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2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)最新文献

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Multi-input LSTM for water level forecasting in Black River at the border of Vietnam-China. 越中边境黑河水位预报的多输入LSTM。
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690558
Truong Thi Hai Yen, Ngo Xuan An, Nguyen Quang Dat, Vijender Kumar Solanki
{"title":"Multi-input LSTM for water level forecasting in Black River at the border of Vietnam-China.","authors":"Truong Thi Hai Yen, Ngo Xuan An, Nguyen Quang Dat, Vijender Kumar Solanki","doi":"10.1109/ICMLANT53170.2021.9690558","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690558","url":null,"abstract":"Black River is an important section in the river system in Northern Vietnam with three of the Vietnamese biggest hydroelectric dams along its bank. Additionally, it plays an indispensable role in the area’s irrigation systems and agricultural activities. Hence, water level prediction on the river is highly necessary. In this paper, we apply the multi-input LSTM model with two datasets (rainfall and water level) to enhance the accuracy of forecasting the water level of the Lai Chau dam on Black River. The usage of additional rainfall data is essential in that rainwater pours straight down into the river due to the surrounding rocky mountainous terrain. The proposed model is compared to RNN model and LSTM model with one input used in 2019 by Le et al. [1], who also conducted a study on the Black River. The results have indicated that our multi-input model surpasses the compared ones with MSE of 0.036687 and MAPE of 0.068576.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122964","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
Feature Generation by K-means for Convolutional Neural Network in Detecting IoT System Attacks 基于K-means的卷积神经网络特征生成检测物联网系统攻击
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690532
Le Thi Hong Van, Pham Van Huong, Ta Quang Hua, Le Duc Thuan, N. H. Minh
{"title":"Feature Generation by K-means for Convolutional Neural Network in Detecting IoT System Attacks","authors":"Le Thi Hong Van, Pham Van Huong, Ta Quang Hua, Le Duc Thuan, N. H. Minh","doi":"10.1109/ICMLANT53170.2021.9690532","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690532","url":null,"abstract":"The research presents a new method to detect attacks on the Internet of Thing (IoT) system using the convolutional neural networks combined with the K-means clustering algorithm. Current studies on machine learning and deep learning also use independent features that have not yet taken advantage of the role of feature groups and the relationship between features. Therefore, in this paper, we use the K-mean algorithm to cluster feature groups, find good groups and generate new linked features from good feature sets to expand the original set of features. From the improved feature set, we use CNN to detect IoT system attacks. The method proposed in this paper is tested on three different data sets, achieving an average accuracy of 97.87% and improved 1.018% compared with the detection method using only CNN.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"177 Pt 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908634","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
Cyberattacks Predictions Workflow using Machine Learning 使用机器学习的网络攻击预测工作流程
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690527
Carlos Eduardo Barrera Pérez, Jairo E. Serrano, J. Martinez-Santos
{"title":"Cyberattacks Predictions Workflow using Machine Learning","authors":"Carlos Eduardo Barrera Pérez, Jairo E. Serrano, J. Martinez-Santos","doi":"10.1109/ICMLANT53170.2021.9690527","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690527","url":null,"abstract":"This research aims to validate the effectiveness of a machine learning model composed of three classifiers: decision tree, logistic regression, and support vector machines. Through the design of a workflow, we demonstrate the effectiveness of the model. First, we execute a network attack, and then monitoring, processing, storage, visualization, and data transfer tools are implemented to create the most realistic environment possible and obtain more accurate predictions.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128209584","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
Kinematics Solution of ALICE: An Exoskeleton Robot for Rehabilitation 康复外骨骼机器人ALICE的运动学解
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690553
M. Cardona
{"title":"Kinematics Solution of ALICE: An Exoskeleton Robot for Rehabilitation","authors":"M. Cardona","doi":"10.1109/ICMLANT53170.2021.9690553","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690553","url":null,"abstract":"In this paper, a complete kinematics analysis of the ALICE (Assistive Lower lImb Controlled Exoskeleton) Re- habilitation Robot is performed. First the ALICE exoskeleton rehabilitation robot is presented, including its mechanical design. Then, the main features of commercial lower limb exoskeleton robots is discussed. Next, the forward kinematics using the Screw theory is presented, finally the inverse kinematics solution using a geometrical approach is presented. MATLAB® as programming tool and testing platform is used.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525308","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
Modeling and Prototype Implementation of an Automated Guided Vehicle for Smart Factories 智能工厂自动导引车的建模与原型实现
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690543
Hiroki Sasamoto, R. Velázquez, S. Gutiérrez, M. Cardona, A. A. Ghavifekr, P. Visconti
{"title":"Modeling and Prototype Implementation of an Automated Guided Vehicle for Smart Factories","authors":"Hiroki Sasamoto, R. Velázquez, S. Gutiérrez, M. Cardona, A. A. Ghavifekr, P. Visconti","doi":"10.1109/ICMLANT53170.2021.9690543","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690543","url":null,"abstract":"This paper overviews the modeling, implementation, and mechanical characterization of an Automated Guided Vehicle (AGV) devoted to transport materials and tools in smart factories. The design is based on a differential-drive mobile robot configuration for which the mathematical model was obtained. Such model allows the simulation of the AGV’s motion and can be used to improve productivity, increase automation, and reduce transportation costs in industrial facilities. The prototype’s implementation was conducted with highly resistant materials and automated manufacturing processes. A finite element analysis (FEA) shows that the prototype is capable of transporting 700 kg loads without suffering any structural damage and that critical cases involving collisions might cause only minimal harm. An IoT approach has been considered for the AGV’s electronic architecture.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116766020","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
Reinforcement learning for optimization hyperparameters of Long Short-Term Memory applied to Electricity load forecasting 长短期记忆超参数优化的强化学习在电力负荷预测中的应用
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690555
Nguyen Thi Ngoc Anh, Dang Tien Dat, Le Anh Ngoc
{"title":"Reinforcement learning for optimization hyperparameters of Long Short-Term Memory applied to Electricity load forecasting","authors":"Nguyen Thi Ngoc Anh, Dang Tien Dat, Le Anh Ngoc","doi":"10.1109/ICMLANT53170.2021.9690555","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690555","url":null,"abstract":"Accurate electricity load forecasting is a key factor in optimizing the operation of the power system. Moreover, more accurate forecasting helps the better efficiency, reliability, and security of the power system. Uncertain weather and uncertain behavior of consumers is the reason for the uncertain behavior of electricity load. The accuracy forecast model is necessary to research. A new model for short-term electricity load forecast having better accuracy performance is investigated in this paper. The proposed method combines long-short term memory and reinforcement learning for short-term electricity load. Concretely, reinforcement learning is used for the optimization of hyperparameters of Long Short-Term Memory (LSTM). The proposed model is applied to the electricity load forecasting of Vietnam. The experimental results show that the proposed model has better performance than the pure LSTM model.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114953910","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
Classification of Activities and Falls within a Multimodal Dataset 多模态数据集中的活动和落类分类
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690559
Evan Kurpiewski, Ilya Samokhvalov, Blythe Layne, Lukmon Rasaq, Gulustan Dogan, Michel J. H. Heijnen
{"title":"Classification of Activities and Falls within a Multimodal Dataset","authors":"Evan Kurpiewski, Ilya Samokhvalov, Blythe Layne, Lukmon Rasaq, Gulustan Dogan, Michel J. H. Heijnen","doi":"10.1109/ICMLANT53170.2021.9690559","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690559","url":null,"abstract":"Accurately predicting fall detection from wearable sensor data has many implications. Detecting falls and other activities from wearable data provide a method by which it assists those who need it. Multiple methods were employed to predict activities from wearable data. One method was using a Recurrent Neural Network(RNN) known as a Long Short Term Memory network (LSTM). In addition, a traditional machine learning approach was explored with the use of a Random Forest Classifler(RFC). This work was adapted from a few previous works, as the dataset being used was that of the Challenge Up competition. Despite utilizing previous methods and works, the highest accuracy attained was 72% which lends itself to the potential difficulty of predicting rare events from time-series data.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360245","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 6PE and 6VPE techniques in MPLS-VPN networks for video streaming 视频流MPLS-VPN网络中6PE和6VPE技术的评估
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690563
Alex Leonel Yautibug Coro, D. Avila-Pesantez, Alberto Arellano-Aucancela
{"title":"Evaluation of 6PE and 6VPE techniques in MPLS-VPN networks for video streaming","authors":"Alex Leonel Yautibug Coro, D. Avila-Pesantez, Alberto Arellano-Aucancela","doi":"10.1109/ICMLANT53170.2021.9690563","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690563","url":null,"abstract":"The work aims to evaluate the performance of the 6PE and 6VPE techniques for infrastructure VPNs MPLS layer 3 transmitting video streaming with IPv4 and IPv6 schema, which transports the data flow of customers to the ISP network. Two emulated test-bed scenarios were carried out in GNS3 by each technique with similar configurations. In the case of 6VPE, VRFs for each client in the PE routers and the VPNv6 routes were created in the ASBRs, with a Hub & Spoke topology. For the experimentation, the DITG software was used by injecting server-client streaming traffic using the VCL media player. The parameters analyzed were jitter, delay, and average packet rate. The result concluded that the 6PE technique is better for the transmission and reception of streaming, with a global significant difference of 8% concerning 6PVE.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126715422","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 Blockchain-based Secured and Privacy-Preserved Personal Healthcare Record Exchange System 基于区块链的安全且隐私保护的个人医疗记录交换系统
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690542
Hien Do Hoang, Do Thi Thu Hien, Thach Canh Nhut, Phan Dang Truc Quyen, Phan The Duy, V. Pham
{"title":"A Blockchain-based Secured and Privacy-Preserved Personal Healthcare Record Exchange System","authors":"Hien Do Hoang, Do Thi Thu Hien, Thach Canh Nhut, Phan Dang Truc Quyen, Phan The Duy, V. Pham","doi":"10.1109/ICMLANT53170.2021.9690542","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690542","url":null,"abstract":"The demand for personal healthcare record (PHR) exchange among electronic medical record (EMR) systems and patients is growing along with remarkable efforts to improve the quality, safety, privacy-preserving, and efficiency of healthcare information delivery. Currently, in EMR systems, there are problems of fragmented communication and lack of interoperability for health care professionals, hospitals, clinics and patients to appropriately access and securely share a patient’s EMR. Moreover, in the context of the recent soaring of security breaches and surveillance in incidents reported, a solution to ensure the safety and privacy of data is essential. Blockchain can ensure trust, security, and privacy of individual data in the digital world. By leveraging blockchain features, we propose HoloCare system for providing a single source from the patient’s PHR for authentication, permission granting and remote access to the health information data from other EMR systems. To demonstrate the practicability and effectiveness of HoloCare, we utilize HL7 FHIR, known as a standard for healthcare data exchange in a wide variety of contexts from PHR-based data sharing, server communication among and within healthcare providers.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133338896","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
Using Neural Networks in River Level Prediction -case study of the river La Leche-Peru 神经网络在河流水位预测中的应用——以秘鲁拉赫河为例
2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690538
Diana Castro Cardenas, F. Altamirano, Merly Yataco Bernaola
{"title":"Using Neural Networks in River Level Prediction -case study of the river La Leche-Peru","authors":"Diana Castro Cardenas, F. Altamirano, Merly Yataco Bernaola","doi":"10.1109/ICMLANT53170.2021.9690538","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690538","url":null,"abstract":"The rugged geographical relief of Peru determines a particular hydrological regime; this includes our Region, which is also under the effect of meteorological phenomena such as El Niño and La Niña that occur unpredictably and whose effects we feel with heavy rains ans floods in the north of Peru, for which we consider essential to be able to forecast river levels, in particular the river La Leche, for this we use the Black-Sholes-Merton stochastic differential equation of the river level, as an input along with other parameters mesaured by meteorological stations within the area of influence of the La Leche river basin, together with an LSMT Neural Network that was trained with data downloaded but conditioned, making forecasts 6, 12, 18 and 24 hours in advance. The performance tests of the obtained neural networks demostrated a high adaptation of the solution to the hydrological model since the NSE is very close to unity; Besides that, the average error is minimal, RMSE of the order of 0.002, and the absolute error is of the order of 0.007.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114618019","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
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