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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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An Intelligent Intrusion Detection using Rule-based CNN with PSO Optimizer 3

An Intelligent Intrusion Detection using Rule-based CNN with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Karpurasundharapondian, P. Selvi, M. Thangaramya, K. Santhosh Kumar, S.V.N. Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Security and secure routing are important design issues in the design of Wireless Sensor Networks (WSNs). Intrusion Detection systems (IDSs) are useful for securing the communication in WSNs. An IDS can be developed by using machine learning based approach. On the other hand, it can be developed using a deep learning classification algorithm. Optimization techniques including Particle Swarm Optimization (PSO) are useful for choosing optimal features from given dataset. This paper proposes an extended CNN algorithm by extending the existing CNN classifier with rules, SVM and PSO named Rule Extended CNN with SVM (RE-SVM-CNN) for developing this IDS which reduces the false alarm rate by enhanced detection accuracy. © 2024 IEEE.

关键词： Wireless sensor networks

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Enhancing Automated Android Application Security through Hybrid Static and Dynamic Analysis Techniques 6

Enhancing Automated Android Application Security through Hyb...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Manukulasooriya, G.E. Munasingha, R.S.I. Fernando, Harinda Uthpali, H.K.I. Yeshani, A.V.S.H. Siriwardana, Deemantha Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The rapid growth of mobile applications has led to serious security challenges, resulting in vulnerabilities. Automation in security testing methods is becoming popular, with the Automated Vulnerability Detection methodology being one such method. This work introduces a hybrid approach for detecting vulnerabilities in Android applications, combining static and dynamic analysis. The approach is accessible to both general users and expert developers, allowing for the identification of ingrained vulnerabilities in the source code. The hybrid model's effectiveness is assessed in various applications with different security flaws, providing a solid solution for automated vulnerability detection in Android applications, enhancing security and resilience in the mobile ecosystem. © 2024 IEEE.

关键词： Automation

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Enhanced Road Safety Through Intelligent Lane Detection and Driver Drowsiness Monitoring 15

Enhanced Road Safety Through Intelligent Lane Detection and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunpandian, J. Gunasekaran, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350370249

The innovative system outlined in the provided research demonstrates a significant stride towards addressing the growing concern of road safety. Lane departure and driver drowsiness are two major causes of road accidents. In this paper, we present an observation of road lane and drowsiness detection system that uses a camera to capture real-time video footage of the road ahead and applies image processing techniques to detect and track road lanes. The system's road lane detection module employs the canny edge detection algorithm and Hough transform to detect lane lines accurately. The drowsiness detection module of the system analyzes the driver's facial features and tracks the driver's eye movements, blink frequency to identify signs of drowsiness. If the driver found to be sleepy, it issues an audio alert to the driver. The system's capability to detect and track road lanes with precision, along with its ability to identify signs of driver drowsiness, underscores its potential as a promising solution for enhancing road safety. © 2024 IEEE.

关键词： Canny edge algorithm drowsiness lane detection road safety vehicles

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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computer Modeling in engineering & Sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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CropViT: A light-weight Transformer Model for Crop Disease Detection 3

CropViT: A light-weight Transformer Model for Crop Disease D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chemmalar Selvi, G. Charan, H.J. Kumar, Dinesh Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on Vision Transformer. This CropViT was developed by fine-tuning the architecture of existing Vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional Neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work. © 2024 IEEE.

关键词： Convolutional neural networks

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Multifunctional Pre-processing Approach for Diabetes Mellitus Prediction Using Ensemble Learning 3

Multifunctional Pre-processing Approach for Diabetes Mellitu...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Divya, S. Sudha, M. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Diabetes mellitus when untreated can result in a number of health issues. It is a metabolic disease marked by abnormal blood glucose levels. Early detection of diabetes improves a person's long-term health by halting its progression. Making better medical decisions can be greatly aided through the implementation into practice automated techniques that can precisely identify pre- and diabetic situations. In this paper Synthetic Minority Oversampling Technique and Adaptive Oversampling Technique is used to address class imbalance problem and Chi-square test, Recursive feature elimination methods are involved in detecting and extracting relevant components from unprocessed data. These techniques are applied to the framework and compared using different ensemble learning classifiers such as XGBoost, AdaBoost, LightGBM and CATBoost. The result shows us CATBoost with ADASYN and RFE provides us with better accuracy of 0.96%. © 2024 IEEE.

关键词： Adaptive boosting

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