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检索条件"机构=Computer Programming"

共 1374 条记录，以下是61-70 订阅

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Hassenstein-Reichardt Detector Using Controllable Single Pulse Time-Delay Circuit for Neuromorphic Hardware 11

Hassenstein-Reichardt Detector Using Controllable Single Pul...

引用

11th International Scientific Conference on computer Science, COMSCI 2023

作者： Trendafilov, Ianislav Gancheva, Veska Neuromorphica Ltd. Sofia1335 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

Inspired by the visual system of the fruit fly, we had created a generic building block for neuromorphic hardware that is vital for third generation neural networks. This enables time delay to be parametrized in a cheap and inexpensive way. The advantage of our proposal is the possibility of building and testing new networks using dynamicist approach in artificial intelligence. © 2023 IEEE.

关键词： Neural networks

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Research of Methods of Identifying the computer Systems State based on Bagging Classifiers 3

Research of Methods of Identifying the Computer Systems Stat...

引用

3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Gavrylenko, Svitlana Hornostal, Oleksii Chelak, Viktor National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

Peculiarities of constructing ensemble bagging classifiers for identifying the state of a computer system under conditions of noisy data are studied. Decision trees and multilayer perceptron were used as basic classifiers. It was found that the accuracy of the bagging algorithm with decision trees as basic classifiers with standard settings ranges from 84.4% to 88.7%. The use of Bootstrap algorithms for the formation of data samples: Pasting, Bootstrapping, Random Subspace, Random Patches Ensemble and the selection of the number of basic classifiers in the ensemble made it possible to increase the classification accuracy to 90.2%. The following parameters were added to improve the accuracy of bagging classifiers based on the multilayer perceptron: the algorithm for forming data samples, the number of basic classifiers in the ensemble, the function of optimizing the neural network, the function of activating hidden layer, size of hidden layers. The recommendation was made to choose the value of the analyzed parameters for the creation of bagging ensembles with multilayer perceptrons, which made it possible to increase the accuracy of computer system identification up to 92.2%. The obtained results have further practical significance and can be used in improving the methods of identifying the state of computer systems. © 2022 IEEE.

关键词： Multilayer neural networks

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Neuromorphic Assisted Sensor Grids 32

Neuromorphic Assisted Sensor Grids

引用

32nd International Scientific Conference Electronics, ET 2023

ISBN: (纸本)9798350302004

A particular problem in using artificial intelligence techniques in the sensor grid is the high power consumption. Remote sensors are usually limited by the amount of power available, thus our general goal is to minimize it while preserving accurate experimentation results. Using emerging technologies like spiking neural networks and neuromorphic hardware, we can create sensor grids that perform data processing with preserving low power consumption. In this paper, we demonstrate a remote node with integrated visual sensor that works on less than 10mW of energy, while performing continuous scene monitoring, object detection and classification. The system has intelligent power management and the ability to send data wirelessly. © 2023 IEEE.

关键词： Object detection

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Software Anomaly Detection Method Based on Artificial Neural Network

Software Anomaly Detection Method Based on Artificial Neural...

引用

IEEE International Conference on e-Business Engineering (ICEBE)

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

A growing range of data sets have been created in recent years; these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. As a result, network security in data transmission is becoming a more important area of study. One well-known method for safeguarding computer systems is the deployment of intrusion detection systems. This study proposes an artificial intelligence based method for data analysis-based anomaly detection. Methods based on machine learning and rules are mixed together. The right rules are created via a genetic algorithm. Relevant features are extracted using principal component analysis with the goal of enhancing performance. The KDD Cup 1999 dataset is used to empirically validate the suggested procedure, satisfying the criterion of using appropriate data. Using the well-known benchmark dataset, the suggested approach is used to identify and examine four different kinds of attacks: Neptune, Ipsweep, Pod, and Teardrop. During the machine learning phase, the data is categorized into categories of attacks and normal behavior after the features set during the training phase are tested. For the purpose of data analysis, the input data is divided into training and testing sets for an artificial neural network. The first 80% of the data are used to train the neural network, and the remaining 20% are used for testing. The estimated accuracy improves with the number of epochs and is higher for training data and lower for validation test data, according to experimental results. Consequently, the trained model can be retained and used to detect discrepancies. The learnt model is used to the system to compute new input parameters that are not used during training or validation.

关键词： Training Accuracy Predictive models Network security Feature extraction Data structures Data models Software Anomaly detection Principal component analysis

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Variable Length Optimization Based on Integrated Particle Swarm Optimization for Data Gathering in Wireless Sensor Networks 4

Variable Length Optimization Based on Integrated Particle Sw...

引用

4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Al-Jumaah, Kareema Inan, Timur Altinbas Univeristy Information Technology Istanbul Turkey Altinbas Univeristy Computer Programming Istanbul Turkey

ISBN: (纸本)9781665468350

Data gathering is an active research topic for wireless sensor networks WSNs with internet of things IoTs. Optimal data gathering enables collecting sensor data efficiently with minimum cost and energy consumption for the limited resources nodes. Enabling mobile sinks for data gathering helps at reducing the cost further and enhancing the energy preservation. This article presents a novel optimization for data gathering in WSNs using multiple mobile sinks by extending multi-objective variable length particle swarm optimization to include tabu search. The developed algorithm is designated as variable length particle swarm optimization with tabu search (SCPSO-TS). It provides the best number of mobile sinks along with their corresponding trajectories that are defined using rendezvous points (RPs). The evaluation has shown a superiority of SCPSO-TS over the benchmarks in terms of domination percentage that is measured by set coverage. © 2022 IEEE.

关键词： Multiobjective optimization

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Parallel Multithreaded Filtering of X-Ray Computed Tomography Images

Parallel Multithreaded Filtering of X-Ray Computed Tomograph...

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Computing in Natural Sciences, Biomedicine and Engineering (COMCONF), International Conference on

作者： Veska Gancheva Kamelia Raynova Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

Medical image quality is very important. High quality ensures the standard of medical diagnosis, treatment, and patient life through health care or automated intelligence systems for medical diagnosis, monitoring, and treatment. The computing difficulties in processing medical images are discussed in the study. Proposing parallel computational models and program implementations based on medical image filtering techniques is one of the main issues. A filter-based parallel computational model is designed. Implementing a multithreaded parallel program verifies the suggested parallel model. An analysis of the effectiveness of medical image filters using a parallel multithreaded computer implementation that generates output images for each type of applied filter and applies filters on a list of compressed medical images. The BlackAndWhiteFilter, UVFilter, BinaryThresholdFilter, and RobertFilter have been applied. Experimental estimates have been analysed for the parallel performance metrics execution time and speedup. The performance estimation and scalability analyses demonstrate the strong scalability of the proposed solution.

关键词： Filters Computational modeling Scalability Instruction sets Computed tomography Image filtering Medical diagnosis Medical diagnostic imaging X-ray imaging Standards

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METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALGORITHM 5

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALG...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Zozulia, Vladislav Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350384499

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models. ©2024 IEEE.

关键词： Network intrusion

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Healthcare Data Analytics Based on Machine Learning

Healthcare Data Analytics Based on Machine Learning

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they enable the identification and creation of models of complicated datasets and the interactions between them. This, in turn, enables the successful prediction of future outcomes associated with a specific sickness type. An artificial intelligence-based approach to healthcare data analytics is presented, which leverages data to build a desired model and solve a particular issue. The suggested approach for healthcare data analytics uses a random forest and feedforward artificial neural network with two hidden layers as its basis to get the best model.

关键词： Analytical models Data analysis Computational modeling Medical services Artificial neural networks Learning (artificial intelligence) Data models Telecommunication computing Informatics Random forests

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Cyclic Quasi-Orthogonal Matrices in Image and Signal Encoding

Cyclic Quasi-Orthogonal Matrices in Image and Signal Encodin...

引用

2022 International Conference on Information Science and Communications Technologies, ICISCT 2022

作者： Alexandr, Sergeev Saint Petersburg State University of Aerospace Instrumentation Department of Computer Technology and Programming Saint Petersburg Russia

ISBN: (纸本)9781665472296

orthogonal and quasi-orthogonal matrices with a limited number of element values and structured in some way are of considerable interest for many technical applications related to image processing and signal coding. The paper classifies and considers cyclic matrices and matrices with cyclic blocks. Descriptions of noise-resistant encoding methods of images and their protective masking using such matrices are given. Based on the analysis of the autocorrelation functions of new code sequences obtained from strings of modified cyclic quasi-orthogonal matrices and the simulation performed, numerical values of the quality parameters of the new code sequences are given. It is shown that their application makes it possible to reduce the maximum level of the side lobe of the aperiodic autocorrelation function, which allows us to conclude that their application is promising and that signal coding algorithms can be revised. © 2022 IEEE.

关键词： Signal encoding

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New approach to predict warranty costs using a new bivariate reliability prediction model 32nd

New approach to predict warranty costs using a new bivariate...

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32nd European Safety and Reliability Conference, ESREL 2022

作者： Krini, Abderrahim Börcsök, Josef Engineering of Reliability and Quality Robert Bosch GmbH Germany Computer and System Programming University of Kassel Germany

ISBN: (纸本)9789811851834

An important marketing strategy of automotive manufacturers is to promote high-quality products by extending warranty periods, which inevitably generates additional costs. In general, different loads on a control unit cause a failure. Therefore, a failure can be traced back to the loads experienced. In reliability theory, the operating time t is commonly used as a reference value for describing the load acting on a system, since the electronic control units (ECUs) are only under current and voltage for the duration t and are therefore only loaded during this time. However, obtaining a suitable database is difficult. The failure data of ECUs collected during the warranty period from the supplier in the event of complaints can be regarded as complete and therefore represent a suitable database for determining reliability parameters. The warranty data are time-censored and reflect the field behaviour of the ECUs during the warranty period. As a rule, when collecting the data, the duration (production and complaint date) of the ECUs in the field is recorded in addition to the cause of failure. With a new multivariate prediction model that uses this data to forecast the expected ECU failures and knowledge of the costs of a warranty case, the expected additional costs of a warranty extension can be calculated. © 2022 ESREL2022 Organizers. Published by Research Publishing, Singapore.

关键词： Automotive industry

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