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

{"title":"Stock Market Price Prediction: Text Analytics of the GameStop Short Squeeze","authors":"Ng Wei Xiang, M. Dabbagh","doi":"10.1109/IICAIET55139.2022.9936756","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936756","url":null,"abstract":"Analytics on the stock market is always a topic of interest by many including researchers to prove that financial outcomes could be analyzed beforehand therefore producing insights. In the year 2020 where the pandemic hit globally, the share price of GameStop suffered an unprecedented short squeeze which was a result of selling activities by major investors and buying activities by netizens primarily active on Reddit. Online media was actively covering surface stories about the short squeeze but detailed and extensive research about the event was not seen and done by many. Upon further investigation, a research gap was found that a limited scale of research had performed analysis on the event with text analytics approach and that formulates the larger goal of this research. In this paper, the scope of analytics was mainly split into two approaches, where we first build a clustering model to understand the text behavior of the community, and then a regression model to predict the changes of share price based on the features of their text. With that, we will not only be able to discover the behaviors and sentiment of the community towards the stock, but also predicting the movement of share price using textual data.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"32 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":"115919644","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}

{"title":"Wasserstein Generative Adversarial Networks with Meta Learning for Fault Diagnosis of Few-shot Bearing","authors":"Chengda Ouyang, N. Abdullah","doi":"10.1109/IICAIET55139.2022.9936741","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936741","url":null,"abstract":"In practical work situations, the bearing fault diagnosis is a small and imbalanced data challenge. However, the intelligent fault diagnosis model relies on a mass of label data. This research, presents a different method, Wasserstein GAN with Meta Learning, for overcoming the difficulty of few-shot fault diagnosis under imbalanced data constraints. The WGAN module can generate synthetic samples for the data argument, and the first-order model agnostic meta-learning (FOMAML) to initialize and modify the network parameters. Validation of the comparative performance has been made using a benchmark dataset, i.e. CWRU datasets, which show that can achieve excellent diagnostic accuracy with small data. It's successfully overcome that the imbalanced data lead to the sample distribution bias and over-fitting. In addition, it can leverage that can precisely identify the bearing fault health types in a variety of working environments, even with noise interference. It is also found that the proposed model performs better in the testing set after training difficult datasets.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 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":"130426763","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}

{"title":"Non-destructive Determination of Sweetness of Philippine Fruits using NIR Technology","authors":"Alvin S. Borras, Ronald Andrew B. Ganotisi, N. Linsangan, Roben A. Juanatas","doi":"10.1109/IICAIET55139.2022.9936746","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936746","url":null,"abstract":"Near-infrared (NIR) spectroscopy is a rapid and non-destructive method for evaluating internal quality, including TSS and firmness. A non-destructive NIR device can be used to accurately determine the sugar content of fruit in degrees Brix, enough to match a destructive commercial refractometer. This study aims to determine if a non-destructive NIR device can be used to accurately determine the sugar content of fruit in degrees Brix, enough to match a destructive commercial refractometer. The lowest R2 value belongs to the Mango with 0.761, while the highest belongs to the Strawberry with 0.9147. For Oranges, Papayas, Chicos, and Grapes - their respective R2 values are 0.8776, 0.8447, 0.7845, and 0.8407. Fruits were cleaned thoroughly before subjecting to NIR spectroscopy to reduce interference, and the AS7265x spectral triad spectrometer captured their respective absorbance spectra. From the ranges gathered, the researchers developed a regression model that can be used with the Arduino UNO to create a program that could detect the sugar content of the fruit.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 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":"128821550","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}

{"title":"Artificial Intelligence-based Safety Helmet Recognition on Embedded Devices to Enhance Safety Monitoring Process","authors":"Sharjeel Anjum, Syed Farhan Alam Zaidi, Rabia Khalid, Chansik Park","doi":"10.1109/IICAIET55139.2022.9936839","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936839","url":null,"abstract":"Construction workers can be adequately protected by wearing a safety helmet while working. Due to the discomfort, the workers take off safety helmets while working, which is unsafe behavior and causes an injury or fatality in case of a fall. Therefore, a practical and handy solution is needed on the construction site to recognize workers safety helmets in order to determine their unsafe behavior. However, conventional safety monitoring methods are labor-intensive, time-consuming, and require a safety manager's presence, which is impossible for him to monitor all the construction workers performing different activities. Therefore, this research presented efficient and cost-effective Artificial Intelligence (Computer Vision) based mobile solution to monitor worker safety helmets and generate an alarming message to the safety manager and the workers. The proposed solution consists of (1) CV based object detection approach to recognize workers with and without a safety helmet, (2) deployment on edge devices such as Android smartphones (3) uses SMS'Manager API and ToneGenerator class to notify safety manager and worker, (4) and real-time firebase database to keep a record of the workers activities (safe and unsafe). Once the worker is detected without a safety helmet, the application generates and sends an SMS on the safety manager's cellphone with workers details and an audible alarm on the device speaker to make the worker aware of his unsafe action. The developed application will be extended with other case scenarios and include rewarding and penalising functionality based on records in the database.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 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":"124584655","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}

{"title":"Optical Character Recognition of Baybayin Writing System using YOLOv3 Algorithm","authors":"Angel Mikaela P. Ligsay, John B. Rivera, J. Villaverde","doi":"10.1109/IICAIET55139.2022.9936792","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936792","url":null,"abstract":"In the Philippines, Baybayin is one of its writing systems that originated in pre-Hispanic colonialism. The centuries-old writing system gained attention and popularity, which later turned into an approved bill in 2018. The recent development of research aimed at translating Baybayin characters into Alphabets, the globally recognizable writing system, uses Artificial Intelligence or A.I. Different researchers have developed an optical character recognition system for the Baybayin script but are incapable of translating multiple characters in single image and are all using object classification algorithms. Therefore, there is a need for a system using a YOLOv3 based CNN architecture capable of recognizing Baybayin scripts in word form. Using the YOLOv3 algorithm, the system was able to achieve an accuracy of 98.92%. It was observed that some of the misclassifications are due to distorted or illegible handwriting. It can be concluded that the optical character recognition of Baybayin characters using the YOLOv3 algorithm is of high accuracy when it comes to detecting and classifying Baybayin characters.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"2020 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":"114909737","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}

{"title":"Comparing Machine Learning Models for Heart Disease Prediction","authors":"S. Chua, V. SIa, P. Nohuddin","doi":"10.1109/IICAIET55139.2022.9936861","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936861","url":null,"abstract":"One of the top causes of death globally is heart disease. Each year, an estimated 17.9 million people die due to heart disease, contributing to 31 percent of all deaths worldwide. Heart diseases, particularly cardiac arrest, could happen anytime and anywhere, without prior warnings or indications. Thus, being able to predict if heart disease is present in a patient can help both the patients and doctors be aware of a potential cardiac arrest and take necessary precautions. Early prognosis of heart disease can essentially help in effective and preventive treatments of patients and reduce the risk of complication of heart disease. In this study, a machine learning approach is used on clinical data of patients to learn models for the prediction of heart disease in patients. A correlation study of the features in the data was carried out to support feature selection for the study. Then, a comparative study of five machine learning techniques, namely Logistic Regression, Naïve Bayes, K-Nearest Neighbour, Decision Tree and Support Vector Machine, was conducted to compare the performance of the models for heart disease prediction. The results obtained were from 13 clinical parameters used to learn models for predicting heart disease. Logistic Regression seemed to perform comparatively well compared the other techniques.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"17 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":"123724793","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}

{"title":"GAN-based Privacy-Conscious Data Augmentation with Finger-Vein Images","authors":"Yusuke Matsuda, Tomo Miyazaki, S. Omachi","doi":"10.1109/IICAIET55139.2022.9936860","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936860","url":null,"abstract":"The lack of sufficient data for evaluation and development is a major problem in biometrics. A novel GAN-based data-augmentation method for finger-vein authentication is proposed and evaluated in this study. Based on the GAN model structure, a subnetwork is added that lowers the similarity between the real data used for training and the fake data from the generator; the fake data looks remarkably similar to the real data, and the correlation between the real and fake data is lowered. Because the real data and fake data are different individuals, the privacy of a particular person is not considered when examining authentication technologies using only generated fake data. Moreover, the possibility of improving the authentication accuracy is confirmed by using both real data and generated fake data for training. The effectiveness of the proposed method is proved experimentally.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"9 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":"117250606","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}

{"title":"Analysis and Forecasting of Blockchain-based Cryptocurrencies and Performance Evaluation of TBATS, NNAR and ARIMA","authors":"Iqra Sadia, A. Mahmood, Laiha Binti Mat Kiah, Saaidal Razalli Azzuhri","doi":"10.1109/IICAIET55139.2022.9936798","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936798","url":null,"abstract":"The rapid growth of cryptocurrencies has gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. Therefore the intelligent management and under-standing the characteristics of cryptocurrencies are becoming more interesting. The price of cryptocurrencies are characterized by linear and nonlinear trend, seasonality and high volatility, which increases the risk factors for investors. This study ex-periments with three different time series forecasting methods, specifically considered for Cryptocurrencies price such as Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Monero (XMR) and Cardano (XRP), and devises a procedure to evaluate their performance. Time series data are collected and examined using descriptive statistics. In next step, the White Neural Network is used for Non-Linearity and Dickey-Fuller for nonstationary and correlation among different settings of datasets. Based on these analyses, we evaluate efficient financial forecasting models such as Autoregressive Integrated Moving Average (ARIMA), Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) and Neural Network Autoregressive (NNAR) with reference to different parameters configuration of these models. The performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criterion and models are ranked by statistical mean and standard deviation of MAPE values. The NNAR model gives minimum MAPE of 2.823 while the minimum convergence time of 4.9835s is observed with TBATS and hence, these are ranked at top amongst other models respectively. These results underpin that neural network-based models perform equally well on both types of nonlinear and linear financial data and, thus, have the potential to improve the impact of financial transaction and cryptocurrencies price bringing more innovation in the decision making process.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 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":"125992869","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}

{"title":"A Hybrid TDNN-HMM Automatic Speech Recognizer for Filipino Children's Speech","authors":"John Andrew Y. Ing, Ronald M. Pascual, Francis D. Dimzon","doi":"10.1109/IICAIET55139.2022.9936815","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936815","url":null,"abstract":"Previous studies presented in the literature in the recent years have shown the feasibility of developing an automatic speech recognition (ASR) system for Filipino-speaking children. However, most of these studies are solely based on the Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM). In this paper, we present the development of a hybrid ASR system using both HMM and Time Delay Neural Network (TDNN). The Filipino Children's Speech Corpus (FCSC), which is purely composed of read speech, was used to train and test all the models. We performed several sets of experiments on various phoneme sets, various numbers of HMM states, and various enhanced models that employed vocal tract length normalization (VTLN), linear discriminant analysis (LDA), and speaker adaptive training (SAT). Our experiments show that a basic TDNN-HMM model could consistently outperform an HMM-GMM model regardless of how many HMM states are present. We also present that VTLN slightly enhances the performance of the model. The best performing model is the 4-state TDNN-HMM hybrid that obtained the lowest word error rate (WER) of 0.97%.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"11 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":"129699174","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}

{"title":"Bacterial Leaf Blight Identification of Rice Fields Using Tiny YOLOv3","authors":"A. Yumang, J. Villaverde, Mc Henry C. Tan, Jeruel Krystian D. Tulfo","doi":"10.1109/IICAIET55139.2022.9936825","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936825","url":null,"abstract":"Rice plantations are frequently affected by various rice diseases, one of which being bacterial leaf blight. Although there are scientific methods for determining bacterial blight using various molecular techniques, these tests are frequently more suitable for specific reasons such as genome identification rather than broad applications due to the same effect of bacterial blight. As a result, image processing techniques such as Convolutional Neural Network (CNN) are commonly utilized for general rice disease identification due to their reliability. The purpose of this research is to identify bacterial leaf blight using the Tiny YOLOv3 algorithm. With a total of 20 test photos, 10 of which were bacterial leaf blight and the other 10 were healthy, the prototype was able to predict bacterial blight infected leaves, with 19 correct predictions and one wrong prediction. During its evaluation, the model used to detect the diseases generated acceptable mean average precision and a precision and accuracy of detecting the disease of 90.91 % and 95%, respectively.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"23 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":"128228366","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}