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9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Verma, Parag Anwar, Shayan Khan, Shadab Mane, Sunil B. Coll Engn Dept Comp Engn & IT Pune Maharashtra India

ISBN: (纸本)9781538644300

An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.

关键词： Intrusion detection XGBoost AdaBoost Machine learning decision trees classification algorithm clustering algorithm gradient boosting

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Research on Auto-Generating Test-Paper Model Based on Spatial-Temporal clustering Analysis 14th

Research on Auto-Generating Test-Paper Model Based on Spatia...

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14th International Conference on Intelligent Computing (ICIC)

作者： Fan, Yuling Dong, Likai Sun, Xuesong Wang, Dong Qin, Wang Cao Aizeng Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China

ISBN: (纸本)9783319959320;9783319959337

In the process of auto-generating test-paper, the category and the difficulty of the title plays a key role in the quality of generating test-paper. It will produce low quality questions and hard to popularize when used the methods of artificial generating test-paper and random generating test-paper, because considering less on the knowledge point classification and difficulty in the subject. To improve the quality of auto-generating test-paper, this paper takes the evaluation data of ACM Online Judge system as the research object. After normalization, (1) we can get three different results by the K-means clustering analysis based on the temporal and spatial characteristics of time variance and average time;(2) On the basis of clustering, the difficulty index of each topic of all the categories is calculated by using the number of submissions and the number of submissions to solve the problem. The ratio of the two is proportional to the difficulty of the problem. In this paper, the ratio of the two to determine the degree of difficulty index;(3) The Gaussian stochastic process is used to extract numbers of questions of each knowledge point, and calculated the difficulty index which were extracted to make sure they are within range to complete the auto-generating test-paper. In the experiment, we try to train and test the automatic test paper model by the number of professional problems (about 50000 data) in the C language test question of the university OJ system. The average difficulty index of the test paper was 0.4663, which meet the requirements, and the difficulty index of the title fit in with the normal distribution. Compared with the traditional generating test-paper method, the automatic test paper model is based on the difficulty and discrimination of the subject, and it can evaluate the level of tester scientifically. The experimental results show that the proposed automatic test model is simple and effective.

关键词： Spatial-temporal feature clustering algorithm Difficult coefficient Auto-generating test paper

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Unsupervised space-time clustering using persistent homology

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ENVIRONMETRICS 2019年第4期30卷

作者： Islambekov, Umar Gel, Yulia R. Univ Texas Dallas Dept Math Sci 800 West Campbell Rd Richardson TX 75080 USA

This paper presents a new clustering algorithm for space-time data based on the concepts of topological data analysis and, in particular, persistent homology. Employing persistent homology-a flexible mathematical tool from algebraic topology used to extract topological information from data-in unsupervised learning is an uncommon and novel approach. A notable aspect of this methodology consists in analyzing data at multiple resolutions, which allows for distinguishing true features from noise based on the extent of their persistence. We evaluate the performance of our algorithm on synthetic data and compare it to other well-known clustering algorithms such as K-means, hierarchical clustering, and DBSCAN (density-based spatial clustering of applications with noise). We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.

关键词： Betti numbers clustering algorithm topological data analysis unsupervised learning

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A Parallel Approach on clustering Traffic Data Stream based on the Density 6

A Parallel Approach on Clustering Traffic Data Stream based ...

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6th International Conference on Advanced Cloud and BigData (CBD)

作者： Lu, Jiamin Feng, Jun Zhang, Jin Xia, Peipei Xiao, Xianjian Hohai Univ Coll Comp & Informat Nanjing Peoples R China Changzhou Inst Technol Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781538680346

Various portable and smart IoT devices are making our life much easier than before. A lot of them provides the positioning function, hence the streams of location information can be uploaded with permissions and be aggregated on the fly, in order to find or predict the areas where high-denstiy actions might happen in the real time. Along with the rapid development of the communication capabilities, such position data comes in a more intensive way, causing a big burden on the processing servers. In order to improve the clustering efficiency, with the help of the modern parallel computation framework, this paper intends to propose a novel parallel approach for the traffic data stream based on moving objects' density, called P-DenStream. The performance study with synthetic and real-world data sets demonstrate that our approach is well suited to high-volume and high-speed traffic stream, producing precise clusters with a minimal delay.

关键词： traffic data stream clustering algorithm parallel processing

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Reactive-voltage Partitioning Method for Wind Power Grid Based on Interval Uncertainty clustering

Reactive-voltage Partitioning Method for Wind Power Grid Bas...

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Sustainable Power and Energy Conference (iSPEC)

作者： ZHANG Xu CHEN Yunlong WANG Yixian ZHA Xiaobing CHENG Xueting GAO Lei School of Electrical and Electronic Engineering North China Electric Power University Nanjing China State Grid Jiangsu Electric Power Co. Ltd Beijing China State Grid Shanxi Electric Power Company Electric Power Research Institute Beijing China Overhaul Branch State Grid Shanxi Electric Power Company Taiyuan China

ISBN: (纸本)9781728149318

In the context of wind power connected to the grid, the uncertainty of wind power prediction will lead to inaccurate reactive partition based on voltage reactive sensitivity. A reactive-voltage partitioning method for wind power grid based on interval uncertainty clustering is proposed in this paper. Based on the Jacobian matrix, the full-dimensional sensitivity matrix is obtained by successive recursive method. The strength of electrical coupling relationship among all nodes of the system is reflected by establishing a full-dimensional spatial electrical distance matrix. The concept of interval number is introduced to establish the full-dimensional electrical distance matrix based on interval uncertainty, which replaces the electrical distance matrix with a single value. In order to improve the accuracy of the partitioning algorithm and reduce the computational complexity, the UIDK-means clustering algorithm based on interval uncertainty is used for reactive partitioning. The simulation results of the improved IEEE-39 node system show that compared with the traditional partitioning method, the partitioning method based on interval uncertainty can accurately obtain the reactive partitioning result considering wind power prediction uncertainty. It is conducive to improve the accuracy of day-ahead dispatch.

关键词： reactive-voltage partitioning wind power access interval uncertainty clustering algorithm Wind energy clustering algorithms partitioning method electrical distance distance matrix Partitioning algorithms Uncertainty monodromy

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Possibilistic Fuzzy C-means clustering under observer-biased framework

Possibilistic Fuzzy C-means clustering under observer-biased...

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International Conference on Intelligent Systems and Computer Vision (ISCV)

作者： El Motaki, Saloua Ali, Yahyaouy Gualous, Hamid Sabor, Jalal Univ Sidi Mohamed Ben Abdellah Fes Morocco Univ Caen Normandy Caen France ENSAM Engn Sch Meknes Morocco

ISBN: (纸本)9781538643969

Ensuring an adaptable and interactive tools to analyze data objects is an advisable objective of machine learning algorithms. Many methods exist, and new methods, or improvements in existing ones are proposed regularly to deal with a variety of problems in different areas. We develop a variant of the well-known Possibilistic Fuzzy c-Means clustering algorithm PFCM that takes into account the observer-biased framework, Possibilistic fuzzy c-means with focal point PFCMFP. the accuracy of the proposed method is verified by cluster validity measures. The experimental results have shown that the accuracy of the new method increases significantly, compared to the initial PFCM algorithm. To elaborate this study, we have used a dataset of individual household electric power consumption, that is accessed publicly at the UCI Machine Learning Repository.

关键词： clustering algorithm Possibilistic fuzzy c -means observer-biased clustering focal point

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Optimization of Volume & Brightness of Android Smartphone through clustering & Reinforcement Learning ("RE-IN") 9

Optimization of Volume & Brightness of Android Smartphone th...

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9th IEEE International Conference on Information and Automation for Sustainability (ICIAfS)

作者： Abeywardhane, J. S. D. M. D. S. De Silva, E. M. W. N. Gallanga, I. G. A. G. S. Rathnayake, L. N. Wiekramarathne, Jagath Sriyaratna, Disni Sri Lanka Inst Informat Technol Fac Comp Malabe Sri Lanka

ISBN: (纸本)9781538694183

Smartphone has become one of the most significant piece of technology that humans were able to produce in the 21st century. It has become our life companion;hence the features of the smartphones have developed in advance. But, some features may not work as expected. For instance, auto brightness changing feature is now actualized with smartphones, yet we alter the brightness according to our preference. In the same manner, considering the volume of our smartphone it doesn't change according to our preference subsequently. This research will develop a mobile application ("RE-IN") to overcome this issue for Android smartphones. Since android smartphones allow accessing its hardware layer we can roll out improvements as we need, yet Apple doesn't permit to proceed with its hardware layer thus hard to do this for the iPhone users. By utilizing the RE-IN mobile application users may have to encounter an optimal brightness and volume on their Android smartphones agreeing the present condition of smartphone users are in. RE-IN application will keep running as a background application on an Android smartphone. When the client changes the brightness and volume as his/her preference. At that point, the reinforcement learning algorithm over the time application will distinguish how to control user's smartphone's brightness and volume relying upon the user's circumstance. When client surrounding is loaded with light, the framework will modify brightness for his/her preference. The client doesn't need to do this manually. Moreover when the client is at the too much boisterous place all of a sudden gets a call from someone;client's smartphone amplifier volume will change consequently and solaces the client's discussion. To actualize this framework it is relied upon to reinforcement learning and machine learning as the research area. By finishing the literature review, research group unable to find an Android mobile application which automates the process of volume and brightness

关键词： Reinforcement learning Optimal brightness and volume clustering algorithm Machine learning

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I-CFSFDP: A Robust and High Accuracy clustering Method Based on CFSFDP 37

I-CFSFDP: A Robust and High Accuracy Clustering Method Based...

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37th Chinese Control Conference (CCC)

作者： Qi, Jingchen Xiao, Bo Chen, Yun Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

ISBN: (纸本)9789881563958

clustering by fast search and find of density peaks (CFSFDP) is honored by its simplicity and speed. However, the hyper-parameter d, can only be determined through empirical experience, and for those datasets containing points that are closer to other cluster center, the algorithm does not perform well. In this work, we proposed a new method called Improved CFSFDP (I-CFSFDP), which makes two modifications compared to CFSFDP. Firstly, a new indicator for density is introduced to eliminate the effect of d, on clustering results. Secondly, cluster diffusion model was proposed to cluster remaining points after finding cluster centers. When regarding datasets as graphs, this process can be abstracted as finding the minimum spanning forest model in a graph, and each spanning tree represents a cluster in the dataset. I-CFSFDP is comprehensively evaluated on several datasets with arbitrary distribution and has demonstrated that l-CFSFDP is distinctly more accurate and robust than CFSFDP and DBSCAN.

关键词： clustering algorithm non-parameter method cluster diffusion Improved CFSFDP

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clustering and Selection Method of Shaft Part Manufacturing Services on Cloud Platform 15

Clustering and Selection Method of Shaft Part Manufacturing ...

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15th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Gao, Xinqin Jing, Yanzhen Yuan, Xin Wang, Xueping Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Econ & Finance Xian 710061 Shaanxi Peoples R China

ISBN: (纸本)9781538650530

Under the cloud mode, it is difficult to select an appropriate shaft part processing service from the rich and huge cloud manufacturing services. The K-means algorithm and the extension theory were applied into the cloud platform to solve this problem. The similarity-based clustering method was proposed to preprocess the shaft part processing cloud services and generate a plurality of class clusters. Based on the extension theory, the matter-element models of service requirement and class clusters of shaft part processing cloud services were established, and the selection method of shaft part processing cloud service sets was presented. The result of the study shows that the clustering and selection method of shaft part manufacturing services on cloud platform is feasible and effective.

关键词： cloud manufacturing clustering algorithm extension theory service selection

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Semi-Dynamic clustering for Large-scale Distributed-Antenna System 10

Semi-Dynamic Clustering for Large-scale Distributed-Antenna ...

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10th International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Zhu, Yanmin Tang, Wei Sun, Qiang Li, Yuhui Zhao, Xinyu Wang, Jue Nantong Univ Sch Elect & Informat Nantong 226019 Peoples R China Nantong Res Inst Adv Commun Technol Nantong 226019 Peoples R China

ISBN: (纸本)9781538693896

Large-scale distributed-antenna system (L-DAS) can further reduce the access distance between user equipments (UEs) and remote antenna units (RAUs), which brings a low propagation loss and high multiplexing gain. However, the interference caused by the overlapping coverage areas of distributed RAUs will severely degrade the sum-rate of the system. Although full cooperation transmission and dynamic clusterinng can effectively suppress inter-RAU interference, it is difficult to share the instantaneous channel state information (CSI) for the L-DAS with the limited-capacity backhaul. In this paper, to reduce the backhaul burden and mitigate interference effectively, a semi-dynamic clustering algorithm with bidirectional selection is proposed by using statistical CSI. Simulation results show that the achievable sum-rate of our proposed algorithm overwhelm that of the static clustering one, as well as, is close to that of the dynamic clustering algorithm using instantaneous CSI.

关键词： large-scale distributed antenna system bidirectional cooperation clustering algorithm

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