2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献_第2页

Real Time Bangla Number Plate Recognition using Computer Vision and Convolutional Neural Network 基于计算机视觉和卷积神经网络的孟加拉车牌实时识别

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257843

Md. Naimul Islam Suvon, R. Khan, Mehebuba Ferdous

{"title":"Real Time Bangla Number Plate Recognition using Computer Vision and Convolutional Neural Network","authors":"Md. Naimul Islam Suvon, R. Khan, Mehebuba Ferdous","doi":"10.1109/IICAIET49801.2020.9257843","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257843","url":null,"abstract":"Automatic number plate identification in today's world plays a vital role in vehicle tracking and organization. Our proposed model of automation in the detection and recognizing vehicles through the use of number plate computerization is expected to create a new scope of evolution for large cities. The system can be used for the parking system of motor vehicles, as well as to collect tolls. The detection of the Bangla number plates from different cities and multi-class vehicles is the first step of the proposed system. The number plate detection has been performed with the computer vision approach, and the You Only Look Once v3 (YOLOv3) algorithm. Next, the Tesseract optical character recognition system, in conjunction with the Bangla character recognition model, has been used for vehicle indexing and convolutional neural network for the character recognition from the detected number plate. Numerical results demonstrate that the accuracies of license plate detection for the computer vision and YOLOv3 are 91% and 95%, respectively. For the character recognition, the accuracy for Tesseract and convolutional neural network are 90% if the license plate is detected and cropped successfully and 91.38%, respectively. Finally, our system has been tested using the convolutional neural network method in an environment of real-world where our system's Pi Camera captured video as input, which has a total of 18 different cars. From 18 cars, it has successfully detected 17 cars, which makes our overall system accuracy 88.89%.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125928203","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: 8

Development of Exercise Promotion System Using Smartphone and AI Speaker 基于智能手机和AI音箱的运动促进系统开发

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257840

Ryota Arai, M. Ohsuga

引用次数: 1

Wideband Conformal Polymeric Based Antenna for Microwave Medical Imaging System 微波医学成像系统宽带共形聚合物天线

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257849

M. Limbah, H. Lago, P. Soh, K. Teo

引用次数: 0

Experimenting Deep Convolutional Visual Feature Learning using Compositional Subspace Representation and Fashion-MNIST 基于组合子空间表示和Fashion-MNIST的深度卷积视觉特征学习实验

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257819

M. Teow

{"title":"Experimenting Deep Convolutional Visual Feature Learning using Compositional Subspace Representation and Fashion-MNIST","authors":"M. Teow","doi":"10.1109/IICAIET49801.2020.9257819","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257819","url":null,"abstract":"This paper introduces a formal framework to model the convolutional visual feature learning in a convolutional neural network, which is called compositional subspace representation. The objective is to explain the convolutional visual feature learning computation using a rigid and structural method. The theoretical basis of the proposed framework is, the best way for representation to model a complex learning function is by using a composition of simple two-dimensional piecewise-linear functions to form a multilayers successive cascaded projection function for complex representation. Under the same hypothesis, the proposed framework also explains the hierarchical feature learning representation in a convolutional neural network, the well-acknowledged significant advantage of convolutional neural networks in visual computing. The proposed framework has experimented with image classification using the Fashion-MNIST dataset. Experimental assessments using learning curves analysis, confusion matrix, and visual assessment are presented and discussed. The experimental results were consistent with the theoretical expectation.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114661591","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

Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications 基于潜在Dirichlet分配的神经网络用于新闻网页分类

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257842

Adel R. Alharbi, Shwaa D. Alharbi, Amer Aljaedi, Oluwatobi Akanbi

{"title":"Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications","authors":"Adel R. Alharbi, Shwaa D. Alharbi, Amer Aljaedi, Oluwatobi Akanbi","doi":"10.1109/IICAIET49801.2020.9257842","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257842","url":null,"abstract":"Any popular news website in our modern life, offering details to millions of users every day. Although computer technology continues to grow, the number of disease data is rising. How to structure the document to enable data recognition dynamically has become one of the main challenges for sophisticated web services. Traditional systematic classification of news text requires not only a lot of human and financial assets but it also hardly accomplishes fast classification function. In this work, we introduce a new method relying on both the Latent Dirichlet Allocation and the Neural Networks that are used in the Arabic document classification. Our approach adopts the Vector Space Model to interpret documents in applications for the text classification. In this process, the text is represented as a term vector; n-grams. These methods can not distinguish semantic or textual content; this results in considerable space for features and semantic losses. In this research, the new proposal utilizes a “topics” sampled as text characteristics by the Latent Dirichlet Allocation method. Effectively it eliminates the problems described. We have extracted important themes (topics) of all the texts. Each theme is identified by a different descriptor distribution, and then each text is depicted on the vectors of certain themes. Our experiments indicate that the proposed solution is capable of achieving high efficiency with an accuracy rate of 85.11% for the Arabic text classification task.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129569203","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: 2

Performance Analysis of Implemented MFCC and HMM-based Speech Recognition System 实现的MFCC和基于hmm的语音识别系统性能分析

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257823

Marlyn Maseri, Mazlina Mamat

引用次数: 1

National and International Advisory Board 国家和国际咨询委员会

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/iicaiet49801.2020.9257855

M. Faizal, Ahmad Fauzi, Abdullah Gani, Yong Jin Park, Shigeru Omatsu, Aslina Baharum, Bablu K. Ghosh, Norbayah Mohd Suki, Yana Mazwin Mohmad Hassim, Y. Leau

引用次数: 0

Improvement of ACTT that is Autonomous Driving Technologies Support Platform for Education and Research 自动驾驶技术教育与科研支撑平台ACTT的改进

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257820

Naohiro Nakamoto, Hiroyuki Kobayashi

引用次数: 0

Fuzzy Logic Control of Exothermic Batch Process 放热间歇过程的模糊逻辑控制

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257832

Wan Ying Chai, M. K. Tan, K. Teo, H. Tham

引用次数: 1

Metaheuristic Multi-Hop Clustering Optimization for Energy-Efficient Wireless Sensor Network 节能无线传感器网络的元启发式多跳聚类优化

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257871

Vincent Chung, N. Tuah, Kit Guan Lim, M. K. Tan, I. Saad, K. Teo

{"title":"Metaheuristic Multi-Hop Clustering Optimization for Energy-Efficient Wireless Sensor Network","authors":"Vincent Chung, N. Tuah, Kit Guan Lim, M. K. Tan, I. Saad, K. Teo","doi":"10.1109/IICAIET49801.2020.9257871","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257871","url":null,"abstract":"Energy-efficient optimization algorithm in wireless sensor network (WSN) is often based on solely cluster routing or multi-hop routing. The cluster optimization algorithm will form a cluster network by dividing the sensor nodes into few clusters where each cluster has a cluster head (CH) for data collection. On the other hand, multi-hop optimization algorithm will form a multi-hop network by transmitting data to base station (BS) through data multi-hopping between sensor nodes. However, cluster optimization algorithm suffers from the overburdens of CH nodes, while multi-hop optimization algorithm suffers from the overburdens of nodes which are near to the BS. Therefore, Genetic Algorithm-Cuckoo Search (GACS) is proposed and developed based on the multi-hop clustering model in this paper. GACS optimizes both intra-cluster and inter-cluster communications to enhance energy efficiency in WSN, extending the network lifetime. Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150009","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: 2