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检索条件"主题词=Partitioning Algorithms"

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A Comparative Analysis of Louvain and Leiden Coloring algorithms in Influencer Detection

A Comparative Analysis of Louvain and Leiden Coloring Algori...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Handrizal Poltak Sihombing Erna Budhiarti Nababan Mohammad Andri Budiman Doctoral Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

In today's digital era, the influence of social media influencers has grown significantly. A commonly used feature by business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, highlighting the need for a more sophisticated approach to influencer detection. This study proposes a novel method for influencer detection that integrates the Leiden coloring algorithm and Degree centrality. This approach leverages network analysis to identify patterns and relationships within large-scale datasets. First, the Leiden coloring algorithm is employed to partition the network into distinct communities, identifying potential influencer clusters. Subsequently, Degree centrality is utilized to identify nodes within these communities exhibiting high connectivity, indicating individuals with significant influence. The proposed method was validated using data crawled from Twitter (X) social media, employing the keyword "GarudaIndonesia." The data was collected using Tweet-Harvest between January 1, 2020, and October 16, 2024, resulting in a dataset of 22,623 rows. The proposed method was compared to the Louvain coloring method, demonstrating an increase in the modularity value of the Leiden coloring algorithm by 0.0195, a reduction in processing time by 13.93 seconds, and a decrease in the number of communities by 649.

关键词： Technological innovation Social networking (online) Blogs Clustering algorithms Network analyzers Feature extraction partitioning algorithms Business

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Advanced Graph-MLPs Distillation based on Global and Local Hyperbolic Geometry Learning

Advanced Graph-MLPs Distillation based on Global and Local H...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yao Guan Wenzhu Yan Ting Yuan Yanmeng Li Nanjing Normal University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have drawn great research attention for graph machine learning. However, graph learning techniques are extremely difficult for practical deployments in the industry applications owing to the scalability challenges incurred by data dependency. Although some works attempt to establish the known efficient MLPs with GNNs based on knowledge distillation (KD), they are primarily designed for graphs in Euclidean spaces, which can not provide the most powerful geometry for graph representation as numerous real- world graphs display a combination of Euclidean and Hyperbolic geometry. To achieve comprehensive expression for complex graph data with high efficiency, in this paper, we proposed a novel Advanced Graph-MLPs Distillation Framework (AGMDF) based on global and local hyperbolic geometry learning. The key point of our method is to fully exploit the complex graph with additional hyperbolic properties based on knowledge distillation. Specifically, the global cross-geometric knowledge fusion can exploit the compensation information learned from Euclidean view and Hyperbolic domain. Then, the local hyperbolic knowledge enhancement can employ prominent tree-likeness components among graph data to improve the graph representation ability. Thus, the distilled MLP model enjoys the high expressive ability of graph context-awareness based on global and local hyperbolic geometry learning. Extensive experiments show that AGMDF achieves competitive accuracy with GNNs and improves over stand-alone MLPs by 21.84% on average while inferring faster than GNNs across five benchmark datasets.

关键词： Geometry Manifolds Scalability Industry applications Signal processing algorithms Machine learning Signal processing Graph neural networks partitioning algorithms Speech processing

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Machine Learning Model for Predicting Non-Performing Agricultural Loans in an Imbalanced Lending Environment

Machine Learning Model for Predicting Non-Performing Agricul...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Mohamed Ahmed Elnaggar Mostafa Abd El Azeem Fahima A. Maghraby Computer Science Dept. College of Computing & Information Technology Arab Academy for Science Technology and Maritime Transport (AASTMT) Cairo Egypt

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Granting a variety of agricultural loans to clients is necessary to support both markets and the agricultural sector. There are many types of risks that affect the banking sector. The most common and important one is the credit risk. The prediction of agricultural credit risk (ACR) has become more important for credit-granting institutions. ACR models seek to predict rather the clients will default on a loan (bad applicant) or non-default (good applicant). Predicting agricultural credit is thus an important part of the decision-making process to consider and to approve loans by the bank. In this paper, we propose a None Performing Agricultural Loans model (NPALSM). We focus only on the binary classifications whether the loan will be default or not by using machine learning classification techniques to build a proactive model to deal with credit approval. Proposed NPALSM consists of oversampling technique to balance data distribution. Empirical results show that the use of methods of resampling the amount of data consistently improves the performance of the original imbalanced data and outperforms other existing base methods using Egyptian credit data from an agricultural bank in Egypt. The meta classifier extra tree algorithm produces the highest accuracy with, 0.97% after sampling.

关键词： Support vector machines Accuracy Computational modeling Training data Predictive models Prediction algorithms Data models Classification algorithms partitioning algorithms Random forests

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Design and testing of Hybrid Slime Mold Algorithm for Real-Time Adaptive Mobile Robot Path Planning in Dynamic Environments

Design and testing of Hybrid Slime Mold Algorithm for Real-T...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Sachin Gupta Shivali Gupta Shaik Mohammed Raith Anand Kumar Salil Bharany Vishnu Kant Ch.Sandeep Department of Robotics and Control Engineering School of Electronics and Control Engineering Lovely Professional University Phagwara India Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Dynamic path planning and collision prevention is one of the key tasks for autonomous vehicles in relation to dynamic movement of obstacles. In this paper, a novel hybrid approach based on integration of Slime Mold Algorithm (SMA), Triangle Inequality Principle (TIP), and Partition Method Strategy (PMS) for solution of this problem statement. The SMA algorithm is very flexible and adaptive by nature due to its biological working mechanism which is associated with TIP and PMS strategies in relation to their optimization and efficient path planning capabilities and robust solutions. This integrated module is tested in virtual environment and the results are compared with other updated algorithms such as A*, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the same static and dynamic environment. It has been observed from the results that this hybrid strategy significantly improves the path planning task several times than other algorithms in terms of multiple aspects. Under testing conditions, the path length was reduced to 25% than other algorithms in a dynamic environment. During this calculation, the computational efficiency of the system has been improved to 60% than conventional methods. The chances of collision have been improved to 34.23% especially in environment where the obstacles are moving. The capability of SMA has been improved due to the synergy between TIP and PMS. The dynamic and adaptive nature of this algorithm is suitable for various applications such as search-and-rescue operations, manufacturing units’ automation and other hazardous environment exploration.

关键词： Navigation Heuristic algorithms Virtual environments Path planning partitioning algorithms Safety Mobile robots Vehicle dynamics Testing Genetic algorithms

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Partitioned Successive-Cancellation List Flip Decoding of Polar Codes

Partitioned Successive-Cancellation List Flip Decoding of Po...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： Charles Pillet Ilshat Sagitov Grégoire Domer Pascal Giard Department of Electrical Engineering École de technologie supérieure Montréal Québec Canada Department of Electrical Engineering Enseirb-Matmeca Bordeaux INP Bordeaux France

ISBN: (数字)9798350373752

ISBN: (纸本)9798350373769

The recently proposed Successive-Cancellation List Flip (SCLF) decoding algorithm for polar codes improves the error-correcting performance of state-of-the-art SC List (SCL) decoding. However, it comes at the cost of a higher complexity. In this paper, we propose the Partitioned SCLF (PSCLF) decoding algorithm, an algorithm that divides a word in partitions and applies SCLF decoding to each partition separately. Compared to SCLF, PSCLF allows early termination but is more susceptible to cyclic-redundancy check (CRC) collisions. In order to maximize the coding gain, a new partition design tailored to PSCLF is proposed as well as the possibility to support different CRC lengths. Numerical results show that the proposed PSCLF algorithm has an error-correction performance gain of up to 0.15 dB with respect to SCLF. Moreover, the proposed CRC structure permits to mitigate the error-correction loss at low frame-error rate (FER) due to CRC collisions, showing a gain of 0.2 dB at a FER of 10– 4 with respect to the regular CRC structure. The average execution time of PSCLF is shown to be 1.5 times lower than that of SCLF, and matches the latency of SCL at FER = 4.10 −3 and lower.

关键词： Measurement Codes Signal processing algorithms Signal processing Performance gain Encoding Decoding partitioning algorithms Polar codes Gain

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Hypergraphic representation for adaptive quantum circuits

Hypergraphic representation for adaptive quantum circuits

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Waldemir Cambiucci Regina Melo Silveira Wilson Vicente Ruggiero Dept. of Computing Engineering and Digital Systems University of São Paulo São Paulo Brasil

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Adaptive quantum circuits enhance flexibility and efficiency over traditional static circuits by dynamically adjusting their structure and parameters in real-time based on intermediate measurement outcomes. This paper introduces a novel hypergraph representation for adaptive quantum circuits, where groups of gates are considered as participants of hyperedges. By incorporating these gate groups into hyperedges, we create an extended hypergraph that includes constraints usable during the partitioning process. This approach guides the partitioning to maintain groups of ports associated with classical operations, ensuring that the resulting partitions prioritize qubits involved in the same sections of classical operations inherent to the adaptive approach. We present a new hypergraph partitioning algorithm based as an extension of Fiduccia-Mattheyses heuristic, to support hypergraphs created from adaptive quantum circuits. Comparative analysis between static and adaptive methods demonstrates the effectiveness of the proposed hypergraph techniques for adaptive circuits. Experimental results using benchmark quantum circuits validate our theoretical insights, showing improvements in circuit representation for partitioning heuristics. These findings highlight the practical benefits of hypergraph representation in adaptive quantum computing.

关键词： Adaptive systems Atmospheric measurements Heuristic algorithms Qubit Logic gates Benchmark testing Particle measurements Real-time systems partitioning algorithms Quantum circuit

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Dedicated Lane Planning for Autonomous Truck Fleets under Hours of Service Regulations

Dedicated Lane Planning for Autonomous Truck Fleets under Ho...

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IEEE Symposium on Intelligent Vehicle

作者： Zhaoming Zeng Xiaotong Sun Qi Luo Department of Civil and Environmental Engineering University of Michigan Ann Arbor MI USA Intelligent Transportation Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Dept. of Business Analytics Tippie College of Business University of Iowa Iowa City IA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

The introduction of automated trucks into the ground freight sector will yield significant transformations, such as the alleviation of labor shortages, the reduction of energy consumption, and the improvement of road safety. Given the current mixed-traffic environment where autonomous vehicles share roads with their human-driven counterparts, support from intelligent transportation infrastructure becomes imperative for ensuring the safe and efficient operations of autonomous trucks. This study proposes an integrated framework for designing dedicated lanes for automated trucks (DLAT) operating under Hours of Service (HOS) Regulations for total cost minimization. This framework considers the truck fleets’ routing and scheduling behavioral changes brought by the implementation of DLAT. We formulate a mixed-integer program (MIP) model and propose an origin-destination(OD)-clustering-based iterative algorithm to tackle it. The algorithm contains two parts: the spectral clustering method for OD pairs partitions and DLAT design within each cluster, and the iterative algorithm addressing the dedicated lanes design problem on overlapping links across clusters. Three numerical experiments on the U.S. freight highway network and algorithm demonstrate the effectiveness of time-varying planning of DLAT, corroborating its benefits to stakeholders and providing managerial insights for future implementations.

关键词： Costs Clustering methods Clustering algorithms Transportation Regulation Planning partitioning algorithms

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Optimizing Privacy and Efficiency in Brain Tumor Classification through Advanced Non-IID Federated Deep Learning

Optimizing Privacy and Efficiency in Brain Tumor Classificat...

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Information and Communication Technology (ICICT), International Conference on

作者： Md Samsuzzaman Rakibul Hasan Sezan Arjon Golder Md. Abdul Masud Golam Md. Muradul Bashir Faculty of Computer Sc. and Engr. Patuakhali Sc. and Tech. University Patuakhali Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Recent advancements in Artificial Intelligence (AI) have significantly transformed various fields, especially medical diagnostics. However, centralized deep learning models face significant challenges due to the increasing data volumes from edge devices, including issues related to data privacy and heterogeneity. In this study, a federated deep learning model for brain tumor classification using MRI data is presented. The model enhances the MobileNetV3 architecture with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) blocks. A novel non-IID (non-Independent and Identically Distributed) data partitioning strategy, proposed in this study, ensures robust data distribution across clients. Utilizing the Flower framework and the FedOpt algorithm, the model achieved a notable accuracy of 97.40% in eight training rounds, operating efficiently on 15GB of RAM and 10GB of GPU memory. This research underscores the potential of Federated Learning (FL) to address data privacy and resource constraints, demonstrating its efficacy in accurate brain tumor classification. These findings pave the way for future scalable and secure medical AI applications.

关键词： Deep learning Data privacy Accuracy Random access memory Brain tumors Brain modeling Data models partitioning algorithms Classification algorithms Artificial intelligence

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Efficient Hardware Platform Architecture for High-Precision Stereo Vision Processing

Efficient Hardware Platform Architecture for High-Precision ...

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IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Yu Yu Yucheng Jiang Xiaohan Zhu Songping Mai Shenzhen International Graduate School Tsinghua University

ISBN: (数字)9798331530723

ISBN: (纸本)9798331530730

To achieve high-precision and efficient endoscopic measurement, this study introduces a highly parallel hardware architecture dedicated to the implementation of binocular stereo matching algorithms, aiming to enhance real-time performance while ensuring accuracy. The system employs a combination of the CENSUS and local-expansion (LE) algorithms for precise disparity extraction. Through pipeline design and optimized memory partitioning, we have enabled efficient parallel operation of various system modules. Experimental results demonstrate that the pipeline implementation increases the processing speed by $\mathbf{1 0 7. 3 \%}$ compared to the serial implementation. On the medical dataset SERV-CT, our algorithm exhibits the highest accuracy. Furthermore, the universality of this hardware architecture allows it to adapt to the implementation of various traditional algorithms, thereby enhancing the system’s applicability and scalability.

关键词： Integrated circuit technology Accuracy Scalability Pipelines Memory management Cameras Hardware Real-time systems partitioning algorithms Stereo vision

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Improved Reinforcement Learning Path Planning Algorithm

Improved Reinforcement Learning Path Planning Algorithm

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Intelligent Systems (ETIS), Emerging Technologies for

作者： S. Anu Santhiya N. Janavee B. Yazhini K. Subramanian Department of ECE National Engineering College (Affiliated by Anna university) Kovilpatti India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

The topic on coverage path planning (CPP) for multiple cooperative mobile robots using a “partition and cover” approach with Voronoi partitioning to prevent task duplication. By incorporating geodesic-distance-based and Manhattan-distance-based Voronoi generalizations, partition continuity is ensured even in the presence of obstacles, avoiding coverage gaps. The area is divided into $\mathbf{2}\mathbf{D} \ast \mathbf{2}\mathbf{D}$ grids, where D represents the robot's footprint size. The proposed partitioning method eliminates coverage gaps and overlaps, as demonstrated by two single-robot CPP techniques: Boustrophedon decomposition and Spanning Tree coverage. Validation is done through simulations and experiments.

关键词： Base stations Navigation Robot kinematics Reinforcement learning Path planning Land vehicles partitioning algorithms Mobile robots Vehicle dynamics Intelligent systems

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