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About 10,000 papers for “algorithm comparison”

Comparison of Tarry’s Algorithm and Awerbuch’s Algorithm

2008

Abstract — Tarry’s algorithm and Awerbuch’s algorithm are two important algorithms for traversing connected networks. This paper compares the two algorithms by doing experiments on them. The experiments measure their time complexity and message complexity while varying the number of nodes in the network and the density of the network. The results shows that Awerbuch’s algorithm is more effective than Tarry’s algorithm in time complexity, and Tarry’s algorithm is more effective than Awerbuch’s algorithm in message complexity. Index Terms—Tarry’s algorithm, Awerbuch’s algorithm, time complexity, message complexity.

Cited by 0

Comparison of algorithm performance.

Xiao Zhou (122661), Ledan Qian (18405498), Haider Aziz (18405501) et al. · Figshare · 2024

<div><p>The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.</p></div>

Cited by 0Open access

A process comparison algorithm /

Nayestani, Naynaz · eScholarship@McGill (McGill) · 2002

0-612-88378-7 L'auteur a accord une licence non exclusive permettant la Bibliothque nationale du Canada de reproduire, prter, distribuer ou vendre des copies de cette thse sous la forme de microfiche/film, de reproduction sur papier ou sur format lectronique.L'auteur conserve la proprit du droit d'auteur qui protge cette thse.Ni la thse ni des extraits substantiels de celle-ci ne doivent tre imprims ou aturement reproduits sans son autorisation.

Cited by 0Open access

Multi-algorithm comparison figure.

Dawei Wang (471687), Bo Gao (106513), Lei Zhang (38117) · Figshare · 2025

<div> In this study, the optimization of construction machinery scheduling within roadbed construction projects is explored, taking into account both personnel fatigue and sequence-dependent setup times. A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. This algorithm reduces the number of iterations required for optimization and, subsequently, cuts down on energy consumption. Through rigorous analysis and comparison with existing algorithms, the proposed IHWGWO demonstrates a significant reduction in both iteration count and financial expenditure. Simulation outcomes confirm the accuracy and practicality of the model and algorithm, establishing a promising new approach for scheduling in construction engineering. </div>

Cited by 0Open access

Algorithm effect comparison chart.

Zhongbo Peng (16884034), Lumeng Wang (16884037), Liang Tong (455446) et al. · Figshare · 2023

&lt;div&gt;&lt;p&gt;Waterway transportation is a crucial mode of transportation, but ensuring navigational safety in waterways requires effective guidance of ships by the Water Resources Bureau. However, supervisors may only be interested in the ship portion of a complex image and need to quickly obtain relevant ship information. Therefore, this paper proposes a two-dimensional OTSU inland ships multi-threshold image segmentation algorithm based on the improved genetic algorithm. The improved algorithm enhances search accuracy and efficiency, improving image thresholding accuracy and reducing algorithm time complexity. Experimental verification shows the algorithm has excellent evaluation indexes and can achieve real-time segmentation of complex images. This method can not only address the challenges of complex inland navigation environments and difficult acquisition of target data sets, but also be applied to optimization problems in other fields by combining various metaheuristic algorithms.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

MSE routing algorithm comparison

Leonard E. Miller, R.H. French, J.S. Lee et al. · 2003

The US Army's Mobile Subscriber Equipment (MSE) communications system uses a flood search algorithm for routing calls. This procedure automatically finds alternate paths to the call destination during the search and requires only a directory of local subscribers to be maintained at each node. Recently, it was suggested that two other routing methods, restricted flood search and hybrid routing, would reduce call delay while retaining some of the alternate routing features of the flood search. In the present work the three algorithms are compared in terms of the MSE backbone switching network's connectivity, throughput, grade of service, and routing delay. These measures are computed as functions of node and link reliabilities to observe the network performance under possible outage conditions in the battlefield environment. It is concluded that the flood search gives the most reliable performance in all aspects under network outages.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 8

Algorithm comparison - top performances.

Pritam Chakraborty (9261302), Anjan Bandyopadhyay (19541099), Sricheta Parui (22049880) et al. · Figshare · 2025

<div> Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing models were found. Afterward, ensemble machine learning methods were used to find the most accurate predictions using the top 5 models ranked by shapely value. The research demonstrates an impressive accuracy of 92.39%, surpassing other proposed models’ performance. This study highlights the utility of combining game theory and machine learning in Ischemic stroke prediction and the potential of ensemble learning methods to increase predictive accuracy in ischemic stroke analysis. </div>

Cited by 0Open access

Machine Learning Algorithm comparison.

Brad Busse (395752), Stephen Smith (117894) · Figshare · 2015

&lt;p&gt;Comparison of various supervised machine learning algorithms. A small training set was used to compare the error rates of multiple MLAs when classifying glutamatergic and GABAergic synapses in an early data set. From left to right: Linear Discriminant Analysis (LDA); Quadratic Discriminant Analysis (QDA); Naive Bayesian filter, gaussian distribution assumption (NB); Naive Bayesian filter, normalized kernel distribution assumption (NBkd); Random Forest Ensemble (RFE); k-means clustering (kNN); Support Vector Machine (SVN). k-means clustering, an unsupervised clustering method, was included for comparison's sake.&lt;/p&gt;

Cited by 0Open access

Comparison of Algorithm accuracy.

Ning Lin (477803), Yue Zuo (19065650) · Figshare · 2024

&lt;div&gt;&lt;p&gt;Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method’s adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Search algorithm performance comparison.

Zhaohui Du (414105), Xiaofeng Zhao (413698), Lin Li (28817) et al. · Figshare · 2025

<div> In recent years, empowered by artificial intelligence technologies, computer-assisted language learning systems have gradually become a hot topic of research. Currently, the mainstream pronunciation assessment models rely on advanced speech recognition technology, converting speech into phoneme sequences, and then determining mispronounced phonemes through sequence comparison. To optimize the phoneme recognition task in pronunciation evaluation, this paper proposes a Chinese pronunciation phoneme recognition model based on the improved Zipformer-RNN-T(Pruned) architecture, aiming to improve recognition accuracy and reduce parameter count. First, the AISHELL1-PHONEME and ST-CMDS-PHONEME datasets for Mandarin phoneme recognition through data preprocessing. Then, three layers of the Zipformer Block architecture are introduced into the Zipformer encoder to significantly enhance model performance. In the stateless Pred Network, the GELU activation function is adopted to effectively prevent neuron deactivation. Furthermore, a hybrid Pruned RNN-T/CTC Loss fusion strategy is proposed, further optimizing recognition performance. The experimental results demonstrate that the method performs excellently in the phoneme recognition task, achieving a Word Error Rate (WER) of 1.92% (Dev) and 2.12% (Test) on the AISHELL1-PHONEME dataset, and 4.28% (Dev) and 4.51% (Test) on the ST-CMDS-PHONEME dataset. Moreover, the model requires only 61.1M parameters, striking a balance between performance and efficiency. </div>

Cited by 0Open access

Algorithm comparison test results.

Hang Ren (1312905), Ping Lan (652758) · Figshare · 2025

<div> Traditional fall detection methods face significant limitations in complex environments, particularly under occlusion and poor lighting conditions. To address these challenges and enhance the detection accuracy in intelligent real-time monitoring systems, this study proposes an optimized BMR-YOLO framework based on the YOLOv8n architecture. Our approach introduces four strategic improvements to effectively overcome environmental constraints. First, we enhance the backbone network by incorporating a BiFormer vision transformer with dual-layer routing attention, enabling dynamic allocation of computational resources, while improving both computational efficiency and feature extraction performance. Next, we replace the C2f module in the backbone with C2f_rvb, enhancing the model’s ability to handle multi-scale features while reducing computational requirements. Furthermore, we strengthen the detection head by adding the MultiSEAM attention mechanism, significantly improving the detection accuracy of occluded objects. Finally, we optimize the regression process by replacing the traditional CIoU loss with a direction-aware SIoU metric, thereby improving both the localization accuracy and training stability of bounding box regression. To validate our method, we constructed a comprehensive dataset, BMR-fall, containing over 10,000 annotated images that capture various fall scenarios, and performed cross-validation using the UR fall detection dataset. Experimental results demonstrate that BMR-YOLO achieves a notable improvement in mAP@0.5, rising from 0.852 to 0.899 on our proprietary dataset, while maintaining a low computational cost of 6.5 GFLOPs. Comparative analysis with existing methods shows that BMR-YOLO outperforms them under occlusion and lighting variation conditions, confirming the model’s robustness and practical applicability for real-world deployment. </div>

Cited by 0Open access