoung Tak, Byungchul Na, Gap-Joo School of Computer Science and Engineering Department of Data Convergence Computing Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of Artifcial Intelligence Research Laboratory Electronics and Telecommunications Research Institute Korea Republic of

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. Also, little has been known about what the potential factors could be that affect the query processing jobs within the GPU DBMSes. To fill this gap, we have conducted a study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. We have also established a set of hypotheses drawn from the model that explained the performance characteristics. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained empirical data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems should resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization. © 2022 ACM.

关键词： Graphics processing unit

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：