2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)最新文献_第4页

GSA based PID controller for Load Frequency Control of Multi-Area Hybrid Power System 基于GSA的多区域混合电力系统负载频率PID控制

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574081

Ajay Kumar, D. Gupta, S. R. Ghatak

引用次数: 1

Image Processing Techniques for Chest Radiography Enhancements and Pneumonia Detection 胸片增强和肺炎检测的图像处理技术

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574077

Dibyajyoti Jena, Natasha Pradhan

引用次数: 0

Bangla-German Language Translation Using GRU Neural Networks 使用GRU神经网络的孟加拉语-德语翻译

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574076

Zerin Jahan, Kazi Fahim Lateef, Joy Paul

{"title":"Bangla-German Language Translation Using GRU Neural Networks","authors":"Zerin Jahan, Kazi Fahim Lateef, Joy Paul","doi":"10.1109/ICETCI51973.2021.9574076","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574076","url":null,"abstract":"Machine translation relates to highly autonomous software which is capable of translating source sentences into different languages. Previously some work was done in this sector where the result was comparatively low. Most of the researchers worked on common languages and none of them gave satisfactory Bilingual Evaluation Understudy (BLEU) score. Depending on these factors, we build a system of Bangla-German translator. This system can be used in various areas (i.e. reliable interpreters, business conduction, e-commerce merchandising, etc.). The system is built based on Gated Recurrent Unit (GRU) which is a gating mechanism of Recurrent Neural Network (RNN). Here, total five types of different RNN algorithms were used like Simple RNN, RNN with Embedding, Encoder-Decoder RNN, Bidirectional RNN, Hybrid RNN. All of them gave good accuracy. But the best result we got from the Hybrid model which was the combination of Embedded and Bidirectional algorithm. The accuracy was 85.69%. For further evaluation, BLEU score was used. The result of BLEU score of unigram to four gram was respectively increasing from 54.40% to 85.88%. Also the comparison between machine translated sentences and Google translated sentences showed that the system works very efficiently.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127172011","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

AVHYAS: A Free and Open Source QGIS Plugin for Advanced Hyperspectral Image Analysis AVHYAS:一个免费和开源的QGIS插件，用于高级高光谱图像分析

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-06-24 DOI: 10.1109/ICETCI51973.2021.9574057

R. Lyngdoh, Anand S. Sahadevan, Touseef Ahmad, P. Rathore, Manoj K. Mishra, P. Gupta, A. Misra

引用次数: 8

Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems 基于体素的实时嵌入式系统三维目标检测方法效率分析

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-05-21 DOI: 10.1109/ICETCI51973.2021.9574075

Illia Oleksiienko, A. Iosifidis

{"title":"Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems","authors":"Illia Oleksiienko, A. Iosifidis","doi":"10.1109/ICETCI51973.2021.9574075","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574075","url":null,"abstract":"Real-time detection of objects in the 3D scene is one of the tasks an autonomous agent needs to perform for understanding its surroundings. While recent Deep Learning-based solutions achieve satisfactory performance, their high computational cost renders their application in real-life settings in which computations need to be performed on embedded platforms intractable. In this paper, we analyze the efficiency of two popular voxel-based 3D object detection methods providing a good compromise between high performance and speed based on two aspects, their ability to detect objects located at large distances from the agent and their ability to operate in real time on embedded platforms equipped with high-performance GPUs. Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances. Moreover, models trained on near objects achieve similar or better performance compared to those trained on all objects in the scene. This means that the models learn object appearance representations mostly from near objects. Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection. This means that the methods can achieve a speed-up of 40–60% by restricting operation to near objects while not sacrificing much in performance.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116164938","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