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Modified light GBM based classification of malicious users in cooperative cognitive radio networks

Cyber-Physical Systems 2024年第1期10卷 104-122页

作者： Sekar, S. Jeyalakshmi, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Signal to noise ratio

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Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

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science China(Information sciences) 2024年第10期67卷 244-260页

作者： Xinru MA Hengyu LI Jun LIU Yueying WANG Shaorong XIE Jun LUO School of Mechatronic Engineering and Automation Shanghai University School of Mathematics and Computer Application Technology Jining University School of Computer Science Shanghai University College of Mechanical and Vehicle Engineering Chongqing University

This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.

关键词： composite formation time-vary tracking information disparity tender-tracking finite-time control nonlinear robots complex networks symmetry consensus

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Ubiquitous WiFi and Acoustic Sensing:Principles,Technologies,and Applications

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Journal of computer science & technology 2023年第1期38卷 25-63页

作者：黄佳玲王云舒邹永攀伍楷舜倪明选 The IoT Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060 China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511455China The Hong Kong University of Science and Technology Hong KongChina

With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn researchers’great *** different sensing medias,WiFi and acoustic signals stand out due to their ubiquity and zero hardware *** on different basic principles,researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition,motion tracking,indoor localization,health monitoring,and the *** enable readers to get a comprehensive understanding of ubiquitous wireless sensing,we conduct a survey of existing work to introduce their underlying principles,proposed technologies,and practical *** we also discuss some open issues of this research *** survey reals that as a promising research direction,WiFi and acoustic sensing technologies can bring about fancy applications,but still have limitations in hardware restriction,robustness,and applicability.

关键词： WiFi sensing acoustic sensing human-computer interaction human activity recognition

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Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

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Differential Evolution Strategy with Chebyshev Chaos Based Mutation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Chaos theory

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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Optimized deep belief network and unsupervised deep learning methods for disease prediction

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Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 9571-9589页

作者： Shenbagavalli, S.T. Shanthi, D. Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul India

Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient feature extraction and classification model for healthcare systems. First, several medical data related to the patient’s health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%. © 2023 – IOS Press. All rights reserved.

关键词： Support vector machines

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Forecasting land use changes in crop classification and drought using remote sensing

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Journal of Arid Land 2025年第5期17卷 575-589页

作者： Mashael MAASHI Nada ALZABEN Noha NEGM Venkatesan VEERAMANI Sabarunisha Sheik BEGUM Geetha PALANIAPPAN Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science Applied College at MahayilKing Khalid UniversityAbha 61421Saudi Arabia Department of Civil Engineering University College of EngineeringAnna UniversityAriyalur 621731India Department of Biotechnology P.S.R.Engineering CollegeSivakasi 626140India Department of Electronics and Communication Engineering School of Engineering and TechnologyDhanalakshmi Srinivasan UniversitySamayapuram 621112India

Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t

关键词： land use and land cover(LULC) crop attributes drought vulnerability machine learning models remote sensing

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Fault Detection of Automotive Engine System Based on Canonical Variate Analysis Combined With Bhattacharyya Distance

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Journal of Dynamic Systems, Measurement and Control 2025年第4期147卷 041004页

作者： Cheng, Zhang Yun-Fei, Wang Yu-Yu, Lao Yuan, Li College of Science Shenyang University of Chemical Technology Liaoning Shenyang110142 China College of Computer Science and Technology Shenyang University of Chemical Technology Liaoning Shenyang110142 China College of Information Engineering Shenyang University of Chemical Technology Liaoning Shenyang110142 China

Aiming at the nonlinear and dynamic characteristics of data in automotive engine systems, a fault detection method based on canonical variate analysis combined with Bhattacharyya distance (CVA-BD) is proposed in this paper. First, CVA is utilized to calculate the state space of the system data. Second, a sliding window is introduced in the state space to quantify the difference in data distribution within the window using Bhattacharyya distance, thereby constructing a novel statistical indicator. Finally, the control limit for statistical indicator is determined to achieve process monitoring of automotive engine systems. CVA-BD effectively enhances the performance of process monitoring by capturing the sequential correlation of data through CVA and eliminating the nonlinear impact between samples using similarity measurement metrics. Simulation experiments are conducted using a numerical case and experimental data from turbocharged spark ignition (TCSI) engines. The simulation results further confirm that, compared with principal component analysis (PCA), dynamic principal component analysis (DPCA), canonical variable analysis (CVA), dissimilar canonical variable analysis (CVDA), auto-encoder (AE), and stacked auto-encoder (SAE) CVA-BD has demonstrated an improvement of at least 41%. Copyright © 2025 by ASME.

关键词： Fault detection

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