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The effect of integration and effectiveness of artificial neural networks on information security tasks

AIP Conference Proceedings 2023年 030021页

作者： Ahmed Jamal Alshaibi Mustafa Majid Al-Ani Juliet Kadum Department Of Complex Information Security of Computer Systems Faculty of Security Tomsk State University of Control Systems and Radioelectronics Tomsk Russian Federation. Department Of Computer Science Collage of Science University of Diyala Diyala Iraq.

Issues related to information security are among the most urgent and priority in the modern world, which is caused by the widespread introduction and use of information and computing tools. One of the urgent tasks related to the field of information security is to improve the efficiency and efficiency of its systems. Thus, today a rather relevant and interesting problem is the problem related to improving the efficiency of information security systems, one of the solutions of which, related to the integration of intellectual tools and methods, will be studied in this paper. The paper pursues the goal of studying neural network technology as an innovative means of developing the information security segment. The paper examines the main information, relevance and effectiveness related to the research topic. Works through the use of statistical data and information, as well as empirical and theoretical research methods. For a more complete disclosure of the topic and obtaining reliable data, in paper uses publications and materials from domestic and foreign sources. A significant part of the article is devoted to the use of neural networks in solving problems related to information security.

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Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis

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Biomedical Signal Processing and control 2025年 109卷

作者： ZainEldin, Hanaa Bamaqa, Amna Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa A. Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, current models fail to combine innovative object identification and classification methods, limiting their diagnostic performance. A bibliometric study of 1145 research articles identified key gaps, including the lack of integration between advanced object detection models like YOLOv9 and YOLOv10 with high-performance classification architectures such as EfficientNet B7, as well as the limited utilization of diverse imaging modalities. This study proposes a novel ensemble model for Breast Cancer Classification and Detection by combining YOLOv9, YOLOv10, and EfficientNet B7 (BC2D-Y/EN) to address these gaps. The proposed model combines the advanced object detection capabilities of YOLOv9 and YOLOv10 with the high-performance classification power of EfficientNet B7. This method aims to improve detection accuracy and computational efficiency in breast cancer identification. The proposed approach is validated using four publicly available datasets: CBIS-DDSM, CBIS-DDSM RGB breast cancer dataset, The Breast1 MRI dataset, and the Breast Ultrasound Tumor Object Detection dataset, all containing expert-annotated cases of benign and malignant tumors. The experimental findings indicate that the suggested model surpasses other models, achieving precision rates of 98.56% with EfficientNet B7, 98.3% with YOLOv9, and 100% with YOLOv9. These findings highlight the advantages of advanced models and diverse imaging methodologies to enhance diagnostic accuracy and effectiveness in breast cancer detection. © 2025 Elsevier Ltd

关键词： Deep learning

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Qualitative Behavior of a Nonlinear Generalized Recursive Sequence with Delay

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Mathematical Problems in Engineering 2021年第1期2021卷

作者： Ibrahim, Tarek F. Khan, Abdul Qadeer Ibrahim, Abdelhameed Department of Mathematics Faculty of Science Mansoura University Mansoura Egypt King Khalid University Abha Saudi Arabia Department of Mathematics University of Azad Jammu and Kashmir Muzaffarabad13100 Pakistan Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Difference equations are of growing importance in engineering in view of their applications in discrete time-systems used in association with microprocessors. We will check out the global stability and boundedness for... 详细信息

关键词： Difference equations

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Simplified Rotor Angular Velocity Estimation for a Permanent Magnets Synchronous Motor by Current and Voltage Measurements 3rd

Simplified Rotor Angular Velocity Estimation for a Permanent...

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3rd International Scientific Conference on Convergent Cognitive Information Technologies, Convergent 2018

作者： Vediakova, Anastasiia O. Vedyakov, Alexey A. Department of Computer Applications and Systems St. Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia Faculty of Control Systems and Robotics ITMO University Kronverkskiy Pr. 49 St. Petersburg197101 Russia

ISBN: (纸本)9783030374358

This paper is devoted to the rotor angular velocity estimation of the permanent-magnet synchronous motor (PMSM). It is an actual problem, for example, in sensorless control. We consider a classical, two-phase model in the stator frame of the unsaturated, non-salient PMSM in the state-space representation. All parameters of the model except the stator windings resistance and rotor inertia are assumed to be known. On the first step, we find the relation between measured signals and angular velocity and excluding the unknown parameters of the motor. This relation is simplified using properties of the measured signals and represented as the first-order regression model, where the unknown parameter is the angular velocity. On the next step, we propose the estimation scheme, which is based on the gradient descent method. The efficiency is illustrated through a set of numerical simulations. © Springer Nature Switzerland AG 2020.

关键词： Sensorless control

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Discrete-time fractional-order dynamical networks minimum-energy state estimation

arXiv

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arXiv 2021年

作者： Chatterjee, Sarthak Alessandretti, Andrea Aguiar, A. Pedro Pequito, Sérgio The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Magneti Marelli S.p.A. Italy The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent (BOLD) data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this paper addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks (DT-FODN), where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Neurophysiology

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Decentralized temperature and storage volume control in multi-producer district heating

arXiv

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arXiv 2022年

作者： Martinez, Juan Eduardo Machado Ferguson, Joel Cucuzzella, Michele Scherpen, Jacquelien M.A. The Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands The School of Engineering The University of Newcastle Australia The Department of Electrical Computer and Biomedical Engineering University of Pavia via Ferrata 5 Pavia27100 Italy

Modern district heating technologies have a great potential to make the energy sector more flexible and sustainable due to their capabilities to use energy sources of varied nature and to efficiently store energy for subsequent use. Central control tasks within these systems for the efficient and safe distribution of heat refer to the stabilization of overall system temperatures and the regulation of storage units state of charge. These are challenging goals when the networked and nonlinear nature of district heating system models is taken into consideration. In this letter, for district heating systems with multiple, distributed heat producers, we propose a decentralized control scheme to provably meet said tasks stably. Copyright © 2022, The Authors. All rights reserved.

关键词： District heating

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Predicting and reducing "hospital-acquired infections" using a knowledge-based e-surveillance system

Predicting and reducing "hospital-acquired infections" using...

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作者： Noaman, Amin Y. Ragab, Abdul Hamid M. Al-Abdullah, Nabeela Jamjoom, Arwa Nadeem, Farrukh Ali, Anser G. Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Clinical Epidemiology and Infection Control Faculty of Nursing King Abdulaziz University Jeddah Saudi Arabia

The use of automated computer methods when detecting hospital-acquired infections (HAIs) enhances the validity of the surveillance in an effective manner. This is because manual infection control systems used by hospitals are time consuming and are often restricted to intensive care units. This paper proposes a new knowledge-based electronic surveillance system to predict and reduce HAIs. The system can gather patient-associated data from hospital databanks to automatically predict patient injury based on the standard central line-associated bloodstream infection algorithm for HAI detection rules. The application of the proposed electronic system reduces the number of central lines associated with infection of the bloodstream and reduces the length of stay for patient treatment and thus reduces costs. The proposed system has several advantages: (a) It is a web-based system that collects actual data from patients from several IT sources, which will help collect patient data safely and quickly, thereby predicting HAIs effectively. (b) It has an integrated simulator to generate patient records, providing the ability to train medical personnel and nurses to enhance their skills. (c) It is a multimedia-based system to improve patient health reporting. (d) It assists policymakers in reviewing and approving control plans and policies to reduce and prevent hospital injuries. (e) The investigational results of the system showed an enhancement value equal to 87%. © 2019 John Wiley & Sons, Ltd

关键词： Patient treatment

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A Backstepping Integral Sliding Mode controller Design for Partial Feedback Linearized Models of Synchronous Generators in Simple Power systems to Improve the Transient Stability 2

A Backstepping Integral Sliding Mode Controller Design for P...

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2nd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2021

作者： Pramanik, M.A.H. Roy, T.K. Anower, M.S. Ghosh, S.K. Mahmud, M.A. Rajshahi University of Engineering Technology Department of Electrical Computer Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering Technology Department of Electronics Telecommunication Engineering Rajshahi Bangladesh Rajshahi University of Engineering Technology Department of Electrical Electronic Engineering Rajshahi6204 Bangladesh Northumbria University Electrical Power Control Systems Research Group Faculty of Engineering and Environment Newcastle United Kingdom

ISBN: (纸本)9781665443197

This paper proposes a backstepping integral sliding mode controller (BISMC) based on the partial feedback linearization (PFBL) of the detailed model of synchronous generators (SGs) in simple power networks. The proposed scheme is designed to obtain the excitation control input using the detailed model of SGs in a simple power system where the PFBL is used to simplify the model. The BISMC is employed on this simplified model to ensure the transient stability during large disturbances. The inherent property of the BISMC tackles the chattering and overcomes the parameter sensitivity problem of the PFBL scheme. Simulation studies are conducted under different operating situations to justify the effectiveness of the proposed PFBL-BISMC over a partial feedback linearizing controller (PFBLC). © 2021 IEEE.

关键词： Synchronous generators

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A Composite Nonlinear controller Design to Enhance the Large-Signal Stability of a DC Distribution System with Constant Power Loads 2

A Composite Nonlinear Controller Design to Enhance the Large...

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2nd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2021

作者： Ghosh, S.K. Roy, T.K. Pramanik, M.A.H. Mahmud, M.A. Rajshahi University of Engineering Technology Department of Electrical Electronic Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering Technology Department of Electronics Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering Technology Department of Electrical Computer Engineering Rajshahi6204 Bangladesh Northumbria University Newcastle Electrical Power Control Systems Research Group Faculty of Engineering and Environment United Kingdom

ISBN: (纸本)9781665443197

A composite backstepping integral sliding mode controller (BISMC) is proposed in this work for a DC-DC boost converter feeding a constant power load (CPL) in a simple DC distribution system. Here, the main objective is to ensure the large-signal stability with wide variations in operating regions. The proposed BISMC is designed based on the feedback linearized model of the DC-DC boost converter with a CPL where the average state-space model is exactly linearized. The use of exact feedback linearization overcomes the non-minimum phase problem of a DC-DC boost converter due to the presence of the CPL that exhibits negative impedance characteristics. The control law is obtained using the combination of backstepping and integral sliding mode control schemes where both approaches ensure the faster convergence of errors for all states and the integral action on the sliding surface is designed in such a fashion that will ensure the constant frequency operation of the power converter feeding the CPL while eliminating the variable frequency associated problem of the existing controllers. In addition, the stability of the whole system is analyzed through the Lyapunov stability theory. Numerical simulations are conducted to verify the performance of the designed composite nonlinear controller whose performance is also compared with a double-loop proportional integral (PI) controller to verify the superiority of the designed controller. © 2021 IEEE.

关键词： Backstepping

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Real-time analysis of neuronal firing patterns via Hawkes processes

Real-time analysis of neuronal firing patterns via Hawkes pr...

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Asilomar Conference on Signals, systems & computers

作者： László Gerencsér György Perczel Systems and Control Lab Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest Hungary

Quick and reliable real-time detection of the onset of epileptic seizures is a key computational component for the development of high quality closed loop neurostimulators. The objective of this paper is the construction of a low-cost algorithm for real-time statistical analysis of EEG signals of epileptic patients. The mathematical methodology is the theory of self-exiting point processes or Hawkes processes, see Truccolo (2016) or Lambert et al. (2018). The main advance is the development of a computationally feasible recursive maximum likelihood method for fitting a Hawkes process the impulse response function of which is a sum of exponential functions. The viability of the method is proven by strong mathematical heuristics and numerical tests on simulated data, based on a priori analysis of human data recorded during both inter-ictal and ictal epochs.

关键词： Maximum likelihood estimation Maximum likelihood detection Statistical analysis Firing Fitting Reliability theory Real-time systems

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