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A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10673-10693页

作者： Yousif, Nada R. Balaha, Hossam Magdy Haikal, Amira Y. El-Gendy, Eman M. Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features;and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches. © 2022, The Author(s).

关键词： Feature extraction

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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control valve simulation method integrable into the global gradient algorithm

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ISH Journal of Hydraulic Engineering 2025年第1期31卷 1-7页

作者： Sivakumar, P. Gorev, N.B. Gorev, V.N. Kodzhespirova, I.F. Shedlovsky, I.A. Itanagar India Department for Functional Elements of Control Systems Institute of Technical Mechanics Dnipro Ukraine Department of Physics Dnipro University of Technology Dnipro Ukraine Department of Automation and Computer Systems Dnipro University of Technology Dnipro Ukraine

This paper presents a refinement of a method that simulates flow- and pressure-regulating valves by replacing them with pipes and adjusting the resistances (diameters) of those pipes to meet the valve settings. The method, referred to as the replacing pipe method (RPM), is readily integrable into the global gradient algorithm, which is the heart of the commonly used EPANET 2 water distribution system modeling software. The RPM proved to be more robust, yet slower than the EPANET 2 valve handling heuristics. In this work, the RPM was modified to increase its computational speed. The modification involved the RPM check valve handling algorithm and condition for updating the diameters of the valve-replacing pipes. The modified RPM was tested as part of a hybrid simulator, which switches from the EPANET 2 valve handling heuristics to the RPM when the former fails. The test networks included a large network with thousands of nodes, pipes, and valves, and a case of an ill-posed problem. As the testing showed, the modified RPM offers a significant reduction in the number of iterations compared to its previous version. © 2024 Indian Society for Hydraulics.

关键词： Safety valves

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A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

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Digital Communications and Networks 2024年第3期10卷 645-654页

作者： Xu Li Gwanggil Jeon Wenshuo Wang Jindong Zhao School of Computer and Control Engineering Yantai UniversityYantai264005China Department of Embedded Systems Engineering Incheon National UniversityAcademyro-119Incheon22012Republic of Korea

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy *** a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy ***,introducing signature technology leads to additional signature verification *** the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature ***,it is necessary to design an efficient multi-signature verification scheme while ensuring *** this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature *** our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data *** results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

关键词： 5G network Crowd-sensing Privacy protection Ring signature Batch verification

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A review of artificial intelligence based malware detection using deep learning

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Materials Today: Proceedings 2023年 80卷 2678-2683页

作者： Mustafa Majid, Al-Ani Alshaibi, Ahmed Jamal Kostyuchenko, Evgeny Shelupanov, Alexander Department of Complex Information Security of Computer Systems Faculty of Security Tomsk State University of Control Systems and Radioelectronics Tomsk634050 Russia

Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activities, it is made possible that now we have malware patterns that are used for training machine learning models. Thus machine learning became indispensable for malware detection. The traditional machine learning models have limitations in performance as the training depth is limited. The emergence of deep learning models paved way for more training possibilities and improvement in detection accuracy with least false positives. This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN, RNN, LSTM and auto encoders. LSTM is found to have memory in the cell to have better possibilities. Auto encoders are found to have better unsupervised approach with encoding and decoding to arrive at abnormalities (malware) detection. There are many contributions found using machine learning and deep learning towards Android malware detection. This paper provides knowledge that leads to further research in deep learning which is essential to improve the state of the art. © 2021

关键词： Malware

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A review on security analysis of cyber physical systems using Machine learning

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Materials Today: Proceedings 2023年 80卷 2302-2306页

作者： Ahmed Jamal, Alshaibi Mustafa Majid, Al-Ani Konev, Anton Kosachenko, Tatiana Shelupanov, Alexander Department of Complex Information Security of Computer Systems Faculty of Security Tomsk State University of Control Systems and Radioelectronics Tomsk634050 Russia

The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks. © 2021

关键词： Intrusion detection

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The Application of Technical Vision for Automation of Measuring Cycles in CNC Machines

The Application of Technical Vision for Automation of Measur...

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2023 International Ural Conference on Electrical Power Engineering, UralCon 2023

作者： Krotova, Nataliya Pushkov, Roman Department of Computer Control Systems Msut 'STANKIN' Moscow Russia

ISBN: (纸本)9798350303391

This article describes a possible method for automating measurements on CNC machines that support measurements in semi-automatic mode by calling parameterized measurement cycles. The proposed method is based on the use of a vision system integrated with a CNC system and approximately determining the geometric parameters and coordinates of the workpiece to fill in the parameters of the measuring cycle. The requirements for the installation of the system hardware on the equipment are formulated. The methodology of measurement using the system is described. Algorithms for obtaining and software processing of images are described, including recognition of the area with the workpiece, automatic correction of brightness and contrast of input images and determination of the contours of the workpieces and their parameters. An algorithm for processing data on the obtained contours for calculating the approximate dimensions of the workpiece relative to the marks previously applied to the machine desktop is also described. The interaction of the vision system with the CNC system is described to run a template control program that causes a filled measuring cycle to determine the position and dimensions of the workpiece accurately. The prototype of the vision system is installed on real equipment. © 2023 IEEE.

关键词： computer vision

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Automation of Configuration of Slave Devices via the EtherCAT Fieldbus for the Collection of Technological Data

Automation of Configuration of Slave Devices via the EtherCA...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Martinova, Liliya Martemianova, Natalia Mstu Stankin Department of Computer Control Systems Moscow Russia

ISBN: (纸本)9798350345551

The article substantiates the necessity and presents an approach to creating a software module for automatic configuration of slave devices when using EtherCAT SOEM master as an application solution without the mediation of TwinCAT software. The proposed solution is designed to contribute to the digitalization of production activities at the workshop level, reducing human participation in the process of setting up control systems for technological equipment. The presented mechanism of automatic configuration of slave devices (drives, PLCs, etc.) over the EtherCAT fieldbus provides flexibility, scalability, adaptability and interoperability of any production intelligent system. The technology of forming a block diagram of the module for auto-configuration of inputs and outputs of subordinate devices is described. The practical implementation of the module for automatic configuration of slave devices is presented on the basis of the digital twin of the "AxiOMA control"CNC system. The results of testing the module using the VIPA baskapler are presented. © 2023 IEEE.

关键词： Intelligent systems

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Development of a Cross-Platform Software Application for Programming CNC Machine Tools

Development of a Cross-Platform Software Application for Pro...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Martinova, Liliya Fokin, Nikolay Mstu Stankin Department of Computer Control Systems Moscow Russia

ISBN: (纸本)9781665475952

This paper describes an approach to the development of a cross-platform application for creating universal cycles of typical types of machining working on machines with different CNC systems. The problem of the portability of control programs to numerical control systems from different manufacturers is outlined and the need for the creation of this toolkit is justified. The concept and implementation of a cross-platform application for programming CNC machines are presented. An architectural model of a cross-platform toolkit for the preparation of control programs for CNC machines is presented, which is based on the application subsystem and a gallery of macro programs, which includes the possibility of flexible expansion of the system without negative consequences for the interaction of system modules. Typical elementary types of processing are represented by macroprograms. The user interface of the software application is based on the principles of constructing the interface of dialog systems embedded in the CNC system, based on the sequential input of information about the processing being performed. The results of testing a cross-platform application on Fanuc, Siemens CNC systems and on the digital twin of the CNC system "AxiOMA control"are described. © 2023 IEEE.

关键词： User interfaces

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Integration of Intelligent Industrial systems into a Workshop-Level Information Network

Integration of Intelligent Industrial Systems into a Worksho...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Martinova, Liliya Sokolov, Sergey Pushkov, Roman MSTU STANKIN Department of Computer Control Systems Moscow Russia

ISBN: (纸本)9781665464291

The article is devoted to the study and solution of problems on the formation of a system for collecting and intermediate accumulation of shop-level data for its subsequent integration into a single information space of digital manufacturing. The technologies and tools of data collection from the manufacturing equipment of the enterprise are analyzed. An approach to building a distributed cloud system for collecting and analyzing data from technological equipment for machine-building industries is described. The processes of controlling equipment and means of automation at the workshop level are analyzed and a five-level management structure is proposed, taking into account the network organization of manufacturing. Criteria for assessing the applicability of technologies for the implementation of the proposed system are defined. The methods of data collection are analyzed and recommended depending on the size of production, the number of technological complexes and individual pieces of equipment. Experimental studies have been carried out on the basis of a developed mock-up of an autonomous data collection system based on ODOT remote I/O modules with interaction via the EtherCAT industrial protocol from sensors and actuators of technological equipment. © 2023 IEEE.

关键词： Distributed cloud

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