2020 8th International Conference on Information and Communication Technology (ICoICT)最新文献_第7页

Classification of subjects of Mild Cognitive Impairment and Alzheimer’s Disease through Neuroimaging modalities and Convolutional Neural Networks 基于神经成像模式和卷积神经网络的轻度认知障碍和阿尔茨海默病受试者分类

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166286

Ahsan Bin Tufail, Yong-Kui Ma, Qiu-Na Zhang

{"title":"Classification of subjects of Mild Cognitive Impairment and Alzheimer’s Disease through Neuroimaging modalities and Convolutional Neural Networks","authors":"Ahsan Bin Tufail, Yong-Kui Ma, Qiu-Na Zhang","doi":"10.1109/ICoICT49345.2020.9166286","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166286","url":null,"abstract":"Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder that is affecting the elderly population worldwide. The staggering costs associated with this disease merits further research in the diagnosis and prognosis of this disease. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are widely used modalities to capture the structural changes in the brain caused by AD in its early stages. Early diagnosis of AD is important from clinical perspective to improve the life of an individual who is at the risk of developing memory deficits. Deep learning architectures such as 2D and 3D Convolutional Neural Networks (CNNs) have shown promising performances in extracting features and building useful representations of data for computer vision tasks. This study is geared towards understanding the performance differences between these architectures. We used transfer and non-transfer learning approaches to study the underlying disease phenomenon. In our experiments on binary classification of early stages of AD, we found the performance of 3D architectures to be better in comparison to their 2D counterparts. For instance, the 3D-CNN architecture which is trained on PET modality data achieved an accuracy of 71.728%, specificity of 73.196%, and sensitivity of 70.213% on the AD class while its 2D-CNN counterpart achieved an accuracy of 56.901%, specificity of 59.764%, and sensitivity of 53.947% on the same class. Further, we found the performance of 3D architecture trained on PET neuroimaging modality data to be the best in terms of performance metrics which shows superior diagnostic power of this type of architecture.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130186913","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

Analysis of Learning Management System Features based on Indonesian Higher Education National Standards using the Feature-Oriented Domain Analysis 基于印尼高等教育国家标准的学习管理系统特征分析

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166459

G. Wicaksono, Ghina Arih Juliani, E. Wahyuni, Y. M. Cholily, Hari Windu Asrini, Budiono

引用次数: 2

Deep Learning Detected Nutrient Deficiency in Chili Plant 深度学习检测辣椒植株营养缺乏

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166224

A. Bahtiar, Pranowo, A. Santoso, Jujuk Juhariah

引用次数: 16

A Data Anonymization Method to Mitigate Identity Attack in Transactional Database Publishing 一种缓解事务性数据库发布中身份攻击的数据匿名化方法

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166262

Dedi Gunawan

{"title":"A Data Anonymization Method to Mitigate Identity Attack in Transactional Database Publishing","authors":"Dedi Gunawan","doi":"10.1109/ICoICT49345.2020.9166262","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166262","url":null,"abstract":"Publishing transactional database becomes more recognized for many institutions such as retails and groceries. Many of them share or publish their data to other institutions as an effort to gain more revenue for their business. However, publishing such a database is problematic since irresponsible parties may associate records in database with specific individuals to disclose personal identity known as identity attack. Data anonymization is an effective technique to protect database from the threat. Unfortunately, applying data anonymization method in transaction database using generalization and suppression based techniques may reduce data utility significantly and cause severe distortion to database properties. A solution to mitigate such drawbacks has been proposed by replacing item with another item instead of applying those techniques. However, selecting an item to replace another item causes other problems specifically when the selected item for the replacement process is not the optimum one. Therefore, in this paper we propose a data anonymization method which performs item replacement that utilizes weighted scoring method to select an optimal item with respect to minimize information loss and maintaining database properties. Experimental results show that the proposed method guarantee higher privacy protection compared with an existing method and it successfully generates an anonymized database while at the same time it maintains data utility by minimizing information loss more than 50% compared with that of an existing method. In addition, the data property of the anonymized database can be well maintained.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129447685","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

Classifying the Polarity of Online Media on the Indonesia Presidential Election 2019 Using Artificial Neural Network 基于人工神经网络的2019年印尼总统大选网络媒体极性分类

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166254

Muhammad Afif Farisi, K. Lhaksmana

{"title":"Classifying the Polarity of Online Media on the Indonesia Presidential Election 2019 Using Artificial Neural Network","authors":"Muhammad Afif Farisi, K. Lhaksmana","doi":"10.1109/ICoICT49345.2020.9166254","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166254","url":null,"abstract":"The 2019 presidential election is one of the mandatory national agendas that is covered by all of the mainstream news media in Indonesia. The function of news media as an information provider reaps criticism because they are suspected of having polarity towards certain candidates. In this paper, the polarity of news media is analyzed by performing sentiment assessment towards every news regarding each candidate. Since manual sentiment analysis is costly and time-consuming, because of the large amount of data that needs to be processed, we adopt a machine learning method to automate the sentiment analysis process. This research employs Artificial Neural Network (ANN) to classify scraped news texts from online media and TF-IDF weighting method for feature extraction. We found that the observed online media kompas.com, liputan tan6.com, republika.co.id, and tempo.co do not have significant polarity toward one of the candidates. In addition to ANN, we also compared other methods to investigate the appropriate methods for our dataset. Our experiment shows that on average, ANN obtains the best accuracy at 84.57%, compares to Decision Tree C4.5 (83.34%), Naive Bayes (SO.42%), and SVM (79.04%).","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294444","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

Applied Internet of Things (IoT): The Prototype Bus Passenger Monitoring System Using PIR Sensor 应用物联网(IoT):使用PIR传感器的巴士乘客监控系统原型

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166420

A. Rahmatulloh, F. M. S. Nursuwars, I. Darmawan, Galih Febrizki

引用次数: 7

Ensemble Learning in Predicting Financial Distress of Indonesian Public Company 集成学习在印尼上市公司财务困境预测中的应用

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166246

Dyah Sulistyowati Rahayu, H. Suhartanto

引用次数: 0

Predicting Users’ Revisitation Behaviour Based on Web Access Contextual Clusters 基于Web访问上下文聚类的用户重访行为预测

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166179

Hapnes Toba, Christopher Starry Jomei, Lotanto Setiawan, Oscar Karnalim, Hui Il

引用次数: 2

Accuracy Improvement on Learning Vector Quantization (LVQ) Using Exponential Smoothing for Driving Activity Classification 利用指数平滑提高学习向量量化(LVQ)的驾驶行为分类准确率

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166370

P. D. Setyorini, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti

引用次数: 1

An Evidence-Based Technical Process for OpenFlow-Based SDN Forensics 基于openflow的SDN取证循证技术流程

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI: 10.1109/ICoICT49345.2020.9166215

S. Mugitama, N. Cahyani, Parman Sukamo

{"title":"An Evidence-Based Technical Process for OpenFlow-Based SDN Forensics","authors":"S. Mugitama, N. Cahyani, Parman Sukamo","doi":"10.1109/ICoICT49345.2020.9166215","DOIUrl":"https://doi.org/10.1109/ICoICT49345.2020.9166215","url":null,"abstract":"Globally planning packets forwarding based on the operator’s objectives by a centralized controller is possible in Software Defined Network (SDN). The chief purpose of the SDN architecture is to manage the network due to centralized control of the network easily. The SDN architecture does not focus on network security since the beginning of its emergence. That matter has created some vulnerabilities due to centralized control of the network. Vulnerability is caused by attacks causing the packet overload on the controller (such as DoS attack). Hence, the controller runs into a race condition. Another vulnerability existed in the controller is the topology poisoning attack utilizing spoofed packet and exploiting LLDP packets in the network. Forensics in a traditional network does not have the capability to deeply analyze the attack because the tools ignore evidence existed in the control and application layer of SDN. This research focuses on technical processes in running forensics on SDN architecture comprehensively and develops modules needed to retrieve log’s evidence existed in the controller by applying forensics’ principles. The result shows DoS attack and topology poisoning can be investigated by utilizing these technical processes. Evidence in the controller can be utilized to create analyses, attribution, and presentation. The technical processes of this study are expected to help forensic investigators in revealing crime incidents in the OpenFlow-based SDN environment.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115292297","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: 4