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3rd Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2024

作者： Mertzios, George B. Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul G. Department of Computer Science Durham University United Kingdom Computer Engineering and Informatics Department University of Patras Greece Department of Mathematics University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959773157

We consider random simple temporal graphs in which every edge of the complete graph Kn appears once within the time interval [0, 1] independently and uniformly at random. Our main result is a sharp threshold on the size of any maximum δ-clique (namely a clique with edges appearing at most δ apart within [0, 1]) in random instances of this model, for any constant δ. In particular, using the probabilistic method, we prove that the size of a maximum δ-clique is approximately 2 log_ n with high probability (whp). What seems surprising is that, even though the random simple log δ1 temporal graph contains Θ(n2) overlapping δ-windows, which (when viewed separately) correspond to different random instances of the Erdős-Rényi random graphs model, the size of the maximum δ-clique in the former model and the maximum clique size of the latter are approximately the same. Furthermore, we show that the minimum interval containing a δ-clique is δ − o(δ) whp. We use this result to show that any polynomial time algorithm for δ-Temporal Clique is unlikely to have very large probability of success. © George B. Mertzios, Sotiris Nikoletseas, Christoforos Raptopoulos, and Paul G. Spirakis.

关键词： Polynomial approximation

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Life Expectancy Prediction using Machine Learning Technique - KNN 3

Life Expectancy Prediction using Machine Learning Technique ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Narooka, Preeti Vashistha, Aarushi Mitra, Priyanka Derashri, Gulisha School of Computer Science Engineering Manipal University Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur303905 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350384598

In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory. © 2024 IEEE.

关键词： Adversarial machine learning

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Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering 24

Dual-Optimized Adaptive Graph Reconstruction for Multi-View ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wen, Zichen Wu, Tianyi Ren, Yazhou Ling, Yawen Cui, Chenhang Pu, Xiaorong He, Lifang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9798400706868

Multi-view clustering is an important machine learning task for multi-media data, encompassing various domains such as images, videos, and texts. Moreover, with the growing abundance of graph data, the significance of multi-view graph clustering (MVGC) has become evident. Most existing methods focus on graph neural networks (GNNs) to extract information from both graph structure and feature data to learn distinguishable node representations. However, traditional GNNs are designed with the assumption of homophilous graphs, making them unsuitable for widely prevalent heterophilous graphs. Several techniques have been introduced to enhance GNNs for heterophilous graphs. While these methods partially mitigate the heterophilous graph issue, they often neglect the advantages of traditional GNNs, such as their simplicity, interpretability, and efficiency. In this paper, we propose a novel multi-view graph clustering method based on dual-optimized adaptive graph reconstruction, named DOAGC. It mainly aims to reconstruct the graph structure adapted to traditional GNNs to deal with heterophilous graph issues while maintaining the advantages of traditional GNNs. Specifically, we first develop an adaptive graph reconstruction mechanism that accounts for node correlation and original structural information. To further optimize the reconstruction graph, we design a dual optimization strategy and demonstrate the feasibility of our optimization strategy through mutual information theory. Numerous experiments demonstrate that DOAGC effectively mitigates the heterophilous graph problem. © 2024 ACM.

关键词： Graph neural networks

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Real-time UAV Resource Monitoring and Alerts with Automated Control Mechanism 16

Real-time UAV Resource Monitoring and Alerts with Automated ...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Parekh, Nisarg Tella, Rajashekhar Reddy Malakalapalli, Lavanya Tammana, Praveen Kondepu, Koteswararao Iit Hyderabad Department of Smart Mobility Hyderabad India Iit Dharwad Department of Computer Science and Engineering Dharwad India Iit Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350383119

Autonomous navigation in Unmanned Aerial Vehicles (UAVs) is a crucial and rapidly advancing field with a wide range of applications. It utilizes various deep learning (DL) algorithms that enable UAVs to operate and navigate autonomously, reducing the need for direct human intervention. However, the use of DL algorithms demands significant computational resources, which can lead to resource constraints, potentially disrupting ongoing operations and compromising UAV safety. This work focuses on establishing a real-time monitoring, alerting, and automated control framework for UAV system resources. The proposed framework alerts the user/operator if system resource utilization exceeds the threshold. Furthermore, the automated controlling mechanism guarantees that resource consumption is adequately monitored and kept below predefined boundaries, lowering the danger of resource depletion and UAV damage. The suggested framework provides a powerful and efficient solution for optimal utilization of the available UAV system resources. By tackling the critical issue of system resource consumption, the suggested framework helps to achieve the larger goal of enabling the widespread adoption of autonomous navigation, © 2024 IEEE.

关键词： Alerting Resources Automated Control Mechanism Grafana Kafka Prometheus UAV resources monitoring

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IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

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computers, Materials & Continua 2024年第2期78卷 2245-2267页

作者： Ashraf S.Mashaleh Noor Farizah Binti Ibrahim Mohammad Alauthman Mohammad Almseidin Amjad Gawanmeh School of Computer Sciences Universiti Sains MalaysiaPenangMalaysia Department Computer Center Al-Balqa Applied UniversitySaltJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science Tafila Technical UniversityTafilaJordan College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic *** IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection *** paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT *** logic addresses IoT threat uncertainties and ambiguities *** component settings are optimized using PSO to improve *** methodology allows for more complex thinking by transitioning from binary to continuous *** of expert inputs,PSO data-driven tunes rules and membership *** study presents a complete IoT botnet risk assessment *** methodology helps security teams allocate resources by categorizing threats as high,medium,or low *** study shows how CICIoT2023 can assess cyber *** research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

关键词： IoT botnet detection risk assessment fuzzy logic particle swarm optimization(PSO) cybersecurity interconnected devices

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NLP Research: A Historical Survey and Current Trends in Global, Indic, and Gujarati Languages 4

NLP Research: A Historical Survey and Current Trends in Glob...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Panchal, Brijeshkumar Y. Shah, Apurva The Maharaja Sayajirao University of Baroda Faculty of Technology and Engineering Computer Science and Engineering Department Gujarat Vadodara India Computer Engineering Department Gujarat Anand India

ISBN: (纸本)9798331529635

This research study presents a comprehensive survey of Natural Language Processing (NLP) research, tracing its historical evolution from its inception to the present. The survey explores the key milestones and advancements in NLP, focusing on global trends, as well as specific contributions from Indic languages, with a particular focus on Gujarati. The paper analyzes the current state of Gujarati NLP research, identifying existing gaps and challenges. By providing a comprehensive overview of NLP research, this study aims to guide future research directions and foster advancements in the field, particularly in the context of Indic languages. © 2024 IEEE.

关键词： Natural language processing systems

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A Study of Contemporary ML Algorithm on Intrusion Detection Using IIoT Datasets 3

A Study of Contemporary ML Algorithm on Intrusion Detection ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Ajagbe, Sunday A. Kumar, T. Ananth Rajmohan, Rajendrane Balogun, Emmanuel O. Olawoyind, Oluwafemi O. Lawrence, Morolake O. Adigun, Matthew O. Computer Science Department University of Zululand Kwadlangezwa 3886 South Africa Computer Engineering Department Abiola Ajimobi Technical University Ibadan200255 Nigeria Computer Science and Engineering Department IFET College of Engineering India Computing Technologies Department SRM Institute of Science and Technology India ICT Unit Ajayi Crowther University Oyo Oyo Nigeria Computer Science Department Baze University Abuja Nigeria

The global digitization of most activities of corporate and non-corporate bodies has come to stay. The interconnection of physical object "things"embedded with different technologies such as sensors, softwares and other technologies to connect and allow for data exchange with different systems and devices over the internet is known as the Internet of Things (IoT). To guarantee lower expenses and increased operational effectiveness, Data-driven insights for prompt decision-making, complete, remote asset and resource management, Prescriptive, predictive, and real-time insights along with enhanced end-user satisfaction Industrial Internet of Things (IIoT) became a given. Implementing machine learning (ML) algorithms for intrusion detection in IIoT systems entails numerous hurdles, such as data imbalance and adversarial attacks. And different ML algorithms handle data imbalance and adversarial attacks for intrusion detection differently. Therefore, there is a need for comparative studies on ML algorithms to be used as a tool to help prevent unauthorised intrusion into various networks. In this research, an experimental approach has been used to compare contemporary ML algorithms such as Linear SVC, random forest RF, XGBoost, decision tree (DT), logistic regression (LR) on IIoT datasets to detect intrusion. Data exploration, Feature engineering, selection and data partition were done at a rate of 80-20 % ratio for the train set and test set respectively. Performance evaluation was carried out using accuracy, recall, precision and F1-score and a confusion matrix was plotted. The performance evaluation shows the accuracy of 100%, 99.99%, 99.35%, 97.49% and 97.27% for RF, XGBoost, DT, Linear SVC and LR algorithms respectively the test set evaluation. RF outperformed the other algorithm that was experimented. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep learning

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data 4

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Baez Hasan, Sarhad Kareem, Shahab Wahhab Ameen, Didar Dlshad Hamad Soran University Faculty of Science Computer Science Department Soran Iraq Erbil Polytechnic University College of Engineering Knowledge University Department of Information System Engineering Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798350368253

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations' big data processing and analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Group Ensemble Block: Subspace Diversity Improves Coarse-to-Fine Image Retrieval

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 60-70页

作者： Zhao, Zhihao Zhao, Shangqing Cheng, Samuel Department of Electrical and Computer Engineering University of Oklahoma NormanOK73019 United States Department of Computer Science University of Oklahoma NormanOK73019 United States

The random subspace method is an ensemble learning technique of great potential. However, its popularity does not match its potential because of its prohibitive cost and the lack of plug-and-play reusable modules. To fill this gap, we propose the random-subspace-based group ensemble block. The proposed ensemble block increases the diversity among subspaces by masking each sampled subspace. It also utilizes the diversity among a large number of subspaces, e.g., 1024, which is facilitated by combining the advantages of both averaging and concatenating strategies for merging different subspaces' outcomes. In terms of cost, the proposed ensemble block contributes few parameters by parameter sharing, negligible computations by the linear transformation, and fast running speed by parallel processing for every subspace. In terms of reusability, it encapsulates every necessary step in one small block, and can be interchangeably used as a linear layer in neural networks. We show that our group ensemble block is complementary to existing methods for image retrieval such as class-level and instance-level discrimination, and can also be deployed for other tasks such as image classification. Experiments show that the proposed group ensemble block achieves the state-of-the-art accuracy on the CIFAR-100 and ImageNet datasets for the coarse-to-fine image retrieval problem, where the model is trained with coarse-level annotation (e.g., trees) and evaluated with fine-grained category (e.g., maple trees and oak trees). © 2022 IEEE.

关键词： Image enhancement

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