2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)最新文献_第4页

[Adverticements] (广告类)

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/idaacs.2019.8924229

引用次数: 0

SOA Based System for Big Genomic Data Analytics and Knowledge Discovery 基于SOA的大基因组数据分析和知识发现系统

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924370

Veska Gancheva, P. Borovska

引用次数: 3

Incident Detection over Unified Threat Management Platform on a Cloud Network 基于云网络统一威胁管理平台的事件检测

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924299

Muhammad Muneeb Saad, Talha Iqbal, Hazrat Ali, Mohammad Farhad Bulbul, Shahid Khan, C. Tanougast

{"title":"Incident Detection over Unified Threat Management Platform on a Cloud Network","authors":"Muhammad Muneeb Saad, Talha Iqbal, Hazrat Ali, Mohammad Farhad Bulbul, Shahid Khan, C. Tanougast","doi":"10.1109/IDAACS.2019.8924299","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924299","url":null,"abstract":"Artificial Intelligence (AI) techniques provide many intelligent methods for security solutions in various domains such as finance, networking, cloud computing, health records and individual's identity. AI achieves security mechanisms like antivirus, firewalls, intrusion detection system (IDS) and cryptography by using machine learning methods and data analysis techniques. As the modern AI techniques help improving security systems, criminal activities are also becoming updated simultaneously. Machine learning methods along with data analysis tools have become popular to prevent security systems from threats and hacking activities. This work contributes to secure cloud networks and help them prevent malicious attacks. In this paper, Bidirectional long short-term memory (BLSTM) is used to detect incidents over unified threat management (UTM) platform operated on cloud network. Results are compared with K-nearest neighbor which is a baseline technique. Time series input samples recorded over UTM platform are used for training and testing purposes. We obtain accuracy score of 98.47% with 0.0186 mean squared error (MSE) using KNN while BLSTM provides 98.6% accuracy score with 0.002 loss, which is better than the KNN.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115222268","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

2D-Deep Neural Network and Its Online Rapid Learning 二维深度神经网络及其在线快速学习

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924281

Yevgeniy V. Bodyanskiy, O. Boiko, I. Pliss, V. Volkova

引用次数: 1

A Fast and Accurate Edge Detection Algorithm for Real-Time Deep-Space Autonomous Optical Navigation 一种用于实时深空自主光学导航的快速精确边缘检测算法

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924336

Hao Xiao, Yanming Fan, Zhang Zhang, Xin Cheng

{"title":"A Fast and Accurate Edge Detection Algorithm for Real-Time Deep-Space Autonomous Optical Navigation","authors":"Hao Xiao, Yanming Fan, Zhang Zhang, Xin Cheng","doi":"10.1109/IDAACS.2019.8924336","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924336","url":null,"abstract":"This paper presents a fast and accurate edge detection algorithm for real-time autonomous optical navigation used in deep-space missions. The proposed algorithm optimizes the non-maximum suppression (NMS) mechanism and the adaptive threshold selection approach of the conventional Canny algorithm. Instead of computing gradient directions, the proposed NMS approach adopts the vertical and horizontal gradients to determine the diagonal directions of gradient directions. In addition, an optimized noise edge suppression mechanism is presented for getting thinner edges without sacrificing the performance in terms of computation complexity. Furthermore, unlike the conventional double-thresholding method, this paper proposes a single-threshold selection approach, thus reducing the computational complexity and easing the real-time embedded implementation. More importantly, the proposed single-threshold scheme can efficiently suppress the noise edges caused by craters and atmosphere covered on celestial bodies. Experimental results show that, compared with the traditional Canny edge detector, the proposed algorithm enables more accurate celestial body edge detection, while reducing a lot of computation complexity.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124859826","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

Research On Short-term Electric Load Forecast Based On Grey Neural Network and Snap-drift Cuckoo Search Algorithm 基于灰色神经网络和瞬时漂移布谷鸟搜索算法的短期电力负荷预测研究

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924273

Feng Chen, Z. Ye, Jun Su, Haofeng Lang, Xiaoxiao Shi, Shuqing Wang

{"title":"Research On Short-term Electric Load Forecast Based On Grey Neural Network and Snap-drift Cuckoo Search Algorithm","authors":"Feng Chen, Z. Ye, Jun Su, Haofeng Lang, Xiaoxiao Shi, Shuqing Wang","doi":"10.1109/IDAACS.2019.8924273","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924273","url":null,"abstract":"In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm(SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization(GWO), moth-fire suppression optimization(MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461843","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

An Improved Association Rule Mining Algorithm Based on Ant Lion Optimizer Algorithm and FP-Growth 基于蚁狮优化算法和FP-Growth的改进关联规则挖掘算法

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924290

Dawei Dong, Z. Ye, Yu Cao, Shiwei Xie, Fengwen Wang, Wei Ming

{"title":"An Improved Association Rule Mining Algorithm Based on Ant Lion Optimizer Algorithm and FP-Growth","authors":"Dawei Dong, Z. Ye, Yu Cao, Shiwei Xie, Fengwen Wang, Wei Ming","doi":"10.1109/IDAACS.2019.8924290","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924290","url":null,"abstract":"Discovering knowledge from the amount of data plays an important role in the era of big data and FP-Growth algorithm is one of the most successful methods for learning association rules. Though the FP-Growth algorithm only needs scan two times, it has a poor efficiency for large datasets. There are already efforts have been made to solve the problem by using some Meta-heuristic optimization algorithms, such as particle swarm optimization algorithm (PSO), immune algorithms etc, which outperform the traditional FP-Growth algorithm and shows strong performance. However, PSO is easy to trap in the local optimums. A novel algorithm ant lion optimizer (ALO) was proposed and with the advantages of global optimization, good robustness, and high convergence accuracy, which was applied to many engineering fields like antenna array synthesis, integrated process planning, scheduling and so on. In the paper, a novel association rule extraction algorithm is put forward based on the ant lion optimization algorithm. A new fitness schema based on confidence and support has been used in this approach, which avoids part of unnecessary searching processes of the FP-Growth algorithm and leads the method of searching the optimization solution more effectively. In order to evaluate the effectiveness of our approach, experiments on various datasets are carried out and experimental results are compared with some other classical meta-heuristic algorithms, experimental results testify the performance of the proposed method.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122821423","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: 7

Application of a Combined Approach for Predicting a Peptide-Protein Binding Affinity Using Regulatory Regression Methods with Advance Reduction of Features 使用预先特征还原的调节回归方法预测肽-蛋白结合亲和力的组合方法的应用

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924244

Oleksandr Murzenko, S. Olszewski, O. Boskin, I. Lurie, N. Savina, M. Voronenko, V. Lytvynenko

引用次数: 4

The Development of Distance Learning in Ukrainian Liberal Arts Institutions Based on EU Experience 基于欧盟经验的乌克兰文科院校远程教育发展

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924293

Halina I. Falfushynska, A. Kłos-Witkowska, B. Buyak, G. Tereshchuk, Uliana Yatsykovska, P. Falat, R. Szklarczyk

引用次数: 4

Comparative Analysis of Key Encapsulation Mechanisms 关键封装机制的比较分析

2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) Pub Date : 2019-09-01 DOI: 10.1109/IDAACS.2019.8924373

M. Yesina, M. Karpinski, V. Ponomar, Y. Gorbenko, T. Gancarczyk, Uliana Yatsykovska

引用次数: 1