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Adaptive Data Forwarding Scheme for Diverse Infotainment Services in Vehicular Named Data Networking

IEEE Transactions on Vehicular technology 2024年第5期74卷 8012-8022页

作者： Khan, Sajjad Ahmad Lim, Huhnkuk Chaudhry, Shehzad Ashraf Kocaeli University Software Engineering Department Izmit41000 Turkey Hoseo University Computer Engineering Department Korea Republic of Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering & Architecture Nisantasi University Istanbul Turkey

Providing diverse infotainment services is crucial for enhancing road safety and improving the overall driving experience in vehicular networks. However, delivering these services within Vehicular Named Data Networking (VNDN) environments presents significant challenges, primarily due to the data broadcast phenomenon and the varying Quality of Service (QoS) requirements of different applications. This paper introduces an adaptive data forwarding scheme designed to effectively manage these challenges and optimize the delivery of infotainment services in VNDN. Our proposed scheme enhances the data packet format by incorporating additional attributes that prioritize data based on its criticality, specifically categorizing it into critical, non-critical, and entertainment data. Furthermore, we introduce an Enhanced Weighted Fair Queuing (EWFQ) policy to optimize the scheduling of data packets according to their importance. Two innovative data forwarding schemes, namely Advanced Push-based Data Forwarding (APDF) and Enhanced Pull-based Data Forwarding (EPDF), are developed to address the needs of critical and non-critical information and entertainment data, respectively. Simulation results demonstrate that our adaptive data forwarding scheme significantly outperforms legacy fragment schemes in delivering infotainment data within dynamic VNDN environments, ensuring reliability and efficiency. This paper contributes to advancing data dissemination strategies in vehicular networks and lays the groundwork for future research in adaptive QoS management. © 1967-2012 IEEE.

关键词： Entertainment industry Quality of service Adaptive systems Delays Data dissemination Vehicle dynamics Scalability Road safety Real-time systems Vehicles

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Heterologous Image Matching Based on Salience Region

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

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CAAI Transactions on Intelligence technology 2023年第3期8卷 849-862页

作者： Ye‐Qun Wang Jian‐Yu Li Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan School of Computer Science and Engineering South China University of TechnologyGuangzhouChina School of Software Engineering South China University of TechnologyGuangzhouChina Hanyang University AnsanSouth Korea

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial ***,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally *** tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)***,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best ***,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate ***,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational *** SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

关键词： deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation

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Cache Algorithms for software Defined Network Efficiency: An Analytical Approach 7

Cache Algorithms for Software Defined Network Efficiency: An...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Haider, Warda Zardari, Shehnila Memon, Shiza Riaz Aamir, Muhammad Ned University of Engineering & Technology Department of Software Engineering Karachi Pakistan Sir Syed University of Engineering & Technology Department of Telecommunication Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

In today's networking environments caching algorithms plays a very important role to get the best use of resources and maximizing performance of networks. In this paper, we conduct a comparative analysis of the three caching algorithms: Dependent set, Cover set and Mixed set. Every algorithm is intended to solve one problem of rule dependency management and cache optimization in the network switches. Each has its strengths and trade-offs, and a detailed comparison across dozens of dimensions, throughput, adaptability, memory usage, cache-hit rate, execution time, resource efficiency, scalability (both operational- and system-level), flexibility in supporting different workloads, robustness to system behavior changes independent of user behavior (like the slow generation asymptote), and operational stability just to name a few popular ones, are relevant for many high-scale web services products. There are many other dependencies in the region, so pay a lot of memory which is not acceptable for the very first versions. The Dependent Set algorithm is designed to be an optimized memory, easy to adapt and resource-efficient rule dependency manager. This further enforces the emphasis on rule coverage policies of the Cover Set algorithm, improving cache performance and scalability. The Mixed Set algorithm combines the strengths of both methods providing a rounded approach, with throughput, adaptability and resource allocation. The study examines the benefits of each algorithm in real-world situations. It shows how they improve network efficiency and adaptability. This research expands our understanding of caching algorithms. It explains how they optimize networks. This paves the way for better network management and performance. By comparing these algorithms, the paper provides useful information. It helps network administrators and researchers enhance caching mechanisms in networks. © 2024 IEEE.

关键词： Caching Cover Set Dependent Set Mixed Set Rules software Defined Networking

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A Novel Lightweight IoT Intrusion Detection Model Based on Self-knowledge Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 16912-16930页

作者： Wang, Zhendong Zhou, Renqiang Yang, Shuxin He, Daojing Chan, Sammy Jiangxi University of Science and Technology School of Information Engineering Ganzhou341000 China Nanchang University Software Engineering Institute Nanchang330047 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China City University of Hong Kong Department of Electrical Engineering Hong Kong

The Internet of Things (IoT) environment contains many different types of devices, each with different functionalities, communication protocols, and security capabilities, which makes the IoT a complex challenge for security protection. Therefore, network intrusion detection (NID) is needed to detect intrusions in the network to secure the IoT. In recent years, deep learning-based intrusion detection systems have achieved excellent results, but they tend to require high computational resources and storage space, which is not feasible for most IoT devices. In this paper, we propose a lightweight intrusion detection model based on self-knowledge distillation, namely TBCLNN, which improves the detection accuracy while also reducing the number of model parameters and computational cost. Specifically, we use the binary Harris Hawk optimization algorithm for dimensionality reduction of traffic features. We use lightweight convolution, such as Tied Block Convolution (TBC), to design lightweight neural network models with residual and inverse residual structures. Moreover, we propose an improved self-knowledge distillation loss function to solve the sample imbalance problem and compensate for the performance degradation caused by lightweight neural networks. The multiclassification accuracy of our proposed method exceeds 99% on all three publicly available IoT datasets. The experimental results show that our method has a small model size and requires only low computational resources, making it suitable for resource-constrained IoT intrusion detection. © 2014 IEEE.

关键词： Network intrusion

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Unveiling the Power of Deep Learning: A Comparative Study of LSTM, BERT, and GRU for Disaster Tweet Classification

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 526-534页

作者： Ullah, Ihsan Jamil, Anum Ul Hassan, Imtiaz Kim, Byung-Seo Department of Software and Communication Engineering Hongik University Korea Republic of Department of Physics NED University of Engineering & Technology Karachi Pakistan Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan

Disasters have serious effects on people's lives and buildings. Therefore, social media platforms, such as Twitter, have become more critical. They are crucial tools for responding to and managing disasters effectively. This study examined the effectiveness of various deep learning models, such as bidirectional encoder representations from transformers (BERT), gated recurrent units (GRU), and long short-term memory (LSTM) for classifying disaster-related tweets. Twitter data related to different disasters were collected using hashtags. The data were then cleaned, preprocessed, and manually annotated by a team. The annotated data were divided into training, validation, and testing sets. The data were used to train three models based on BERT, GRU, and LSTM for the categorical classification of disaster tweets. Finally, the three models were evaluated and compared using the test data. BERT achieved an accuracy of 96.2%, making it the most effective model. In contrast, the LSTM and GRU models achieved an accuracy of 93.2% and 88.4%, respectively. These findings underscore the potential effectiveness of deep learning models in classifying disaster-related tweets, offering insights that could enhance disaster management strategies, refine social media monitoring processes, bolster public safety, and provide directions for future research. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Disasters

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AI-Based Early Detection of Coronary Artery Disease Using Atherosclerosis Inflammatory and MicroRNA Biomarkers in Angina Patients 6

AI-Based Early Detection of Coronary Artery Disease Using At...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Sajid, Muhammad Hassan, Ali Khan, Dilshad Ahmed Abbas Gilani, Sayed Tanveer Khan, Shoab Ahmed Bakhshi, Asim Dilawar CEME National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Rawalpindi Pakistan MCS National University of Sciences and Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest and is responsible for a high percentage of global mortality. Early diagnosis of CAD in patients with chest pain is challenging. Coronary angiography, an invasive procedure that is not widely available in developing countries, confirms the diagnosis of CAD. It is imperative to establish noninvasive biomarkers for the early diagnosis of CAD in patients with angina. Motivated by two recent medical diagnostic studies that used novel molecular (microRNA) and chemical (atherosclerosis-inflammatory) biomarkers for early detection of CAD, the proposed study explores the hidden potential of novel biomarkers using machine learning (ML). The study combines the biomarkers of two medical studies and presents a NUMS-NIHD dataset. From the complete dataset, the best feature set is shortlisted after an extensive evaluative search in the biomarker feature space. Ten ML classifiers are applied to the selected biomarker inputs, including clinical, chemical, and molecular cardiac biomarkers. To select optimal biomarker-classifier combinations, experiments use K-fold cross-validation. The results of K-fold cross-validation are validated against a hold-out set of data. The results are then compared to current state-of-the-art benchmark studies. The detection strategy achieved an accuracy of 90.18%, a sensitivity of 92.33 % and a specificity of 100.00%. The proposed study validated previous medical diagnostic studies and significantly improved the results. These ML biomarkers-classifier combinations can be used to exclude healthy individuals non-invasively before angiography. © 2024 IEEE.

关键词： Diseases

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Intelligent control of additively manufactured beads on the basis of target morphologies and thermal field

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Frontiers of Mechanical engineering 2024年第6期19卷 87-98页

作者： Xuhui YANG Rui LI Kelong HU Aidong SUN Xiaoyao MA Mingxin LIU Mingtian WANG Runsheng LI Gang ZHAO Wenzheng ZHAI Hao SONG Zili LI Haiou ZHANG School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Software Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Mechanical and Electronic Engineering China University of PetroleumQingdao 266580China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and TechnologyWuhan 430065China

Hybrid deposition with microrolling is a promising arc-based direct energy deposition technique to rapidly build complex parts,whose performance is comparable to that of their wrought *** forming conditions and bead morphologies pose difficulties in controlling the morphologies from a single weld bead to built part profiles,and these difficulties hinder the widespread application of the ***,a model that can automatically generate optimal process parameters on the basis of the infrared image,including thermal information and the point cloud information of the target weld bead,is *** show that the errors in critical parameters,namely,feed speed,travel speed,and rolling force,are below 0.4%,0.9%,and 2%,respectively,indicating that the proposed technique outperforms the compared ***,validation reveals that the actual depositing bead is similar(deviation below 0.05 mm)to the target *** proposed strategy provides an effective foundation for dynamic path planning and can considerably improve printing efficiency and precision.

关键词： wire arc additive manufacturing deep learning thermal field point cloud stainless steel

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