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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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Explainable ensemble deep learning-based model for brain tumor detection and classification

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Neural Computing and Applications 2025年第3期37卷 1289-1306页

作者： Hosny, Khalid M. Mohammed, Mahmoud A. Salama, Rania A. Elshewey, Ahmed M. Department of Information Technology Faculty of Computers and Information Zagazig University P.O.BOX 44519 Zagazig Egypt Department of Computer Science Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt Department of Information Technology Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt

Brain tumors are very dangerous as they cause death. A lot of people die every year because of brain tumors. Therefore, accurate classification and detection in the early stages can help in recovery. Various deep learning techniques have achieved good results in brain tumor classification. The traditional deep learning methods and training the neural network from scratch are time-consuming and can last for weeks of training. Therefore, in this work, we proposed an ensemble approach depending on transfer learning that utilizes pre-trained models of DenseNet121 and InceptionV3 to detect three forms of brain tumors: meningioma, glioma, and pituitary. While developing the ensemble model, some changes were made to the architecture of pre-trained models by replacing their classifiers (fully connected and SoftMax layers) with a new classifier to adopt the recent task. In addition, gradient-weighted class activation maps (Grad-CAM) are an explainable model to verify results and achieve high confidence. The suggested model was validated using a publicly available dataset and achieved 99.02% accuracy, 98.75% precision, 98.98% recall, and a 98.86% F1 score. The suggested approach outperformed others in detecting and classifying brain tumor MRI data, and verifying results using the explainable model achieved a high degree of trust. © The Author(s) 2024.

关键词： Transfer learning

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Pothole road detection and identification based on transfer learning 4

Pothole road detection and identification based on transfer ...

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4th International Conference on Sensors and information technology, ICSI 2024

作者： Mao, Yuying Su, Wenzhe Chen, Haotian College of Information Science and Technology Hangzhou Normal University Hangzhou China College of Computer Science and Technology Qingdao University Qingdao China East China Survey and Design Institute Hangzhou China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

Building a powerful country in transportation is a major strategic decision made based on national conditions, focusing on the overall situation and facing the future. The identification of potholes has important applications in the field of reducing accident rates as well as geological exploration and autonomous driving. In this paper, based on 301 road pictures, the training samples of the model are increased by data augmentation method. After normalization and standardization of the augmented training data, the random weighted sampling method is adopted as the training input of the model. In this paper, the VGG16 model is adopted as the classification model of road images, and the model parameters of lmageNet 1000 data set are taken as the feature extraction layer of the model in the way of transfer learning, and the model is trained on the modified classification layer. Two indexes, F1-score and Kappa coefficient, were used to evaluate the model. The model F1-score fluctuates around 0.5 and is higher than 0.4, and the Kappa coefficient is close to 0.6 in most cases. This shows that the model trained in this paper has high generalization ability and classification accuracy. © 2024 SPIE.

关键词： Roads and streets

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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An Optimized Deep Learning Model for Emotion Classification in Tweets

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computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania Badria Sulaiman Alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques *** main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better *** step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关键词： Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes

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Randomized Active Learning to Identify Phishing URL 1st

Randomized Active Learning to Identify Phishing URL

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Ponni, P. Prabha, D. Department of Computer Science and Engineering CMS College of Engineering and Technology Coimbatore641032 India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore641008 India

ISBN: (纸本)9783031250873

Data analytics is rapidly being employed in cybersecurity concerns, and has been found to be beneficial in situations where large amounts of data and heterogeneity make human assessment by security specialists difficult. Obtaining data with annotations is a tough and well-known restrictive constraint for various supervised security analytics tasks in real-world cyber-security situations using data-driven analytics. Because annotation is largely manual and involves a great deal of expert effort, vast sections of large datasets are frequently left unlabeled. We adopt a randomly ranked feature active learning strategy to create a semi-supervised solution in this research to address this constraint in an applied cyber-security challenge of phishing classification. An early classifier is trained on a slight sample of interpreted data, and then iteratively updated by selecting just relevant samples from a huge pool of unlabeled data that are most likely to effect classifier presentation quickly. Randomly ranked feature Active Learning has a lot of potential in terms of achieving quicker convergence in relationships of classification presentation in a group learning environment, needing even less human annotation labor. Without requiring a significant number of marked training examples to be accessible during training, a helpful feature rank update strategy paired with active learning displays good classification results for labeling phishing/malicious URLs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cybersecurity

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Performance Analysis of YOLO versions for Real-time Pothole Detection 16

Performance Analysis of YOLO versions for Real-time Pothole ...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Fortin, Leonhel V. Llantos, Orven E. Department of Computer Applications Philippines Department of Computer Science Philippines College of Computer Studies MSU-Iligan Institute of Technology Tibanga Iligan City9200 Philippines

The integration of lightweight computer vision models emerges as a promising solution for automating road inspection processes. The evolution of YOLO architectures, particularly their lightweight variants, enables significant improvements in both detection accuracy and computational efficiency. Recent iterations through YOLOv9-tiny, YOLOv10-nano, and YOLOv11-nano enhance real-time performance capabilities while maintaining acceptable accuracy through optimized network architectures. The implementation of these lightweight YOLO variants presents a compelling approach to automated pothole detection. Previous studies utilizing limited datasets of approximately 1,388 images established initial feasibility but left significant room for improvement in model robustness and generalization. This research addresses these limitations by employing an expanded dataset of 16,054 images, encompassing diverse environmental conditions and road surface characteristics. The comparative analysis establishes new benchmarks for pothole detection in resource-constrained environments, demonstrating the viability of nano/tiny models for practical applications. © 2025 Elsevier B.V.. All rights reserved.

关键词： Object detection

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A Hierarchical-Based Sequential Caching Scheme in Named Data Networking

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China Communications 2025年第5期22卷 48-60页

作者： Zhang Junmin Jin Jihuan Hou Rui Dong Mianxiong Kaoru Ota Zeng Deze College of Computer Science South-Central Minzu UniversityWuhan 430074China Department of Information and Electronic Engineering Muroran Institute of TechnologyMuroranHokkaido 050-8585Japan School of Computer Science China University of GeosciencesWuhan 430074China

Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load ***,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN *** mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is *** this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router *** aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache *** results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).

关键词： hierarchical router named data networking sequential caching

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A Proposal of the Gamification of Programming Learning Assistant System

A Proposal of the Gamification of Programming Learning Assis...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Kyaw, Htoo Htoo Sandi Kaneko, Keiichi Fuanbiki, Nobuo Tokyo University of Agriculture and Technology Department of Computer and Information Science Tokyo Japan Okayama University Department of Information and Communication Systems Okayama Japan

ISBN: (纸本)9798331530839

Despite the rapid expansion of the ICT sector, there's a disconnect with STEM subjects, leading to high dropout rates in computer science and engineering programs. This contributes to many unfilled positions in the ICT job market. One key issue is that traditional teaching methods struggle to engage students in challenging and abstract STEM subjects like programming. To address this, innovative technology-enhanced learning solutions are needed to be developed. In the past, we have developed a Programming Learning Assistant System (PLAS) to support programming learning for novice students. PLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In this paper, we propose the initial prototype of the gamification of PLAS to enhance students' comprehension of programming concepts, improve their confidence, cultivate their interest in STEM fields, and increase course engagement through dynamic and engaing scenarios. © 2024 IEEE.

关键词： Students

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Autonomous Navigation Development of Solar Panel Cleaning Vehicle 28th

Autonomous Navigation Development of Solar Panel Cleaning Ve...

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28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Cheng, Wen-Chang Hsiao, Hung-Chou Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City Taiwan Department of Information Management Chaoyang University of Technology Taichung City Taiwan

ISBN: (纸本)9789819717132

This study presents an automated navigation system for solar panel cleaning vehicles. Since self-positioning and navigation cannot be achieved on solar panels through self-built mapping, we utilize the vanishing points formed by the perspective transformation of many coplanar parallel lines on the solar panel to accomplish navigation. This is complemented by fuzzy control to align the vehicle with the parallel lines and proceed along the straight lines of the solar panel. When the solar panel cleaning vehicle reaches the boundary, it will turn to the next row's direction to continue cleaning along the straight lines of the solar panel until the cleaning is complete or autonomous navigation is deactivated. Preliminary experimental results show that the average standard deviation of the x-coordinate during straight-line movement is 1.70 pixels, and the average error between the maximum and minimum x-coordinates is approximately 6.33 pixels. After adjusting the speeds of the two-wheel servo motors, calibrating the camera’s centre point, and repeating the experiments, the results demonstrate an average standard deviation of 0.43 pixels, and the maximum and minimum errors in the x-coordinate of straight lines have decreased to 2 pixels, achieving practical usability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hough transforms

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