2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)最新文献

Convolutional Neural Networks for Raman Spectral Analysis of Chemical Mixtures 卷积神经网络用于化学混合物的拉曼光谱分析

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664686

M. Mozaffari, L. Tay

{"title":"Convolutional Neural Networks for Raman Spectral Analysis of Chemical Mixtures","authors":"M. Mozaffari, L. Tay","doi":"10.1109/SLAAI-ICAI54477.2021.9664686","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664686","url":null,"abstract":"In the spectroscopy domain, one-dimensional Convolutional Neural Networks (1D CNN) assist researchers in recognizing one pure chemical compound and distinguishing it from unknown substances. The novelty of this approach is that a trained CNN operates automatically with almost no pre-or post-processing of data. However, the application of 1-D CNNs has typically been restricted to a binary classification of pure chemical substances. This study highlights a new approach in spectral recognition and quantification of components in chemical mixtures. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose as two multi-label classifiers. Depending on data availability, there is no limit to the number of compounds in an unknown mixture to recognize by RaMixNet models. We trained RaMixNet models using generated Raman spectra utilizing a novel data augmentation technique that adds random noise and different baselines to each spectrum as well as random wavenumber shifts for Raman peaks. The experimental results over hundreds of generated synthetic test mixtures revealed that the classification accuracy of RaMixNet I and II is 100%; at the same time, the RaMixNet II model could reach an average means square error rate of 0.06 and R2 score of 0.76 for the quantification of each component. In a comparison study, RaMixNet models could distinguish components of six actual chemical mixtures better than well-established distance-based techniques in the literature.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122233804","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: 5

SLAAI-ICAI 2021 Agenda

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/slaai-icai54477.2021.9664691

引用次数: 0

Determine the Architecture of ANNs by Using the Peak Search Algorithm and Delta Values 利用峰值搜索算法和Delta值确定人工神经网络的结构

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664680

Mihirini Wagarachchi, A. Karunananda, Dinithi Navodya

{"title":"Determine the Architecture of ANNs by Using the Peak Search Algorithm and Delta Values","authors":"Mihirini Wagarachchi, A. Karunananda, Dinithi Navodya","doi":"10.1109/SLAAI-ICAI54477.2021.9664680","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664680","url":null,"abstract":"The solution obtained by an Artificial Neural Network does not guarantee that it always yields with the simplest neural network architecture for particular problem. This causes computational complexity of training, deployment, and usage of the trained of an artificial neural network. It has observed that the hidden layer architecture of an artificial neural network significantly influences on its solution. However, still modeling of the hidden layer architecture of an artificial neural network remains as a research challenge. This paper presents a theoretically-based approach to prune hidden layers of trained artificial neural networks, ensuring better or the same performance of a simpler network as compared with the original network and then discusses how to extend the proposed method to deep learning nets. The method was inspired by the facts of neuroplasticity. It achieves the solution by two phases. First, the number of hidden layers is determined by using a peak search algorithm and then newly discovered simpler network with lesser number of hidden layers and highest generalization power considered for pruning of its hidden neurons. Experiments have shown that the resultant architecture generated by this approach exhibits same or better performance as compared with the original network architecture.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127577655","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

Adaptive Stock Market Portfolio Management and Stock Prices Prediction Platform for Colombo Stock Exchange of Sri Lanka 斯里兰卡科伦坡证券交易所自适应证券市场投资组合管理与股价预测平台

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664735

Samudith Nanayakkara, A. Wanniarachchi, D. Vidanagama

{"title":"Adaptive Stock Market Portfolio Management and Stock Prices Prediction Platform for Colombo Stock Exchange of Sri Lanka","authors":"Samudith Nanayakkara, A. Wanniarachchi, D. Vidanagama","doi":"10.1109/SLAAI-ICAI54477.2021.9664735","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664735","url":null,"abstract":"Over the past few years various studies have been conducted to develop an optimum stock market related portfolio management platform that will assists investors to actively perform the portfolio management process. Risk and level of investor participation is considered to be one of the challenging aspects identified for optimum portfolio management. Along with portfolio management, stock price prediction is one of the key contributing factors that helps an investor to arrive mid- and long-term strategic investment decisions. Various deep learning concepts are evaluated to determine the most accurate algorithm to implement the stock price-based prediction system. Currently Colombo Stock Exchange have identified a desperate requirement of a portfolio management system with prediction capabilities to support the local and foreign investors to actively engage in trading activities among different stock exchanges in different countries. A critical study has been conducted using supportive research papers, similar applications developed and using various requirement elicitation techniques to determine the functional requirements, non-functional requirements, investor requirements, UI/UX considerations etc. The paper further describes various technological mechanisms implemented and system architectures used to develop the portfolio management and stock price prediction system. Accordingly, the implementation of Brownian Motion algorithm-based model and LSTM (Long Short-Term Memory) model are in detailed presented by the author. Finally, evaluation and testing results of the completed system and stock price prediction models are presented to prove the successfulness of the completed application and accuracy of the models implemented.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324624","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

Main Organizing Committee 主要组织委员会

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/apace.2012.6457701

引用次数: 0

Comparative Study of Face Detection Methods for Robust Face Recognition Systems 鲁棒人脸识别系统中人脸检测方法的比较研究

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664689

Thilinda Edirisooriya, E. Jayatunga

{"title":"Comparative Study of Face Detection Methods for Robust Face Recognition Systems","authors":"Thilinda Edirisooriya, E. Jayatunga","doi":"10.1109/SLAAI-ICAI54477.2021.9664689","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664689","url":null,"abstract":"Face detection systems are used in various computer vision-based applications such as biometrics, security, surveillance, etc. Computationally immoderate face detection methods may not be convenient for devices with inadequate resources. On the other hand, an appropriate face detection approach should be considered in order to achieve high accuracy and substantial performance. This paper deliberates different methods of facial detection and contrasts them to find a better approach for a robust facial recognition system. Five methods of face detection were used in this comparison namely, ViolaJones, Histogram of Oriented Gradient with Support Vector Machine (HOG-SVM), Multi-task Cascaded Convolutional Network (MTCNN), Single Shot Multibox Detector (SSD) and Maxmargin Object Detection (MMOD). Each method was evaluated by varying illumination intensity, angle of the face, the scale of the face and different occlusion types. Video data and WIDERFACE image samples were used for the analysis. Obtained experimental results depict that SSD performs better on the task of face detection with high accuracy and performance, while MMOD has the lowest performance and Viola-Jones gives the lowest accuracy.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134550446","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

Evaluation of Deep Learning Approaches for Anomaly Detection 异常检测的深度学习方法评价

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664669

Asela Hevapathige

引用次数: 1

DDoS Attack Traffic Identification Using Recurrent Neural Network 基于递归神经网络的DDoS攻击流量识别

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664685

Yu Li, Hao Shi, Mingyu Fan

{"title":"DDoS Attack Traffic Identification Using Recurrent Neural Network","authors":"Yu Li, Hao Shi, Mingyu Fan","doi":"10.1109/SLAAI-ICAI54477.2021.9664685","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664685","url":null,"abstract":"Cyber security plays a very important role in all walks of our life, especially in information industries. We all know, there are a lot of cyber attacks in network. Among all, DDoS attacks are more common and harmful than other types. Nowadays, with the rapid development of distributed computing technologies, cloud technologies and Internet, the scope of DDoS attacks is increased. These DDoS attacks are of different types like denial of service, distributed denial of service, Slowloris, and so on. We know that there are a number of technologies to detect the attacks, and the most popular way is machine learning. In this paper, we propose a recurrent neural network-based solution for DDoS attack traffic flow detection. This solution can be used for online intrusion detection systems and intrusion prevention systems. Firstly, we need to collect dataset. Due to the lack of reliable test and validation datasets, the existing datasets illustrate that most of them are out of date and useless, we use DDoS 2019 dataset for our experiment. Secondly, we extract features by CICFlowMeter tool. Thirdly, the extracted features are converted into grayscale images by a certain algorithm. Finally, the grayscale images are used as input to the RNN classifier. Regardless of a feature appears in the image, through RNN classifier, we will get the same output, this is a fundamental and most important benefit of RNN classifiers. With this implementation, we can achieve an accuracy of 99.95%.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126127816","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

Evaluation and Validation of Subfertility Ontology and Decision Support System 亚生育本体与决策支持系统的评价与验证

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664683

T. Yogarajah, Kuhaneswaran Banujan, S. Vasanthapriyan

{"title":"Evaluation and Validation of Subfertility Ontology and Decision Support System","authors":"T. Yogarajah, Kuhaneswaran Banujan, S. Vasanthapriyan","doi":"10.1109/SLAAI-ICAI54477.2021.9664683","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664683","url":null,"abstract":"Evaluation and Validation of ontology verify the quality and perfection of the ontology. Decision Support System ’s Evaluation and Validation provides an error-free and accurate system for users. This paper describes the validation of ontology procedures which verifies the content of the ontology and examines the application of developed subfertility treatment method ontology. We gathered ontology expert suggestions and assessment methods for developed ontology also from the users’ feedback from the doctors and medical students. Ontology accuracy and quality validated by Corpus-based Approach, Delphi Method, Modified Delphi Method, and OntOlogy Pitfall Scanner (web-based pitfall scanner tool) are some approaches employed for ontology evaluation. Certain concepts such as DL Query and SPARQL Query have been utilized to assess ontology, which is included in the Protégé OWL Ontology editor 5.5. The technique of verification adheres to the coherence and structure of ontology while the methods of validation focus to assess their application in the real world. Validity and assessment determine the ontology model’s consistency and user satisfaction. Decision Support System (DSS) evaluated and validated by the field test and user’s feedback. This Evaluation and Validation provide the perfectness of decision-making in a specified domain.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738187","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

Deep COVID-19 Recognition Using Chest X-ray Images: A Comparative Analysis 使用胸部x射线图像进行COVID-19深度识别:比较分析

2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) Pub Date : 2021-12-06 DOI: 10.1109/SLAAI-ICAI54477.2021.9664727

S. Thuseethan, C. Wimalasooriya, S. Vasanthapriyan

{"title":"Deep COVID-19 Recognition Using Chest X-ray Images: A Comparative Analysis","authors":"S. Thuseethan, C. Wimalasooriya, S. Vasanthapriyan","doi":"10.1109/SLAAI-ICAI54477.2021.9664727","DOIUrl":"https://doi.org/10.1109/SLAAI-ICAI54477.2021.9664727","url":null,"abstract":"The novel coronavirus variant, which is also widely known as COVID-19, is currently a common threat to all humans across the world. Effective recognition of COVID-19 using advanced machine learning methods is a timely need. Although many sophisticated approaches have been proposed in the recent past, they still struggle to achieve expected performances in recognizing COVID-19 using chest X-ray images. In addition, the majority of them are involved with the complex pre-processing task, which is often challenging and time-consuming. Meanwhile, deep networks are end-to-end and have shown promising results in image-based recognition tasks during the last decade. Hence, in this work, some widely used state-of-the-art deep networks are evaluated for COVID-19 recognition with chest X-ray images. All the deep networks are evaluated on a publicly available chest X-ray image datasets. The evaluation results show that the deep networks can effectively recognize COVID-19 from chest X-ray images. Further, the comparison results reveal that the EfficientNetB7 network outperformed other existing state-of-the-art techniques.","PeriodicalId":252006,"journal":{"name":"2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133942884","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