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检索条件"主题词=Partitioning Algorithms"

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Tourist Spot Recommendation by Clustering Using an SDP-Based Max-Cut

Tourist Spot Recommendation by Clustering Using an SDP-Based...

引用

International Conference on Knowledge and Smart Technology (KST)

作者： Yuichi Tanigawa Virach Sornlertlamvanich Thatsanee Charoenporn Graduate School of Data Science Musashino University Tokyo Japan Faculty of Data Science Musashino University Tokyo Japan Faculty of Engineering Thammasat University Pathum Pathum Thani Thailand

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Collaborative filtering, a widely used recommended algorithm, prioritizes the generation of highly similar item lists. However, this approach specializes in aggregating similar items, which poses challenges in ensuring diversity, user autonomy, and explainability. Based on the opposite idea of conventional recommendation methods, we propose a clustering-based recommendation approach that intentionally separates highly similar items to ensure diversity. A Bag-of-Words corpus was constructed by collecting 600 English reviews from 60 major tourist attractions in Kyoto and applying noise reduction techniques. Using the Latent Dirichlet Allocation Method (LDA), each tourist spot was transformed into a 15-dimensional vector and a similarity graph was created based on cosine similarity. Max-Cut was then used to maximize dissimilarity between clusters by placing nodes with higher edge weights in different partitions to ensure diverse group formation. Experimental results demonstrate that the SDP-based Max-Cut method successfully generates clusters with high variance and outperforms comparable methods using traditional k-means clustering in terms of intra-cluster diversity. In addition, we analyze the impact of partition granularity on the recommended system and investigate its applicability to a wider range of areas beyond the tourism sector.

关键词： Technological innovation Reviews Diversity reception Refining Clustering algorithms Vectors partitioning algorithms Resource management Recommender systems Optimization

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Chronic Kidney Disease Prediction Using Machine Learning and Deep Learning Models

Chronic Kidney Disease Prediction Using Machine Learning and...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Shaik Rafi Nuti Revanth K Veera Raghava Reddy K Mahesh Babu Y Likhith Prasanna Kumar N Vijay Kumar Dept of CSE Narasaraopeta Engineering College Narasaraopet Palnadu Andhra Pradesh India Dept of CSE Narasaraopeta Engineering College Narasaraopet Andhra Pradesh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Chronic kidney disease is a noticeable health condition that can persist throughout an individual’s life, resulting from either kidney malignancy or diminished kidney function. In this work, we investigate how several machine learning techniques might provide an early CKD diagnosis. While previous research has extensively explored this area, our aim is to refine our approach by employing predictive modeling techniques. Initially, we considered 25 variables alongside the class property. The data set used in this study underwent extensive processing, including changing the names of colours for clarity, converting identified colours to numbers, treating unique values with letters handling of partitioned values, fixing incorrect values, filling null values with mean, and encoding categorical values into mathematical notation. In addition, Principal component analysis (PCA) was also employed to lower dimensionality. Our findings demonstrated that the XG Boost classifier surpassed e very other algorithm, with an accuracy of 0.991.

关键词： Technological innovation Image color analysis Null value Predictive models Prediction algorithms Chronic kidney disease Mathematical models partitioning algorithms Random forests Principal component analysis

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Dynamic Space Partition Algorithm with an Archimedean Spiral for Wildfire Detection Using a Swarm of UAVs

Dynamic Space Partition Algorithm with an Archimedean Spiral...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Yinan Shi Georgios Tzoumas Sabine Hauert Bristol Robotics Laboratory University of Bristol Bristol UK

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Due to climate change in recent years, wildfires have become one of the most harmful hazards to the environment and society. In firefighting operations, the early stages are crucial to controlling wildfires successfully. In this paper, we propose an improvement to an existing dynamic space partition (DSP) algorithm by adding an Archimedean spiral to enable wildfire detection in large areas on the scale of California. Compared to the baseline DSP controller, the improved algorithm provides more efficient area coverage with the same number of robots in the simulation. With a swarm of 30 robots, the DSP algorithm with an Archimedean spiral (DSP-A) can identify 87.81% static fires. With the same configuration, the baseline DSP algorithm covered 79.77% of total fires. Furthermore, the DSP-A controller is resilient when the number of robots decreases. When the number of robots in the swarm drops from 30 to 10, the DSP-A algorithm can still cover 70% of wildfires, while the performance of the baseline DSP controller is reduced to 44%.

关键词： Wildfires Heuristic algorithms Prevention and mitigation Aerospace electronics Hazards partitioning algorithms Particle swarm optimization Robots Monitoring Climate change Autonomous aerial vehicles

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FedLCA: Synchronous Layer-Wise Compensated Aggregation for Straggler Mitigation

FedLCA: Synchronous Layer-Wise Compensated Aggregation for S...

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IEEE Conference on Wireless Communications and Networking

作者： Jiale Wang Nan Li Xinghan Wang Tingting Yang Dalian Maritime University Dalian China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we introduce a layer-wise compensated aggregation algorithm designed for federated learning in dynamic environments. To reduce data transmission and maintain model continuity, our method implements a hierarchical update strategy that avoids artificially modular partition of neural networks and thus more flexible in practice. Specifically, weight layers closer to the output, which significantly impact model performance, receive direct updates. Conversely, layers further from the output with minimal influence, are updated using gradient compensation from the previous iteration. To ensure consistency across the model, we compensate these less influential weight layers with residual values from earlier rounds, thus enhancing integration and continuity. Experimental results from two datasets confirm that our algorithm not only secures stable convergence but also matches the generalization performance of traditional federated averaging, even under conditions with dropout rates as high as 90%.

关键词： Training Performance evaluation Machine learning algorithms Federated learning Heuristic algorithms Prevention and mitigation Artificial neural networks partitioning algorithms Time-varying systems Convergence

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Enumerating the K-Fold Configurations in Multi-Class Classification Problems

Enumerating the K-Fold Configurations in Multi-Class Classif...

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Conference on Data Science and Machine Learning Applications (CDMA)

作者： Attila Fazekas György Kovács University of Debrecen Debrecen Hungary Analitycal Minds Ltd. Beregsurány Hungary

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

K-fold cross-validation is a widely used tool for assessing classifier performance. The reproducibility crisis [6] faced by artificial intelligence partly results from the irreproducibility of reported k-fold cross-validation-based performance scores. Recently, we introduced numerical techniques [10] to test the consistency of claimed performance scores and experimental setups. In a crucial use case, the method relies on the combinatorial enumeration of all k-fold configurations, for which we proposed an algorithm in the binary classification case. In this paper, the algorithm is generalized to multi-class classification.

关键词： Machine learning algorithms Machine learning Data science Reproducibility of results partitioning algorithms Classification algorithms Multiaccess communication

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Efficient Policy Iteration and Value Iteration via State Space Clustering

Efficient Policy Iteration and Value Iteration via State Spa...

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IEEE Southeastcon

作者： Avinash Paidi Pabitra Mitra Department of Artificial Intelligence Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This paper presents a novel state-space clustering technique specifically designed for Reinforcement Learning (RL) problems with discrete state and action spaces. Building on this clustering methodology, we propose a Hierarchical Reinforcement Learning (HRL) framework to improve the efficiency of policy iteration (PI) and value iteration (VI) algorithms. Our approach partitions the state space into well-defined, homogeneous clusters and identifies representative prototype states for each cluster. Transitioning between clusters is facilitated through macro-actions, which are derived using the Depth First Search (DFS) algorithm. By focusing value function updates exclusively on these prototype states during PI and VI operations, the proposed method substantially reduces computational overhead without compromising performance. Experimental evaluations demonstrate that the proposed technique generates highly cohesive clusters, enhances policy learning quality, and accelerates the convergence of the PI and VI algorithms.

关键词： Buildings Clustering algorithms Prototypes Focusing Reinforcement learning partitioning algorithms Convergence

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Online Music Recommendation Algorithm Based on Heterogeneous Neural Network

Online Music Recommendation Algorithm Based on Heterogeneous...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Bo Li Chao Dai Qinghai Normal University Xining City China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Traditional recommendation algorithms mainly rely on historical interaction information between users and music projects, but ignore the collaborative signals in user interactions, and are limited by the sparsity of interaction data. To solve these problems, this paper combines heterogeneous graph neural networks and designs the HNMRA algorithm by splitting the graph structure, decomposing the complex network into multiple focused on specific relationships subgraphs. By using the user's explicit feedback, such as user comments, we need to calculate preference weights. On each subgraph, the GNN learns the user and music attributes and behavior preference representations by using the attention mechanism to fuse information from different subgraphs, generating more precise user and music embedding representations. Based on these embedding representations, the top-K recommendation is performed, and the K most likely interesting music is recommended to the user. The advantage of the algorithm is that it can handle multidimensional relationships simultaneously, improve the recommendation accuracy by using explicit feedback, and achieve personalized preference modeling. To solve the challenges of computational complexity and data sparsity, this paper proposes optimizing the GNN structure design, adopting efficient graph partitioning and parallel processing technologies, and combining implicit feedback, such as playback times, to enhance model robustness. Experimental results show that the proposed research not only improves the comprehensiveness and accuracy of recommendations, but also provides personalized recommendation results, which has high research value and application potential.

关键词： Accuracy Computational modeling Scalability Parallel processing Graph neural networks User experience Robustness partitioning algorithms Computational complexity Recommender systems

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A Parameter Selection Strategy for the Generalized Fuzzy-Possibilistic C-Means Algorithm

A Parameter Selection Strategy for the Generalized Fuzzy-Pos...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Mirtill-Boglárka Naghi Levente Kovács László Szilágyi Doctoral School of Applied Mathematics and Applied Informatics Óbuda University Budapest Hungary Comput. Intell. Research Group Sapientia Univ. Tg. Mureş Romania Physiological Controls Research Center Óbuda University Budapest Hungary Computational Intelligence Research Group Sapientia University Tg. Mureş Romania

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The generalized fuzzy-possibilistic $c$ -means algorithm (GFPCM) was proposed as a correction of the so-called fuzzy-possibilistic $c$ -means, (FPCM) that failed to adapt its possibilistic term to the number of clustered data. GPFCM added an extra parameter to the objective function of FPCM that set up a tradeoff between the strength of the probabilistic and possibilistic term. However, a formula for the appropriate value of this extra parameter $\beta$ was not given at its introduction. This paper aims to establish a parameter selection technique that would ensure fine partitioning by the use of the GFPCM algorithm.

关键词： Systematics Noise Clustering algorithms Approximation algorithms Probabilistic logic Linear programming partitioning algorithms Noise measurement Informatics Machine intelligence

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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation partitioning algorithms Maintenance Convergence Genetic operators

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Design and Research of AI Accelerators for Onboard Computing Platforms Targeting YOLOs Network

Design and Research of AI Accelerators for Onboard Computing...

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International Conference on Computer Research and Development (ICCRD)

作者： Yunlong Wang Hui Zhou Xia Liu Bing Li Yifan Ge Beijing Aerospace Automatic Control Institute Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

Convolutional Neural Networks (CNNs) play a crucial role in computer vision, with wide applications in image recognition, object tracking, and trajectory optimization. Due to constraints in computation, power consumption, and size in aerospace vehicles, deploying CNN algorithms on target platforms presents a significant challenge in aerospace applications. This paper proposes an accelerator design for the YOLOv3-tiny network tailored to aerospace applications. By employing techniques such as loop partitioning, loop unrolling, and weight sharing, the proposed accelerator enhances computational performance and data reuse efficiency. Additionally, by integrating convolution, activation, and pooling operations into a unified module, this approach reduces the need for intermediate buffers, saving memory and improving acceleration performance. Compared to accelerators that require buffering of convolution results, the proposed design demonstrates superior performance. The accelerator is implemented on the ZYNQ7020 FPGA platform, achieving a peak computational performance of 114.53 GOPS.

关键词： Convolution Computational efficiency partitioning algorithms Timing Convolutional neural networks Field programmable gate arrays Trajectory optimization Research and development Pipeline processing Radio spectrum management

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