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2024 International Conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： An, Zizhen Deng, Mingcong Tokyo University of Agriculture and Technology Graduate School of Engineering Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350366204

In this note, a new structure of Right Coprime Factorization (RCF) for nonlinear systems with uncertainty has been proposed based on a time-varying Bezout identity. This is inspired from the concept of dilation from homogeneous theory. However, this time-varying dilation in right coprime factorized uncertain nonlinear systems is not limited to homogeneous plants but is integrated into Bezout identity so as to ensure the bounded input bounded output (BIBO) stability of uncertain systems. Additionally, simulation results of two numerical examples are exhibited to validate the proposition. © 2024 IEEE.

关键词： Nonlinear simulations

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Learning Multiagent Options for Tabular Reinforcement Learning using Factor Graphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1141-1153页

作者： Chen, Jiayu Chen, Jingdi Lan, Tian Aggarwal, Vaneet Purdue University School of Industrial Engineering West LafayetteIN47907 United States George Washington University Department of Electrical and Computer Engineering WashingtonDC20052 United States Purdue University School of Industrial Engineering The School of Electrical and Computer Engineering West LafayetteIN47907 United States King Abdullah University of Science and Technology Department of Computer Science Thuwal23955 Saudi Arabia

Covering option discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios, where only sparse reward signals are available. It aims to connect the most distant states identified through the Fiedler vector of the state transition graph. However, the approach cannot be directly extended to multiagent scenarios, since the joint state space grows exponentially with the number of agents, thus prohibiting efficient option computation. Existing research adopting options in multiagent scenarios still relies on single-agent algorithms and fails to directly discover joint options that can improve the connectivity of the joint state space. In this article, we propose a new algorithm to directly compute multiagent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as the Kronecker product of individual agents' state spaces, based on which we can directly estimate the Fiedler vector of the joint state space using the Laplacian spectrum of individual agents' transition graphs. This decomposition enables us to efficiently construct multiagent joint options by encouraging agents to connect the subgoal joint states, which are corresponding to the minimum or maximum of the estimated joint Fiedler vector. Evaluation on multiagent collaborative tasks shows that our algorithm can successfully identify multiagent options and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher cumulative rewards. © 2020 IEEE.

关键词： Reinforcement learning

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Malicious Attack Detection Using Deep Learning in IoT Network 8th

Malicious Attack Detection Using Deep Learning in IoT Networ...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Srivastava, Amisha Narayanan, V. Anantha Sumesh, A.K. Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819784219

The IOT (Internet of Things) engulfs a widespread ecosystem made up of networks, processing technologies, and smart items. IoT is a popular platform that allows many of the objects in our environment to interact with one another via sensors and, most significantly, the internet, thereby rendering our lives easier. Today, users and attackers are paying close attention to anomaly detection on the Internet of Things. The development we are noticing is the outcome of enhanced sensor monitoring technologies. Gathering data is crucial for creating an estimate for analyzing unhealthy behavior. IoT networks are more accessible to cyberattacks, and the deployment of attacks within the network and user privacy are the primary concerns. Intrusion detection systems might be a smart plan to safeguard IoT networks from various threats. The following strategies were employed in this study to find anomalies: Decision Tree, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Logistical Classification. An assessment is made using the Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. The UNSW-NB 15 dataset was used. The model’s predicted output is the identification of attacks or anomalies harmful to the devices and potentially damaging to user privacy and security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks

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Soft computing 2023年第5期27卷 2739-2750页

作者： Javadpour, Amir Rezaei, Samira Sangaiah, Arun Kumar Slowik, Adam Mahmoodi Khaniabadi, Shadi School of Computer Science and Technology Guangzhou University Guangzhou510006 China Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands School of Computing Science and Engineering VIT University Vellore India Department of Industrial Engineering and Management National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland Department of Computer Science and Engineering USM Engineering Campus Pulau Pinang George Town14300 Malaysia

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Lossless Image Compression using K-Means Clustering in Color Pixel Domain 5

Lossless Image Compression using K-Means Clustering in Color...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumari, Rimjhim Sriramulu, Srinivasan Galgotias University Department of Computer Science and Engineering Greater Noida India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Compressing images is a method for shrinking the image's dimensions using a particular algorithm. Image compression is a solution associated with transmitting and storing large amounts of data for digital images. While image storage is necessary for medical images, satellite images, documents, and pictures, image transmission includes a variety of applications like television broadcasting, remote sensing via satellite, and other long-distance communication. These kinds of applications deal with image compression. Lossy compression and lossless compression are two separate methods for compressing images. While unneeded metadata is deleted in lossless compression to minimize file size, lossy compression permanently removes part of the original data to reduce file size. The terms 'supervised learning' and 'unsupervised learning' refer to two different machine learning techniques. As opposed to a non-supervised learning algorithm, which uses unlabeled input data, supervised learning uses labeled data. While clustering is a subtype of unsupervised learning, classification, and regression are additional subcategories of supervised learning. This paper discusses the application of clustering using K-means. This methodology explores the concept of lossless picture compression is the process of reducing the size of an image without sacrificing its quality which is achieved using k-mean clustering. The proposed methodology achieves higher compression ratios than other existing approaches to image compression algorithms. © 2024 IEEE.

关键词： clustering compression image k-mean lossless unsupervised

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Image-based Plant Diseases Detection using Deep Learning

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Materials Today: Proceedings 2023年 80卷 3500-3506页

作者： Panchal, Adesh V. Patel, Subhash Chandra Bagyalakshmi, K. Kumar, Pankaj Khan, Ihtiram Raza Soni, Mukesh Department of Computer Science & Engineering GEC Patan India School of Computer Science and Engineering VIT Bhopal University Sehore MP Bhopal India Electrical and Electronics Engineering SRI Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science & Engineering Institute of Engineering & Technology Uttar Pradesh Greater Noida India Jamia Hamdard Computer Science Department Delhi India Jagran Lakecity University Bhopal India

Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. © 2021

关键词： Crops

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Detecting ugly and derogatory words in Serbian language using a web browser extension 32

Detecting ugly and derogatory words in Serbian language usin...

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32nd Telecommunications Forum, TELFOR 2024

作者： Milakovic, Adrian Jocovic, Vladimir Cincovic, Jelica Micovic, Marko Radenkovic, Uros Draskovic, Drazen University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Serbia

ISBN: (纸本)9798350391053

In natural language processing, data acquisition and preprocessing techniques are significant for experiments involving training models on cleaned data. This paper describes the formation of a dataset of ugly and derogatory words in the Serbian language and the development of a web extension to analyze texts in which such words appear and then their censoring. Developing such an add-on is essential in creating a safer and healthier digital environment for young users who are just developing their Internet habits and adults who want to avoid unwanted content. It is crucial to point out that there are few software tools for natural languages with limited resources, such as the Serbian language. This software could analyze the frequency of certain foul words on different sites, which would contribute to a better understanding of the impact of such language on users. © 2024 IEEE.

关键词： Web browsers

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Deep Learning-Powered Camouflaged Object Recognition Model 2

Deep Learning-Powered Camouflaged Object Recognition Model

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military Technology Object Recognition Situational Awareness YOLOv8

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in computer science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham science Publishers. All rights reserved.

关键词： Electronic health record

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Relationship of IQ with Causal Connections Between Brain Salient Network Regions 31

Relationship of IQ with Causal Connections Between Brain Sal...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Ghoorchian, Mina Soltanian-Zadeh, Hamid Pedram, Mir Mohsen Sadeghi, Mohsen Mir Mohammad Institute for Cognitive Science Studies Department of Cognitive Science Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran Kharazmi University Department of Electrical and Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798331529710

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people's lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value © 2024 IEEE.

关键词： Support vector regression

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