2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)最新文献

Malaysian Coins Recognition Using Machine Learning Methods 使用机器学习方法识别马来西亚硬币

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574175

Nur Nadirah Zainuddin, Muhammad Sadiq Naim Bin Noor Azhari, W. Hashim, A. Alkahtani, A. Mustafa, G. Alkawsi, Fuad M. Noman

{"title":"Malaysian Coins Recognition Using Machine Learning Methods","authors":"Nur Nadirah Zainuddin, Muhammad Sadiq Naim Bin Noor Azhari, W. Hashim, A. Alkahtani, A. Mustafa, G. Alkawsi, Fuad M. Noman","doi":"10.1109/AiDAS53897.2021.9574175","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574175","url":null,"abstract":"Coins have become essential to our daily lives. As a legal tender, identifying, validating, classifying, and sorting coins is a complex process. In essence, automated coin detectors should identify deteriorated or older coins and distinguish between genuine and fake coins. Nonetheless, we identified limited literature examining different Machine Learning (ML) approaches for detecting Malaysian coins. This study investigates machine learning approaches and identifies the most efficient and accurate for Malaysian coin recognition. The model was trained on 311 images of coins and classified into four object categories: 5, 10, 20, and 50 cents. Six classifiers are used to test the training model. For the Grey-Level Co-occurrence Matrix (GLCM) feature extraction, AdaBoost classifiers were the most accurate, whereas K-Nearest Neighbors (KNN) classifiers were the least accurate. Moreover, the Artificial Neural Networks (ANN) classifier had the highest accuracy in the Histogram of Oriented Gradients (HOG) feature, while the Linear Discriminant Analysis (LDA) classifier had the lowest. The study findings and future directions are discussed.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116923177","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

A NARX Neural Network Model To Predict One-Day Ahead Movement of The Stock Market Index of Malaysia 用NARX神经网络模型预测马来西亚股市指数一日走势

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574394

Ali H. Dhafer, F. M. Nor, W. Hashim, N. Shah, Khairil Faizal Bin Khairi, G. Alkawsi

{"title":"A NARX Neural Network Model To Predict One-Day Ahead Movement of The Stock Market Index of Malaysia","authors":"Ali H. Dhafer, F. M. Nor, W. Hashim, N. Shah, Khairil Faizal Bin Khairi, G. Alkawsi","doi":"10.1109/AiDAS53897.2021.9574394","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574394","url":null,"abstract":"Stock price prediction is one of the most challenging tasks confronting investors in the stock market. Academics and practitioners have examined a variety of techniques for predicting stock price movement. Artificial intelligence models have attracted many researchers interested in financial forecasting in the stock market. This paper investigates the use of commodity prices and exchange rates to predict the KLCI index price one day ahead using the NARX-ANN model. The methodology employs various experiments on the sample dataset using various exogenous variables and neural training algorithms. The model's prediction performance using four input variables (three commodity prices and the exchange rate) is compared with the model's prediction performance when only the KLCI price data with price lags is used as input variables. The model's performance shows promising results in terms of error rate and hit rate. However, using commodity prices and exchange rate combinations did not improve the model's short-term next-day prediction of the KLCI price.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106075","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

Ontology for Traditional Modernized Dance Jaipong Oray Welang 传统现代舞蹈的本体——斋蓬·奥雷·威朗

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574214

Winangsari Pradani, D. Wulandari

引用次数: 0

Human Embryo Classification Using Self-Supervised Learning 使用自监督学习的人类胚胎分类

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574328

R. Wicaksono, Ali Akbar Septiandri, A. Jamal

{"title":"Human Embryo Classification Using Self-Supervised Learning","authors":"R. Wicaksono, Ali Akbar Septiandri, A. Jamal","doi":"10.1109/AiDAS53897.2021.9574328","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574328","url":null,"abstract":"The implementation of computer vision generally focuses on resolving a problem of one of two types of image data, whether it was natural image datasets or medical image datasets. Self-supervised learning (SSL), as the most recent approach in computer vision, is typically used to solved tasks related only to natural image datasets such as ImageNet, CIFAR-10, and PASCAL. This study focuses on analyzing the involvement of natural images in the SSL implementation when classifying medical images. We used the ResNet50 architecture comparing the results of human embryo image classification using the conventional transfer learning method and SSL methods. We apply three SSL approaches, namely Rotation, Jigsaw Puzzle, and SimCLRv1. The results of our research, with 1226 embryo images divided into three classes, show that the SSL implementations cannot be said to be efficient enough compared to the conventional model produced by ResNet50 with pretrained weight ImageNet. However, when the models were trained using random weight initialization, the Rotation model with an average accuracy of 78.15% and the Jigsaw Puzzle model with an average accuracy of 80.30% are significantly superior to the conventional model with average accuracy is 71.07%. Moreover, related to our embryos dataset, we discuss the differences between the SSL context-context contrastive learning approach and the context instance contrastive learning approach.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114314577","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: 3

Automated Information Security Awareness Profiling Framework of Malaysian 马来西亚的自动信息安全意识分析框架

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574351

H. Yeo, Yichiet Aun, Ming-Lee Gan, J. Khaw

引用次数: 0

Covid-19 Detection from Chest X-Ray Images using Convolutional Neural Network 基于卷积神经网络的胸部x线图像新冠肺炎检测

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574345

Usup bin Keram, Mohd Adib bin Mohd Ramli, N. Kamal, Luqmanul Hakim bin Mohd Abas

{"title":"Covid-19 Detection from Chest X-Ray Images using Convolutional Neural Network","authors":"Usup bin Keram, Mohd Adib bin Mohd Ramli, N. Kamal, Luqmanul Hakim bin Mohd Abas","doi":"10.1109/AiDAS53897.2021.9574345","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574345","url":null,"abstract":"COVID-19 reported cases in Malaysia is increasing every day. The lab facilities for testing COVID-19 has almost reached their capacity. Traditionally, to detect the COVID-19, a swab test is used. This method will take some time and be costly. The swab test kits are very scarce, and the human resources to do this test are limited. Through modern technology, there is a probability of detecting positive COVID-19 using X-ray images with deep learning. In this paper, Convolutional Neural Network (CNN) approach is used to detect Covid-19 through digital X-ray images. The 2D convolution kernel layer consists of three layers. The first layer has a $3 times 3$ kernel, the second part has a $5 times 5$ kernel, and the third part has a $7 times 7$ kernel. Then, the output will be combined into one layer. Afterwards, the concatenated layer continued with another sequential process consisting of two convolution processes, ReLU and max pooling. Next, the model is then flattened, dropout and dense. A total of 2100 positive Covid-19 and negative Covid-19 images from Github and Kaggle databases have been used in this research. Based on the experiment done, the accuracy was almost 96%.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117046671","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: 3

Gender Voiceprint Identification using Machine Learning Algorithms 基于机器学习算法的性别声纹识别

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574389

J. Ong, S. Ismail, Aida Nabilah Sadon, Nur'aina A. Rahman, K. Tay, Nan Mad Sahar

引用次数: 0

Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory 基于长短期记忆的电力负荷预测的无监督电力异常点检测

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574184

Nur Shakirah Md Salleh, M. Saripuddin, A. Suliman, B. Jørgensen

{"title":"Electricity Anomaly Point Detection using Unsupervised Technique Based on Electricity Load Prediction Derived from Long Short-Term Memory","authors":"Nur Shakirah Md Salleh, M. Saripuddin, A. Suliman, B. Jørgensen","doi":"10.1109/AiDAS53897.2021.9574184","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574184","url":null,"abstract":"Electricity theft caused a major loss for electricity power provider. The anomaly detection helps to predict the abnormal load usage of a consumer. Usually, the classification method used in anomaly detection. This research paper proposed to identify the potential anomaly points by using threshold and outliers. The prediction in time-series applied Long Short-Term Memory (LSTM) algorithm. The historical electricity load dataset of a single industrial consumer was used to generate the prediction of electricity load. There were five optimizers used to produce the model: Adam, Adadelta, Adagrad, RMSProp, and Stochastic gradient descent (SGD). The prediction model was evaluated using mean squared error (MSE) and mean absolute error (MAE). The best model among all five models was generated by Adadelta optimizer with the error rate value of 0.091982 for MSE and 0.018433 for MAE. The prediction values were generated by this model. The anomaly point was detected by using threshold and outliers. The threshold value was 0.218983. One week in August 2019 was chosen to detect any anomaly load occurrences. There were 24 outliers were found within the selected week. The study shall expand on the electricity usage trend during COVID-19 pandemic period.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131161799","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

Assessing undergraduate student's perceived stress during open distance learning 开放远程学习中大学生感知压力的评估

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574172

N. I. Jamil, Soo Kum Yoke, Norziha Che Him

引用次数: 1

Long Short-Term vs Gated Recurrent Unit Recurrent Neural Network For Google Stock Price Prediction 长期短期vs门控递归单元递归神经网络用于谷歌股价预测

2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574312

Aida Nabilah Sadon, S. Ismail, Nur Syahira Jafri, S. M. Shaharudin

{"title":"Long Short-Term vs Gated Recurrent Unit Recurrent Neural Network For Google Stock Price Prediction","authors":"Aida Nabilah Sadon, S. Ismail, Nur Syahira Jafri, S. M. Shaharudin","doi":"10.1109/AiDAS53897.2021.9574312","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574312","url":null,"abstract":"Deep Learning has proven its powerful performance in many fields as it is the sub-component of Artificial Intelligence. The use of traditional statistics methods in forecasting time series are less practicality and gives less valuable prediction. The aim of this study is to propose Recurrent Neural Network (RNN) model that suitable for forecasting Google Stock Price time series data. In this study, RNN with Long Short-Term (LSTM) and Gated Recurrent Unit (GRU) architectures are proposed as predictive models known as RNN-LSTM (2), RNN-LSTM (3), RNN-GRU (2), and RNN-GRU (3). The experimental results revealed that RNN-GRU (3) was the best model with lowest error measurements of Root Mean Square Error (RMSE), Median Absolute Percentage Error (MdAPE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Directional Accuracy (MDA). The proposed model showed its capability and applicability in predicting the future values of Google stock price data with good accuracy and it can be used to predict multi-step ahead values. Evident from this analysis, it is proven that the proposed RNN-GRU (3) provides a promising alternative technique in forecasting time-series data.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114796961","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