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

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Face Detection and Face Re-identification System Using Deep Learning and OpenVINO 基于深度学习和OpenVINO的人脸检测和人脸再识别系统
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574201
S. Teoh, Yiek Heng Wong, C. F. Leong, Lyk Yin Tan
{"title":"Face Detection and Face Re-identification System Using Deep Learning and OpenVINO","authors":"S. Teoh, Yiek Heng Wong, C. F. Leong, Lyk Yin Tan","doi":"10.1109/AiDAS53897.2021.9574201","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574201","url":null,"abstract":"Face detection and face re-identification are two important processes commonly used in the surveillance system especially in Intelligent Surveillance System to identify and track people from the video footages collected from a network surveillance system that are stored inside a centralized server. This system will be helpful in crime detection, criminal tracking, and factory visitor tracking if it can be done in real time and has high accuracy. It will open the potential to revolutionize the industry and the way businesses work today. In this paper, an embedded system equipped with fast and accurate face reidentification using a modified single shot detector (SSD), a landmark regressor and a MobileNet V2 model is proposed. From the result obtained, the proposed system is tested on multiple faces and is able to achieve 99.01% re-identification accuracy. Furthermore, the proposed system also achieves an average processing time of 60ms using the low-cost Intel UP board with Intel Atom CPU and OpenVINO (a neural network acceleration platform).","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":"126307807","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
PicToText: Text Recognition using Firebase Machine Learning Kit PicToText:使用Firebase机器学习工具包的文本识别
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574187
Desmond Kim Seng Neoh, Eric Chun Meng Lock, Kem Ho Lew, Hong Yip Tang, D. L. Tong, Tun Tai Tan, Kwan Lee Tseu
{"title":"PicToText: Text Recognition using Firebase Machine Learning Kit","authors":"Desmond Kim Seng Neoh, Eric Chun Meng Lock, Kem Ho Lew, Hong Yip Tang, D. L. Tong, Tun Tai Tan, Kwan Lee Tseu","doi":"10.1109/AiDAS53897.2021.9574187","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574187","url":null,"abstract":"Back in the days, documents, payment receipts, and books are being published in printed format. To make a digital format, readers need to manually type them into a computer. However, things have change rapidly especially in the information technology (IT) industry. Optical character recognition (OCR) has been introduced to simplify the digitalization process for users. However, OCR not able to translate scanned text images into different human readable languages. Therefore, this paper proposed an Android-based application programming interface (API) developed using Firebase as an improvement text recognition program that able to translate the scanned text images into language of user preference. This proposed API processes text images, detects text from the scanned images, and translate the text into user-preferred languages. The used data in this paper is the research papers available in the university library.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"137 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":"115792044","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
Predictive Model using SVM to Improve the Effectiveness of Direct Marketing 利用支持向量机预测模型提高直销效果
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574440
N. Sagala, S. D. Permai
{"title":"Predictive Model using SVM to Improve the Effectiveness of Direct Marketing","authors":"N. Sagala, S. D. Permai","doi":"10.1109/AiDAS53897.2021.9574440","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574440","url":null,"abstract":"Direct marketing through phone calls is one of the most general marketing campaigns. Identifying profitable or risky customers are needed to improve the success of bank telemarketing. In this study, a comprehensive data analysis is proposed before developing a predictive model on Portuguese retail bank. The process starts by understanding business and data used, followed by data preparation, then apply SVM algorithm to build predictive models. During business and data understanding, how the business works is analyzed, the problem of the bank and gather the data needed for modelling. Later, univariate, bivariate, and multivariate analysis are conducted to have a general idea about the variables. Data preparation is performed to ensure the predictive models built comes from a good quality data set. Two schemas with SVM are applied on the data set. The performance of models is assessed by the specificity, sensitivity, and accuracy rate. From the experimental results, SVM with hyper tune parameter achieves specificity at 82.5, sensitivity at 72.4, and accuracy at 76.48%.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"3 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":"132210335","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
Gaussian Mixture Model - Expectation Maximization Algorithm for Brain Images 高斯混合模型-脑图像期望最大化算法
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574309
Fatin Amelia Binti Kasim, H. S. Pheng, Syarifah Zyurina Binti Nordin, Ong Kok Haur
{"title":"Gaussian Mixture Model - Expectation Maximization Algorithm for Brain Images","authors":"Fatin Amelia Binti Kasim, H. S. Pheng, Syarifah Zyurina Binti Nordin, Ong Kok Haur","doi":"10.1109/AiDAS53897.2021.9574309","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574309","url":null,"abstract":"Segmentation of human brain can be performed with the aid of mathematical algorithm as well as computer-based system to assist radiologists and medical related profession to monitor the condition of one's brain comprehensively. Due to the complex structure of the human brain, one cannot simply analyze them just by looking at the MRI images. This research examines the brain segmentation and the validation of the segmentation using ground truth data for seven subjects. The segmentation of brain regions such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) can be accomplished by using Gaussian Mixture Model (GMM) and Expectation-Maximization (EM) Algorithm. The results of segmentation are shown by the Gaussian distribution graph that indicates the volume of brain regions. The segmentation results are validated by the value of Dice index, Jaccard index, and positive predictive value (PPV). It is found that all seven subjects have high value for every index as the values ranging from more than 0.6 to almost approaching 1. For all subjects, the lowest percentage for Dice is 77.82% while the highest is 84.28%, the lowest percentage for Jaccard is 63.70% while the highest is 72.84%, and the lowest percentage for PPV is 94.44% while the highest is 98.75%. In conclusion, the index values for all subjects are acceptable and this means the segmentation by using GMM and EM Algorithm is accurate after going through the process of validation of segmentation.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"240 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":"133698279","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
Sentiment Analysis on Movie Review using Naïve Bayes 基于Naïve贝叶斯的电影评论情感分析
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574419
Noor Latiffah Adam, Nor Hanani Rosli, S. C. Soh
{"title":"Sentiment Analysis on Movie Review using Naïve Bayes","authors":"Noor Latiffah Adam, Nor Hanani Rosli, S. C. Soh","doi":"10.1109/AiDAS53897.2021.9574419","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574419","url":null,"abstract":"Sentiment analysis is needed because the analysis of the emotion is somehow complicated due to slang phrases, misspellings, short-forms, recurring characters, the use of dialects, and modern emoticons. The aim of this project is to develop a system to analyze the sentiment of movie reviews and visualize the result of sentiment analysis. The dataset was captured from IMDb movie reviews. This dataset contains 50,000 instances with two columns which are the reviews and their sentiments. The data set undergoes a cleaning process to extract meaningful data. Two types of features have been investigated, namely, the Bag of Words (BoW) and the TF-IDF modelling with Naïve Bayes classifiers. The result of accuracy is 89% which indicates that the Naïve Bayes can be used for sentiment analysis in any movie review task.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"81 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":"133919978","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
Sex Estimation Model For Asian based on Random Forest Using Length of Left Hand Bone 基于随机森林的亚洲人左手骨长度性别估计模型
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574387
Mohd. Faaizie Bin Darmawan, M. Z. Osman, D. Nasien
{"title":"Sex Estimation Model For Asian based on Random Forest Using Length of Left Hand Bone","authors":"Mohd. Faaizie Bin Darmawan, M. Z. Osman, D. Nasien","doi":"10.1109/AiDAS53897.2021.9574387","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574387","url":null,"abstract":"Sex estimation is used in forensic anthropology to assist in the identification of human remains. The estimation model, on the other hand, is usually one-of-a-kind and only applies to a specific group of people. In addition, the study on Asian population also limited. This paper proposed sex estimation model using Random Forest based on the length of bones in x-ray images of left hand taken from Asian dataset. All the nineteen bones of the hand were appraised in centimeter using free image software and the Random Forest model was performed using Matlab. The result shows that the Random Forest model produced the best average of accuracy percentage on age group 16 to 18 years old which is 91.67% compare to the other group. The result also reveals that only this age group can be used for sex estimation due to the results produced more than 80%. However, due to the limited sample size, this study may not precisely reflect the overall Asian population, which it may impact the data's interpretation.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"32 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":"116179800","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
Comparisons Of Data Mining Classification Algorithms For Customers' Shopping Intention In E-Commerce 电子商务中顾客购物意向的数据挖掘分类算法比较
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574307
Ke Xuan, S. Ismail, Intan Syazwani Noorain, Nur Aliaa Dalila A. Muhaime
{"title":"Comparisons Of Data Mining Classification Algorithms For Customers' Shopping Intention In E-Commerce","authors":"Ke Xuan, S. Ismail, Intan Syazwani Noorain, Nur Aliaa Dalila A. Muhaime","doi":"10.1109/AiDAS53897.2021.9574307","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574307","url":null,"abstract":"Online shopping provides an excellent opportunity and platform for today's traditional businesses. The application of data mining for online shopping provides helps in understanding consumer behaviour, purchasing patterns and increased customer experience. The aim of this study is to identify the potential factor that affects customer to purchase on e-commerce and classify the potential customer by using various single and ensemble method classification algorithms. The experimental results revealed that Random Forest has the best performance in term of accuracy, precision, recall, F1, and Receiver Operating Characteristic Curve. However, Logistic Regression has the lowest computational time as compares to other algorithms. The resulting model provides a different selection of model into classification model of a potential customer which directly benefit the company to select the suitable model in lowering the cost and time when providing more personalized customer experience for the customer.","PeriodicalId":152187,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":"96 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":"132208157","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
Recognizing Human Emotion Using Computer Vision 使用计算机视觉识别人类情感
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Pub Date : 2021-09-08 DOI: 10.1109/AiDAS53897.2021.9574411
Muladi Muhamad, Kendrew Gian Widjaja, Muhammad Faisal Adinata, Maria Susan Anggreainy
{"title":"Recognizing Human Emotion Using Computer Vision","authors":"Muladi Muhamad, Kendrew Gian Widjaja, Muhammad Faisal Adinata, Maria Susan Anggreainy","doi":"10.1109/AiDAS53897.2021.9574411","DOIUrl":"https://doi.org/10.1109/AiDAS53897.2021.9574411","url":null,"abstract":"Detecting and recognizing human emotion is not an easy thing to do. This will be a big challenge in computer vision because many factors have to be taken while developing an emotion recognition system like facial hair, the presence of spectacles and diverse environments. This paper will use CNN (Convolution Neural Network) model to recognize human emotion from the livefeed using the FER2013 (Facial Expressions Recognition-2013)dataset to train the model. This model delivers 54% accuracy from recognizing seven basic human emotions such as neutral, angry, disgust, fear, happy, sad, and surprise.","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":"128640508","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
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