Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence最新文献_第2页

A Novel Approach to Low Light Object Detection Using Exclusively Dark Images 一种基于纯暗图像的低光目标检测新方法

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533064

Ankit Kumar, Bijal Talati, Mihir Rajput, H. Trivedi

{"title":"A Novel Approach to Low Light Object Detection Using Exclusively Dark Images","authors":"Ankit Kumar, Bijal Talati, Mihir Rajput, H. Trivedi","doi":"10.1145/3533050.3533064","DOIUrl":"https://doi.org/10.1145/3533050.3533064","url":null,"abstract":"The efficiency of our vision highly depends on the light’s intensity. In dark images, the intensity of light in our surroundings is generally lower, reducing the efficiency of vision and the capability to distinguish different objects. An analysis of lowlight images is possible with handcrafted and learned features. This process of object recognition also needs to take into consideration the intensity of light that is produced by a particular pixel varies depending on the color space used for a particular image since different colors produce different intensities of light. Therefore, the exclusively dark dataset has been used recently as a benchmark dataset for object recognition in the dark that contains 10 low light illumination types and 12 different categories of objects, and it has the potential to be used as the standard database for benchmarking research in the domain of low light. CSPNet is essential for the purpose of feature extraction. This reduces the computational load required by our model and also ensures that the accuracy does not significantly reduce. When it is coupled with the CNN, the results show potential for practical applications. The goal of this paper is to further improve the recognition rate of various objects in the dark.","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127624883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Coevolutionary Algorithm for Evolving Competitive Strategies in the Weapon Target Assignment Problem 武器目标分配问题中演化竞争策略的协同进化算法

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533052

E. Elfeky, Madeleine Cochrane, L. Marsh, S. Elsayed, B. Sims, Simon Crase, D. Essam, R. Sarker

引用次数: 1

Application of Hybrid PSO and SQP Algorithm in Optimization of the Retardance of Citrate Coated Ferrofluids 混合粒子群算法与SQP算法在柠檬酸盐包覆铁磁流体缓速优化中的应用

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533060

Jing-Fung Lin, J. Sheu

{"title":"Application of Hybrid PSO and SQP Algorithm in Optimization of the Retardance of Citrate Coated Ferrofluids","authors":"Jing-Fung Lin, J. Sheu","doi":"10.1145/3533050.3533060","DOIUrl":"https://doi.org/10.1145/3533050.3533060","url":null,"abstract":"The citrate (citric acid, CA) coated ferrofluids with great magneto-optical retardance can meet the high magnetic responsive demand, especially in widely potential biomedical applications such as hyperthermia and magnetic resonance imaging. In this study, the measured retardances are based on the Taguchi method with nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The retardance obtained from the double centrifugation test is also included. Three optimization algorithms including the particle swarm optimization (PSO), the sequential quadratic programming (SQP), and a hybrid PSO-SQP algorithm are executed to obtain high retardance. The comparisons are made among the retardance results obtained from these algorithms. Seven start points chosen from the orthogonal test are input into the SQP, the PSO is applied to the stepwise regression equation, and while executing the hybrid PSO-SQP algorithm, the parametric combination obtained by the PSO is adopted as the start point in the SQP simulation. The global optimum retardance and the corresponding parameter values are effectively assured by the global search ability of the PSO and the local search ability of the SQP.","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115971671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the Effects of Ant Algorithms on Path Planning with Gain-Ant Colony Optimization 理解蚁群算法对增益-蚁群优化路径规划的影响

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533058

V. Sangeetha, R. Krishankumar, K. S. Ravichandran, A. Gandomi

引用次数: 0

Static Polynomial Approximation Using Set-based Particle Swarm Optimisation 基于集合的粒子群优化静态多项式逼近

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533061

Donovan Edeling, A. Engelbrecht

引用次数: 0

A New Discrete Whale Optimization Algorithm with a Spiral 3-opt Local Search for Solving the Traveling Salesperson Problem 一种新的带有螺旋3-选择局部搜索的离散鲸鱼优化算法求解旅行销售人员问题

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533056

Elias Rotondo, S. Heber

引用次数: 0

Evolutionary Algorithm for Solving Supervised Classification Problems: An Experimental Study 解决监督分类问题的进化算法:实验研究

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533054

Daniel Soto, Wilson Soto

引用次数: 0

AxDFM:Position Prediction System Based on the Importance of High-Order Features 基于高阶特征重要性的位置预测系统

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2022-04-09 DOI: 10.1145/3533050.3533065

Chang Su, Haoxiang Feng, Xianzhong Xie

{"title":"AxDFM:Position Prediction System Based on the Importance of High-Order Features","authors":"Chang Su, Haoxiang Feng, Xianzhong Xie","doi":"10.1145/3533050.3533065","DOIUrl":"https://doi.org/10.1145/3533050.3533065","url":null,"abstract":"The exploration and combination of high-level features is crucial for many machine learning tasks. At the same time, we cannot ignore the different importance of high-level features. In traditional machine learning predictive models, analyzing and combining the original data and manually making these features will undoubtedly increase the complexity and cost of the system. The emergence of factorization machines can use the vector product to represent the interaction of features, and automatically learn features The combination of to get high-order feature interactions not only reduces the complexity of the system, but also increases the diversity of high-order features. We refer to the depth factorization machine (xDeepFM) to generate high-level feature interactions at the display mode and vector level, and The importance of different features is dynamically learned through the squeeze-incentive (SENET) mechanism, and different weights are used for interaction.Then, use the attention mechanism to extract the importance of the obtained high-order features and assign weights, and finally get the prediction classification through the fully connected layer. We further summarized these methods into a unified model, and named the model the Advanced Attention Depth Factorization Machine (AxDFM).","PeriodicalId":109214,"journal":{"name":"Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131410217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence 2022年第六届智能系统、元启发式和群体智能国际会议论文集

Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 1900-01-01 DOI: 10.1145/3533050

引用次数: 0