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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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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 NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Tire - Road Friction Characteristics Estimation Based on ESO Observer and LSTM Neural Networks

Tire - Road Friction Characteristics Estimation Based on ESO...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Tianyu, Hou Kulik, Alexey A. Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798331511241

To enhance the perception of road surface information during autonomous driving, this paper proposes a Tire-Road Friction Characteristics (TRFC) estimation algorithm that integrates a 7-DOF vehicle dynamics model, the Magic Formula, an improved adaptive Extended State Observer (ESO), and Long Short-Term Memory (LSTM) neural networks. The vehicle's key state parameters are obtained through the dynamics model, while the tire forces and the ESO observer provide accurate estimates of the utilized TRFC and slip ratio. Furthermore, an improved LSTM model, incorporating the Physics-Informed Neural Network (PINN) approach based on physical constraints, is employed to estimate the TRFC and critical vehicle state parameters. The algorithm's reliability and robustness are validated through MATLAB/Simulink and CarSim co-simulation experiments. The results demonstrate that the proposed method maintains high accuracy even under highly dynamic and uncertain conditions. This approach can serve as a complementary technique for fusion with vision and LiDAR-based TRFC identification methods, providing theoretical support for improving the safety and stability of autonomous vehicles. © 2025 IEEE.

关键词： Stiction

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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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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System 4

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Ovono, Gerald Zuva, Travos Otunniyi, Temidayo Faculty of Applied and Computer Sciences Department of Computer South Africa VaalUniversity of Technology Faculty of Engineering and Technology Department of Process Control and Computer Systems South Africa

ISBN: (纸本)9798350387988

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems. © 2024 IEEE.

关键词： Algorithms Evaluation Metrics Novelty Recommender systems Scholarly Research Serendipity

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Impact of Gravitational Anomalies and Vertical Deviations on the Accuracy of Inertial Navigation systems and Compensation Methods

Impact of Gravitational Anomalies and Vertical Deviations on...

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2024 International Ural Conference on Electrical Power Engineering, UralCon 2024

作者： Tianyu, Hou Bauman Moscow State Technical University Faculty of Computer Science and Control Systems Moscow Russia

ISBN: (纸本)9798350386028

The accuracy of Inertial Navigation systems (INS) has significantly improved, making previously negligible sensor errors, such as gravitational disturbances, major sources of errors. This paper analyzes the impact of gravitational anomalies on various stages of position calculation in INS and their influence on accuracy. The study includes the examination of gravitational disturbances using spherical harmonic decomposition and Legendre series, employing models like EIGCN-6C4 and WGS-84. It also explores the impact of these disturbances on initial alignment, the overall INS navigation process, and methods for compensating gravitational disturbances. Finally, simulations are conducted to validate the proposed hypotheses. This comprehensive analysis aims to enhance the understanding of gravitational disturbances and improve the accuracy and reliability of INS through detailed simulations and practical solutions. © 2024 IEEE.

关键词： Inertial navigation systems

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A MATHEMATICAL MODEL ILLUSTRATING THE INHIBITORY EFFECT OF THE MICRO RNA ON THE PROTEIN PRODUCTION

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2023年第4期85卷 101-106页

The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote... 详细信息

关键词： Proteins

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Improving Data Transmission Efficiency in UAV Networks Through Adaptive Coding 7

Improving Data Transmission Efficiency in UAV Networks Throu...

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7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Safadi, Majd M. Amoursky, Vladislav B. Mathematical and Software of Computing Systems Complexes and Computer Networks Faculty of Information Science and Control Systems Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798331531836

In recent years, unmanned aerial vehicles (UAV) have become widely used in civil and military applications such as surveillance, data collection, and cargo delivery. With the increasing use of this technology, there is a need for improved communication systems to ensure reliable and efficient data transmission. UAV networks face unique challenges due to dynamic motion, environmental interference, and energy and bandwidth limitations. The objective of this study is to propose an adaptive coding scheme using Rate-Compatible Punctured Coding (RCPC) technology, which assigns different coding rates to different data depending on their importance and channel conditions. The proposed scheme includes a mechanism to dynamically adjust the coding rates based on the change in link quality. This study demonstrates that adaptive coding can improve resource efficiency, reduce latency, and enhance communication reliability. © 2025 IEEE.

关键词： Drones

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