2020 International Conference on Advanced Information Technologies (ICAIT)最新文献_第2页

Grape Leaf Diseases Classification using Convolutional Neural Network 基于卷积神经网络的葡萄叶片病害分类

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261801

Khaing Zin Thet, Khine Khine Htwe, M. Thein

{"title":"Grape Leaf Diseases Classification using Convolutional Neural Network","authors":"Khaing Zin Thet, Khine Khine Htwe, M. Thein","doi":"10.1109/ICAIT51105.2020.9261801","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261801","url":null,"abstract":"Most of the grape plant diseases are severe and spread rapidly. The grape plant diseases start on the leaf and then spread to stem, fruit, and root. It has problems with time-consuming and lacking knowledge for farmers in remote areas to classify grapes leaf diseases because of limited access to human experts in developing countries. In recent research, grape leaf diseases are classified with fine-tuning VGG16 Network. But the classification was not received good accuracy results with VGG16 Architecture. This system proposes transfer learning via fine-tuning of VGG16 network, one of CNN Architecture, to classify diseases on grape leaf The system used Global Average Pooling (GAP) layer instead of VGG16's two fully connected layers before final classification SoftMax layer to improve accuracy result of fine-tuning VGG16 for grape leaf diseases classification. The proposed system mainly analyzed healthy leaves and five leaves diseases, named anthracnose, downy mildew, black measles, isariopsis leaf spot, nutrient insufficient, on 6000 images dataset of Myanmar Grapevine Yard. The proposed system compared the accuracy with VGG16 using fully connected layers and VGG16 using SVM classifier. The proposed system outperformed with 98.4% accuracy than others.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428379","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: 15

Predicting Customer Class using Customer Lifetime Value with Random Forest Algorithm 基于随机森林算法的客户终身价值预测客户类别

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261792

Thanda Win, Khin Sundee Bo

{"title":"Predicting Customer Class using Customer Lifetime Value with Random Forest Algorithm","authors":"Thanda Win, Khin Sundee Bo","doi":"10.1109/ICAIT51105.2020.9261792","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261792","url":null,"abstract":"As there are a lot of booming online retailers in e-commerce industry in the Internet age, the need of maintaining competitive advantages has become to pay attention to customer relationship management (CRM). To build a successful CRM strategy, it is needed to know individual customer class which can be calculated from Customer Lifetime Value (CLV): the monetary value of customers purchased from the business during their lifetime. CLV modelling allows us to identify customer's predicted business value. It provides the retailers for effectively allocating the resource in their business. This predictive model has been taken on the global Super Store Retail dataset with almost ten thousand transactions. Our model will predict the customers' class of the next year based on their CLV that will help the online retailer to decide which customer should be invested to get long term CRM. Random Forest (RF) algorithm is utilized to train our model and Random Search tuning is conducted to get the best predictive accuracy. The experimental analysis is performed to compare with AdaBoost algorithm on the same dataset.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132316177","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

Attractiveness of Single-Agent Game: Case Study Using Sliding Puzzle 单智能体博弈的吸引力:以滑动谜题为例

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261794

Chang Liu, Shunqi Huang, Mohd Nor Akmal Khalid, H. Iida

引用次数: 2

An Investigation of Delay-guaranteed Mechanism in Wi-Fi Networks with Multiple Traffic Flows 多流量 Wi-Fi 网络中的延迟保证机制研究

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261800

Hiroaki Motohashi, Kien Nguyen, H. Sekiya

引用次数: 0

Informational Analysis of Go, Part 2: Zuozi and Huanqitou 围棋的信息分析(二):左子和环七头

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261773

Wu Yicong, Chang Liu, Mohd Nor Akmal Khalid, H. Iida

引用次数: 1

A GUI based Self-learning Tool for Polar Codes using Successive Cancellation and List Decoders 一个基于GUI的使用连续消去和列表解码器的极码自学习工具

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261811

Rafee Al Ahsan, Sunita Khichar, W. Phakphisut, L. Wuttisittikulkij, Audsadang Ngamjob, P. Sasithong, Ambar Bajpai, M. Saadi, Amir Parnianifard, Irfan Ullah, May Pyone Han, Wai Phyo, P. Vanichchanunt

{"title":"A GUI based Self-learning Tool for Polar Codes using Successive Cancellation and List Decoders","authors":"Rafee Al Ahsan, Sunita Khichar, W. Phakphisut, L. Wuttisittikulkij, Audsadang Ngamjob, P. Sasithong, Ambar Bajpai, M. Saadi, Amir Parnianifard, Irfan Ullah, May Pyone Han, Wai Phyo, P. Vanichchanunt","doi":"10.1109/ICAIT51105.2020.9261811","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261811","url":null,"abstract":"Recently, the importance of channel coding has become prominent for next-generation 5G wireless communication networks. Channel coding is becoming an active area of research and scholars are aiming to improve their knowledge about channel coding schemes which will be playing a crucial part in high-performance 5G networks. polar codes are the class of channel coding techniques that have been standardized in 5G. Moreover, it is a fact that activity tool-based learning raises the learning efficiency and generates intrinsic motivation for learning. Therefore, the main motivation for designing this paper is to develop a Graphical User Interface (GUI) based learning tool to help students easily learn channel coding techniques. In our teaching curriculum, we have used figure-based interactive representations to teach the basic concepts of polar codes efficiently. This learning tool provides users the option to learn about the encoding technique and two different decoding techniques named as Successive Cancellation (SC) and Successive Cancellation List (SCL) for different lengths of user-defined data bits. This complete model has been designed in Python.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114687285","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

Analysis of Jump & Jump Game and its Possible Enhancement 跳远游戏分析及其可能的改进

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261784

Hong Ri, Mohd Nor Akmal Khalid, H. Iida

引用次数: 2

Myanmar Sign Language Classification using Deep Learning 缅甸手语分类使用深度学习

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261775

Sai Myo Htet, Bawin Aye, Myo Min Hein

引用次数: 1

Multi-Criteria Decision Analysis in Urban Transport System by applying Function Resonance Analysis Method 基于功能共振分析法的城市交通系统多准则决策分析

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261795

Shin Mon Phyo, Swe Zin Hlaing, M. Lwin

{"title":"Multi-Criteria Decision Analysis in Urban Transport System by applying Function Resonance Analysis Method","authors":"Shin Mon Phyo, Swe Zin Hlaing, M. Lwin","doi":"10.1109/ICAIT51105.2020.9261795","DOIUrl":"https://doi.org/10.1109/ICAIT51105.2020.9261795","url":null,"abstract":"Traffic congestion is one of the major problems facing Yangon and is attributed to many factors including rapid population increase, poor road infrastructure, city structure, the rapid increase in the number of cars, and a lack of physical car parking plan. For this congestion problem, people can be lost their valuable time and cost. To avoid traffic congestion problems, the best choice of the urban transport system with multiple criteria such as Car Capacity (CC), Required Time (Time), Car Parking Place (PP), Required Cost (Cost), and Travelling Cost (Cost) are introduced in the proposed approach. Before applying the multicriteria analysis method, this paper analyzes the traffic congestion problem by using the Functional Resonance Analysis Method (FRAM). In this analysis, data collected from the Yangon Traffic Control Center (TCC) and show the traffic congestion status using Queuing Model. Based on the traffic congestion status, AHP is applied by identifying suitable alternative solutions of the urban transport system such as rail transport system, public bus system, taxi system, and private car system for the dedicated urban area in Yangon. As a result, the government can consider the urban transport system to avoid traffic congestion problem.","PeriodicalId":173291,"journal":{"name":"2020 International Conference on Advanced Information Technologies (ICAIT)","volume":"621 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132685202","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

Comparative Analysis of Video Streaming in Software Defined Networking 软件定义网络中视频流的比较分析

2020 International Conference on Advanced Information Technologies (ICAIT) Pub Date : 2020-11-04 DOI: 10.1109/ICAIT51105.2020.9261786

Hla Myo Su, A. Maw

引用次数: 2