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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Capsule Network Approach for Image Classification 4th

Capsule Network Approach for Image Classification

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Marjit Singh, Moirangthem Kumar Sarkar, Nayan Department of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Arunachal Pradesh Itanagar India

ISBN: (纸本)9789819994410

Numerous deep learning (DL) models based on convolutional neural network (CNN) have been developed for image classification. Though the CNN models attained outstanding performance, its architecture contains certain limitations such as loss of information in the pooling layer and no spatial relationship among the features of an object. The development of capsule network (CapsNet) has overcome the drawbacks of CNN. The CapsNet works on a set of capsules, and each capsule comprises a set of features. The CapsNet consists of a convolution layer, a primary capsule layer, image caps and a fully connected (FC) layer. In this paper, a variant of CapsNet approach is proposed by tuning the parameters of the original CapsNet and replacing ReLU with AReLU activation function in each FC layer. The classification performance of the proposed approach is evaluated on the Plant Village (PV) and Tomato datasets. 80% of the datasets were trained and 20% were tested. The proposed approach considering filter size of 5 × 5 in the feature extraction layer with AReLU activation function in each FC layer delivers 94.02% and 94.06% classification accuracies on PV and Tomato datasets respectively. Compared to other modern approaches, the proposed approach delivered better results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Brain Tumor Detection Using Xception Model

Brain Tumor Detection Using Xception Model

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Philip, Joshwa Joy Rani, Ruchi Kumar, Sumit School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Science and Engineering Pune India School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350388800

Numerous morphological and histological characteristics of brain tumors make diagnosis and treatment very challenging. Traditional diagnosis methods often depend on the analysis of medical images, which can lead to unclear conclusions and errors. In recent years, machine learning (ML) and deep learning techniques (DL) have shown great scope in improving the detection and classification of brain tumors. These methods extract the features and classify medical imaging data, such as MRI, CT, and PET scans. Convolutional and recurrent neural networks give better performance even though there are challenges in the dataset. In the proposed work, Xception architecture is used which is a type of convolution neural network. This model is pretrained on the ImageNet dataset. The proposed model achieves 99.90% accuracy in brain tumor detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Ransomware Detection Utilizing Ensemble Based Interpretable Deep Learning Model 2

Ransomware Detection Utilizing Ensemble Based Interpretable ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Chisty, Tanjir Alam Rahman Rahman, Md. Mahbubur University of Professionals Department of Information and Communication Technology Bangladesh Dhaka Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

Ransomware, a form of malicious software, encrypts victim data and renders it inaccessible until a ransom is paid. Different ransomware variants employ various tactics to avoid detection during their attacks. Understanding the techniques used in ransomware development and deployment is crucial to counter these threats. This study presents a stacked ensemble-based machine learning (ML) approach for ransomware detection. The ensemble model consists of Naive Bayes (NB), Random Forest (RF), and K-Nearest Neibors (KNN) as base classifiers and Artificial Neural Network (ANN) as meta-classifiers. Additionally, Explainable AI (XAI) utilizing LIME and SHAP has been applied to gain insights into prediction outcomes and validate model correctness. The use of LIME and SHAP ensures the inter-pretability of the model, which is a crucial factor in real-world implications. The research employs a publicly available dataset and evaluates the model using multiple metrics. Experimental results reveal that the proposed model achieves an impressive accuracy of 98.57%. The model is benchmarked against existing ML techniques, demonstrating its superior performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Pathological Analysis of Blood Cells Using Deep Learning Techniques

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Recent Advances in computer science and Communications 2022年第3期15卷 397-403页

作者： Ranga, Virender Gupta, Shivam Agrawal, Priyansh Meena, Jyoti Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. However, this method has a number of limitations such as there is no standard way of diagnosis, there are certain chances of human errors and besides, it burdenizes the laboratory personnel to diagnose a large number of slides on a daily basis. Methods: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to get an insight into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When an input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are applied in accordance with the proposed algorithm, then the model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, and testing loss for the proposed model is plotted using matplotlib and trends. Discussion: The performance of the proposed model is better than the existing standard architectures and other works done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory personnel. . This can also in turn help the pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyp

关键词： Diagnosis

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AntiMicrobial Resistance Prediction Using Advanced Image Encoding Techniques 1

AntiMicrobial Resistance Prediction Using Advanced Image Enc...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Ghadekar, Premanand Jaggi, Arjun Swain, Debabrata Ghadekar, Aryan Vishwakarma Institute of Technology Department of Information Technology Pune India HCLTech CA United States Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331520540

Antimicrobial resistance (AMR) is one of the significant problems in the health sector and requires resources to create and develop accurate forecasting systems capable of recognizing resistant microbes and their treatment approaches. The traditional machine learning models used in AMR prediction usually suffer from poor genomic sequences feature extraction and finding it hard to solve big biological problems with big data. K-mer based methods or other simpler classifiers have been able to provide certain accuracy, but these have strived when dealing with many genomic strains and variants. To address these shortcomings, deep learning models are able to learn data representation at various abstraction levels which understanding complex data of fundamental and high dimensional patterns. In this work we provide a new deep learning approach exploiting CNNs on FCGR images of microbial genomes. The sequence is represented as frequency chaos game representation making it better for the enn structural application as resnet and densenet where features are deeply embedded in classifiers. When it comes to string and image based methods it is clear that they both have their own benefits and shortcomings. String based analysis is a faster and more accurate way to analyze naked sequence data conversion, but it may not be as informative as visual presentation. Our study verified the hypothesis that it is feasible to encode genomic data for further FCGR, so that the data might be utilized as an input for machine learning and deep learning methods. The model is effective with substantial improvements with the accuracy of over 90% in test dataset. This represents a significant improvement in accuracy score with better systems for predicting antibiotic resistance among many types of microbes. © 2025 IEEE.

关键词： AntiMicrobial Resistance Prediction CARD Frequency Chaos Game Representation microbes NCBI

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Optimal Deep Belief Network Enabled Malware Detection and Classification Model

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Intelligent Automation & Soft Computing 2023年第3期35卷 3349-3364页

作者： P.Pandi Chandran N.Hema Rajini M.Jeyakarthic Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of CSE Alagappa Chettiar Government College of Engineering and TechnologyKaraikudi630003India Department of Computer and Information Science Annamalai UniversityChidambaram608002India

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily *** Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading *** recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify *** this motivation,this study focuses on the design of mayﬂy optimization with a deep belief network for PDF malware detection and classiﬁcation(MFODBN-MDC)*** major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the *** proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature *** addition,Adamax optimizer with the DBN model is used for PDF malware detection and classiﬁ*** design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-ﬁcation demonstrates the novelty of the *** demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various *** comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

关键词： PDF malware data classiﬁcation security deep learning feature selection metaheuristics

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Teleoperation in the Age of Mixed Reality: VR, AR, and ROS Integration for Human-Robot Direct Interaction 4

Teleoperation in the Age of Mixed Reality: VR, AR, and ROS I...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Nandhini, P. Chellammal, P. Jaslin, J.S. Harthy Ruby Priya, S. Uma, M. Kaviyaraj, R. Sri Shanmugha College of Engineering and Technology Department of Computer Science and Engineering Sankari India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350300093

Robotic systems that can be operated remotely are becoming more and more common in a variety of industries, including construction, disaster relief, space exploration, and industrial applications. Teleoperation, however, necessitates skilled operators who can handle sophisticated data processing and programming. This research introduces a unique teleoperation method that makes use of Virtual Reality (VR) and the Robot Operating System (ROS) to overcome this problem. By creating a 3D representation of the robot's workspace in Unity and connecting it to the actual robot with ROS, this technique optimizes control. Programming is not necessary because the robot's vision serves as the foundation for the user interface. Through ROS integration, a target point is established in Unity and sent to the robot as the end effector position. By simply setting up a new environment, reducing the requirement for knowledgeable operators, and improving situational awareness, this method may be used to any sort of robot. For even greater situational awareness, this technique may also be used with augmented reality (AR). AR can give the operator real-time information about the robot's surroundings by superimposing digital information over the physical world. This can be achieved by including AR markers in the physical surroundings that the VR system can detect. In the operator's field of view, the system can then superimpose information such as sensor data or robot status onto the real-world environment. © 2023 IEEE.

关键词： Augmented reality

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Optimistic Multi-Agent Policy Gradient 41

Optimistic Multi-Agent Policy Gradient

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41st International Conference on Machine Learning, ICML 2024

作者： Zhao, Wenshuai Zhao, Yi Li, Zhiyuan Kannala, Juho Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Aalto University Finland University of Oulu Finland

Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art *** address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO *** approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in *** optimism prevents individual agents from quickly converging to a local ***, we provide a formal analysis to show that the proposed method retains optimality at a fixed *** extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest. Copyright 2024 by the author(s)

关键词： Multi agent systems

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VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

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Recent Advances in computer science and Communications 2022年第1期15卷 32-39页

作者： Patel, Warish D. Patel, Chirag Patel, Monal Department of Computer Science Engineering and Technology Parul University Vadodara India Department of Computer Science BITS Edu Campus Vadodara India

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for signifi-cant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. Therefore, even for the daily activity in the life of aged people, an automatic fall detecting system and vital signs examining system have become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people Methods: In comparison with present algorithms, there are various benefits regarding privacy, suc-cess rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algo-rithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%. ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS mod-ules so that the aged people can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The results of the experiment show the ade-quacy of the proposed approach. © 2022 Bentham science Publishers.

关键词： Fall detection

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