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Blockchain-Based Trust Model for Inter-Domain Routing

computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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Dynamic Trust Management and AI-Based Multi-Factor Authentication for 6G Machine Type Communications 1

Dynamic Trust Management and AI-Based Multi-Factor Authentic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sinha, Garima Agarwal, Ankita Faculty of Engineering and Technology Department of Computer Science Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

New security concerns about the transmission of sensitive data over enormous networks of linked devices have arisen with the advent of the 6G era and the broad adoption of massive machine-type communication (MTC). The authors of this paper provide a fresh strategy for MFA and dynamic trust management in 6G networks that make use of AI to solve these issues and improve MTC security. This study presents a novel approach to behavioural profiling that takes into account the strengths and weaknesses of machine-type devices. It was necessary to create simulated data to represent many situations due to the wide range of possible network circumstances, device characteristics, ambient variables, applications load of work, and security needs. The efficacy of the suggested remedy was evaluated during six simulation cycles. The findings showed that the framework can adapt to changing network circumstances and device behaviours, with stability demonstrated across many situations. The accuracy rates for implementing MFA ranged from 0.49 to 0.57. This MFA and AI-driven dynamic trust management framework has great potential to improve 6G network MTC security;its adaptability and resilience provide a solid basis for meeting the security concerns that will inevitably arise in the future of 6G networks. To maximize performance and assure successful deployment, more modifications and real-life testing are suggested. © 2025 IEEE.

关键词： Authentication Protocol

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Empowering Dysarthric Communication: Hybrid Transformer-CTC-Based Speech Recognition System

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IEEE Access 2025年 13卷 82479-82491页

作者： Vinotha, R. Hepsiba, D. Anand, L. D. Vijay Bruntha, P. Malin Dinh, Linh Dang, Hien Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore641114 India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore641114 India Karunya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641114 India Suffolk University Department of Information Systems BostonMA02108 United States Thuyloi University Faculty of Computer Science and Engineering Hanoi116705 Viet Nam Molloy University Department of Mathematics and Computer Science Rockville CentreNY11570 United States

Dysarthria, a motor speech disorder, impairs the muscles involved in speech production, leading to challenges in articulation, pronunciation, and overall communication. This results in slow, slurred speech that is difficult to understand. Augmentative and Alternative Communication (AAC) aids integrated with speech recognition technology offer a promising solution for individuals with dysarthria. However, Automatic Speech Recognition (ASR) systems trained on typical speech data often struggle to recognize dysarthric speech due to its unique speech patterns and limited training data. To address these challenges, a hybrid Transformer-CTC model has been proposed for improving ASR performance on dysarthric speech. The Transformer architecture employs a self-attention mechanism that models complex dependencies between speech features, enabling it to identify and emphasize important patterns even when training data is limited. This ability is particularly crucial for dysarthric speech, where speech signals often exhibit high variability. On the other hand, Connectionist Temporal Classification (CTC) acts as an effective transcription layer. It aligns speech features with character sequences without requiring precise input-output alignment, making it well-suited for handling the inconsistencies and distortions present in dysarthric speech. The integration of these components creates a powerful architecture capable of learning nuanced speech patterns and delivering accurate transcriptions for dysarthric speech. The model was trained using the UA speech corpus, containing 13 hours of speech from 15 speakers with varying dysarthria levels. The proposed hybrid system achieves an impressive Word Recognition Accuracy (WRA) of 89%, demonstrating its effectiveness in accurately transcribing dysarthric speech. This innovative approach significantly advances the development of ASR technologies tailored to diverse and variable speech patterns, ultimately enhancing communication for in

关键词： Speech analysis

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A Hybrid Approach for Cardiovascular Disease Risk Evaluation 6

A Hybrid Approach for Cardiovascular Disease Risk Evaluation

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sesadri, U. Jumlesha, Shaik Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jadala, Vijaya Chandra Department of CSE Vardhaman College of Engineering Telangana Hyderabad India AP Tirupati517501 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur522502 India Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam531162 India Faculty of Computer Technology Assam down town University Assam Guwahati India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India

ISBN: (纸本)9798331522667

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. There were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. The risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. The integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. The ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. The early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual. © 2025 IEEE.

关键词： Accuracy Cardio-vascular disease Efficiency Hybridization Modifiable attributes Prediction Risk estimator

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Img2Neuro: brain-trained neural activity encoders for enhanced object recognition

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Machine Learning: science and Technology 2025年第2期6卷

作者： Aboelnaga, Mona A. El-Kharashi, Mohamed W. Eldawlatly, Seif Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

For decades, the brain’s visual pathway has inspired machine and deep learning models, yet these models oversimplify the brain’s complex visual processing. This is manifested in the significant superiority of the brain in comparison to the developed models in terms of the accuracy and the amount of data needed for training. Therefore, rather than using the brain as an inspiration, in this paper, we introduce Img2Neuro;a convolutional neural network model feature extractor that predicts the visual brain’s response to images by encoding neural activity. Img2Neuro is trained on natural scene images paired with single-neuron recordings from the visual cortex and thalamus of mice and monkeys. We explore the feasibility of using Img2Neuro as a feature extractor for object recognition, where the output of Img2Neuro in response to unseen images is used as input to classifiers with the task of recognizing the object in the image. We evaluated our approach on three benchmark datasets;namely, MNIST, Fashion-MNIST, and Cifar10. In our experiments, we examined the classification performance when Img2Neuro is used as a feature extractor compared to using the images as direct input to the classifier, using five different classifiers;namely, linear discriminant analysis, perceptron, logistic regression, ridge classifier, and a single-layer neural network. The results demonstrate superior performance when using Img2Neuro in most datasets and across all classifiers, reaching an enhancement in accuracy of 9% on the MNIST dataset, 2% on FashionMNIST, and 18% on Cifar10 in some cases compared to using raw images as an input in the classifiers. The performance enhancements suggest that brain-trained encoders can effectively capture image features for object recognition tasks. By leveraging neural response data, Img2Neuro demonstrates a promising avenue for bridging the gap between biological and artificial visual processing, ultimately leading to novel strategies for improving state-of-t

关键词： Discriminant analysis

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LightHand99K: A Synthetic Dataset for Hand Pose Estimation With Wrist-Worn Cameras

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IEEE Access 2025年 13卷 81423-81433页

作者： Lee, Jeongho Kim, Changho Kim, Jaeyun Kim, Seon Ho Choi, Younggeun Choi, Sang-Il Dankook University Department of Computer Science and Engineering Yongin-si16890 Korea Republic of University of Southern California Integrated Media Systems Center Los AngelesCA90089 United States

Although hand-pose estimation using external camera systems has made significant progress driven by large annotated datasets, wrist-worn camera-based hand-pose estimation offers unique advantages owing to its ability to capture nearby images. However, datasets primarily collected from multi-angle external systems are unsuitable for wrist-camera-based estimation. To address this gap, we developed LightHand99K, a synthetic dataset comprising 99,792 synthetic RGB hand images designed explicitly for hand-pose estimation using wrist-worn cameras. The dataset was generated using a Unity 3D-based hand image generator optimized to efficiently enhance the performance of hand-pose estimation models for wrist-worn camera images by incorporating human finger joint movement information while minimizing computational cost. This tool allows users to customize their poses, camera angles, backgrounds, lighting, and hand-orientation settings. Incorporating three data augmentation techniques, LightHand99K demonstrated a 36% increase in area under the curve (AUC) and a 6.2-mm reduction in average endpoint error (EPE) compared to existing datasets. These results underscore the value of LightHand99K in advancing hand-pose estimation, particularly in wrist-worn camera applications. © 2013 IEEE.

关键词： Deep learning

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TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images 4th

TinyML Model for Solar Cell Defect Classification Based on E...

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4th International Conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Boubaker, S. Moussaoui, C. Mellit, Adel Benghanem, M. Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia Faculty of Sciences and Technology University of Jijel Jijel18000 Algeria Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

ISBN: (纸本)9789819606436

Being largely utilized as alternative sources of energy, photovoltaic (PV) panels are being deployed in many locations. However, those panels are extensively being prone to faults. A careful detection and diagnosis of those faults becomes a must for solar energy engineers, practitioners and decision-makers in order to extract the maximum of energy from them. The integration of artificial intelligence (AI) in the domain of solar energy management has become a trend in the few previous years because of the high potential provided by machine learning (ML) and deep learning (DL) algorithms in handling issues related to PV systems such as control and fault detection and isolation (FDI). In this paper, TinyML models for defect classification of photovoltaic modules based on electroluminescence (EL) images were developed. An open-source platform, Edge Impulse, was used to design and deploy the models. Two convolutional neural network models (mobileNetV1 and mobileNetV2) have been used and compared. A dataset of EL with three anomalies was used to develop the models. The validation test shows an accuracy of 88.44 and 76.38% for respectively MobileNet-V1 and MobileNet-V2. The quantized (Int8) model has been integrated into a microcontroller (Arduino Portenta H7) for real-time application. The obtained results can be considered as promising and can be extended to other case studies with more defects and larger datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

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Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

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The Role of Artificial Intelligence in Improving Histopathological Diagnosis of Prostate Cancer: A Review

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Journal of engineering and science in Medical Diagnostics and Therapy 2025年第2期8卷 020801页

作者： Afifi, Shereen Faragallah, M. Hamdy Taha, Radwa Baig, Mirza Ullah, Ehsan Gholam Hosseini, Hamid Hassanein, Sally I. Computer Science and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Biochemistry Department Faculty of Science Helwan University Cairo11795 Egypt Digital Media and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Digital Front Door Orion Health Auckland1010 New Zealand Department of Pathology and Laboratory Medicine Health New Zealand Te Toka Tumai Auckland1061 New Zealand School of Engineering Computer and Mathematical Sciences Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand Biochemistry Department Faculty of Pharmacy and Biotechnology German University Cairo11835 Egypt

This review investigates the effectiveness of exploiting the massive Artificial Intelligence (AI) technology in the diagnosis of prostate cancer histopathological images. It focuses on studying and analyzing the current state and practice for utilizing AI tools, including significant machine learning and deep learning models in the histopathological image analysis process. The preferred reporting items for systematic reviews and meta-Analyses (PRISMA) methodology was adopted for conducting this systematic review to include recent research articles that have been published since 2017. Leveraging novel deep learning models and advanced imaging techniques, AI demonstrates promising capabilities in improving accuracy and efficiency in detecting and classifying prostate cancer. A comprehensive comparison of existing works has been presented with in-depth discussions around current limitations and key challenges while proposing some future advancements. This study aims to pave the way for future research and further integration of AI into the diagnostic processes toward early detection, personalized treatment strategies, and enhanced patient outcomes in the context of a prostate cancer diagnosis. Copyright © 2025 by ASME.

关键词： Contrastive Learning

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A Comparison of Selected Agent-Based Modelling Frameworks 15th

A Comparison of Selected Agent-Based Modelling Frameworks

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Gacek, Paulina Bocheńczyk, Dominika Krajewski, Maciej Kruczek, Bartosz Lazarz, Krzysztof Magnuszewski, Pawel Zięciak, Antoni Kamiński, Filip Wąs, Jaroslaw Department of Applied Computer Science Faculty of Electrical Engineering Automatics IT and Biomedical Engineering AGH University of Krakow Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031857027

In recent years, a variety of new frameworks streamlining the process of agent-based modeling has emerged. These frameworks serve different purposes and each offers a unique set of features. In this practical comparative study, we evaluate the performance of various ABM frameworks through a series of benchmark simulations. By comparing the distinct functionalities offered by these tools, we aim to assist in the selection of an appropriate ABM toolkit for developing system models. This review presents a concise overview of seven popular agent-based modeling tools aiming to inspire further exploration and investigation into this subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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