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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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Security framework for IoT and deep belief network-based healthcare system using blockchain technology

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International Journal of Electronic Business 2022年第3期17卷 226-243页

作者： Nagarajan, Senthil Murugan Anandhan, Prathik Muthukumaran, V. Uma, K. Kumaran, U. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Mathematics School of Applied Sciences REVA University Bengaluru India School of Advance Sciences Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Nowadays, the computerisation and information systems need very fast, easier and secure data analysis models. So, blockchain technology and deep learning concepts play important roles for data security and analysis in field of healthcare applications. Blockchain technology has optimal reliability in various sectors like healthcare, security, information management, smart home, etc. In this paper, blockchain technology is used for the healthcare system to provide a secure and reliable data transmission. Furthermore, IoT-based wearable devices are used to collect the data from the patients. Moreover, deep belief network (DBN) is proposed to classify the obtained data in order to predict the type of disease or problems of the patients. The experimental results shows that the proposed framework and classification model abruptly outperforms when compared with other existing techniques. The efficiency of the proposed framework is analysed based on the performance metrics like throughput and latency. The obtained loss ratio for the proposed model seems very less of 0.0126 when compared with other existing techniques like MLP, SVM, ANN, and CNN. The accuracy obtained for the proposed DBN classifier is above 95% for the increase in the number of patients. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Internet of things

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IoT Devices Authentication Using Artificial Neural Network

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computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： Syed Shabih Ul Hasan Anwar Ghani Ikram Ud Din Ahmad Almogren Ayman Altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for Internet of Things(IoT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： Implicit authentication behavioral authentication artificial neural network processed pattern authentication

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PiGPDS: Perspective Independent Ground Plane Detection and Segmentation for Complex 3D Indoor Scenes

PiGPDS: Perspective Independent Ground Plane Detection and S...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Ebrahimi, Ali Czarnuch, Stephen Memorial University of Newfoundland Faculty of Engineering and Applied Science Department of Electrical and Computer Engineering St. John's Canada Memorial University of Newfoundland Discipline of Emergency Medicine Department of Electrical and Computer Engineering St. John's Canada

ISBN: (纸本)9798350382204

Ground plane detection and segmentation techniques can benefit and help improve the accuracy and robustness of a wide range of computer vision applications, from 3D object segmentation and autonomous navigation to mixed and augmented reality. Existing approaches often rely on restrictive assumptions to simplify the problem, such as the ground plane being the largest plane in the scene or the camera location or orientation being ideal. We present a ground plane segmentation technique for real-world 3D indoor scenes where the position and orientation of the sensor are unrestricted and unknown. Our method only requires one 3D point cloud of an indoor scene and assumes that the scene contains at least one surface parallel to the actual ground plane, which is generally true for 3D indoor scenes. We begin by utilizing a voxelized grid downsampling method to enhance the speed of the algorithm. Subsequently, we use K-medoids clustering and an angular-based zone determination technique to identify the ground zone. Next, we divide the ground zone into several clusters using the Euclidean clustering algorithm, and we employ the M-estimator Sample Consensus (MSAC) algorithm to fit the largest plane in each cluster with a specific orientation. Finally, based on the geometric relationship between the fitted planes of the ground zone, we estimate the ground plane, verify it and segment all its associated points using a distance-based approach. We evaluated our method on public and self-generated datasets, in which we positioned a depth sensor at various locations, pitches, and yaws. Our experimental results demonstrate that our proposed method can robustly and efficiently detect and segment the ground plane of complex 3D indoor scenes and supports varied sensor locations and orientations. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, with average experimental results of 98.28, 95.48, 96.64, a

关键词： Clustering algorithms

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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First-Order Federated Bilevel Learning 39

First-Order Federated Bilevel Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yifan Xiao, Peiyao Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States Department of Computational Applied Math and Operations Research Rice University United States

ISBN: (纸本)157735897X

Federated bilevel optimization (FBO) has garnered significant attention lately, driven by its promising applications in meta-learning and hyperparameter optimization. Existing algorithms generally aim to approximate the gradient of the upper-level objective function (hypergradient) in the federated setting. However, because of the nonlinearity of the hypergradient and client drift, they often involve complicated computations. These computations, like multiple optimization sub-loops and second-order derivative evaluations, end up with significant memory consumption and high computational costs. In this paper, we propose a computationally and memory-efficient FBO algorithm named MemFBO. MemFBO features a fully single-loop structure with all involved variables updated simultaneously, and uses only first-order gradient information for all local updates. We show that MemFBO exhibits a linear convergence speedup with milder assumptions in both partial and full client participation scenarios. We further implement MemFBO in a novel FBO application for federated data cleaning. Our experiments, conducted on this application and federated hyper-representation, demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Leveraging XGBoost for Predictive Analytics in Healthcare: Enhancing Disease Diagnosis 7

Leveraging XGBoost for Predictive Analytics in Healthcare: E...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Kotiyal, Arnav Habelalmateen, Mohammed I. Rana, Ajay Devi, V.S Anusuya Rao, Bolleddu Devananda Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Departyment of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in healthcare predictive analytics to improve the speed and precision of illness detection. Thanks to its built-in regularisation algorithms that avoid overfitting and its capacity to handle big datasets, XGBoost has proved useful in a broad variety of prediction applications. It is a scalable and high-performance machine learning algorithm. In order to improve patient outcomes, healthcare providers must ensure that patients are diagnosed with chronic and life-threatening conditions including cancer, heart disease, and diabetes as soon as possible. To test how well XGBoost performs in comparison to more conventional machine learning techniques, the research makes use of healthcare datasets collected from the actual world. The effectiveness of the model in predicting illness outcomes is evaluated using critical metrics including accuracy, precision, recall, and F1-score. Based on the findings, XGBoost is the best classifier for real-time healthcare applications because to its quicker processing efficiency and greater predictive ability. Additionally, the article delves into the model's interpretability and how it may be integrated into clinical decision support systems. By demonstrating how XGBoost may improve diagnostic accuracy and bolster individualised treatment plans, this study adds to the expanding area of AI-driven healthcare. © 2024 IEEE.

关键词： Predictive analytics

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Enhancing Cooperative Spectrum Sensing Performance with Fusion Rule in Cognitive Radio: Hybrid Techniques 2

Enhancing Cooperative Spectrum Sensing Performance with Fusi...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Deepthi, K. Sreevani, N. Raj, Vijilius Helena Sangeetha, A. Sobti, Rajeev Salman, Z. Avanthi Insistute of Engineering and Technology Department of Mechanical Engineering India Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

The issue of limited wireless spectrum may be addressed with the use of Cognitive Radio (CR). Without affecting the primary users (PU), or licenced users, it allows other users, known as secondary users or unlicensed users, to exploit spectrum gaps, improving spectrum efficiency. Energy detection, a matching filter, and cyclostationary detection make up the CR, which determines whether the spectrum is idle or not. Cognitive Radio Networks (CRNS) are hindered in their ability to improve performance by noise. No signal-to-noise ratio (SNR) may be efficiently achieved with a cooperative spectrum employing a set sensing time in terms of throughput performance. There are two ways that this research suggests they might improve the effectiveness of energy detectors in the wireless spectrum. At its core, the first method is an energy detector-based hybrid of the Additive Wavelet Transform as well as the Homomorphic (AWTH). Mixing the Haar Discrete Wavelet Transform with the Homomorphic technique (HDWTH) is the basis of the second approach. The Two Quality Metrics Used: False Alarm Probability (Pf) and Probability Detection (Pd). The efficiency of the energy detector is enhanced by implementing the two methods that have been suggested. Metrics for quality demonstrate that the first method improves the wireless spectrum's Pd point perspective. But from a throughput perspective, the second strategy succeeds in maximising. © 2024 IEEE.

关键词： Signal to noise ratio

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Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines 6

Comparative Evaluation between Accelerated RISC- V and ARM A...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Christofas, Vasileios Amanatidis, Petros Karampatzakis, Dimitris Lagkas, Thomas Goudos, Sotirios K. Psannis, Konstantinos E. Sarigiannidis, Panagiotis International Hellenic University Department of Computer Science Kavala Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece University of Macedonia Department of Applied Informatics Thessaloniki Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350339505

Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application. © 2023 IEEE.

关键词： computer vision

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Resource Consumption of Federated Learning Approach applied on Edge IoT Devices in the AGV Environment 1

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23rd International Conference on Computational science, ICCS 2023

作者： Shubyn, Bohdan Grzesik, Piotr Maksymyuk, Taras Kostrzewa, Daniel Benecki, Pawel Syu, Jia-Hao Lin, Jerry Chun-Wei Sunderam, Vaidy Mrozek, Dariusz Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine Department of Computer Science Emory University AtlantaGA30322 United States Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

ISBN: (数字)9783031360305

ISBN: (纸本)9783031360299

Federated learning is a distributed machine learning method that is well-suited for the Industrial Internet of Things (IIoT) as it enables the training of machine learning models on distributed datasets. One of the most important advantages of using Federated Learning for Automated Guided Vehicles (AGVs) is its capability to optimize resource consumption. AGVs are typically resource-constrained systems and must operate within tight power and computational limits. By using Federated Learning, AGVs can perform model training and updating on-board, which reduces the amount of data that needs to be transmitted. This paper presents experiments to assess the consumption of resources of the Jetson Nano edge IoT device while training the Federated Learning model, and compares it with referential machine learning approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recurrent neural networks

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