2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献_第3页

Going Big and Deep: Using Convolutional Neural Network to Leverage Training Data from Multiple Domains for Cross-Domain Sentiment Classification on Product Reviews 走向大而深入:使用卷积神经网络利用来自多个领域的训练数据对产品评论进行跨领域情感分类

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257815

Aditi Gupta, Jasy Liew Suet Yan, Cheah Yu-N

{"title":"Going Big and Deep: Using Convolutional Neural Network to Leverage Training Data from Multiple Domains for Cross-Domain Sentiment Classification on Product Reviews","authors":"Aditi Gupta, Jasy Liew Suet Yan, Cheah Yu-N","doi":"10.1109/IICAIET49801.2020.9257815","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257815","url":null,"abstract":"Training a classifier for sentiment polarity detection in product reviews when labeled data is not available for a particular domain poses a challenge, which can be addressed through cross-domain sentiment analysis. We experimented with Convolutional Neural Network (CNN) to learn sentiment polarity (positive or negative) from labeled data available in many different source domains and test its performance on a target domain that it is not trained on. Extensive experiments were conducted on 14 different domains using Amazon product reviews. Our preliminary findings show that cross-domain CNN models trained with multiple source domains achieved accuracy of above 80% across all the domains and outperform the in-domain models trained using limited labeled data from the same domain. In fact, the cross-domain CNN models demonstrated better performance when a larger number of source domains are used for training. Therefore, going deep and big is a promising direction to explore for cross-domain sentiment classification.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660727","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

Conceptual and design framework for smart stormwater filtration 智能雨水过滤的概念和设计框架

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257828

A. Ali, Mohd. Yazid Mohd. Anas Khan, N. Bolong, K. A. Maarof, Siti Hasnah Tanalol

引用次数: 1

Drowsiness detection using EEG and ECG signals 利用脑电图和心电信号检测睡意

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257867

S. Yaacob, Nur Afrina Izzati Affandi, P. Krishnan, Amir Rasyadan, M. Yaakop, Mohamed Fredj

引用次数: 4

Copyright 版权

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/iicaiet49801.2020.9257861

引用次数: 0

Stock Market Prediction using Ensemble of Deep Neural Networks 基于深度神经网络集成的股票市场预测

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257864

Lu Sin Chong, K. Lim, C. Lee

引用次数: 16

The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks 卷积神经网络在黑色素瘤皮肤癌预测中的数据增强效果

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257859

Kin Wai Lee, R. Chin

{"title":"The Effectiveness of Data Augmentation for Melanoma Skin Cancer Prediction Using Convolutional Neural Networks","authors":"Kin Wai Lee, R. Chin","doi":"10.1109/IICAIET49801.2020.9257859","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257859","url":null,"abstract":"Melanoma skin cancer has been a serious threat due to its high fatality. For this reason, early detection and treatments are given more attention as countermeasures. In recent years, skin cancer detection has been utilizing artificial intelligence techniques, specifically deep convolutional neural network. However, the performance of the convolutional neural network is highly vulnerable to different data constraints, such as the quality and quantity of the data. Therefore, this study explores the synthetization of training data using different data augmentation methods. The work presented in this paper utilizes four different categories of data augmentation methods, which include geometrical transformation, noise addition, colour transformation, and image mix. Multiple layers data augmentation approach is also explored. Dataset expansion strategies and optimized dataset expansion scale are determined to improve the performance of the skin cancer classification. The core findings in our study revealed that single-layer augmentation has better performance than multiple layers augmentation approaches, where region of interest (ROI) image mix has the highest effectiveness compared to other methods. In addition, the best dataset expansion strategy is random ROI image mix. Finally, the optimized dataset expansion is determined at 300%, which yielded the best overall test accuracy at 82.9%, 4.6% improvement compared to unprocessed raw dataset.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116531050","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: 9

Heart Rate Extraction from Photoplethysmography Signal: A Multi Model Machine Learning Approach 从光容积脉搏波信号中提取心率:一种多模型机器学习方法

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257869

Md. Sazal Miah, Shikder Shafiul Bashar, A. Z. Karim, Zahid Hasan

{"title":"Heart Rate Extraction from Photoplethysmography Signal: A Multi Model Machine Learning Approach","authors":"Md. Sazal Miah, Shikder Shafiul Bashar, A. Z. Karim, Zahid Hasan","doi":"10.1109/IICAIET49801.2020.9257869","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257869","url":null,"abstract":"The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it is not difficult to separate pulse from that point. An optimistic component in this day, HR checking is Photoplethysmography (PPG). Moreover, during physical workout HR extraction precision is truly influenced by clamor and movement artifact (MA). To extract HR variability there are numerous ordinary techniques. In this research, a novel way is utilized to extract HR which is known as a multi-model machine learning technique. In this study, initially training and testing of our developed algorithm is done for various features and various dataset. In addition, separation of noisy and non noisy information is done by K means clustering. Then, the machine gain information from noisy and non noisy dataset. The Linear Regression model is utilized to estimate HR by using dataset. In this study, the feature engineering is also done, as it were, we choose an alternate set of features and know their conduct with our recommended technique and we discover error percentage for each set of features. There were 12 subject from where trial dataset were recorded. The root mean square (RMS) and the mean absolute error of HR was extracted. The lowest absolute mean error we find in this research is 3.06 beats per minute (BPM).","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127247107","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

Acoustic Event Detection with MobileNet and 1D-Convolutional Neural Network 基于MobileNet和一维卷积神经网络的声事件检测

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257865

Pooi Shiang Tan, K. Lim, C. Lee, C. Tan

{"title":"Acoustic Event Detection with MobileNet and 1D-Convolutional Neural Network","authors":"Pooi Shiang Tan, K. Lim, C. Lee, C. Tan","doi":"10.1109/IICAIET49801.2020.9257865","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257865","url":null,"abstract":"Sound waves are a form of energy produced by a vibrating object that travels through the medium that can be heard. Generally, the sound is used in human communication, music, alert, and so on. Furthermore, it also helps us to understand what are the events that occurring in the moment, and thereby, provide us hints to understand what is happening around us. This has prompt researchers to study on how humans understand what event is occurring based on the sound waves. In recent years, researchers also study on how to equip the machine with this ability, i.e. acoustic event detection. This study focuses on the acoustic event detection which leverage both frequency spectrogram technique and deep learning methods. Initially, a spectrogram image is generated from the acoustic data by using the frequency spectrogram technique. Then, the generated frequency spectrogram is fed into a pre-trained MobileNet model to extract robust features representations. In this work, 1 Dimensional Convolutional Neural Network (1D-CNN) is adopted to train a model for acoustic event detection. The feature representations are extracted from a pre-trained MobileNet. The proposed 1D-CNN consist of several alternatives of convolution and pooling layers. The last pooling layer is flattened and fed into a fully connected layer to classify the events. Dropout is employed to prevent overfitting. The proposed frequency spectrogram with pre-trained MobileNet and 1D-CNN is then evaluated with three datasets, which are Soundscapes1, Soundscapes2, and UrbanSound8k. From the experimental results, the proposed method obtained 81, 86, and 70 F1-score, for Soundscapes1, Soundscapes2, and UrbanSound8k, respectively.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122902845","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

Feasibility Analysis of a Rule-Based Ontology Framework (ROF) for Auto-Generation of Requirements Specification 基于规则的本体框架用于需求规范自动生成的可行性分析

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257838

Amarilis Putri Yanuarifiani, Fang-Fang Chua, Gaik-Yee Chan

{"title":"Feasibility Analysis of a Rule-Based Ontology Framework (ROF) for Auto-Generation of Requirements Specification","authors":"Amarilis Putri Yanuarifiani, Fang-Fang Chua, Gaik-Yee Chan","doi":"10.1109/IICAIET49801.2020.9257838","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257838","url":null,"abstract":"Writing requirements specification documents plays an important role in determining the success of information system development. To compile documents that are consistent, complete and in accordance with standards, both from a technical and business perspective require enough knowledge. Some previous approaches, such as GUI-F framework, propose automated requirements specification document creation with a variety of different methods. However, most of them do not provide detailed guidance on how stakeholders can identify their needs to support the company's business needs. In addition, some methods only focus on documenting high level requirements specification, such as use case diagram. As for the code development process, this only represents very basic information and lack of technical aspects. In our previous work, we proposed a Rule-Based Ontology Framework (ROF) for Auto-Generating Requirements Specification. ROF covers 2 processes in requirements engineering, namely: elicitation and documentation. The output of the elicitation process is a list of final requirements that are stored in an ontology structure, called Requirements Ontology (RO). Using RO, the documentation process automatically generates 2 outputs: process model in the Business Process Model and Notation (BPMN) standard and Software Requirements Specification (SRS) documents in the IEEE standard. The aim of this paper is to conduct a feasibility analysis to prove that ROF is feasible to be implemented in an Information System (IS) projects. ROF is implemented in a case study, an IS project that calculates lecturer workload activity at a university in Indonesia. The feasibility analysis is carried out in stages for each output using qualitative and quantitative methods. The results of the analysis show that that the framework is feasible to be implemented in the IS project to minimize effort in generating requirements specification.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115224172","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

Early driver drowsiness detection using electroencephalography signals 基于脑电图信号的早期驾驶员睡意检测

2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2020-09-26 DOI: 10.1109/IICAIET49801.2020.9257833

S. Yaacob, Nur Iman Zahra Muhamad'Arif, P. Krishnan, Amir Rasyadan, M. Yaakop, Mohamed Fredj

{"title":"Early driver drowsiness detection using electroencephalography signals","authors":"S. Yaacob, Nur Iman Zahra Muhamad'Arif, P. Krishnan, Amir Rasyadan, M. Yaakop, Mohamed Fredj","doi":"10.1109/IICAIET49801.2020.9257833","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257833","url":null,"abstract":"This study aims to provide a solution in determining the drowsiness state among drivers at the early stage. The revolving issue nowadays is the increasing number of traffic crashes due to drowsiness are considerably at an alarming stage. Drowsiness is a state of sleepiness, which leads to the lapse of attention and focuses. Numerous factors caused drowsiness, which can be determined through the biosignals of an individual. A thorough analysis of the bio-signals of drivers, which is the electroencephalogram (EEG), is applied as one of the solutions in handling drowsiness. EEG is significant in measuring drowsiness levels as it shows the electrical activity of the brain. This study analyzes driver behaviour by measuring the brain wave pattern to detect drowsiness. In this study, the brain signals from the subjects were collected using an EEG headset interfaced with the OpenBCI software. The subjective approach, namely, the Karolinska Sleepiness Scale (KSS), is performed to validate the data. This study involves signal processing in examining brain wave patterns by using MATLAB. An alpha frequency band is extracted from the estimation of power spectral density (PSD) using the periodogram method. Classification of all the extracted features by using a decision tree showed high accuracy ranges from 77.1%-97.20% for each of the subjects. Drowsiness managed to be determined based on increasing alpha power.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122885005","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