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ML assisted enhanced recommendation system for reliable volunteering environment

Procedia Computer science 2024年 235卷 22-31页

作者： Prabakaran N Meenakshi K J. Kumarnath Rajasekaran P School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 India Department of Mathematics Presidency University Bengaluru 560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul 624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu 603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created.

关键词： Decision Tree ID3 C4.5 CART Volunteers NGO

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Intelligent Cutter State Detection Platform based on Edge-Enhanced FCN 5

Intelligent Cutter State Detection Platform based on Edge-En...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, Pan Zheng, Liaomo Lian, Mengjia School of Information Science and Engineering Shenyang Ligong University Shenyang110159 China Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang110168 China School of Mathematics and Information Engineering Longyan University Longyan361000 China

ISBN: (纸本)9798350357950

In the manufacturing industry, machining is a crucial process, and tool wear is often a significant concern. During high-speed machining, tool breakage and excessive wear can lead to workpiece rejection and even damage to the tool holder and machine tool. Therefore, in-depth research on the detection, assessment, and prevention of tool wear is of paramount importance to the manufacturing industry. Tool wear manifests in various forms, including chipping, wear on the rake face, built-up edge, and edge wear. By developing efficient tool wear detection techniques, manufacturers can rapidly and accurately assess the quality and performance of tools from different batches and manufacturers, thereby enhancing product quality and production efficiency. Detection methods based on image segmentation can intuitively and efficiently batch detect tool conditions, providing operators with a visual interface to reduce their workload, improve production efficiency and safety, and lower production costs. © 2023 IEEE.

关键词： Edge Attention Mechanism Image Segmentation Tool Wear Detection

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

A Heterogeneous Network-based Contrastive Learning Approach ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen Huazhong Agricultural University College of Informatics Hubei Wuhan China Charles Sturt University School of Computing Mathematics & Engineering Wagga WaggaNSW Australia Charles Sturt University Artificial Intelligence & Cyber Futures Institute BathurstNSW Australia University of Mines and Technology Faculty of Engineering Tarkwa Ghana Xinyu University School of Mathematics and Computer Science China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. © 2024 IEEE.

关键词： Contrastive Learning

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FINITE ELEMENT SPACES BY WHITNEY k-FORMS ON CUBICAL MESHES

arXiv

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arXiv 2024年

作者： Zhang, Shuo LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Finite element spaces by Whitney k-forms on cubical meshes in Rn are presented. Based on the spaces, compatible discretizations to HΛk problems are provided, and discrete de Rham complexes and commutative diagrams ar... 详细信息

关键词： Mesh generation

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms 25

TensorMD: Molecular Dynamics Simulation with Ab Initio Accur...

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Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

作者： Yucheng Ouyang Ying Liu Honghui Shang Zhenchuan Chen Jiahao Shan Huimin Cui Xiaobing Feng Xin Chen Xingyu Gao Lifang Wang Haifeng Song Rongfen Lin Fang Li Institute of Computing Technology Chinese Academy of Sciences Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

ISBN: (纸本)9798400714436

Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. This inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. This paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.

关键词： GPU

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Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs

arXiv

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arXiv 2025年

作者： Zhang, Juan Luo, Junyue Zhang, Fangfang Yue, Xiaoqiang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper explores the application of kernel learning methods for parameter prediction and evaluation in the Algebraic Multigrid Method (AMG), focusing on several Partial Differential Equation (PDE) problems. AMG is an efficient iterative solver for large-scale sparse linear systems, particularly those derived from elliptic and parabolic PDE discretizations. However, its performance heavily relies on numerous parameters, which are often set empirically and are highly sensitive to AMG’s effectiveness. Traditional parameter optimization methods are either computationally expensive or lack theoretical support. To address this, we propose a Gaussian Process Regression (GPR)-based strategy to optimize AMG parameters and introduce evaluation metrics to assess their effectiveness. Trained on small-scale datasets, GPR predicts nearly optimal parameters, bypassing the time-consuming parameter sweeping process. We also use kernel learning techniques to build a kernel function library and determine the optimal kernel function through linear combination, enhancing prediction accuracy. In numerical experiments, we tested typical PDEs such as the constant-coefficient Poisson equation, variable-coefficient Poisson equation, diffusion equation, and Helmholtz equation. Results show that GPR-predicted parameters match grid search results in iteration counts while significantly reducing computational time. A comprehensive analysis using metrics like mean squared error, prediction interval coverage, and Bayesian information criterion confirms GPR’s efficiency and reliability. These findings validate GPR’s effectiveness in AMG parameter optimization and provide theoretical support for AMG’s practical application. © 2025, CC BY.

关键词： Poisson equation

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Distributed quantum architecture search

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Physical Review A 2024年第2期110卷 022403-022403页

作者： Haozhen Situ Zhimin He Shenggen Zheng Lvzhou Li College of Mathematics and Informatics School of Electronic and Information Engineering Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China Institute of Quantum Computing and Software School of Computer Science and Engineering

Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures. In this study, we propose an end-to-end distributed quantum architecture search framework, where we aim to automatically design distributed quantum circuit structures for interconnected quantum processing units with specific qubit connectivity. We devise a circuit generation algorithm which incorporates TeleGate and TeleData methods to enable nonlocal gate implementation across quantum processing units. While taking into account qubit connectivity, we also incorporate qubit assignment from logical to physical qubits within our quantum architecture search framework. A two-stage progressive training-free strategy is employed to evaluate extensive circuit structures without circuit training costs. Through numerical experiments on three VQE tasks, the efficacy and efficiency of our scheme is demonstrated. Our research into discovering efficient structures for distributed quantum circuits is crucial for near-term quantum computing where a single quantum processing unit has a limited number of qubits. Distributed quantum circuits allow for breaking down complex computations into manageable parts that can be processed across multiple quantum processing units.

关键词： Machine learning Quantum circuits

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αSetup-PCTL:An Adaptive Setup-Based Two-Level Preconditioner for Sequence of Linear Systems of Three-Temperature Energy Equations

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Communications in computational Physics 2022年第10期32卷 1287-1309页

作者： Silu Huang Xiaoqiang Yue Xiaowen Xu Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100088China National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105China

The iterative solution of the sequence of linear systems arising from threetemperature(3-T)energy equations is an essential component in the numerical simulation of radiative hydrodynamic(RHD)***,due to the complicated application features of the RHD problems,solving 3-T linear systems with classical preconditioned iterative techniques is *** address this difficulty,a physicalvariable based coarsening two-level(PCTL)preconditioner has been proposed by dividing the fully coupled system into four individual easier-to-solve *** its nearly optimal complexity and robustness,the PCTL algorithm suffers from poor efficiency because of the overhead associatedwith the construction of setup phase and the solution of ***,the PCTL algorithm employs a fixed strategy for solving the sequence of 3-T linear systems,which completely ignores the dynamically and slowly changing features of these linear *** address these problems and to efficiently solve the sequence of 3-T linear systems,we propose an adaptive two-level preconditioner based on the PCTL algorithm,referred to as α*** adaptive strategies of the αSetup-PCTL algorithm are inspired by those of αSetup-AMG algorithm,which is an adaptive-setup-based AMG solver for sequence of sparse linear *** proposed αSetup-PCTL algorithm could adaptively employ the appropriate strategies for each linear system,and thus increase the overall *** results demonstrate that,for 36 linear systems,the αSetup-PCTL algorithm achieves an average speedup of 2.2,and a maximum speedup of 4.2 when compared to the PCTL algorithm.

关键词： Sequence of linear systems sparse linear solver preconditioning methods radiation hydrodynamics simulation

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Giant retardation effect in electron-electron interaction

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Physical Review A 2022年第3期105卷 L030801-L030801页

作者： Zhimin Hu Gang Xiong Zhencen He Zhihao Yang Naoki Numadate Chengwu Huang Jiamin Yang Ke Yao Baoren Wei Yaming Zou Chensheng Wu Yulong Ma Yong Wu Xiang Gao Nobuyuki Nakamura Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064 China Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China Laser Fusion Research Center China Academy of Engineering Physics Mianyang 621900 China Institute for Laser Science The University of Electro-Communications Tokyo 182-8585 Japan Institute for Applied Physics and Computational Mathematics Beijing 100088 China Beijing Computational Science Research Center Beijing 1000193 China

In electron-electron interactions in electromagnetic systems, retardation in the exchange of a virtual photon is essentially important as the first-order quantum electrodynamics correction. However, the retardation effect is generally so small that it is buried in unretarded electric and magnetic interactions and thus has yet to be directly probed. Here, we present a giant contribution of the retardation effect in an electron-electron interaction via observing strong electric-dipole-allowed radiative transition rates. The relative transition rates are obtained for two dominant radiative transitions from the 1s2s22p1/22p3/2 inner-shell excited state of boronlike tungsten and bismuth ions to 1s22s22p1/2 and 1s22s22p3/2, and it was found that the transition rate ratio between the two transitions is affected by the retardation effect up to more than 100%.

关键词： Electronic transitions Relativistic & quantum electrodynamic effects in atoms, molecules,& ions

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