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Dynamic Access Control in Multi-Layer Satellite Remote Sensing System Using Multi-Agent Deep Reinforcement Learning

IEEE Transactions on Wireless Communications 2025年

作者： Hu, Han Lyu, Yifeng Fan, Rongfei Sui, Xiufeng Zhan, Cheng Niyato, Dusit Beijing Institute of Technology School of Information and Electronics Beijing100081 China Norinco Group Institute of Computer Application Technology Beijing100089 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Southwest University School of Computer and Information Science Chongqing400715 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

The Multi-Layer Satellite Remote Sensing (SRS) integrates data collection by Low Earth Orbit (LEO) satellites and data processing assistance from Medium Earth Orbit (MEO) satellites, thereby playing a crucial role in scientific exploration. However, effectively controlling access to LEO satellites for processing data, especially considering the frequent handovers caused by speed differences, presents a significant challenge to achieving high energy efficiency services. To address this challenge, we explore cooperative dynamic access control based on efficient communication mechanisms, with the aim of prioritizing processed data volume and meeting energy consumption requirements for satellites. Specifically, we formulate the access control issue as an optimization problem and integrate it into the framework of partially observable Markov decision process (POMDP), considering MEO satellites' limited observation ability. By employing Multi-agent Deep Reinforcement Learning (MADRL), we propose a novel dynamic access control algorithm named DAC to solve our featured problem. Specifically, for improving performance, communication-efficient cooperation among MEOs is enhanced through modeling decision-relevant information of fellow MEO satellites and maximizing mutual information with their actual data to extract precise awareness and enable the generation of concise message. Finally, we conduct comprehensive experiments and an ablation study spanning the Starlink, OneWeb, and Telesat mega-constellations. The results demonstrate that DAC increases the average system data processing volume by at least 13.5%, while meeting energy consumption constraints and outperforming baseline algorithms. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Online social network automation attack detection methods for energy analysis and consumption modelling

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International Journal of information technology (Singapore) 2024年 1-13页

作者： Rawat, Romil Sikarwar, Ranjana Maravi, Prakash Kumar Ingle, Mandakini Bhardwaj, Vivek Rawat, Anjali Rawat, Hitesh Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore Indore India Department of CSE Amity University Madhya Pradesh Gwalior India Department of Computer Science Engineering Parul University Gujarat Vadodara India Department of CSE Medicaps University Indore India School of Computer Science and Engineering Manipal University-Jaipur Rajasthan Jaipur India Department of Computer and Communication Technology University of Extremadura Badajoz Spain Department of Business Management and Economics University of Extremadura Badajoz Spain

Preventing the spread of Domain Name System (DNS) tunnelling and the use of automation infrastructure is one of the key problems that the idea of a contemporary smart OSN (online social network) must address. The proposed technique focuses on online social network automation attacks (OSNAA) based on smart social network devices (SSND) energy consumption (Ecomp) analysis based on user’s selection modes (USM) for the detection of OSNAA and Man-in-the-Middle (MiTM) attacks. Automated opcode sequence analysis (OSA) is used to improve the accuracy of OSNAA detection and localise MiTM attacks. It analyses the performance of the SSND for MiTM attacks by localising suspicious automated tools on these devices with applicable accuracy and is based on the monitoring of energy consumption, which helps to analyse and categorise the behaviour of the SSND in the state of normal or in compromised conditions by the threat agents. The suggested technique enables high-efficiency detection of SSND threats, such as MiTM attacks, at a level of about 99.91% and localization of suspicious automation tools on these machines with an accuracy of around 99.73%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cyberattack Data mining Energy efficiency and consumption MiTM Opcode analysis OSN-Automation

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Lung Cancer Prediction from CT Images and using Deep Learning Techniques 2

Lung Cancer Prediction from CT Images and using Deep Learnin...

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2nd International Conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2023

作者： Mamatha, Botla Rashmi, D. Tiwari, Kanchan S. Sikrant, Poornima A Jovith, A Arokiaraj Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Information Science and Engineering Bengaluru India Electronics and Telecommunications MES Wadia College of Engineering Pune Maharasta India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Management Sciences Tamilnadu Chennai India Networking and Commun. College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Lovely Professional University School of Computer Science and Engineering Phagwara Punjab India

ISBN: (纸本)9798350339949

Cellular proliferation in the lung tissues is a hallmark of lung cancer. Lung cancer is particularly dangerous since the lungs are responsible for both breathing in oxygen and exhaling carbon dioxide-two of the body's most vital functions. The application of deep-learning (DL) for the identification of lymph node involvement on histopathology slides has gained a lot of attention due to the potential impact it could have on patient diagnosis and therapy. Recognition accuracy, precision, sensitivity, F-Score, specificity, etc., are all significantly lower with the current approach. Convolution-Neural-Network (CNN), CNN Gradient-Descent (CNN GD), VGG-16, VGG-19, and Resnet-50 are just few of the deep learning algorithms that exhibit improved performance in the metrics with the proposed methodology. CT scan pictures and histopathology images are used to evaluate the suggested method. When histopathological tissues are analyzed, the results demonstrate that detection accuracy improves. © 2023 IEEE.

关键词： computerized tomography

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Deep Learning Algorithm for Brain Tumor Detection and Classification using MRI Images

Deep Learning Algorithm for Brain Tumor Detection and Classi...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： A. Harshavardhan N. Uma Maheswari M. Prakash Naresh Sammeta Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Department of Computer Science and Business Systems Panimalar Engineering College School of Computing Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering R.M.K College of Engineering and Technology

Brain tumor classification is beneficial for identifying and diagnosing the tumor’s specific location. According to the medical imaging system, early diagnosis and categorization of a tumor extend a person’s life. Clinical specialists rely heavily on magnetic resonance imaging (MRI) among numerous imaging modalities since it provides contrast information on brain malignancies. The primary purpose of this project is to use a competent automated approach that improves tumour identification accuracy. Several segmentation strategies have been developed throughout the years to achieve and improve the categorization precision of brain tumours. Brain picture segmentation has long been recognised as a difficult and time-consuming aspect of medical image processing. This method for detecting brain tumors Brain pictures are classified using the Full Resolution Convolutional Network (FRCN) classification architecture after pre-processing and segmentation. This study presents a Full Resolution Convolutional Network (FRCN) with Support Vector Machine (SVM) approach for detecting tumors on MRI scans. The procedure is broken down into four steps. In the first phase, the anisotropic filter is utilized to pre-process raw MRI images, followed by segmentation using the Support vector machine (SVM) and skull classification. The singular value decomposition and primary component analysis operations are performed in the third step. Tumors are then detected and classified using the Full Resolution Convolutional Network (FRCN) approach. Simultaneously, the Support Vector Machine (SVM) technique is employed to improve the classification precision of the study model. The experimental results showed an amazing accuracy rate of nearly 100% in detecting both normal and diseased tissues from brain MR images, confirming the efficacy of the suggested technique.

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary Pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： Local binary pattern

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Roadmap for Implementing Business Intelligence systems in Higher Education Institutions: Validation of a Case Study at the University of Trás-os-Montes and Alto Douro 21

Roadmap for Implementing Business Intelligence Systems in Hi...

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21st International Conference on Smart Business Technologies, ICSBT 2024

作者： Sequeira, Romeu Reis, Arsénio Branco, Frederico Alves, Paulo School of Science and Technology University of Trás-os-Montes and Alto Douro Quinta dos Prados Vila Real Portugal Instituto Politécnico de Bragança Bragança Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9789897587108

The adoption of effective policies and access to relevant information are critical to improving strategic management and performance monitoring in Higher Education Institutions (HEIs), which is essential to promote data-driven decision-making. This article describes how an HEI can carry out the validation process when implementing a Business Intelligence (BI) system, and provides a detailed guide to doing so. Through a case study at the University of Trás-os-Montes and Alto Douro, a structured roadmap is validated, which acts as a visual and sequential guide to facilitate the effective implementation of BI solutions. The validation carried out through semi-structured interviews with experts and evaluation of dashboards by user groups, not only confirms the applicability and efficiency of the proposed model but also emphasises its practical relevance, providing valuable insights for the adaptation and use of BI in different educational contexts. Copyright © 2024 by SCITEPRESS - science and technology Publications, Lda.

关键词： Competitive intelligence

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Involution symmetry quantification using recurrences

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Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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A Hybrid Federated Learning Model for Insurance Fraud Detection

A Hybrid Federated Learning Model for Insurance Fraud Detect...

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IEEE International Conference on Communications Workshops, ICC

作者： Supriya Y Nancy Victor Gautam Srivastava Thippa Reddy Gadekallu School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Dept. of Math and Computer Science Brandon University Brandon MB Canada

Mission-critical systems are significant for the survival of any organization. Financial systems, communication systems, and electricity grid systems are some examples of mission-critical applications. Any damage to such systems can cause business operations to fail. Insurance fraud is one of the challenges faced by mission-critical systems today. Insurance fraud detection has historically been a manual task left to claim agents, who look over the evidence and draw an inference based on their intuitions. Recently, machine learning (ML) has been predominantly used to automate the process of insurance fraud detection. Despite the success of ML models in detecting insurance fraud, privacy preservation of insured personal data continues to be a problem. To address this issue, we present a novel hybrid approach. The proposed work suggests an automated way to control the process of vehicle insurance claim fraud detection in the insurance industry. The proposed study combines Federated Learning (FL), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to incorporate the advantages of each technology. The suggested model uses GA for optimal feature subset extraction. The optimized feature subset is then fed into a Federated learning with a Particle swarm optimization (FPSO) model. The findings show that the suggested hybrid model has an accuracy of 94.47% and that it may be improved even further by using other nature-inspired algorithms that are only used for fraud detection.

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Optimizing Greenhouse Operations Through IoT-Integrated Environmental Control systems: A New Era of computer-Based Farming 2

Optimizing Greenhouse Operations Through IoT-Integrated Envi...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kumar K, Rajesh Palani, Hari Kumar Sundravadivelu, K. Senthilvel, P. Gururama Thirupurasundari, D.R. Madha Engineering College Kundrathur Department of Electronics and Communication Engineering TamilNadu Chennai India School of Engineering Ajeenkya D y Patil University India Madurai Kamaraj University Department of Computer Science School of Information Technology Tamil Nadu Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

technology is widely adopted across today's agricultural landscape to improve output, efficiency, and longevity. The Internet of Things (IoT) deployment inside greenhouse settings is one of the promising areas of technology integration. To maximize efficiency, this research investigates how the Internet of Things (IoT)-integrated Environmental Control systems (ECS) might be used in greenhouses. Optimal growing conditions were maintained by constantly monitoring and adjusting factors like temperature, humidity, light intensity, and carbon dioxide levels using various Internet of Things (IoT) sensors and devices. Using a cloud-based data analytics platform, we analyzed massive data streams, discovered useful insights, and used those to inform our operational strategy development efforts. The study uncovered significant gains in agricultural productivity, less resource waste, and improved fore castability of crop development. In addition, the IoT-ECS's adaptability allowed for automated responses to unexpected environmental changes, protecting crops from destruction. This study demonstrates the promise of Internet of Things integration in greenhouses as a game-changing strategy for contemporary agriculture, ushering in a new era of precision farming that is good for the bottom line and the environment. © 2023 IEEE.

关键词： Environmental Control systems Greenhouse operations Internet of Things Precision Farming Sensors

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Investigating the Feasibility of Using Machine Learning for Real-Time Data Analysis

Investigating the Feasibility of Using Machine Learning for ...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Latha, B. Ghule, Vijaykumar R. Geetha, G. Singh, Shweta Kharbas, Vikash Kumar Ahluwalia, Gunveen Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350329773

This paper seeks to determine the feasibility of the usage of device learning for actual-time facts evaluation. System gaining knowledge of can be used to research massive datasets and apprehend styles, in addition to automate selection-making methods. This may be utilized in studying the consequences of digital techniques in real-time and tracking adjustments in patron traits. numerous device studying algorithms, consisting of selection trees, Naïve Bayes, support Vector system (SVM), Logistic Regression, Random wooded area, and Neural Networks, are evaluated if you want to show their overall performance for real-time statistics assessment. A framework based on machine gaining knowledge of techniques is proposed for real-time facts evaluation. Open source datasets are used to evaluate and compare the overall performance of different algorithms. The assessment results show the practicality and effectiveness of the usage of device studying for actual-time records evaluation. In end, the feasibility of using device studying for real-time data analysis is affirmed via this research. © 2023 IEEE.

关键词： Support vector machines

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