2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献_第3页

Speech-Based Number Recognition Using KNN and SVM 基于KNN和SVM的语音数字识别

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936761

R. R. Porle, Suzanih Embok

引用次数: 0

Performance Evaluation of HMI based on AHP and GRT for GUI 基于AHP和GRT的人机界面性能评价

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936844

Jianbang Liu, M. Ang, J. Chaw, K. Ng

引用次数: 1

Efficient Distributed Consensus Algorithm For Swarm Robotic 群机器人的高效分布式共识算法

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936787

S. Ranganathan, M. Mariappan, Karthigayan Muthukaruppan

引用次数: 1

Snail Recognition Using YOLO 使用YOLO识别蜗牛

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936736

Juan Ricardo I. Borreta, Justin A. Bautista, A. Yumang

引用次数: 2

Real-time Detection of Aquarium Fish Species Using YOLOv4-tiny on Raspberry Pi 4 利用YOLOv4-tiny在树莓派4上实时检测观赏鱼种类

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936790

Cyril Jay L. Chan, Ethan James A. Reyes, N. Linsangan, Roben A. Juanatas

{"title":"Real-time Detection of Aquarium Fish Species Using YOLOv4-tiny on Raspberry Pi 4","authors":"Cyril Jay L. Chan, Ethan James A. Reyes, N. Linsangan, Roben A. Juanatas","doi":"10.1109/IICAIET55139.2022.9936790","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936790","url":null,"abstract":"A version of the YOLO detection algorithm, the YOLOv4, has yet to find much use on aquatic species. Detection systems optimized for aquarium fish species are also currently lacking. This study provides a detection program for select fish species, namely the dwarf gourami, guppy, and zebrafish, using the YOLOv4-tiny detection model. The program was implemented in the Raspberry Pi 4 Model B single-board computer with an 8MP camera. The YOLOv4-tiny model was trained using images from Kaggle, FishBase, and the Global Biodiversity Information Facility, along with local images. The program was tested on live samples of the three fish species along with one irrelevant fish species, the petticoat tetra. There were three live samples of each species. Close shots for each sample were taken from the aquarium's front, left, right, and back sides, making a total of 48 images for detection. Training data and the confusion matrix from the experiment were utilized to determine the program's reliability in detecting the fish species. For the results, the trained model achieved a mAP of 97.81% during training and a global accuracy of 91.67% during the experiment. The program exhibited reliable performance across the board, achieving above 90% AP and accuracy in all classes.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126319784","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

Maximizing Power Generation in Variable Speed Micro-Hydro with Power Point Tracking 基于功率点跟踪的变速微水力发电最大化

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936859

M. K. Tan, Norafe Maximo Javinez, Kit Guan Lim, A. Haron, Pungut Ibrahim, K. Teo

{"title":"Maximizing Power Generation in Variable Speed Micro-Hydro with Power Point Tracking","authors":"M. K. Tan, Norafe Maximo Javinez, Kit Guan Lim, A. Haron, Pungut Ibrahim, K. Teo","doi":"10.1109/IICAIET55139.2022.9936859","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936859","url":null,"abstract":"Conventional variable speed micro-hydro control systems suffer from non-optimal input control. The controllers estimate the changes in flow rate without anticipating the global maximum power curve. As such, this paper aims to explore and develop a feasible maximum power point tracker (MPPT) with perturb and observe (P&O) and genetic algorithm (GA) in providing optimal power generation for variable speed micro-hydro system. This research first introduces a mathematical model for an experimental variable speed micro-hydro platform and then simulates the micro-hydro in MATLAB. Conventional P&O MPPT algorithm used fixed perturbation size which requires large computation time when the perturbation size is small and suffers from power fluctuation issues when the perturbation size is large. Thus, a GA-based P&O MPPT algorithm with adaptive perturbation size is proposed to provide a large perturbation size during transient response and a small perturbation size at a steady state. The simulation results showed that the proposed GA-based P&O MPPT algorithm was able to track the global maximum power point (MPP).","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121882167","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

Forest Fire Detection for Edge Devices 边缘设备的森林火灾探测

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936786

Teo Khai Xian, Hermawan Nugroho

{"title":"Forest Fire Detection for Edge Devices","authors":"Teo Khai Xian, Hermawan Nugroho","doi":"10.1109/IICAIET55139.2022.9936786","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936786","url":null,"abstract":"It is observed that the forest land mass was reducing rapidly from 1990 to 2020. As many plants and animals are depending on the forest, this is very alarming. Forest fire is one of the major causes of such loss. Forest fires tend to spread quickly and are difficult to control in a short time. Early detection of these forest fires is the key to mitigate the forest fire. There are many methods developed by researchers to monitor forest fire. An aerial-based detection system with unmanned aerial vehicles (U A V s) is one of the emerging methods which can provider a bird's eye view of the forest from above. Monitoring with UAVs however requires trained personnel to operate and manually monitor the forest. In this paper, we develop a fire detection algorithm that can analyzed images taken by UAVs and can be equipped into an autonomous UA V. The developed method does not require a lot computing power. It is based on YOLOv5 which is build and converted into optimized model that can run on an embedded board. Result shows that the method has a high MAP (>97%) with acceptable inference time indicating a good potential of the developed model.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131794032","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

Hand-Foot-Mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data 基于光纤光栅生物传感器光谱数据特征的手足口病分类

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936818

A. Mahmood, S. Azzuhri, Adnan N. Qureshi, Palwasha Jaan, Iqra Sadia

{"title":"Hand-Foot-Mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data","authors":"A. Mahmood, S. Azzuhri, Adnan N. Qureshi, Palwasha Jaan, Iqra Sadia","doi":"10.1109/IICAIET55139.2022.9936818","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936818","url":null,"abstract":"Hand, Foot and Mouth disease (HFMD) is a common viral childhood disease affected by the family of enterovirus and Coxsackie. Current laboratory identification is based on the RT-PCR test, which is expensive, time-consuming, and unsuitable for the pandemic. The SPR- TFBG was biofunctionalized with monoclonal antibody (Mab). Mab is a bioreceptor with an affinity for the virus for detecting EV-A71. A dataset of reflectance spectra of 660 samples of different virus impurities measured with SPR-TFBG biosensor to detect EV-71 virus was developed. The extracted signal has around 4000 different features based on wavelength information. The first subset was selected based on the region of interest analysis, and the dimension has reduced from 4000 to 1496 features. The dimensionality of the large feature set is reduced based on the statistical feature engineering procedure using 10 features including mean, variance, skewness, RMS, kurtosis, standard deviation, range, crest factor, impulse factor and shape factor. Subsequently, classification of the virus (signal) data is achieved through SVM and it is evaluated with different types of kernels. For the evaluation of classifiers, we used accuracy, sensitivity, precision and F1 score performance metrics. The obtained results of accuracy are 87.88 for linear SVM, 86.06 for radial basis, 75.76 for sigmoid SVM, and 75.15 for polynomial SVM, respectively. The results show that for our experiments, Linear SVM performs better than radial, polynomial and sigmoid kernels. This is because projecting the data onto higher dimensions is not required as data exhibits linear properties confirmed by White Neural Network (WNN) test for nonlinearity.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132247635","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

Performance of Content-Based Features to Detect Depression Tendencies in Different Text Lengths 基于内容的特征在不同文本长度中检测抑郁倾向的性能

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936811

N. Z. Zulkarnain, N. Yusof, Sharifah Sakinah Syed Ahmad, Zuraini Othman, Azura Hanim Hashim

引用次数: 0

Pre-trained Deep Learning Models for COVID19 Classification: CNNs vs. Vision Transformer covid - 19分类的预训练深度学习模型:cnn与Vision Transformer

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936852

Mai Sufian, E. Moung, J. Dargham, Farashazillah Yahya, S. Omatu

{"title":"Pre-trained Deep Learning Models for COVID19 Classification: CNNs vs. Vision Transformer","authors":"Mai Sufian, E. Moung, J. Dargham, Farashazillah Yahya, S. Omatu","doi":"10.1109/IICAIET55139.2022.9936852","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936852","url":null,"abstract":"The fast proliferation of the coronavirus disease 2019 (COVID19) has pushed many countries' healthcare systems to the brink of disaster. It has become a necessity to automate the screening procedures to reduce the ongoing cost to the healthcare systems. Although the use of the Convolutional Neural Networks (CNNs) is gaining attention in the field of COVID19 diagnosis based on medical images, these models have disadvantages due to their image-specific inductive bias, which contradict to the Vision Transformer (ViT). This paper conducts comparative study of the use of the three most established CNN models and a ViT to deal with the classification of COVID19 and Non-COVID19 cases. This study uses 2481 computed tomography (CT) images of 1252 COVID19 and 1229 Non-COVID19 patients. Confusion metrics and performance metrics were used to analyze the models. The experimental results show all the pre-trained CNNs (VGG16, ResNet50, and IncetionV3)outperformed the pre-trained ViT model, with InceptionV3 as the best performing model (99.20% of accuracy).","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133097263","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