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Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

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International Journal of applied and Computational Mathematics 2024年第4期10卷 1-15页

作者： Venkatesh, R. Rao, Deepak Dasaratha Sangeetha, V. Subbalakshmi, Ch. Bala Dhandayuthapani, V. Mekala, R. Department of Mathematics Hindusthan Institute of Technology Coimbatore 641032 India Indian Institute of Technology Patna India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Shinas Oman Department of Information Technology M.Kumarasamy College of Engineering Tamilnadu Karur India

Traditional control methods may struggle to adapt to the nonlinear and uncertain characteristics in Autonomous Vehicle Control. In recent years, fuzzy control techniques, such as the Takagi–Sugeno fuzzy controller, have emerged as promising approaches for handling such complexities. Fuzzy controllers utilize linguistic variables and fuzzy logic to model system behavior, offering flexibility and robustness in dealing with uncertainties. Furthermore, Lyapunov function analysis provides a powerful tool for assessing the stability of dynamical systems. By employing Lyapunov functions, researchers can mathematically prove the stability of a system and derive stability criteria, contributing to a deeper understanding of system behavior. This paper investigates the enhancement of stability in control systems by employing Fuzzy Gain Scheduling combined with Lyapunov function analysis. Stability is a crucial aspect of control systems, ensuring their reliable and efficient operation in various dynamic environments. Traditional control techniques often struggle to handle the nonlinear and uncertain nature of modern systems. FGS offers a flexible and adaptive approach to control by adjusting controller gains based on system operating conditions. Additionally, Lyapunov function analysis provides a rigorous mathematical framework for stability assessment, enabling the verification of system stability properties. By integrating FGS and Lyapunov function analysis, this research aims to develop a robust control strategy capable of ensuring stability across a range of operating conditions. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed approach in enhancing stability and performance in control systems. Specifically, the settling time was reduced by 20%, and overshoot was minimized to 5% of the steady-state value. Furthermore, in experimental tests conducted on a real-world control system setup, the proposed approach demonstrated rob

关键词： Control systems Fuzzy gain scheduling (FGS) Lyapunov function analysis Performance Simulation Stability

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An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

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2023 International Conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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Computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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EXISTENCE AND APPROXIMATE CONTROLLABILITY FOR A CLASS OF FRACTIONAL ORDER HEMIVARIATIONAL INEQUALITIES

arXiv

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arXiv 2024年

作者： Gupta, Garima Dabas, Jaydev Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India

This paper discusses the approximate controllability of a fractional differential control problem driven by a nonlinear hemivariational inequality in a Hilbert space. First, we prove the existence of a mild solution for a fractional control inclusion problem which is equivalent to a hemivariational inequality by using the nonsmooth analysis and fixed point technique. Further, we established sufficient conditions for the approximate controllability of our inclusion problem by taking corresponding linear system is approximately controllable. The existence and controllability results obtained for the inclusion problem are valid for considered nonlinear hemivariational problem. Finally, we provide an example to illustrate the efficiency of the developed results. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllability

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THE EXISTENCE AND CONTROLLABILITY OF NONAUTONOMOUS SYSTEM INFLUENCED BY IMPULSES ON BOTH STATE AND CONTROL.

arXiv

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arXiv 2024年

作者： Gupta, Garima Dabas, Jaydev Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India

This paper examines impulsive controls related to nonautonomous impulsive integro-differential equations in Hilbert space, highlighting their significance. We establish the existence of the mild solution by using fixed point approach and present conditions for approximate controllability using impulsive resolvent operators and the adjoint problem, supported by an illustrative example. Copyright © 2024, The Authors. All rights reserved.

关键词： Controllability

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Optimizing Phishing URL Detection: A Comparative Analysis of Feature Counts and Training Times 5

Optimizing Phishing URL Detection: A Comparative Analysis of...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Alajlouni, Moayyad Tashtoush, Yahya Dossari, Saud Alhajaj Al. Darwish, Omar Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University Department of Electrical Engineering Wadi Addwasir Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic web sites to obtain sensitive information such as passwords and bank statements. One common technique used in these attacks is malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. This paper presents a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the required features and the training time by using fewer features after applying XAI techniques. Our technique only uses statistical features extracted from n-grams and the n-gram itself, without requiring any high-level features. We applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features and 87 features used in the original experiment and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by optimizing feature selection and analyzing the impact on training times. Our findings show the importance of using minimal statistical features to identify malicious URLs effectively and efficiently. © 2024 IEEE.

关键词： Phishing

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data 2

Customer Churn Prediction in the Telecom Sector with Machine...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

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