Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems最新文献

Genetic algorithm for Traveling Salesman Problem 旅行商问题的遗传算法

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581798

Haojie Xu, Yisu Ge, Guodao Zhang

引用次数: 1

The Spatial Topological Shape of the Rough Surface is Simulated and Generated by a New Gaussian Filtering Algorithm 采用一种新的高斯滤波算法模拟并生成了粗糙表面的空间拓扑形状

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581797

Jianan Zhang, Min Yang, Can Zhao, Cheng-Wu Liu

引用次数: 0

Research on the Design of Shared Intelligent Pet Management Cage in the Context of Mobile Internet 移动互联网环境下共享智能宠物管理笼的设计研究

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581806

Xiaolong Hu, Ruiting Zhang, Zefan Zhang

引用次数: 0

Research on Object Tracking Technology Based on Region Proposal Siamese Network 基于区域提议暹罗网络的目标跟踪技术研究

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581793

Zhe Gu, Yanjing Lei, Di Cao, Lue Zhan

引用次数: 0

Route Optimization for Sailing Vessels using Artificial Intelligence Techniques 基于人工智能技术的帆船航线优化

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581803

John M. Anderson, S. Sithungu, E. M. Ehlers

{"title":"Route Optimization for Sailing Vessels using Artificial Intelligence Techniques","authors":"John M. Anderson, S. Sithungu, E. M. Ehlers","doi":"10.1145/3581792.3581803","DOIUrl":"https://doi.org/10.1145/3581792.3581803","url":null,"abstract":"Sailing is not a new concept. The use of artificial intelligence (AI) within the field of sailing however is a relatively new concept. AI is being applied to a variety of different aspects within the field of sailing such as AI plotting unmanned vessels across the globe as well as in the optimization of racing vessels in a variety of prestigious regatta. This paper aims to explore the application of AI to the route creation and plotting aspect of AI in sailing, the objective being to create a system that efficiently assists sailors with their route creation within a complex environment. The approach used is through the creation of a model that uses an adapted A* (star) search algorithm where the weightings of the heuristic values used better represent the costs associated with the complex environment the algorithm operates in. The environment devised in this paper is a combination of geographical and meteorological data and coordinates to create a semi-dynamic environment in which the system operates. The resulting system successfully creates optimized routes within the semi-dynamic environment devised by the system.","PeriodicalId":436413,"journal":{"name":"Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121697173","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

A Gene Expression Programming Inspired Evolution Symbiont Agent for Real-Time Strategy Generation 基于基因表达编程的实时策略生成进化共生代理

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581801

S. Sithungu, E. M. Ehlers

{"title":"A Gene Expression Programming Inspired Evolution Symbiont Agent for Real-Time Strategy Generation","authors":"S. Sithungu, E. M. Ehlers","doi":"10.1145/3581792.3581801","DOIUrl":"https://doi.org/10.1145/3581792.3581801","url":null,"abstract":"AdaptiveSGA is a method for achieving Adaptive Game Artificial Intelligence-Based Dynamic Difficulty Balancing through the Symbiotic Game Agent Model. Previous work has shown that AdaptiveSGA can achieve Dynamic Difficulty Balancing in simulated soccer by effectively changing a team's strategy based on the opponent's performance. AdaptiveSGA pre-existing strategies and switches between them during runtime to increase the game's replayability by adapting the challenge it poses to the human player. Although this method works, its limitation is that if the human player surpasses the most intelligent strategy of the computer opponent, there is no way for the model to generate a new strategy during runtime that can potentially overcome the human player. AdaptiveSGA can only maintain engagement with the human player if the human player has not overcome the best strategy for the pool of pre-existing strategies. Current work addresses this limitation by introducing an Evolution Symbiont Agent whose purpose is to generate new strategies in real-time (during gameplay) through evolutionary mechanisms using Gene Expression Programming. Experimental results show that the presence of the evolution symbiont agent can use Gene Expression Programming to generate strategies capable of outperforming an opposing strategy.","PeriodicalId":436413,"journal":{"name":"Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133318519","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

Traffic Incident Detection System Based on Video Analysis 基于视频分析的交通事件检测系统

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581795

Zhe Gu, Yanjing Lei, Sixian Chan, Di Cao, Kongkai Zhang

引用次数: 0

Motorcycle Helmet Detection and Usage Classification in the Philippines using YOLOv5 Algorithm 基于YOLOv5算法的菲律宾摩托车头盔检测与使用分类

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581796

J. P. Tomas, B. Doma

{"title":"Motorcycle Helmet Detection and Usage Classification in the Philippines using YOLOv5 Algorithm","authors":"J. P. Tomas, B. Doma","doi":"10.1145/3581792.3581796","DOIUrl":"https://doi.org/10.1145/3581792.3581796","url":null,"abstract":"Motorcycles are becoming the primary option for mobility worldwide, and the number of motorcycle riders has been exponentially increasing over the years. In the Philippines, it is reported that the increase in registered motorcycles was greater than the increase in total registered vehicles. However, motorcycles are notorious as one of the most dangerous and fatal modes of transportation. Hence, it is heavily enforced that motorcycle riders wear the proper motorcycle helmets that meet the safety standards of motorcycle riding. The study introduced a YOLOv5 algorithm-based motorcycle rider detection and helmet usage classification model. The study utilized two pieces of footage captured by the researchers in Makati City. The footage underwent frame segmentation and preprocessing before being loaded into the model for training. The results of the model showed a desirable performance in the detection and classification capabilities of the trained model. The optimal hyperparameter values were also found using the babysitting method for model validation. It is recommended that future studies ensure consistency in data samples to eliminate bias in any class of the model. The study also recommends using smaller increments for tuning the hyperparameter values to further investigate the effects of increasing and decreasing hyperparameter values and utilize separate models for the detection and classification tasks.","PeriodicalId":436413,"journal":{"name":"Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116360863","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

Classification of Flood Disaster Risks with the Use of Gradient Boosting Algorithm 基于梯度增强算法的洪水灾害风险分类

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581799

J. P. Tomas, Gabriela Andes, Razmin Bernadette Ellazar, Ayesha Keith Santos

引用次数: 0

Single Node Acceleration of Generative Adversarial Networks using HPC for Image Analytics 基于HPC的图像分析生成对抗网络的单节点加速

Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems Pub Date : 2022-11-04 DOI: 10.1145/3581792.3581802

A. Ravikumar, H. Sriraman

{"title":"Single Node Acceleration of Generative Adversarial Networks using HPC for Image Analytics","authors":"A. Ravikumar, H. Sriraman","doi":"10.1145/3581792.3581802","DOIUrl":"https://doi.org/10.1145/3581792.3581802","url":null,"abstract":"Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic Processing Unit and Tensor Processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central Processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic Processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.","PeriodicalId":436413,"journal":{"name":"Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122205431","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