2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献_第2页

Optimization of Signalized Traffic Network using Swarm Intelligence 基于群体智能的信号交通网络优化

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573784

M. K. Tan, Mohd. Riezman Ladillah, H. S. Chuo, Kit Guan Lim, R. Chin, K. Teo

{"title":"Optimization of Signalized Traffic Network using Swarm Intelligence","authors":"M. K. Tan, Mohd. Riezman Ladillah, H. S. Chuo, Kit Guan Lim, R. Chin, K. Teo","doi":"10.1109/IICAIET51634.2021.9573784","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573784","url":null,"abstract":"Traffic lights are the signaling devices located at a road intersection for granting right-of-way movement to road users. Thus, optimization of traffic signalization is essential to improve road service as it is the cost-effective way. Commonly, the signal optimization aims to minimize the average travel delay by manipulating the green signal timing. Besides to optimize the signal timing, the local intersection controller needs to collaborate with neighboring intersection controllers for minimizing the average delay for whole network as the congestion will be propagated to the downstream intersection. However, the current fixed-time signal controller is inadequate to manage the high growing demands of traffic as it is tuned offline using the nominal traffic flow data. Thus, this work aims to explore the potential of using Particle Swarm Optimization (PSO) to optimize the traffic signal timing for the traffic network. The proposed algorithm is texted using a benchmarked 1x2 traffic model and its performances are compared with the classical Genetic Algorithm (GA). The simulated results show the proposed PSO has improved the performances in minimizing average travel delay by 3.94 %.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123394974","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

[Copyright notice] (版权)

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/iicaiet51634.2021.9573577

引用次数: 0

Evaluation of Assistance System to Predict Sit-to-stand Speed using Trunk Angle and Lower Limb EMG 利用躯干角度和下肢肌电图预测坐立速度的辅助系统评价

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573655

Tsuyoshi Inoue, Kosuke Uehata, Chihiro Tomoda

引用次数: 0

Optimization of Crop Disease Classification using Convolution Neural Network 基于卷积神经网络的作物病害分类优化

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573934

Kit Guan Lim, Chii Soon Huong, M. K. Tan, C. F. Liau, Min Yang, K. Teo

{"title":"Optimization of Crop Disease Classification using Convolution Neural Network","authors":"Kit Guan Lim, Chii Soon Huong, M. K. Tan, C. F. Liau, Min Yang, K. Teo","doi":"10.1109/IICAIET51634.2021.9573934","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573934","url":null,"abstract":"This paper presents the deep learning model by Convolution Neural Network (CNN) in training the crop disease classifier via image classification. A camera will be equipped and applied in artificial intelligent drone to operate as a crop monitoring system used for agriculture. Agriculture productivity is a key component of country economy. Crop diseases can lead to a drop in the quality and quantity of agricultural products. Famers are facing problems to detect the crop diseases accurately in huge region of crops. Therefore, CNN based method for crop disease detection is proposed. Dataset contains of 16,257 color images which has a total of categories have been fed into the model, out of which 10 categories are of diseased crop leaves. The CNN model contains 7 convolution layers with the number of filters 32, 64, two layers with 128 filters, three layers with 256 filters and filter size $3times 3$ is the proposed approach to perform crop disease classification, with the best testing accuracy of 99.02%. The crops are classified correctly using the suggested CNN design. The suggested CNN design is validated and evaluated which achieves accuracy of 99.02%, 0.98% error, 99% recall, 99% precision and 0.99 score of F-measure. In this paper, achievement of the proposed CNN model is reaching a promising result and simulated successfully in classifying the crop disease.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121191862","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

Convolutional Autoencoder for Image Denoising: A Compositional Subspace Representation Perspective 图像去噪的卷积自编码器:一种组合子空间表示的视角

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573657

M. Teow

引用次数: 0

Securing mHealth Applications with Grid-Based Honey Encryption 使用基于网格的蜂蜜加密保护移动健康应用程序

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573645

S. Tan, Ka-Man Chirs Lo, Y. Leau, G. Chung, F. Ahmedy

{"title":"Securing mHealth Applications with Grid-Based Honey Encryption","authors":"S. Tan, Ka-Man Chirs Lo, Y. Leau, G. Chung, F. Ahmedy","doi":"10.1109/IICAIET51634.2021.9573645","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573645","url":null,"abstract":"Mobile healthcare (mHealth) application and technologies have promised their cost-effectiveness to enhance healthcare quality, particularly in rural areas. However, the increased security incidents and leakage of patient data raise the concerns to address security risks and privacy issues of mhealth applications urgently. While recent mobile health applications that rely on password-based authentication cannot withstand password guessing and cracking attacks, several countermeasures such as One-Time Password (OTP), grid-based password, and biometric authentication have recently been implemented to protect mobile health applications. These countermeasures, however, can be thwarted by brute force attacks, man-in-the-middle attacks and persistent malware attacks. This paper proposed grid-based honey encryption by hybridising honey encryption with grid-based authentication. Compared to recent honey encryption limited in the hardening password attacks process, the proposed grid-based honey encryption can be further employed against shoulder surfing, smudge and replay attacks. Instead of rejecting access as a recent security defence mechanism in mobile healthcare applications, the proposed Grid-based Honey Encryption creates an indistinct counterfeit patient's record closely resembling the real patients' records in light of each off-base speculation legitimate password.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132369533","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

Kiwi Fruit IoT Shelf Life Estimation During Transportation with Cloud Computing 基于云计算的猕猴桃物联网运输保质期评估

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573602

J. Khoo, Solomon Haw, Nicholas Su, Shakeeb Mulaafer

引用次数: 0

Multi-Scale Texture Analysis For Finger Vein Anti-Spoofing 手指静脉抗欺骗的多尺度纹理分析

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9574036

Nurul Nabihah binti Ashari, T. Ong, C. Tee, J. H. Teng, Yu Fan Leong

引用次数: 2

Federated Learning: Optimizing Objective Function 联邦学习:优化目标函数

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573567

Aishwarya Asesh

{"title":"Federated Learning: Optimizing Objective Function","authors":"Aishwarya Asesh","doi":"10.1109/IICAIET51634.2021.9573567","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573567","url":null,"abstract":"A universal server coordinates the training of a single model on a largely distributed network of computers in federated learning. This setting can easily be expanded to a multi-task learning system in order to manage real-world federated datasets with high statistical heterogeneity across devices. Federated learning is very useful as a framework for real-world data and federated multi-task learning has been applied to convex models. This research work discusses and evaluates possibility of sparser gradient changes to outperform the existing state-of-the-art for federated learning on real-world federated datasets as well as imputed data values. The experiments investigate the effect of rolling data or data randomization and adaptive global frequency update scheduling on the convergence of the federated learning algorithm. The results show that convergence speed and gradient curve are considerably affected by number of contact rounds between worker and aggregator and is unaffected by data heterogeneity or client sampling. The research is the core part of an extended experimental setup that will follow to better understand the behavior of distributed learning, by developing a simulation to track weights and loss function gradients during the training.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116257036","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

Mobile Machine Vision for Railway Surveillance System using Deep Learning Algorithm 基于深度学习算法的铁路监控系统移动机器视觉

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2021-09-13 DOI: 10.1109/IICAIET51634.2021.9573772

Kit Guan Lim, Daniel Siruno, M. K. Tan, C. F. Liau, Shan Huang, K. Teo

{"title":"Mobile Machine Vision for Railway Surveillance System using Deep Learning Algorithm","authors":"Kit Guan Lim, Daniel Siruno, M. K. Tan, C. F. Liau, Shan Huang, K. Teo","doi":"10.1109/IICAIET51634.2021.9573772","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573772","url":null,"abstract":"Trains have been a popular transportation in our daily life. However, there is no proper surveillance system for obstacle detection at the railway, leading to the happen of unwanted accidents. In order to overcome this issue, machine vision embedded with deep learning algorithm can be implemented. Obstacle detection can be achieved through vision-based object detection, where the object classification model computes the images similarity to its respective classes, classifying its potential as an obstacle. In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). The detection model used in this paper is Single-Shot multibox Detection (SSD) MobileNet detection model. This model can be implemented with Raspberry Pi to simulate the object detection algorithm virtually. During simulation, the object recognition algorithm is able to detect and classify various objects into its respective classes. By applying past research approaches, the developed object detection model is able to analyze image as well as real-time video feed to identify multiple objects. Any object that has been detected at the Region of Interest (ROI) can be characterized as an obstacle.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124769801","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