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检索条件"主题词=Partitioning Algorithms"

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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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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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Comparative Analysis of Outlier Detection algorithms in Agricultural IoT Datasets

Comparative Analysis of Outlier Detection Algorithms in Agri...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Ahmed Amamou Bilal Ben Mahria Younes Balboul Said Hraoui Computer Science Department National School of Applied Sciences Fes Morocco Computer Science Department Faculty of Science and Technology Fes Morocco Network and Telecommunications Department National School of Applied Sciences Fes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This paper evaluates the efficiency of eleven outlier detection algorithms applied to three agricultural IoT datasets: Weather in Szeged, Greenhouse, and Crop Recommendation. It focuses on how data quality affects IoT systems' reliability in agriculture. The analyzed algorithms include statistical, distance-based, density-based, ensemble, and hybrid methods, and their ability to detect outliers is investigated using recall, precision, and F1-score. The results demonstrate that algorithm performance is influenced by dataset characteristics such as dimensionality, nonlinearity, and temporal dependencies. Isolation Forest (iForest) consistently outperformed other algorithms due to its non-parametric design and recursive partitioning. In contrast, algorithms like Cluster-Based Local Outlier Factor (CBLOF), K-Nearest Neighbors (KNN), and Gaussian Mixture Model (GMM) showed variable effectiveness depending on dataset features. The study emphasizes the need for selecting the appropriate outlier detection method tailored to specific agricultural datasets and recommends future research on hybrid models or deep learning approaches to enhance anomaly detection in agricultural IoT systems.

关键词： Deep learning Data integrity Clustering algorithms Forestry Nearest neighbor methods partitioning algorithms Internet of Things Anomaly detection Gaussian mixture model Meteorology

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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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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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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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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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Word Break on SLP-Compressed Texts

Word Break on SLP-Compressed Texts

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Data Compression Conference (DCC)

作者： Rajat De Dominik Kempa Department of Computer Science Stony Brook University Stony Brook NY USA

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Word Break is a prototypical factorization problem in string processing: Given a word $w$ of length $N$ and a dictionary $\mathcal{D}=\{d_{1},\ d_{2},\ \ldots,\ d_{K}\}$ of $K$ strings, determine whether we can partition $w$ into words from $\mathcal{D}$ . We propose the first algorithm that solves the Word Break problem over the SLP-compressed input text $w$ . Specifically, we show that, given the string $w$ represented using an SLP of size $g$ we can solve the Word Break problem in $\mathcal{O}(g\cdot m^{\omega}+M)$ time, where $m=\max_{i=1}^{K}\vert d_{i}\vert M=\sum_{i=1}^{K}\vert d_{i}\vert$ , and $\omega\geq 2$ is the matrix multiplication exponent. We obtain our algorithm as a simple corollary of a more general result: We show that in $\mathcal{O}(g\cdot m^{\omega}+M)$ time, we can index the input text $w$ so that solving the Word Break problem for any of its substrings takes $\mathcal{O}(m^{2}\log N)$ time (independent of the substring length). Our second contribution is a lower bound: We prove that, unless the Combinatorial $k$ -Clique Conjecture fails, there is no combinatorial algorithm for Word Break on SLP-compressed strings running in $\mathcal{O}(g\cdot m^{2-\epsilon}+M)$ time for any $\epsilon > 0$ .

关键词： Lower bound Dictionaries Data compression partitioning algorithms Indexes

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