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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...

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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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Soft Matrix Game: A Hesitant Fuzzy MCDM Approach

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American Journal of Mathematical and Management Sciences 2021年第2期40卷 107-119页

作者： Jana, Jishu Kumar Roy, Sankar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University MidnaporeWest Bengal India

Soft set theory has emerged recently as a new mathematical tool to handle uncertainty. Sometimes decision makers are not sure about the decision-making criteria, where soft set theory provides an idea to deal with such uncertainties. Multi-criteria decision making (MCDM) involves choosing the best from several alternatives. MCDM methods such as TOPSIS and VIKOR depend on an aggregating function for presenting "closeness to the ideal" which arises due to the compromise solution. The VIKOR method of compromise ranking describes a compromise solution, providing a maximum for the "maximizing player" and minimum for the "opponent", which is an effective approach in an MCDM game. TOPSIS method presents a solution with the shortest distance to the positive ideal solution (PIS) and largest distance from the negative ideal solution (NIS). Also, hesitant fuzzy soft set is an appropriate tool to tackle the imprecise parameters introduced in MCDM problems by the decision maker (DM). In this paper, we extend the VIKOR and TOPSIS methods for solving MCDM game problems with hesitant fuzzy soft payoffs to determine the optimal strategies. Finally, a numerical example is incorporated to verify the extended VIKOR approach and the results are compared with those for the TOPSIS method. The paper ends with conclusions and outlooks. © 2020 Taylor & Francis Group, LLC.

关键词： Numerical methods

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Ensuring the reliability of remote online monitoring systems

Ensuring the reliability of remote online monitoring systems

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

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

ISBN: (纸本)9781665472296

the issue of ensuring the reliability of the functioning of global systems of remote online monitoring of the condition of patients is considered. Remote monitoring is in demand when supervision the health status of disabled people with cardiovascular diseases, pregnant women and children. It is especially in demand in the context of a pandemic and the accompanying restrictions on movement. Monitoring is carried out 'transparently' for the patient, without involving him in the subtleties of the process of interaction with a medical institution, ensuring the preservation of his mobility due to the proposed concept of implementing a wearable online monitor device. The procedures of the online monitor initialization and distribution of functions in it are considered. Threats to the normal functioning of distributed systems with online monitors are presented, including existing problems of a general nature related mainly to global communications. Solutions are offered to ensure reliable operation of wearable devices as part of online monitors. © 2022 IEEE.

关键词： Reliability

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Effects of Different Optimizations at Detection of Vehicles and Pedestrians with YOLOv8

Effects of Different Optimizations at Detection of Vehicles ...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Ecem İren Computer Programming Program İzmir Kavram Vocational School İzmir Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Improvements in deep learning has facilitated traffic management by providing analyzing and prediction capabilities on the road. In this study, vehicle and pedestrian detection with YOLOv8 was carried out with different optimizations such as Adam, Stochastic Gradient Descent (SGD) and RMSprop, at the end of the experiments, the model with SGD which produced $87.1 \%$ in mAP was seen as the best optimizer when compared with others at detecting objects.

关键词： Deep learning YOLO Pedestrians Roads Stochastic processes Optimization methods Traffic control Real-time systems Pattern analysis Monitoring

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Rewriting for Monoidal Closed Categories 7

Rewriting for Monoidal Closed Categories

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7th International Conference on Formal Structures for Computation and Deduction, FSCD 2022

作者： Alvarez-Picallo, Mario Ghica, Dan Sprunger, David Zanasi, Fabio Programming Languages Laboratory Huawei Research Centre United Kingdom Department of Computer Science University of Birmingham United Kingdom Programming Languages Laboratory Huawei Research Centre Reading United Kingdom Department of Computer Science University College London United Kingdom

ISBN: (纸本)9783959772334

This paper develops a formal string diagram language for monoidal closed categories. Previous work has shown that string diagrams for freely generated symmetric monoidal categories can be viewed as hypergraphs with interfaces, and the axioms of these categories can be realized by rewriting systems. This work proposes hierarchical hypergraphs as a suitable formalization of string diagrams for monoidal closed categories. We then show double pushout rewriting captures the axioms of these closed categories. © Mario Alvarez-Picallo, Dan Ghica, David Sprunger, and Fabio Zanasi

关键词： Algebra

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Design of a Novel Safe Approach to Monitor Drivers While Operating Their Cars 7

Design of a Novel Safe Approach to Monitor Drivers While Ope...

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7th International Conference on Advances in Biomedical Engineering, ICABME 2023

作者： Elhawawsha, Aseel Zeine, Carla Zeid Daou, Roy Abi Kalinowski, Dominik Khattar, Joseph Telawi, Samer Boercsoek, Josef University of Kassel Department of Computer Architecture and System Programming Kassel Germany Université la Sagesse Saculty of Engineering Mechatronics Eng. Department Beirut Lebanon

ISBN: (纸本)9798350325850

Road accidents have been known to be one of the leading death causes around the world for a long time. Thus, cars and all kinds of road vehicles form a huge source of danger, and they relate to multiple high risks. There are many factors that can contribute to road traffic accidents such as the health condition of the driver if a medical emergency event happened while driving, drowsiness, or mind-wandering. The proposed system can monitor the behavior of the driver while driving as well as his/her health status. Moreover, it analyzes the monitored data, alerts the driver in a timely manner, and assists him/her in adjusting the driving behavior. Different dangerous and risky situations which can be faced during driving can be captured and avoided by using such a system which leads at the end to increase road traffic safety and reduce vehicle collisions. Notifications are developed in cases of drowsiness, loss of focus, or other concerns that may emerge while driving. However, when the driver's ability to operate the vehicle safely is hampered by a health concern, the implemented system is only able at this stage to alarm the driver itself and the surrounding cars as a first level of alarms. © 2023 IEEE.

关键词： Roads and streets

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Two-stage multi-item 4-dimensional transportation problem with fuzzy risk and substitution

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9469-9496页

作者： Devnath, Sudeshna De, Manoranjan Mondal, Seema Sarkar Maiti, Manoranjan Department of Mathematics National Institute of Technology Durgapur Durgapur713209 India Department of Mathematics Mugberia Gangadhar Mahavidyalaya West Bengal Purba Medinipur India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore721102 India

With the worldwide transport infrastructural development, there are several connecting roads between the cities for transportation. Some of these roads are smooth, and some are very rough, which invites some risks during transportation. For public distribution of some commodities, Govts. of developing countries collect those from the open markets, transport those to some warehouses for temporary storage, and from there, again transport to the fair-price shops for public distribution. With these facts, in this investigation, Govt.’s above scheme is modelled as two-stage four-dimensional transportation problems with different choices of conveyances and routes. There are some path and conveyance-wise uncertain travel risks. In fair-price shops, substitutable essential items (say rice and wheat) are sold with the necessary ones (say, mustard oil, etc.), which are sold by push-sale against some concession in an essential item (say, more rice, etc.). Thus, there is a trade-off between the ‘push-sale’ and ‘concession’, and hence an optimum decision is derived. Several models are developed depending on management’s and customers’ decisions, substitutability, push-sale, and environments. For road risk, a two-parameter (road’s roughness and distance) Mamdani-type fuzzy logic is implemented. The system’s profits for different models are maximised in both crisp and fuzzy environments and illustrated numerically using Generalised Reduced Gradient method through Lingo software. Here, the Govt.’s policy of introducing new products (termed as necessary item) among the public against a concession in an essential item has been introduced and analysed. This push-sale is effective up to a certain level, and after that, profit goes down. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fuzzy logic

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