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A new online field feature selection algorithm based on streaming data

Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids

Reinforcement Learning based MIMO Controller for Virtual Ine...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skiparev, Vjatseslav Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios. © 2022 IEEE.

关键词： Reinforcement learning

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CD-FL:Cataract Images Based Disease Detection Using Federated Learning

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computer systems Science & engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

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Ontological Analysis of the Digital Crime Conceptual Model 18

Ontological Analysis of the Digital Crime Conceptual Model

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Vlasenko, Lidiia Lutska, Nataliia Zaiets, Nataliia Lysenko, Vitaliy State University of Trade and Economics dept. of Software Engineering and Cyber Security Kyiv Ukraine National University of Food Technologies dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine dept. of Automation and Robotic Systems Kyiv Ukraine

ISBN: (纸本)9798350360462

A conceptual ontological model of Digital Crime has been developed, consisting of five non-empty classes. Identification and classification were carried out according to the experience of domestic and foreign experts. The central place of the model is occupied by objects of the Digital Crime class, which have their own attributes and are connected by various links with other objects. Objects of some classes are filled in by users, others are formed by knowledge engineers. Based on the ontological analysis, SWRL-rules are constructed that automatically form links between objects of the custom classes Digital crime and Digital evidence with objects of the Type of crime and Criminal Liability classes. The last class contains articles of the Criminal Code of Ukraine on criminal liability for digital crimes. The ontological model is developed taking into account possible requests to it. Such queries can be formed on the basis of direct relationships between human and automatic concepts. And also contain logical chains to concepts that do not contain an explicit link in the ontology. It is assumed that the model can be expanded and supplemented. The ontological model of Digital Crime can be used to share information, develop new systems and applications, improve information retrieval and ensure its accordance. © 2023 IEEE.

关键词： Ontology

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Business Intelligence Model for Customer Targeting Using Fuzzy-C-Means and FP-Growth

Business Intelligence Model for Customer Targeting Using Fuz...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Sam Abdulilah Ahmed Mohammed Mogeeb A. Saeed Software Engineering Taiz University Taiz Yemen Computer Networks and Distributed systems Taiz University Taiz Yemen

In the era of business, business intelligence (BI) technology can inform the decision-maker of the right data and insight. In this study, a business intelligence model is built to help the marketing department build an efficient promotion strategy. This paper aims to understand the impact of segmenting customers using the soft clustering technique (fuzz-c-means) based on their spending behavior or Recency, Frequency, and Monetary Model (RFM model) on product affiliation or market basket analysis to develop more efficient promotions to target customers. This aim is approached first by, downloading a dataset called online retail from Kaggle, exploring data to remove the missing value and detect and remove outliers, and constructing an RFM model (Recency, frequency, and monetary). Second, the clustering algorithm is performed fuzzy-c-means, to segment customers into clusters with similar characteristics. Third, product affiliation or association rules are obtained as per cluster using the FP-growth algorithm. Eventually, spending behavior is concluded to show a significant impact on product affiliation which means that clustering based on spending behavior increases the efficiency of market basket analysis (MBA).

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Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

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Interface Protection Against Transient Faults

Interface Protection Against Transient Faults

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IEEE Design and Diagnostics of Electronic Circuits and systems (DDECS)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava Slovakia

Increasing bus frequency to fulfill the performance requirements of dependable applications may increase the susceptibility of the system to transient faults. This paper describes an integration of protection against transient bus faults into the interface of the Hardisc RISC-V core. The protection is based on information redundancy with spatial redundancy features. It enables uninterrupted execution in the presence of transient faults and provides a hardware-software interface for its reporting. The benchmarking results indicate that most of the applications will be impacted minimally. The protection has a negligible impact on the maximal frequency and 8% area and power consumption overhead.

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Fingerprint matching,spoof and liveness detection:classification and literature review

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Frontiers of computer Science 2021年第1期15卷 179-196页

作者： Syed Farooq ALI Muhammad Aamir KHAN Ahmed Sohail ASLAM Department of Software Engineering University of Management and TechnologyLahore 54770Pakistan Department of Informatics and Systems University of Management and TechnologyLahore 54770Pakistan Department of Computer Science University of Management and TechnologyLahore 54770Pakistan

Fingerprint matching,spoof mitigation and liveness detection are the trendiest biometric techniques,mostly because of their stability through life,uniqueness and their least risk of *** recent decade,several techniques are presented to address these challenges over well-known *** study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few *** divides the research on fingerprint into nine different approaches including feature based,fuzzy logic,holistic,image enhancement,latent,conventional machine learning,deep learning,template matching and miscellaneous *** these,deep learning approach has outperformed other approaches and gained significant attention for future *** reviewing fingerprint literature,it is historically divided into four eras based on 106 referred papers and their cumulative citations.

关键词： computer society template matching fingerprint recognition survey deep learning machine learning

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Lockstep Vs Microarchitecture: A Comparison

Lockstep Vs Microarchitecture: A Comparison

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IEEE International SOC Conference

ISBN: (数字)9798350377569

ISBN: (纸本)9798350377576

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles).

关键词： Fault tolerance Microarchitecture Power demand Event detection Fault tolerant systems Registers System-on-chip Protection Transient analysis Clocks

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Tutorial: Safe, Secure, and Trustworthy Artificial Intelligence (AI) via Formal Verification of Neural Networks and Autonomous Cyber-Physical systems (CPS) with NNV

Tutorial: Safe, Secure, and Trustworthy Artificial Intellige...

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IEEE/IFIP International Conference on Dependable systems and Networks â€“ Supplemental Volume (DSN-S)

作者： Taylor T. Johnson Diego Manzanas Lopez Hoang-Dung Tran Computer Science Vanderbilt University Nashville TN Institute for Software Integrated Systems Vanderbilt University Nashville TN Computer Science and Engineering University of Nebraska Lincoln Lincoln NE

ISBN: (数字)9798350395709

ISBN: (纸本)9798350395716

Ensuring safe, secure, and trustworthy artificial intelligence (AI), particularly within safety-critical systems like autonomous cyber-physical stems (CPS), is of paramount importance and of crucial urgency for dependability research. One approach to establishing such desiderata of AI is through formal verification, particularly in machine learning (ML) components like neural networks, to establish they meet certain formal specifications. The Neural Network Verification (NNV) software tool implements automated formal methods for this purpose, specifically reachability analysis, and this interactive tutorial will demonstrate these to formally verify specifications in neural networks, as well as in closed-loop CPS. The tutorial begins with a lecture on the emerging research area of neural network verification, followed by interactive demos of these methods implemented in NNV. Examples will be shown from the security, medicine, and CPS domains.

关键词： Tutorials Artificial neural networks Machine learning Cyber-physical systems Security Formal specifications Artificial intelligence

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