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共 585 条记录，以下是561-570 订阅

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Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification

Artificial Neural Networks Optimized with Unsupervised Clust...

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international Conference on Smart Systems and data Science (ICSSD)

作者： Ichrak Lafram Naoual Berbiche Jamila El Alami Laboratoire d’Analyse des systèmes et Traitement d’Image et Management Mohammed V University Rabat Agdal Rabat Morocco

The following topics are dealt with: learning (artificial intelligence); data mining; pattern classification; Big data; production engineering computing; computer aided instruction; Internet; ontologies (artificial in... 详细信息

ISBN: (数字)9781728143682

ISBN: (纸本)9781728143699

关键词： Clustering algorithms Intrusion detection Support vector machines machine learning data models Classification algorithms Artificial neural networks

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Intelligent Computing and Applications: Proceedings of the international Conference on ICA, 22-24 December 2014 1

引用

丛书名： Advances in Intelligent Systems and Computing 343

2015年

作者： Durbadal Mandal, Rajib Kar, Swagatam Das, Bijaya Ketan Panigrahi (eds.)

ISBN: (数字)9788132222682

ISBN: (纸本)9788132222675;9788132222682

The idea of the 1st international Conference on Intelligent Computing and Applications (ICICA 2014) is to bring the Research Engineers, Scientists, Industrialists, Scholars and students together from in and around the globe to present the on-going research activities and hence to encourage research interactions between universities and industries. The conference provides opportunities for the delegates to exchange new ideas, applications and experiences, to establish research relations and to find global partners for future collaboration. The proceedings covers latest progresses in the cutting-edge research on various research areas of Image, Language Processing, Computer Vision and pattern recognition, machine learning, data mining and Computational Life Sciences, Management of data including Big data and Analytics, Distributed and Mobile Systems including Grid and Cloud infrastructure, Information Security and Privacy, VLSI, Electronic Circuits, Power Systems, Antenna, Computational fluid dynamics & Heat transfer, Intelligent Manufacturing, Signal Processing, Intelligent Computing, Soft Computing, Bio-informatics, Bio Computing, Web Security, Privacy and E-Commerce, E-governance, Service Orient Architecture, data Engineering, Open Systems, Optimization, Communications, Smart wireless and sensor Networks, Smart Antennae, Networking and Information security, machine learning, Mobile Computing and Applications, Industrial Automation and MES, Cloud Computing, Green IT, IT for Rural Engineering, Business Computing, Business Intelligence, ICT for Education for solving hard problems, and finally to create awareness about these domains to a wider audience of practitioners.

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Looking for an MR? Try METWiki Today

Looking for an MR? Try METWiki Today

引用

IEEE/ACM international workshop on Metamorphic Testing (MET)

作者： Xiaoyuan Xie Jiahao Li Chen Wang Tsong Yueh Chen State Key Lab of Software Engineering Computer School Wuhan University China Department of Computer Science and Software Engineering Swinburne University of Technology Australia

ISBN: (纸本)9781509001309

Metamorphic Testing (MT) has been demonstrated to successfully alleviate oracle problems in many areas, including machine learning, compilers, bioinformatics, etc. However, given a new MT task, it is still very challenging to identify enough Metamorphic Relations (MRs) which are key components of MT. Aiming at this problem, we revisited previous MT applications and realized that they could form a very valuable database. Currently there is a lack of efficient link to get testers access these historical data. Therefore, in this paper, we propose to build METWiki, an MR repository, for collection and retrieval of these MRs. By providing exploration and search facilities, testers can find their desired MRs for reuse, reference, or inference. We also illustrate three sample usages of METWiki, which show important illuminations on how MRs can be obtained in practice.

关键词： Testing Software data mining Mathematics Software engineering Bioinformatics Libraries

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Improving Detection Accuracy for Imbalanced Network Intrusion Classification using Cluster-based Under-sampling with Random Forests

Improving Detection Accuracy for Imbalanced Network Intrusio...

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international Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

作者： Md. Ochiuddin Miah Sakib Shahriar Khan Swakkhar Shatabda Dewan Md. Farid Department of Computer Science & Engineering United International University Dhaka Bangladesh

Network intrusion classification i n t he imbalanced big data environment becomes a significant and important issue in information and communications technology (ICT) in this digital era. Presently, intrusion detection systems (IDSs) are commonly using tool to detect and prevent internal and external network attacks/intrusions. IDSs are majorly bifurcated into host-based and network-based systems, and use pattern-matching techniques to detect intrusions that known as misuse-based intrusion detection system. machine learning (ML) and data mining (DM) algorithms are widely using for classifying intrusions in IDS over the last few decades. One of the major challenges for building IDS employing machine learning and data mining algorithms is to improve the intrusion classification accuracy and also reducing the false-positive rate. In this paper, we have introduced a new method for improving detection rate to classify minority-class network attacks/ intrusions using cluster-based under-sampling with Random Forest classifier. The proposed method is a multi-layer classification approach, which can process the highly imbalanced big data to correctly identify the minority/ rare class-intrusions. Initially, the proposed method classify a data point/ incoming data is attack/ intrusion or not (like normal behaviour), if it's an attack then the proposed method try to classify attack type and later sub-attack type. We have used cluster-based under-sampling technique to deal with class-imbalanced problem and popular ensemble classifier Random Forest for addressing overfitting problem. We have used KDD99 intrusion detection benchmark dataset for experimental analysis and tested the performance of proposed method with existing machine learning algorithms like: Artificial N eural Network (ANN), naïve Bayes (NB) classifier, Random Forest, and Bagging techniques.

关键词： Random forests Classification algorithms Intrusion detection Network intrusion machine learning algorithms Decision trees data mining

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Role of machine learning in Acute stroke Imaging: A Technical Review

Role of Machine Learning in Acute Stroke Imaging: A Technica...

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Advanced Computing and Emerging Technologies (ACET), international Conference on

作者： Gurmeet Singh Mahender Singh Akansha Gupta Deepjyot Singh Department of Electronics and Communication Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India Department of Computer Science and Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The utilization of machine learning (ML) and pattern recognition algorithms, which is used for analysis of complex datasets to detect similarities, differences, or trends, which can be particularly useful in medical diagnostics, has become essential in enhancing the prognostics of various neurological diseases such as Acute Ischemic stroke (AIS). These advanced techniques have particularly transformed the evaluation and exploitation of neuroimaging data in the assessment and management of Acute Ischemic stroke (AIS). In addition, these techniques also enable rapid and accurate analysis of complex imaging data, aiding clinicians in timely diagnosis, personalized treatment planning, and outcome prediction for stroke patients. This paper focuses on a comprehensive examination of machine learning algorithm strategies to enhance the accuracy and effectiveness of predicting strokes. The author has explored and reviewed different ML algorithms and has also delved into stroke prevention to advance our understanding.

关键词： Neuroimaging machine learning algorithms Accuracy Prevention and mitigation Imaging machine learning stroke (medical condition) Prediction algorithms Planning pattern recognition

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Schematic interpretation and the CLARET consolidated learning algorithm

Schematic interpretation and the CLARET consolidated learnin...

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international Conference on Knowledge-Based Intelligent Electronic Systems (KES)

作者： A. Pearce T. Caelli Department of Computer Science Curtin University of Technology Perth Australia

A new learning algorithm, the Consolidated learning Algorithm based on Relational Evidence Theory (CLARET) is presented, which relies on inductive logic programming and graph matching techniques. In relational evidence theory, two different approaches to evidential learning are consolidated in how they apply to generalising within relational data structures. Attribute-based discrimination (decision trees) is integrated with part-based interpretation (graph matching) for evaluating and updating representations in spatial domains. This allows an interpretation stage to be incorporated into the generalization process. A systematic approach to finding interpretations is demonstrated for an online, hand drawn, schematic diagram and symbols recognition system. The approach uses an adaptive representational bias and search strategy during learning by efficiently grounding the learning procedures in the relational spatial constraints of their application.

关键词： pattern matching Logic programming Decision trees Indexing learning systems data structures Computer science Grounding machine learning Information retrieval

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Generative Adversarial Network-a Comparative Analysis

Generative Adversarial Network-a Comparative Analysis

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international Conference on Computational Science and Technology (ICCst)

作者： S. Gurusubramani B. Latha Dept. of CSE Sri Sai Ram Engineering College

ISBN: (纸本)9781665476560

machine learning (ML) is a subset of artificial intelligence domain that incorporates a wide range of techniques including Genetic algorithms, Artificial Neural Networks, Bayesian learning, decision trees. Analytical learning and other supervised or unsupervised learning models. These techniques exploit present and arriving data to recognize the patterns and foresee forthcoming clinical events. There are number of evolving applications of ML in other aspects of patient management that have not yet been analyzed. Generative Adversarial networks (GANs), a deep neural networks which are capable of generating or transforming images and can support in faster imaging by creatingrealistic images utilizing the current imaging protocols in a variety of contrast and modalities. This paper deals with the big picture analysis of GAN along with architecture, comparison with existing approaches, application areas and the challenges associated with it.

关键词： Deep learning Analytical models Protocols Scientific computing learning (artificial intelligence) Generative adversarial networks pattern recognition

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Does the learning Principle Help Us Improve Hand-Crafted Finger-Knuckle-Print Features?

Does the Learning Principle Help Us Improve Hand-Crafted Fin...

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Communications, Control Systems and Signal Processing (CCSSP), 2020 1st international Conference on

作者： Mohamed Yassine Haouam Abdallah Meraoumia Issam Bendib Lakhdar Laimeche LAboratory of Mathematics Informatics and Systems (LAMIS) Larbi Tebessi University - Tebessa ALGERA

ISBN: (数字)9781728158358

ISBN: (纸本)9781728158365

One of the most critical steps in pattern recognition systems is to extract the most distinctive features of the image. Therefore, the accuracy of these systems is strongly related to the efficiency of this step to represent the image. In general, hand-craft image features are obtained using feature extraction methods that often have no prior knowledge about the image type, which may result in poor features. In this paper, a hand-craft image feature learning method, based on Local Binary pattern (LBP), for Finger-Knuckle-Print representation and recognition is applied. Thus, in the learning phase, each training finger image is first divided into several non-overlapping regions, and the feature filter and the codebook of each region are individually learned. For the enrolment phase, the obtained learned filter and codebook are applied to each image to extract the histogram feature of each region. Then, the different histograms are concatenated in a single feature vector to represent the finger features. Finally, an SVM classifier is used for the identification phase. The experimental results clearly show that the learning principle offers a good gain in the system accuracy, which can provide an excellent identification rate and thus improve security.

关键词： Feature extraction Biometrics (access control) Training Histograms data mining Face

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Virtual astronomy, information technology, and the new scientific methodology

Virtual astronomy, information technology, and the new scien...

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Computer Architectures for machine Perception (CAMP)

作者： S.G. Djorgovski Division of Physics Mathematics and Astronomy and with Center for Advanced Computing Research California Institute of Technology Pasadena CA USA

All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transform the scientific practice, but also poses a number of common technological challenges. The virtual observatory concept is the astronomical community's response to these challenges: it aims to harness the progress in information technology in the service of astronomy, and at the same time provide a valuable testbed for information technology and applied computer science. Challenges broadly fall into two categories: data handling (or "data farming"), including issues such as archives, intelligent storage, databases, interoperability, fast networks, etc., and data mining, data understanding, and knowledge discovery, which include issues such as automated clustering and classification, multivariate correlation searches, pattern recognition, visualization in highly hyperdimensional parameter spaces, etc., as well as various applications of machine learning in these contexts. Such techniques are forming a methodological foundation for science with massive and complex data sets in general, and are likely to have a much broader impact on the modern society, commerce, information economy, security, etc. There is a powerful emerging synergy between the computationally enabled science and the science-driven computing, which will drive the progress in science, scholarship, and many other venues in the 21st century.

关键词： Astronomy Information technology Space technology Observatories Testing Computer science data handling Deductive databases Intelligent networks learning systems

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A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification 1st

A Novel Intrusion Detection System Based on Advanced Naive B...

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1st international Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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