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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Ismail, Ahmad Vishnyakov, Sergey Networks Moscow Power Engineering Institute Department of Computing Machines Systems Moscow Russia

ISBN: (纸本)9798350350890

Object tracking based on deep learning is a challenging task. Accurate object detection and tracking requires a neural network that is deeper than a typical network. Therefore, it requires more levels of processing and takes more time. The YOLO model is considered one of the most state-of-the-art neural networks. Its wide application in object tracking depends on the speed of processing and extracting object features. In this article, we develop the YOLO model by changing the final layer from a fully connected layer to a fully convolutional layer, changing the activation function, and adding a batch normalization layer. A comparison of the results with the old YOLO model shows improved accuracy and speed. Additionally, the developed model is less sensitive to noisy environments. © 2024 IEEE.

关键词： Target tracking

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Using Machine Learning Methods to Analyze Optimal Oil Drilling Sites 6

Using Machine Learning Methods to Analyze Optimal Oil Drilli...

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6th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2024

作者： Durova, M.A. Zein, A.N. Tsaplin, D.O. Filippov, D.S. Krasnova, A.A. Ismailova, A.D. National Research University 'MPEI' Computing Machines Systems and Networks Department Moscow Russia National Research University 'MPEI' Computing Technologies Department Moscow Russia

ISBN: (纸本)9798350382891

The article is devoted to the problem of determining the optimal coordinates for drilling oil wells. This article discusses various mathematical solutions for determining the coordinates of drilling points with minimal deviation and presents regression and interpolation methods. Each method has been analyzed and implemented to solve the problem of determining the location of the drilling site. Datasets (coordinates of points) and maps are considered as input data. The input data is used both to calculate the coordinates of the drilling point and to compare the accuracy of the results. The Euclidean distance is used as a guideline, and after comparing the final result, it became clear that the polynomial regression of the fifth degree method is 300 times more accurate than linear regression. © 2024 IEEE.

关键词： Oil fields

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Design a Method for Tracking Moving Object and Predicting its Trajectory in a Noisy Environment 6

Design a Method for Tracking Moving Object and Predicting it...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Ismail, Ahmad Vishnyakov, Sergey Moscow Power Engineering Institute Department of Computing Machines Systems and Networks Moscow Russia

ISBN: (纸本)9798350325485

Target tracking is significant due to the fact that detection and tracking methods are being developed in order to accommodate the system's requirements regarding accuracy and speed. This article will propose a method of target tracking that is intended to address the practical issues of signal loss or a significant amount of noise that affects the process of target detection. The procedure of predicting the course of the object is one of the most promising tasks at the present time, the value of the predictions is apparent in the use of robots, automation and smart tracking radars. The mathematical foundation of the process of predication is related to the previous behavior of the target, the preceding motion of the object conveys information about its general trajectory. This is similar to the Kalman *** created a module that predicts the movement of an object, the module is a hybrid of a Kalman filter and a numerical algorithm that estimates the path of any trajectory in terms of polynomial. The results indicate that the supposed method is a viable solution to the predication problem with the absence of a signal. © 2023 IEEE.

关键词： Target tracking

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Design a Hybrid Neural Network Tracking System Using Multiple Cameras 7

Design a Hybrid Neural Network Tracking System Using Multipl...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Ismail, Ahmad Vishnyakov, Sergey Zedan, Mohammad Moscow Power Engineering Institute Department of Computing Machines Systems and Networks Moscow Russia Moscow Power Engineering Institute Department of Diagnostic Information Technologies Moscow Russia

ISBN: (纸本)9798350350890

Neural networks have become a transformative technology, enabling significant advances over the past few decades. In particular, detection and tracking systems benefit from the ability of neural networks to learn and extract features, resulting in accurate object classification and localization. In this paper, we propose a hybrid neural network framework that integrates data from multiple cameras positioned along an object's path. This multi-camera configuration expands the system's field of view and improves prediction accuracy when tracking an object's position. We conduct extensive experiments to evaluate the performance of our proposed model, which demonstrates superior tracking accuracy and robustness across different environments compared to traditional single-camera systems. © 2024 IEEE.

关键词： Multiple cameras Multiple objects Neural Network Prediction trajectory Tracking system

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Digital Maturity Assessment Model (DMAM): assimilation of Design Science Research (DSR) and Capability Maturity Model Integration (CMMI)

Digital Transformation and Society

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Digital Transformation and Society 2025年第2期4卷 128-152页

作者： Omol, Edwin Juma Mburu, Lucy Waruguru Abuonji, Paul Anyango Department of Software Development and Information Systems (SDIS) School of Technology KCA University Nairobi Kenya Chair of Department - Networks and Applied Computing (NAC) School of Technology KCA University Nairobi Kenya KCA University Nairobi Kenya

Purpose: This study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT). Design/methodology/approach: DMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts. Findings: DMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization. Originality/value: DMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature. © 2024, Edwin Juma Omol, Lucy Waruguru Mburu and Paul Anyango Abuonji.

关键词： Capability Maturity Model Integration (CMMI) Design Science Research (DSR) Digital Maturity Assessment Model (DMAM) Goal Question Metric (GQM) Proof-of-Concept (POC) & Minimum Viable Product (MVP) Resource Dependence Theory (RDT)

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The Concept of Cognition as Categorization in the Development of New Metaheuristics and Algorithms Inspired by Nature 46

The Concept of Cognition as Categorization in the Developmen...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Tulicic, Domagoj Ivkovic, Nikola College for Information Technologies Department of Computer Systems and Networks Zagreb Croatia Faculty of Organization and Informatics Department of Computing and Technology Varaždin Croatia

ISBN: (纸本)9789532331042

The sensorimotor system is a crucial mechanism that enables organisms to categorize information in their environment, a skill necessary for survival. According to cognitive scientist Steven Harnad's 'To Cognize is to Categorize: Cognition is Categorization all living things are essentially sensorimotor systems. This approach is proposed to serve as a novel concept for the development of algorithms inspired by swarm intelligence. Many authors of such algorithms have drawn inspiration from animal species, attempting to emulate their abilities to interact with and navigate their environment. However, these abilities are essentially rooted in the organisms' categorization skills, which are a fundamental characteristic of all sensorimotor systems. Therefore, when designing new algorithms and metaheuristics, it is unnecessary to seek out new species with specific behaviors;instead, the entire sensorimotor system can be observed, and its sensor capabilities can be incorporated even if they do not exist in nature. In order to justify this, abstraction was used as a way to demonstrate the creation of concepts. The paper offers a new perspective on the development of swarm intelligence algorithms and promotes a holistic approach to algorithm design based on Steven Harnad's viewpoint of sensory motor systems. © 2023 MIPRO Croatian Society.

关键词： Swarm intelligence

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Organization of Support and Education for Adults in the Context of Transition to the Concept of Information Society 7

Organization of Support and Education for Adults in the Cont...

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7th International Conference on Information Technologies in Engineering Education, Inforino 2024

作者： Filina, Elizaveta Shindina, Tatiana National Research University MPEI Department of Computing Machines Systems and Networks Moscow Russia Institute of Remote and Additional Education National Research University MPEI Moscow Russia

ISBN: (纸本)9798350371369

Development of an information-based society is a priority for the future. Educational system is one of the fundamental components, and teaching adults who require new digital competencies is one of the main directions of the development. The article is focused on researching interactions between adult students and educational organizations on the basis of a cross-functional mechanism that will help to compare different states of interaction and select their balanced combinations. The article represents the structural flow chart of the formation of a system of sustainable business development and innovative solutions under the influence of the educational sector and government support measures. The feature of the proposed approach is a development of digital competencies on the basis of entropy approach, which allows assessing the effectiveness of information exchange between subjects of educational activities. © 2024 IEEE.

关键词： Entropy

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External Fields Boundaries Estimation of High-Q Shielded Dielectric Resonator 4

External Fields Boundaries Estimation of High-Q Shielded Die...

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4th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2022

作者： Gevorkyan, Vladimir M. Kasantsev, Yuri A. Shutov, Aleksandr V. National Research University 'MPEI' Department of Computing Machines Systems and Networks Moscow Russia

ISBN: (纸本)9781665414340

Dielectric resonators are widely used in modern microwave technology. They are small in size and weight and contribute to the miniaturization of microwave devices. This article discusses the electromagnetic field of a dielectric resonator from the point of view of division into internal and external fields. This paper presents a numerical method for estimating the boundaries of external fields of a high-Q dielectric resonator. The method is based on the calculation of heat losses in imperfect shielding walls (waveguide) at a distance from the dielectric resonator. A numerical experiment shows that the external fields of the resonator are negligibly small at distances of the order of one wavelength in the waveguide. © 2022 IEEE.

关键词： Q factor measurement

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A Real-Time Object Tracking Method 5

A Real-Time Object Tracking Method

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Ismail, Ahmad Vishnyakov, Sergey Moscow Power Engineering Institute Department of Computing Machines Systems and Networks Moscow Russia

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Artificial Intelligence is one of the most vital prospects in our current life;its applications vary from robotics, transport, houses, to industrial facilities and planes. While neural networks offer the ability to recognize and track new objects, it takes time during learning process especially deep neural networks. There are specific computer vision systems, which require high accuracy and high speed conditions, regardless the knowledge of tracked object. Such as a tracking system for a measurement instrument which scans a certain area and follows the location of object(s). In this paper, we introduce a new tracking method for one object in real time system, based on a theory, building a new powerful algorithm to track the center of object in sub-pixel accuracy. © 2022 IEEE.

关键词： Real time systems

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